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b/translations/kn/3-Data-Visualization/11-visualization-proportions/README.md index 0656aed9..d6d0845b 100644 --- a/translations/kn/3-Data-Visualization/11-visualization-proportions/README.md +++ b/translations/kn/3-Data-Visualization/11-visualization-proportions/README.md @@ -93,7 +93,7 @@ plt.show() ``` ಇದು, ಈ ಎರಡು ಮುಷ್ರೂಮ್ ವರ್ಗಗಳ ಪ್ರಕಾರ ಈ ಡೇಟಾದ ಪ್ರಮಾಣಗಳನ್ನು ತೋರಿಸುವ ಪೈ ಚಾರ್ಟ್. ಲೇಬಲ್‌ಗಳ ಕ್ರಮವನ್ನು ಸರಿಯಾಗಿ ಪಡೆಯುವುದು ಬಹಳ ಮುಖ್ಯ, ವಿಶೇಷವಾಗಿ ಇಲ್ಲಿ, ಆದ್ದರಿಂದ ಲೇಬಲ್ ಅರೆ ಅನ್ನು ರಚಿಸುವ ಕ್ರಮವನ್ನು ಖಚಿತಪಡಿಸಿಕೊಳ್ಳಿ! -![pie chart](../../../../translated_images/pie1-wb.e201f2fcc335413143ce37650fb7f5f0bb21358e7823a327ed8644dfb84be9db.kn.png) +![pie chart](../../../../translated_images/kn/pie1-wb.e201f2fcc335413143ce37650fb7f5f0bb21358e7823a327ed8644dfb84be9db.png) ## ಡೋನಟ್ಸ್! @@ -123,7 +123,7 @@ plt.title('Mushroom Habitats') plt.show() ``` -![donut chart](../../../../translated_images/donut-wb.be3c12a22712302b5d10c40014d5389d4a1ae4412fe1655b3cf4af57b64f799a.kn.png) +![donut chart](../../../../translated_images/kn/donut-wb.be3c12a22712302b5d10c40014d5389d4a1ae4412fe1655b3cf4af57b64f799a.png) ಈ ಕೋಡ್ ಒಂದು ಚಾರ್ಟ್ ಮತ್ತು ಮಧ್ಯದ ವೃತ್ತವನ್ನು ರಚಿಸುತ್ತದೆ, ನಂತರ ಆ ಮಧ್ಯದ ವೃತ್ತವನ್ನು ಚಾರ್ಟ್‌ನಲ್ಲಿ ಸೇರಿಸುತ್ತದೆ. ಮಧ್ಯದ ವೃತ್ತದ ಅಗಲವನ್ನು `0.40` ಅನ್ನು ಬೇರೆ ಮೌಲ್ಯಕ್ಕೆ ಬದಲಾಯಿಸುವ ಮೂಲಕ ಸಂಪಾದಿಸಬಹುದು. @@ -171,7 +171,7 @@ fig = plt.figure( ವಾಫಲ್ ಚಾರ್ಟ್ ಬಳಸಿ, ನೀವು ಈ ಮುಷ್ರೂಮ್ ಡೇಟಾಸೆಟ್‌ನ ಕ್ಯಾಪ್ ಬಣ್ಣಗಳ ಪ್ರಮಾಣಗಳನ್ನು ಸ್ಪಷ್ಟವಾಗಿ ನೋಡಬಹುದು. ಆಸಕ್ತಿದಾಯಕವಾಗಿ, ಇಲ್ಲಿ ಹಲವಾರು ಹಸಿರು ಕ್ಯಾಪ್ ಮುಷ್ರೂಮ್ಗಳು ಇವೆ! -![waffle chart](../../../../translated_images/waffle.5455dbae4ccf17d53bb40ff0a657ecef7b8aa967e27a19cc96325bd81598f65e.kn.png) +![waffle chart](../../../../translated_images/kn/waffle.5455dbae4ccf17d53bb40ff0a657ecef7b8aa967e27a19cc96325bd81598f65e.png) ✅ Pywaffle ಚಾರ್ಟ್‌ಗಳಲ್ಲಿ [Font Awesome](https://fontawesome.com/) ನಲ್ಲಿ ಲಭ್ಯವಿರುವ ಯಾವುದೇ ಐಕಾನ್‌ಗಳನ್ನು ಬಳಸುವ ಐಕಾನ್‌ಗಳನ್ನು ಬೆಂಬಲಿಸುತ್ತದೆ. ಚೌಕಗಳ ಬದಲು ಐಕಾನ್‌ಗಳನ್ನು ಬಳಸಿ ಇನ್ನಷ್ಟು ಆಸಕ್ತಿದಾಯಕ ವಾಫಲ್ ಚಾರ್ಟ್ ರಚಿಸಲು ಪ್ರಯೋಗ ಮಾಡಿ. diff --git a/translations/kn/3-Data-Visualization/12-visualization-relationships/README.md b/translations/kn/3-Data-Visualization/12-visualization-relationships/README.md index 76e1e98c..533c8fdb 100644 --- a/translations/kn/3-Data-Visualization/12-visualization-relationships/README.md +++ b/translations/kn/3-Data-Visualization/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ honey.head() ```python sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ``` -![scatterplot 1](../../../../translated_images/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.kn.png) +![scatterplot 1](../../../../translated_images/kn/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.png) ಈಗ, ವರ್ಷಗಳ ಪ್ರಗತಿಯನ್ನು ತೋರಿಸಲು ಜೇನುತುಪ್ಪ ಬಣ್ಣದ ಯೋಜನೆಯನ್ನು ಬಳಸಿ ಅದೇ ಡೇಟಾವನ್ನು ತೋರಿಸಿ. ವರ್ಷದಿಂದ ವರ್ಷಕ್ಕೆ ಬದಲಾವಣೆಯನ್ನು ತೋರಿಸಲು 'hue' ಪರಿಮಾಣವನ್ನು ಸೇರಿಸಬಹುದು: @@ -60,7 +60,7 @@ sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ```python sns.relplot(x="priceperlb", y="state", hue="year", palette="YlOrBr", data=honey, height=15, aspect=.5); ``` -![scatterplot 2](../../../../translated_images/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.kn.png) +![scatterplot 2](../../../../translated_images/kn/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.png) ಈ ಬಣ್ಣ ಯೋಜನೆಯ ಬದಲಾವಣೆಯಿಂದ, ಜೇನುತುಪ್ಪ ಬೆಲೆ ಪ್ರತಿ ಪೌಂಡ್ ವರ್ಷದಿಂದ ವರ್ಷಕ್ಕೆ ಸ್ಪಷ್ಟವಾಗಿ ಹೆಚ್ಚುತ್ತಿರುವುದು ಕಾಣುತ್ತದೆ. ನಿಜವಾಗಿಯೂ, ಡೇಟಾದ ಒಂದು ಮಾದರಿ ಸೆಟ್ (ಉದಾಹರಣೆಗೆ, ಅರಿಜೋನಾ ರಾಜ್ಯ) ನೋಡಿದರೆ, ವರ್ಷದಿಂದ ವರ್ಷಕ್ಕೆ ಬೆಲೆ ಏರಿಕೆಯ ಮಾದರಿಯನ್ನು ಕೆಲವು ಅಪವಾದಗಳೊಂದಿಗೆ ಕಾಣಬಹುದು: @@ -89,7 +89,7 @@ sns.relplot(x="priceperlb", y="state", size="year", data=honey, height=15, aspec ``` ನೀವು ಬಿಂದುಗಳ ಗಾತ್ರ ಕ್ರಮೇಣ ಹೆಚ್ಚುತ್ತಿರುವುದನ್ನು ಕಾಣಬಹುದು. -![scatterplot 3](../../../../translated_images/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.kn.png) +![scatterplot 3](../../../../translated_images/kn/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.png) ಇದು ಸರಳ ಸರಬರಾಜು ಮತ್ತು ಬೇಡಿಕೆ ಪ್ರಕರಣವೇ? ಹವಾಮಾನ ಬದಲಾವಣೆ ಮತ್ತು ಕಾಲೋನಿ ಕಾಲಾಪ್ಸ್ ಮುಂತಾದ ಕಾರಣಗಳಿಂದ, ವರ್ಷದಿಂದ ವರ್ಷಕ್ಕೆ ಕಡಿಮೆ ಜೇನುತುಪ್ಪ ಲಭ್ಯವಿದೆಯೇ, ಆದ್ದರಿಂದ ಬೆಲೆ ಏರಿಕೆಯಾಗುತ್ತಿದೆಯೇ? @@ -104,7 +104,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); ``` ಉತ್ತರ: ಹೌದು, 2003 ರ ಸುತ್ತಲೂ ಕೆಲವು ಅಪವಾದಗಳೊಂದಿಗೆ: -![line chart 1](../../../../translated_images/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.kn.png) +![line chart 1](../../../../translated_images/kn/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.png) ✅ Seaborn ಒಂದು ಲೈನ್ ಸುತ್ತಲೂ ಡೇಟಾವನ್ನು ಸಂಗ್ರಹಿಸುವುದರಿಂದ, "ಪ್ರತಿ x ಮೌಲ್ಯದ ಮೇಲೆ ಹಲವಾರು ಅಳೆಯುವಿಕೆಗಳನ್ನು ಸರಾಸರಿ ಮತ್ತು ಸರಾಸರಿಯ ಸುತ್ತಲೂ 95% ವಿಶ್ವಾಸಾಂಶವನ್ನು ಪ್ಲಾಟ್ ಮಾಡುತ್ತದೆ". [ಮೂಲ](https://seaborn.pydata.org/tutorial/relational.html). ಈ ಸಮಯ ತೆಗೆದುಕೊಳ್ಳುವ ವರ್ತನೆಯನ್ನು `ci=None` ಸೇರಿಸುವ ಮೂಲಕ ನಿಷ್ಕ್ರಿಯಗೊಳಿಸಬಹುದು. @@ -114,7 +114,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); sns.relplot(x="year", y="totalprod", kind="line", data=honey); ``` -![line chart 2](../../../../translated_images/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.kn.png) +![line chart 2](../../../../translated_images/kn/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.png) ಉತ್ತರ: ಅಲ್ಲ. ಒಟ್ಟು ಉತ್ಪಾದನೆ ನೋಡಿದರೆ, ಆ ವಿಶೇಷ ವರ್ಷದಲ್ಲಿ ಅದು ಹೆಚ್ಚಿದಂತೆ ತೋರುತ್ತದೆ, ಆದರೂ ಸಾಮಾನ್ಯವಾಗಿ ಈ ವರ್ಷಗಳಲ್ಲಿ ಜೇನುತುಪ್ಪ ಉತ್ಪಾದನೆ ಕಡಿಮೆಯಾಗುತ್ತಿದೆ. @@ -139,7 +139,7 @@ sns.relplot( ``` ಈ ದೃಶ್ಯೀಕರಣದಲ್ಲಿ, ನೀವು ವರ್ಷದಿಂದ ವರ್ಷಕ್ಕೆ ಕಾಲೋನಿಗಳ ಸಂಖ್ಯೆ ಮತ್ತು ಪ್ರತಿ ಕಾಲೋನಿಯ ಉತ್ಪಾದನೆಯನ್ನು ಹೋಲಿಸಬಹುದು, ಕಾಲಮ್‌ಗಳಿಗೆ 3 ರಲ್ಲಿ ರ್ಯಾಪ್ ಹೊಂದಿಸಿ: -![facet grid](../../../../translated_images/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.kn.png) +![facet grid](../../../../translated_images/kn/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.png) ಈ ಡೇಟಾಸೆಟ್‌ಗೆ, ಕಾಲೋನಿಗಳ ಸಂಖ್ಯೆ ಮತ್ತು ಅವರ ಉತ್ಪಾದನೆ ವರ್ಷದಿಂದ ವರ್ಷಕ್ಕೆ ಮತ್ತು ರಾಜ್ಯದಿಂದ ರಾಜ್ಯಕ್ಕೆ ವಿಶೇಷವಾಗಿ ಹೊರಹೊಮ್ಮುವುದಿಲ್ಲ. ಈ ಎರಡು ವ್ಯತ್ಯಾಸಗಳ ನಡುವಿನ ಸಂಬಂಧವನ್ನು ಕಂಡುಹಿಡಿಯಲು ಬೇರೆ ವಿಧಾನವಿದೆಯೇ? @@ -162,7 +162,7 @@ sns.despine(right=False) plt.ylabel('colony yield') ax.figure.legend(); ``` -![superimposed plots](../../../../translated_images/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.kn.png) +![superimposed plots](../../../../translated_images/kn/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.png) 2003 ರ ಸುತ್ತಲೂ ಯಾವುದೇ ಸ್ಪಷ್ಟ ಬದಲಾವಣೆ ಕಾಣಿಸದಿದ್ದರೂ, ಈ ಪಾಠವನ್ನು ಸ್ವಲ್ಪ ಸಂತೋಷಕರವಾಗಿ ಮುಗಿಸಲು ಇದು ಸಹಾಯ ಮಾಡುತ್ತದೆ: ಒಟ್ಟು ಕಾಲೋನಿಗಳ ಸಂಖ್ಯೆ ಕಡಿಮೆಯಾಗುತ್ತಿದೆಯಾದರೂ, ಕಾಲೋನಿಗಳ ಸಂಖ್ಯೆ ಸ್ಥಿರವಾಗುತ್ತಿದೆ ಮತ್ತು ಅವರ ಉತ್ಪಾದನೆ ಪ್ರತಿ ಕಾಲೋನಿಗೂ ಕಡಿಮೆಯಾಗುತ್ತಿದೆ. diff --git a/translations/kn/3-Data-Visualization/13-meaningful-visualizations/README.md b/translations/kn/3-Data-Visualization/13-meaningful-visualizations/README.md index dc81f8b5..29ee3575 100644 --- a/translations/kn/3-Data-Visualization/13-meaningful-visualizations/README.md +++ b/translations/kn/3-Data-Visualization/13-meaningful-visualizations/README.md @@ -47,25 +47,25 @@ CO_OP_TRANSLATOR_METADATA: ಒಂದು ಡೇಟಾ ವಿಜ್ಞಾನಿ ಸರಿಯಾದ ಡೇಟಾಗೆ ಸರಿಯಾದ ಚಾರ್ಟ್ ಆಯ್ಕೆಮಾಡಿದರೂ ಸಹ, ಡೇಟಾವನ್ನು ತೋರಿಸುವ ಹಲವು ಮಾರ್ಗಗಳಿವೆ, ಅವು ಡೇಟಾವನ್ನು ಹಾಳುಮಾಡುವ ಬೆಲೆಗಾಗಿ ಒಂದು ವಿಷಯವನ್ನು ಸಾಬೀತುಪಡಿಸಲು. ಮೋಸಮಯ ಚಾರ್ಟ್‌ಗಳು ಮತ್ತು ಇನ್ಫೋಗ್ರಾಫಿಕ್ಸ್‌ಗಳ ಅನೇಕ ಉದಾಹರಣೆಗಳಿವೆ! -[![ಅಲ್ಬೆರ್ಟೋ ಕೈರೋ ಅವರ "ಹೌ ಚಾರ್ಟ್ಸ್ ಲೈ"](../../../../translated_images/tornado.9f42168791208f970d6faefc11d1226d7ca89518013b14aa66b1c9edcd7678d2.kn.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "ಹೌ ಚಾರ್ಟ್ಸ್ ಲೈ") +[![ಅಲ್ಬೆರ್ಟೋ ಕೈರೋ ಅವರ "ಹೌ ಚಾರ್ಟ್ಸ್ ಲೈ"](../../../../translated_images/kn/tornado.9f42168791208f970d6faefc11d1226d7ca89518013b14aa66b1c9edcd7678d2.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "ಹೌ ಚಾರ್ಟ್ಸ್ ಲೈ") > 🎥 ಮೇಲಿನ ಚಿತ್ರವನ್ನು ಕ್ಲಿಕ್ ಮಾಡಿ ಮೋಸಮಯ ಚಾರ್ಟ್‌ಗಳ ಬಗ್ಗೆ ಸಮ್ಮೇಳನ ಮಾತುಕತೆಗಾಗಿ ಈ ಚಾರ್ಟ್ X ಅಕ್ಷವನ್ನು ತಿರುಗಿಸಿ ಸತ್ಯದ ವಿರುದ್ಧ ತೋರಿಸುತ್ತದೆ, ದಿನಾಂಕ ಆಧಾರಿತವಾಗಿ: -![ಕೆಟ್ಟ ಚಾರ್ಟ್ 1](../../../../translated_images/bad-chart-1.93130f495b748bedfb3423d91b1e754d9026e17f94ad967aecdc9ca7203373bf.kn.png) +![ಕೆಟ್ಟ ಚಾರ್ಟ್ 1](../../../../translated_images/kn/bad-chart-1.93130f495b748bedfb3423d91b1e754d9026e17f94ad967aecdc9ca7203373bf.png) [ಈ ಚಾರ್ಟ್](https://media.firstcoastnews.com/assets/WTLV/images/170ae16f-4643-438f-b689-50d66ca6a8d8/170ae16f-4643-438f-b689-50d66ca6a8d8_1140x641.jpg) ಇನ್ನೂ ಹೆಚ್ಚು ಮೋಸಮಯವಾಗಿದೆ, ಕಣ್ಣು ಬಲಕ್ಕೆ ಸೆಳೆಯುತ್ತದೆ ಮತ್ತು ಸಮಯದೊಂದಿಗೆ COVID ಪ್ರಕರಣಗಳು ವಿವಿಧ ಜಿಲ್ಲೆಗಳಲ್ಲಿ ಕಡಿಮೆಯಾಗಿವೆ ಎಂದು ತೀರ್ಮಾನಿಸುತ್ತದೆ. ವಾಸ್ತವವಾಗಿ, ದಿನಾಂಕಗಳನ್ನು ಗಮನದಿಂದ ನೋಡಿದರೆ, ಅವು ಮೋಸಮಯ ಇಳಿಜಾರಿನ ಪ್ರವೃತ್ತಿಯನ್ನು ನೀಡಲು ಮರುಕ್ರಮಿಸಲಾಗಿದೆ. -![ಕೆಟ್ಟ ಚಾರ್ಟ್ 2](../../../../translated_images/bad-chart-2.c20e36dd4e6f617c0c325878dd421a563885bbf30a394884c147438827254e0e.kn.jpg) +![ಕೆಟ್ಟ ಚಾರ್ಟ್ 2](../../../../translated_images/kn/bad-chart-2.c20e36dd4e6f617c0c325878dd421a563885bbf30a394884c147438827254e0e.jpg) ಈ ಪ್ರಸಿದ್ಧ ಉದಾಹರಣೆ ಬಣ್ಣ ಮತ್ತು ತಿರುಗಿದ Y ಅಕ್ಷವನ್ನು ಬಳಸಿಕೊಂಡು ಮೋಸ ಮಾಡುತ್ತದೆ: ಗನ್-ಸ್ನೇಹಿ ಕಾನೂನುಗಳ ಅಂಗೀಕಾರದ ನಂತರ ಗನ್ ಸಾವುಗಳು ಏರಿಕೆಯಾಗಿವೆ ಎಂದು ತೀರ್ಮಾನಿಸುವ ಬದಲು, ಕಣ್ಣು ವಿರುದ್ಧವನ್ನು ನಂಬುವಂತೆ ಮೋಸ ಮಾಡುತ್ತದೆ: -![ಕೆಟ್ಟ ಚಾರ್ಟ್ 3](../../../../translated_images/bad-chart-3.6865d0afac4108d737558d90a61547d23a8722896397ec792264ee51a1be4be5.kn.jpg) +![ಕೆಟ್ಟ ಚಾರ್ಟ್ 3](../../../../translated_images/kn/bad-chart-3.6865d0afac4108d737558d90a61547d23a8722896397ec792264ee51a1be4be5.jpg) ಈ ವಿಚಿತ್ರ ಚಾರ್ಟ್ ಅನುಪಾತವನ್ನು ಹೇಗೆ ಮ್ಯಾನಿಪ್ಯುಲೇಟ್ ಮಾಡಬಹುದು ಎಂಬುದನ್ನು ಹಾಸ್ಯಾಸ್ಪದ ಪರಿಣಾಮದೊಂದಿಗೆ ತೋರಿಸುತ್ತದೆ: -![ಕೆಟ್ಟ ಚಾರ್ಟ್ 4](../../../../translated_images/bad-chart-4.68cfdf4011b454471053ee1231172747e1fbec2403b4443567f1dc678134f4f2.kn.jpg) +![ಕೆಟ್ಟ ಚಾರ್ಟ್ 4](../../../../translated_images/kn/bad-chart-4.68cfdf4011b454471053ee1231172747e1fbec2403b4443567f1dc678134f4f2.jpg) ತೋಲಿಸಲಾಗದ ವಸ್ತುಗಳನ್ನು ಹೋಲಿಸುವುದು ಇನ್ನೊಂದು ಅನುಮಾನಾಸ್ಪದ ತಂತ್ರ. 'ಸ್ಪ್ಯೂರಿಯಸ್ ಕೊರಿಲೇಶನ್ಸ್' ಬಗ್ಗೆ ಸಂಪೂರ್ಣ ವೆಬ್‌ಸೈಟ್ ಇದೆ, ಇದು ಮೇನ್‌ನ ವಿಚ್ಛೇದನ ದರ ಮತ್ತು ಮಾರ್ಗರಿನ್ ಸೇವನೆ ಮುಂತಾದ ವಿಷಯಗಳನ್ನು ಹೊಂದಿರುವ 'ತಥ್ಯಗಳನ್ನು' ತೋರಿಸುತ್ತದೆ. ರೆಡಿಟ್ ಗುಂಪು ಕೂಡ ಡೇಟಾ ಬಳಕೆಯ [ಕಿರುಕುಳಗಳನ್ನು](https://www.reddit.com/r/dataisugly/top/?t=all) ಸಂಗ್ರಹಿಸುತ್ತದೆ. @@ -100,13 +100,13 @@ CO_OP_TRANSLATOR_METADATA: ನಿಮ್ಮ ಡೇಟಾ ಪಠ್ಯರೂಪದಲ್ಲಿದ್ದರೆ ಮತ್ತು X ಅಕ್ಷದಲ್ಲಿ ಬಹುಶಃVerbose ಆಗಿದ್ದರೆ, ಓದಲು ಸುಲಭವಾಗಲು ಪಠ್ಯವನ್ನು ತಿರುಗಿಸಬಹುದು. [Matplotlib](https://matplotlib.org/stable/tutorials/toolkits/mplot3d.html) 3D ಪ್ಲಾಟಿಂಗ್ ಅನ್ನು ಒದಗಿಸುತ್ತದೆ, ನಿಮ್ಮ ಡೇಟಾ ಅದನ್ನು ಬೆಂಬಲಿಸಿದರೆ. ಸುಧಾರಿತ ಡೇಟಾ ದೃಶ್ಯೀಕರಣಗಳನ್ನು `mpl_toolkits.mplot3d` ಬಳಸಿ ಉತ್ಪಾದಿಸಬಹುದು. -![3D ಪ್ಲಾಟ್‌ಗಳು](../../../../translated_images/3d.0cec12bcc60f0ce7284c63baed1411a843e24716f7d7425de878715ebad54a15.kn.png) +![3D ಪ್ಲಾಟ್‌ಗಳು](../../../../translated_images/kn/3d.0cec12bcc60f0ce7284c63baed1411a843e24716f7d7425de878715ebad54a15.png) ## ಅನಿಮೇಶನ್ ಮತ್ತು 3D ಚಾರ್ಟ್ ಪ್ರದರ್ಶನ ಇಂದಿನ ಅತ್ಯುತ್ತಮ ಡೇಟಾ ದೃಶ್ಯೀಕರಣಗಳಲ್ಲಿ ಕೆಲವು ಅನಿಮೇಟೆಡ್ ಆಗಿವೆ. ಶಿರ್ಲಿ ವು D3 ಬಳಸಿ ಅದ್ಭುತವಾದವುಗಳನ್ನು ಮಾಡಿದ್ದಾಳೆ, ಉದಾಹರಣೆಗೆ '[ಫಿಲ್ಮ್ ಫ್ಲವರ್ಸ್](http://bl.ocks.org/sxywu/raw/d612c6c653fb8b4d7ff3d422be164a5d/)', ಇಲ್ಲಿ ಪ್ರತಿ ಹೂವು ಒಂದು ಚಲನಚಿತ್ರದ ದೃಶ್ಯೀಕರಣವಾಗಿದೆ. ಇನ್ನೊಂದು ಉದಾಹರಣೆ ಗಾರ್ಡಿಯನ್‌ನ 'ಬಸ್ಸ್ಡ್ ಔಟ್', ಇದು ಗ್ರೀನ್ಸಾಕ್ ಮತ್ತು D3 ಜೊತೆಗೆ ಸ್ಕ್ರೋಲಿಟೆಲಿಂಗ್ ಲೇಖನ ಫಾರ್ಮ್ಯಾಟ್ ಅನ್ನು ಸಂಯೋಜಿಸಿ NYC ತನ್ನ ಬಡವರ ಸಮಸ್ಯೆಯನ್ನು ಬಸ್ಸಿಂಗ್ ಮೂಲಕ ಹೇಗೆ ನಿರ್ವಹಿಸುತ್ತದೆ ಎಂಬುದನ್ನು ತೋರಿಸುವ ಇಂಟರಾಕ್ಟಿವ್ ಅನುಭವ. -![ಬಸ್ಸಿಂಗ್](../../../../translated_images/busing.7b9e3b41cd4b981c6d63922cd82004cc1cf18895155536c1d98fcc0999bdd23e.kn.png) +![ಬಸ್ಸಿಂಗ್](../../../../translated_images/kn/busing.7b9e3b41cd4b981c6d63922cd82004cc1cf18895155536c1d98fcc0999bdd23e.png) > "ಬಸ್ಸ್ಡ್ ಔಟ್: ಅಮೆರಿಕ ತನ್ನ ಬಡವರನ್ನು ಹೇಗೆ ಸಾಗಿಸುತ್ತದೆ" [ಗಾರ್ಡಿಯನ್](https://www.theguardian.com/us-news/ng-interactive/2017/dec/20/bussed-out-america-moves-homeless-people-country-study) ನಿಂದ. ದೃಶ್ಯೀಕರಣಗಳು ನಾಡಿಯೆ ಬ್ರೆಮರ್ ಮತ್ತು ಶಿರ್ಲಿ ವು ಅವರಿಂದ @@ -116,7 +116,7 @@ CO_OP_TRANSLATOR_METADATA: ನೀವು ಈ ಸಾಮಾಜಿಕ ಜಾಲತಾಣದ ಅನಿಮೇಟೆಡ್ ವೀಕ್ಷಣೆಯನ್ನು ಪ್ರದರ್ಶಿಸುವ ವೆಬ್ ಅಪ್ಲಿಕೇಶನ್ ಅನ್ನು ಪೂರ್ಣಗೊಳಿಸುವಿರಿ. ಇದು Vue.js ಮತ್ತು D3 ಬಳಸಿ [ಜಾಲತಾಣದ ದೃಶ್ಯ](https://github.com/emiliorizzo/vue-d3-network) ರಚಿಸಲು ನಿರ್ಮಿಸಲಾದ ಗ್ರಂಥಾಲಯವನ್ನು ಬಳಸುತ್ತದೆ. ಅಪ್ಲಿಕೇಶನ್ ಚಾಲನೆಯಲ್ಲಿದ್ದಾಗ, ನೀವು ಡೇಟಾವನ್ನು ಮರುಕ್ರಮಿಸಲು ಪರದೆ ಮೇಲೆ ನೋಡ್‌ಗಳನ್ನು ಎಳೆಯಬಹುದು. -![ಲಿಯಾಸನ್ಸ್](../../../../translated_images/liaisons.7b440b28f6d07ea430244fdf1fc4c64ff48f473f143b8e921846eda1c302aeba.kn.png) +![ಲಿಯಾಸನ್ಸ್](../../../../translated_images/kn/liaisons.7b440b28f6d07ea430244fdf1fc4c64ff48f473f143b8e921846eda1c302aeba.png) ## ಪ್ರಾಜೆಕ್ಟ್: D3.js ಬಳಸಿ ಜಾಲತಾಣವನ್ನು ತೋರಿಸಲು ಚಾರ್ಟ್ ನಿರ್ಮಿಸಿ diff --git a/translations/kn/3-Data-Visualization/R/09-visualization-quantities/README.md b/translations/kn/3-Data-Visualization/R/09-visualization-quantities/README.md index 69c37418..d8410463 100644 --- a/translations/kn/3-Data-Visualization/R/09-visualization-quantities/README.md +++ b/translations/kn/3-Data-Visualization/R/09-visualization-quantities/README.md @@ -66,7 +66,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` ಇಲ್ಲಿ, ನೀವು `ggplot2` ಪ್ಯಾಕೇಜ್ ಅನ್ನು ಸ್ಥಾಪಿಸಿ ನಂತರ `library("ggplot2")` ಕಮಾಂಡ್ ಬಳಸಿ ವರ್ಕ್‌ಸ್ಪೇಸ್‌ಗೆ ಆಮದುಮಾಡುತ್ತೀರಿ. ggplot ನಲ್ಲಿ ಯಾವುದೇ ಪ್ಲಾಟ್ ರಚಿಸಲು `ggplot()` ಫಂಕ್ಷನ್ ಬಳಸಲಾಗುತ್ತದೆ ಮತ್ತು ಡೇಟಾಸೆಟ್, x ಮತ್ತು y ಚರಗಳನ್ನು ಗುಣಲಕ್ಷಣಗಳಾಗಿ ಸೂಚಿಸಲಾಗುತ್ತದೆ. ಈ ಪ್ರಕರಣದಲ್ಲಿ, ನಾವು ರೇಖಾ ಪ್ಲಾಟ್ ರಚಿಸಲು `geom_line()` ಫಂಕ್ಷನ್ ಬಳಸುತ್ತೇವೆ. -![MaxWingspan-lineplot](../../../../../translated_images/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.kn.png) +![MaxWingspan-lineplot](../../../../../translated_images/kn/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.png) ನೀವು ತಕ್ಷಣವೇ ಏನು ಗಮನಿಸುತ್ತೀರಿ? ಕನಿಷ್ಠ ಒಂದು ಹೊರಗಿನ ಮೌಲ್ಯವಿದೆ - ಅದು ತುಂಬಾ ದೊಡ್ಡ ರೆಕ್ಕೆ ವ್ಯಾಪ್ತಿ! 2000+ ಸೆಂ.ಮೀ. ರೆಕ್ಕೆ ವ್ಯಾಪ್ತಿ 20 ಮೀಟರ್‌ಗಿಂತ ಹೆಚ್ಚು - ಮಿನೆಸೋಟಾದಲ್ಲಿ ಪ್ಟೆರೋಡ್ಯಾಕ್ಟೈಲ್ಸ್ ಓಡಾಡುತ್ತಿವೆಯೇ? ಪರಿಶೀಲಿಸೋಣ. @@ -84,7 +84,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` ನಾವು `theme` ನಲ್ಲಿ ಕೋನವನ್ನು ಸೂಚಿಸುತ್ತೇವೆ ಮತ್ತು `xlab()` ಮತ್ತು `ylab()` ನಲ್ಲಿ ಕ್ರಮವಾಗಿ x ಮತ್ತು y ಅಕ್ಷದ ಲೇಬಲ್ಗಳನ್ನು ನಿಗದಿಪಡಿಸುತ್ತೇವೆ. `ggtitle()` ಗ್ರಾಫ್/ಪ್ಲಾಟ್‌ಗೆ ಹೆಸರು ನೀಡುತ್ತದೆ. -![MaxWingspan-lineplot-improved](../../../../../translated_images/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.kn.png) +![MaxWingspan-lineplot-improved](../../../../../translated_images/kn/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.png) ಲೇಬಲ್ಗಳ ತಿರುಗುವಿಕೆಯನ್ನು 45 ಡಿಗ್ರಿ ಗೆ ಹೊಂದಿಸಿದರೂ, ಓದಲು ತುಂಬಾ ಹೆಚ್ಚು ಇದೆ. ಬೇರೆ ತಂತ್ರವನ್ನು ಪ್ರಯತ್ನಿಸೋಣ: ಹೊರಗಿನ ಮೌಲ್ಯಗಳಿಗೆ ಮಾತ್ರ ಲೇಬಲ್ಗಳನ್ನು ನೀಡಿ ಮತ್ತು ಲೇಬಲ್ಗಳನ್ನು ಚಾರ್ಟ್ ಒಳಗೆ ಸೆಟ್ ಮಾಡಿ. ಲೇಬಲಿಂಗ್‌ಗೆ ಹೆಚ್ಚು ಜಾಗ ಮಾಡಲು ಸ್ಕ್ಯಾಟರ್ ಚಾರ್ಟ್ ಬಳಸಬಹುದು: @@ -100,7 +100,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ನೀವು ಏನು ಕಂಡುಹಿಡಿದಿರಿ? -![MaxWingspan-scatterplot](../../../../../translated_images/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.kn.png) +![MaxWingspan-scatterplot](../../../../../translated_images/kn/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.png) ## ನಿಮ್ಮ ಡೇಟಾವನ್ನು ಫಿಲ್ಟರ್ ಮಾಡಿ @@ -119,7 +119,7 @@ ggplot(data=birds_filtered, aes(x=Name, y=MaxWingspan,group=1)) + ``` ನಾವು ಹೊಸ ಡೇಟಾಫ್ರೇಮ್ `birds_filtered` ರಚಿಸಿ ನಂತರ ಸ್ಕ್ಯಾಟರ್ ಪ್ಲಾಟ್ ರಚಿಸಿದ್ದೇವೆ. ಹೊರಗಿನ ಮೌಲ್ಯಗಳನ್ನು ಫಿಲ್ಟರ್ ಮಾಡಿದ ನಂತರ, ನಿಮ್ಮ ಡೇಟಾ ಈಗ ಹೆಚ್ಚು ಸಮ್ಮಿಲಿತ ಮತ್ತು ಅರ್ಥಮಾಡಿಕೊಳ್ಳಬಹುದಾಗಿದೆ. -![MaxWingspan-scatterplot-improved](../../../../../translated_images/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.kn.png) +![MaxWingspan-scatterplot-improved](../../../../../translated_images/kn/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.png) ಈಗ ರೆಕ್ಕೆ ವ್ಯಾಪ್ತಿಯ ದೃಷ್ಟಿಯಿಂದ ಸ್ವಚ್ಛವಾದ ಡೇಟಾಸೆಟ್ ಇದ್ದು, ಈ ಪಕ್ಷಿಗಳ ಬಗ್ಗೆ ಇನ್ನಷ್ಟು ತಿಳಿದುಕೊಳ್ಳೋಣ. @@ -159,7 +159,7 @@ birds_filtered %>% group_by(Category) %>% ``` ಕೆಳಗಿನ ಸ্নಿಪೆಟ್‌ನಲ್ಲಿ, ಡೇಟಾ ಮ್ಯಾನಿಪ್ಯುಲೇಶನ್ ಮತ್ತು ಗುಂಪು ಮಾಡಲು ಸಹಾಯ ಮಾಡುವ [dplyr](https://www.rdocumentation.org/packages/dplyr/versions/0.7.8) ಮತ್ತು [lubridate](https://www.rdocumentation.org/packages/lubridate/versions/1.8.0) ಪ್ಯಾಕೇಜ್‌ಗಳನ್ನು ಸ್ಥಾಪಿಸುತ್ತೇವೆ. ಮೊದಲು, ನೀವು ಪಕ್ಷಿಯ `Category` ಮೂಲಕ ಡೇಟಾವನ್ನು ಗುಂಪುಮಾಡಿ ನಂತರ `MinLength`, `MaxLength`, `MinBodyMass`, `MaxdyMass`, `MinWingspan`, `MaxWingspan` ಕಾಲಮ್‌ಗಳನ್ನು ಸಾರಾಂಶಗೊಳಿಸುತ್ತೀರಿ. ನಂತರ, `ggplot2` ಪ್ಯಾಕೇಜ್ ಬಳಸಿ ಬಾರ್ ಚಾರ್ಟ್ ಅನ್ನು ಪ್ಲಾಟ್ ಮಾಡಿ ವಿಭಿನ್ನ ವರ್ಗಗಳಿಗೆ ಬಣ್ಣಗಳನ್ನು ಮತ್ತು ಲೇಬಲ್ಗಳನ್ನು ಸೂಚಿಸುತ್ತೀರಿ. -![Stacked bar chart](../../../../../translated_images/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.kn.png) +![Stacked bar chart](../../../../../translated_images/kn/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.png) ಈ ಬಾರ್ ಚಾರ್ಟ್ ಓದಲು ಅಸಾಧ್ಯವಾಗಿದೆ ಏಕೆಂದರೆ ಅತಿಯಾದ ಗುಂಪುಮಾಡದ ಡೇಟಾ ಇದೆ. ನೀವು ಪ್ಲಾಟ್ ಮಾಡಲು ಬಯಸುವ ಡೇಟಾವನ್ನು ಮಾತ್ರ ಆಯ್ಕೆಮಾಡಬೇಕು, ಆದ್ದರಿಂದ ಪಕ್ಷಿಗಳ ಉದ್ದವನ್ನು ಅವುಗಳ ವರ್ಗದ ಆಧಾರದ ಮೇಲೆ ನೋಡೋಣ. @@ -174,7 +174,7 @@ ggplot(birds_count,aes(Category,n))+geom_bar(stat="identity")+coord_flip() ``` ಮೊದಲು ನೀವು `Category` ಕಾಲಮ್‌ನ ವಿಶಿಷ್ಟ ಮೌಲ್ಯಗಳನ್ನು ಎಣಿಸಿ ನಂತರ ಅವುಗಳನ್ನು ಹೊಸ ಡೇಟಾಫ್ರೇಮ್ `birds_count` ಗೆ ಸರಿಸುಮಾರು ಮಾಡುತ್ತೀರಿ. ಈ ಸರಿಗೊಳಿಸಿದ ಡೇಟಾವನ್ನು ಅದೇ ಮಟ್ಟದಲ್ಲಿ ಫ್ಯಾಕ್ಟರ್ ಮಾಡಿ ಆದ್ದರಿಂದ ಅದು ಸರಿಗೊಳಿಸಿದ ರೀತಿಯಲ್ಲಿ ಪ್ಲಾಟ್ ಆಗುತ್ತದೆ. ನಂತರ `ggplot2` ಬಳಸಿ ಬಾರ್ ಚಾರ್ಟ್ ರಚಿಸುತ್ತೀರಿ. `coord_flip()` ಹೋರಿಜಾಂಟಲ್ ಬಾರ್‌ಗಳನ್ನು ಪ್ಲಾಟ್ ಮಾಡುತ್ತದೆ. -![category-length](../../../../../translated_images/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.kn.png) +![category-length](../../../../../translated_images/kn/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.png) ಈ ಬಾರ್ ಚಾರ್ಟ್ ಪ್ರತಿ ವರ್ಗದಲ್ಲಿನ ಪಕ್ಷಿಗಳ ಸಂಖ್ಯೆಯ ಉತ್ತಮ ದೃಶ್ಯವನ್ನು ತೋರಿಸುತ್ತದೆ. ಕ್ಷಣದಲ್ಲಿಯೇ ನೀವು ಈ ಪ್ರದೇಶದಲ್ಲಿ Ducks/Geese/Waterfowl ವರ್ಗದಲ್ಲಿರುವ ಪಕ್ಷಿಗಳ ಸಂಖ್ಯೆ ಅತ್ಯಂತ ಹೆಚ್ಚು ಎಂದು ನೋಡಬಹುದು. ಮಿನೆಸೋಟಾ '10,000 ಸರೋವರಗಳ ಭೂಮಿ' ಆಗಿರುವುದರಿಂದ ಇದು ಆಶ್ಚರ್ಯಕರವಲ್ಲ! @@ -197,7 +197,7 @@ ggplot(birds_grouped,aes(Category,MaxLength))+geom_bar(stat="identity")+coord_fl ``` ನಾವು `birds_filtered` ಡೇಟಾವನ್ನು `Category` ಮೂಲಕ ಗುಂಪುಮಾಡಿ ನಂತರ ಬಾರ್ ಗ್ರಾಫ್ ರಚಿಸುತ್ತೇವೆ. -![comparing data](../../../../../translated_images/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.kn.png) +![comparing data](../../../../../translated_images/kn/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.png) ಇಲ್ಲಿ ಏನೂ ಆಶ್ಚರ್ಯಕರವಿಲ್ಲ: ಹುಮ್ಮಿಂಗ್‌ಬರ್ಡ್‌ಗಳು ಪೆಲಿಕಾನ್ಸ್ ಅಥವಾ ಗೀಸೆಗಳಿಗಿಂತ ಕಡಿಮೆ ಗರಿಷ್ಠ ಉದ್ದ ಹೊಂದಿವೆ. ಡೇಟಾ ತಾರ್ಕಿಕವಾಗಿದ್ದರೆ ಚೆನ್ನಾಗಿದೆ! @@ -209,7 +209,7 @@ ggplot(data=birds_grouped, aes(x=Category)) + geom_bar(aes(y=MinLength), stat="identity", position="identity", fill='orange')+ coord_flip() ``` -![super-imposed values](../../../../../translated_images/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.kn.png) +![super-imposed values](../../../../../translated_images/kn/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.png) ## 🚀 ಸವಾಲು diff --git a/translations/kn/3-Data-Visualization/R/10-visualization-distributions/README.md b/translations/kn/3-Data-Visualization/R/10-visualization-distributions/README.md index e666e1a1..d3d474e8 100644 --- a/translations/kn/3-Data-Visualization/R/10-visualization-distributions/README.md +++ b/translations/kn/3-Data-Visualization/R/10-visualization-distributions/README.md @@ -45,7 +45,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + geom_point() + ggtitle("Max Length per order") + coord_flip() ``` -![max length per order](../../../../../translated_images/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.kn.png) +![max length per order](../../../../../translated_images/kn/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.png) ಇದು ಪಕ್ಷಿ ಕ್ರಮದ ಪ್ರತಿ ದೇಹ ಉದ್ದದ ಸಾಮಾನ್ಯ ವಿತರಣೆಯ ಅವಲೋಕನವನ್ನು ನೀಡುತ್ತದೆ, ಆದರೆ ಇದು ನಿಜವಾದ ವಿತರಣೆಯನ್ನು ಪ್ರದರ್ಶಿಸಲು ಅತ್ಯುತ್ತಮ ವಿಧಾನವಲ್ಲ. ಆ ಕಾರ್ಯವನ್ನು ಸಾಮಾನ್ಯವಾಗಿ ಹಿಸ್ಟೋಗ್ರಾಮ್ ರಚಿಸುವ ಮೂಲಕ ನಿರ್ವಹಿಸಲಾಗುತ್ತದೆ. ## ಹಿಸ್ಟೋಗ್ರಾಮ್‌ಗಳೊಂದಿಗೆ ಕೆಲಸ @@ -56,7 +56,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=10)+ylab('Frequency') ``` -![distribution over entire dataset](../../../../../translated_images/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.kn.png) +![distribution over entire dataset](../../../../../translated_images/kn/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.png) ನೀವು ನೋಡಬಹುದು, ಈ ಡೇಟಾಸೆಟ್‌ನ 400+ ಪಕ್ಷಿಗಳ ಬಹುತೇಕವು ಗರಿಷ್ಠ ದೇಹ ಭಾರದ 2000 ಕ್ಕಿಂತ ಕಡಿಮೆ ವ್ಯಾಪ್ತಿಯಲ್ಲಿ ಬರುತ್ತವೆ. `bins` ಪರಿಮಾಣವನ್ನು 30 ರಂತಹ ಹೆಚ್ಚಿನ ಸಂಖ್ಯೆಗೆ ಬದಲಾಯಿಸುವ ಮೂಲಕ ಡೇಟಾದ ಬಗ್ಗೆ ಹೆಚ್ಚಿನ ಒಳನೋಟವನ್ನು ಪಡೆಯಿರಿ: @@ -64,7 +64,7 @@ ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![distribution-30bins](../../../../../translated_images/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.kn.png) +![distribution-30bins](../../../../../translated_images/kn/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.png) ಈ ಚಾರ್ಟ್ ಸ್ವಲ್ಪ ಹೆಚ್ಚು ಸೂಕ್ಷ್ಮ ರೀತಿಯಲ್ಲಿ ವಿತರಣೆಯನ್ನು ತೋರಿಸುತ್ತದೆ. ಎಡಕ್ಕೆ ಕಡಿಮೆ ತಿರುವು ಹೊಂದಿರುವ ಚಾರ್ಟ್ ಅನ್ನು ನೀವು ನಿರ್ದಿಷ್ಟ ವ್ಯಾಪ್ತಿಯೊಳಗಿನ ಡೇಟಾವನ್ನು ಮಾತ್ರ ಆಯ್ಕೆಮಾಡುವ ಮೂಲಕ ರಚಿಸಬಹುದು: @@ -76,7 +76,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![filtered histogram](../../../../../translated_images/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.kn.png) +![filtered histogram](../../../../../translated_images/kn/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.png) ✅ ಇನ್ನಷ್ಟು ಫಿಲ್ಟರ್‌ಗಳು ಮತ್ತು ಡೇಟಾ ಪಾಯಿಂಟ್‌ಗಳನ್ನು ಪ್ರಯತ್ನಿಸಿ. ಡೇಟಾದ ಸಂಪೂರ್ಣ ವಿತರಣೆಯನ್ನು ನೋಡಲು, ಲೇಬಲ್ ಮಾಡಲಾದ ವಿತರಣೆಯನ್ನು ತೋರಿಸಲು `['MaxBodyMass']` ಫಿಲ್ಟರ್ ಅನ್ನು ತೆಗೆದುಹಾಕಿ. @@ -90,7 +90,7 @@ ggplot(data=birds_filtered_1, aes(x=MaxBodyMass, y=MaxLength) ) + ``` ಈ ಎರಡು ಅಂಶಗಳ ನಡುವೆ ನಿರೀಕ್ಷಿತ ಅಕ್ಷದ ಮೇಲೆ ನಿರೀಕ್ಷಿತ ಸಂಬಂಧವಿದೆ, ಒಂದು ವಿಶೇಷವಾಗಿ ಬಲವಾದ ಸಂಯೋಜನೆಯ ಬಿಂದುವಿನೊಂದಿಗೆ: -![2d plot](../../../../../translated_images/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.kn.png) +![2d plot](../../../../../translated_images/kn/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.png) ಹಿಸ್ಟೋಗ್ರಾಮ್‌ಗಳು ಸಂಖ್ಯಾತ್ಮಕ ಡೇಟಾಗಾಗಿ ಡೀಫಾಲ್ಟ್‌ನಲ್ಲಿ ಚೆನ್ನಾಗಿ ಕೆಲಸ ಮಾಡುತ್ತವೆ. ಪಠ್ಯ ಡೇಟಾ ಪ್ರಕಾರ ವಿತರಣೆಯನ್ನು ನೋಡಬೇಕಾದರೆ ಏನು ಮಾಡಬೇಕು? ## ಪಠ್ಯ ಡೇಟಾ ಬಳಸಿ ವಿತರಣೆಯನ್ನು ಅನ್ವೇಷಿಸಿ @@ -121,7 +121,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + scale_fill_manual(name="Conservation Status",values=c("red","green","blue","pink"),labels=c("Endangered","Near Threathened","Vulnerable","Least Concern")) ``` -![wingspan and conservation collation](../../../../../translated_images/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.kn.png) +![wingspan and conservation collation](../../../../../translated_images/kn/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.png) ಕನಿಷ್ಠ ರೆಕ್ಕೆ ವಿಸ್ತಾರ ಮತ್ತು ಸಂರಕ್ಷಣಾ ಸ್ಥಿತಿಯ ನಡುವೆ ಉತ್ತಮ ಸಂಬಂಧವಿಲ್ಲದಂತೆ ತೋರುತ್ತದೆ. ಈ ವಿಧಾನವನ್ನು ಬಳಸಿ ಡೇಟಾಸೆಟ್‌ನ ಇತರ ಅಂಶಗಳನ್ನು ಪರೀಕ್ಷಿಸಿ. ನೀವು ಯಾವುದೇ ಸಂಬಂಧವನ್ನು ಕಂಡುಕೊಳ್ಳುತ್ತೀರಾ? @@ -135,7 +135,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + geom_density() ``` -![density plot](../../../../../translated_images/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.kn.png) +![density plot](../../../../../translated_images/kn/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.png) ನೀವು ನೋಡಬಹುದು, ಈ ಪ್ಲಾಟ್ ಕನಿಷ್ಠ ರೆಕ್ಕೆ ವಿಸ್ತಾರದ ಡೇಟಾಗಾಗಿ ಹಿಂದಿನದನ್ನು ಪ್ರತಿಧ್ವನಿಸುತ್ತದೆ; ಅದು ಸ್ವಲ್ಪ ಸ್ಮೂತ್ ಆಗಿದೆ. ನೀವು ಎರಡನೇ ಚಾರ್ಟ್‌ನಲ್ಲಿ ರಚಿಸಿದ ಜಾಗೃತ MaxBodyMass ರೇಖೆಯನ್ನು ಮರುಸೃಷ್ಟಿಸುವ ಮೂಲಕ ಅದನ್ನು ಚೆನ್ನಾಗಿ ಸ್ಮೂತ್ ಮಾಡಬಹುದು: @@ -143,7 +143,7 @@ ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density() ``` -![bodymass density](../../../../../translated_images/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.kn.png) +![bodymass density](../../../../../translated_images/kn/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.png) ನೀವು ಸ್ಮೂತ್ ಆದರೆ ತುಂಬಾ ಸ್ಮೂತ್ ಅಲ್ಲದ ರೇಖೆಯನ್ನು ಬಯಸಿದರೆ, `adjust` ಪರಿಮಾಣವನ್ನು ಸಂಪಾದಿಸಿ: @@ -151,7 +151,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density(adjust = 1/5) ``` -![less smooth bodymass](../../../../../translated_images/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.kn.png) +![less smooth bodymass](../../../../../translated_images/kn/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.png) ✅ ಈ ರೀತಿಯ ಪ್ಲಾಟ್‌ಗೆ ಲಭ್ಯವಿರುವ ಪರಿಮಾಣಗಳ ಬಗ್ಗೆ ಓದಿ ಮತ್ತು ಪ್ರಯೋಗ ಮಾಡಿ! @@ -161,7 +161,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + ggplot(data=birds_filtered_1,aes(x = MaxBodyMass, fill = Order)) + geom_density(alpha=0.5) ``` -![bodymass per order](../../../../../translated_images/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.kn.png) +![bodymass per order](../../../../../translated_images/kn/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.png) ## 🚀 ಸವಾಲು diff --git a/translations/kn/3-Data-Visualization/R/11-visualization-proportions/README.md b/translations/kn/3-Data-Visualization/R/11-visualization-proportions/README.md index ecfe6124..ae3c2f58 100644 --- a/translations/kn/3-Data-Visualization/R/11-visualization-proportions/README.md +++ b/translations/kn/3-Data-Visualization/R/11-visualization-proportions/README.md @@ -97,7 +97,7 @@ pie(grouped$count,grouped$class, main="Edible?") ``` ಇದು, ಈ ಡೇಟಾದ ಪ್ರಮಾಣಗಳನ್ನು ಈ ಎರಡು ಮಶ್ರೂಮ್ ವರ್ಗಗಳ ಪ್ರಕಾರ ತೋರಿಸುವ ಪೈ ಚಾರ್ಟ್. ಲೇಬಲ್‌ಗಳ ಕ್ರಮವನ್ನು ಸರಿಯಾಗಿ ಪಡೆಯುವುದು ಬಹಳ ಮುಖ್ಯ, ವಿಶೇಷವಾಗಿ ಇಲ್ಲಿ, ಆದ್ದರಿಂದ ಲೇಬಲ್ ಅರೆ ಅನ್ನು ರಚಿಸುವ ಕ್ರಮವನ್ನು ಖಚಿತಪಡಿಸಿಕೊಳ್ಳಿ! -![pie chart](../../../../../translated_images/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.kn.png) +![pie chart](../../../../../translated_images/kn/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.png) ## ಡೋನಟ್ಸ್! @@ -132,7 +132,7 @@ library(webr) PieDonut(habitat, aes(habitat, count=count)) ``` -![donut chart](../../../../../translated_images/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.kn.png) +![donut chart](../../../../../translated_images/kn/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.png) ಈ ಕೋಡ್ ಎರಡು ಲೈಬ್ರರಿಗಳನ್ನು ಬಳಸುತ್ತದೆ - ggplot2 ಮತ್ತು webr. webr ಲೈಬ್ರರಿಯ PieDonut ಫಂಕ್ಷನ್ ಬಳಸಿ, ನಾವು ಸುಲಭವಾಗಿ ಡೋನಟ್ ಚಾರ್ಟ್ ರಚಿಸಬಹುದು! @@ -170,7 +170,7 @@ waffle((cap_color$count/10), rows = 7, title = "Waffle Chart")+scale_fill_manual ವಾಫಲ್ ಚಾರ್ಟ್ ಬಳಸಿ, ನೀವು ಈ ಮಶ್ರೂಮ್ ಡೇಟಾಸೆಟ್‌ನ ಕ್ಯಾಪ್ ಬಣ್ಣಗಳ ಪ್ರಮಾಣಗಳನ್ನು ಸ್ಪಷ್ಟವಾಗಿ ನೋಡಬಹುದು. ಆಸಕ್ತಿದಾಯಕವಾಗಿ, ಹಸಿರು ಕ್ಯಾಪ್ ಇರುವ ಮಶ್ರೂಮ್‌ಗಳು ಬಹಳಿವೆ! -![waffle chart](../../../../../translated_images/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.kn.png) +![waffle chart](../../../../../translated_images/kn/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.png) ಈ ಪಾಠದಲ್ಲಿ, ನೀವು ಪ್ರಮಾಣಗಳನ್ನು ದೃಶ್ಯೀಕರಿಸುವ ಮೂರು ವಿಧಾನಗಳನ್ನು ಕಲಿತಿರಿ. ಮೊದಲು, ನಿಮ್ಮ ಡೇಟಾವನ್ನು ವರ್ಗಗಳಾಗಿ ಗುಂಪುಮಾಡಬೇಕು ಮತ್ತು ನಂತರ ಡೇಟಾವನ್ನು ಪ್ರದರ್ಶಿಸುವ ಅತ್ಯುತ್ತಮ ವಿಧಾನವನ್ನು ನಿರ್ಧರಿಸಬೇಕು - ಪೈ, ಡೋನಟ್ ಅಥವಾ ವಾಫಲ್. ಎಲ್ಲವೂ ರುಚಿಕರವಾಗಿವೆ ಮತ್ತು ಬಳಕೆದಾರರಿಗೆ ಡೇಟಾಸೆಟ್‌ನ ತಕ್ಷಣದ ದೃಶ್ಯವನ್ನು ನೀಡುತ್ತವೆ. diff --git a/translations/kn/3-Data-Visualization/R/12-visualization-relationships/README.md b/translations/kn/3-Data-Visualization/R/12-visualization-relationships/README.md index fe4d59b3..8a73a25a 100644 --- a/translations/kn/3-Data-Visualization/R/12-visualization-relationships/README.md +++ b/translations/kn/3-Data-Visualization/R/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ library(ggplot2) ggplot(honey, aes(x = priceperlb, y = state)) + geom_point(colour = "blue") ``` -![scatterplot 1](../../../../../translated_images/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.kn.png) +![scatterplot 1](../../../../../translated_images/kn/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.png) ಈಗ, ವರ್ಷಗಳ ಪ್ರಗತಿಯನ್ನು ತೋರಿಸಲು ಜೇನುತುಪ್ಪ ಬಣ್ಣದ ಸ್ಕೀಮ್ ಬಳಸಿ ಅದೇ ಡೇಟಾವನ್ನು ತೋರಿಸಿ. ವರ್ಷದಿಂದ ವರ್ಷಕ್ಕೆ ಬದಲಾವಣೆಯನ್ನು ತೋರಿಸಲು 'scale_color_gradientn' ಪರಿಮಾಣವನ್ನು ಸೇರಿಸಬಹುದು: @@ -61,7 +61,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ggplot(honey, aes(x = priceperlb, y = state, color=year)) + geom_point()+scale_color_gradientn(colours = colorspace::heat_hcl(7)) ``` -![scatterplot 2](../../../../../translated_images/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.kn.png) +![scatterplot 2](../../../../../translated_images/kn/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.png) ಈ ಬಣ್ಣದ ಸ್ಕೀಮ್ ಬದಲಾವಣೆಯಿಂದ, ಜೇನುತುಪ್ಪದ ಪೌಂಡ್ ಪ್ರತಿ ಬೆಲೆಯು ವರ್ಷದಿಂದ ವರ್ಷಕ್ಕೆ ಸ್ಪಷ್ಟವಾಗಿ ಹೆಚ್ಚುತ್ತಿರುವುದು ಕಾಣುತ್ತದೆ. ನಿಜವಾಗಿಯೂ, ಡೇಟಾದ ಒಂದು ಮಾದರಿ ಸೆಟ್ (ಉದಾಹರಣೆಗೆ ಅರೆಜೋನಾದ ಒಂದು ರಾಜ್ಯ) ನೋಡಿದರೆ, ವರ್ಷದಿಂದ ವರ್ಷಕ್ಕೆ ಬೆಲೆ ಏರಿಕೆಯ ಮಾದರಿಯನ್ನು ಕಾಣಬಹುದು, ಕೆಲವೊಂದು ಹೊರತುಪಡಿಸಿ: @@ -92,7 +92,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ``` ನೀವು ಬಿಂದುಗಳ ಗಾತ್ರ ಕ್ರಮೇಣ ಹೆಚ್ಚುತ್ತಿರುವುದನ್ನು ಕಾಣಬಹುದು. -![scatterplot 3](../../../../../translated_images/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.kn.png) +![scatterplot 3](../../../../../translated_images/kn/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.png) ಇದು ಸರಳ ಸರಬರಾಜು ಮತ್ತು ಬೇಡಿಕೆ ಪ್ರಕರಣವೇ? ಹವಾಮಾನ ಬದಲಾವಣೆ ಮತ್ತು ಕಾಲೋನಿ ಕಾಲಾಪ್ಸ್ ಮುಂತಾದ ಕಾರಣಗಳಿಂದ, ವರ್ಷದಿಂದ ವರ್ಷಕ್ಕೆ ಖರೀದಿಗೆ ಲಭ್ಯವಿರುವ ಜೇನುತುಪ್ಪ ಕಡಿಮೆಯಾಗುತ್ತಿದೆಯೇ, ಆದ್ದರಿಂದ ಬೆಲೆ ಏರುತ್ತಿದೆಯೇ? @@ -107,7 +107,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab ``` ಉತ್ತರ: ಹೌದು, 2003 ರ ಸುತ್ತಲೂ ಕೆಲವು ಹೊರತುಪಡಿಸಿ: -![line chart 1](../../../../../translated_images/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.kn.png) +![line chart 1](../../../../../translated_images/kn/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.png) ಪ್ರಶ್ನೆ: 2003 ರಲ್ಲಿ ಜೇನುತುಪ್ಪದ ಸರಬರಾಜಿನಲ್ಲಿ ಏರಿಕೆಯನ್ನು ನೋಡಬಹುದೇ? ಒಟ್ಟು ಉತ್ಪಾದನೆಯನ್ನು ವರ್ಷದಿಂದ ವರ್ಷಕ್ಕೆ ನೋಡಿದರೆ? @@ -115,7 +115,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab qplot(honey$year,honey$totalprod, geom='smooth', span =0.5, xlab = "year",ylab = "totalprod") ``` -![line chart 2](../../../../../translated_images/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.kn.png) +![line chart 2](../../../../../translated_images/kn/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.png) ಉತ್ತರ: ಅಷ್ಟು ಅಲ್ಲ. ಒಟ್ಟು ಉತ್ಪಾದನೆಯನ್ನು ನೋಡಿದರೆ, ಆ ವಿಶೇಷ ವರ್ಷದಲ್ಲಿ ಅದು ಹೆಚ್ಚಿದಂತೆ ತೋರುತ್ತದೆ, ಆದರೂ ಸಾಮಾನ್ಯವಾಗಿ ಈ ವರ್ಷಗಳಲ್ಲಿ ಜೇನುತುಪ್ಪದ ಉತ್ಪಾದನೆ ಕಡಿಮೆಯಾಗುತ್ತಿದೆ. @@ -135,7 +135,7 @@ ggplot(honey, aes(x=yieldpercol, y = numcol,group = 1)) + ``` ಈ ದೃಶ್ಯೀಕರಣದಲ್ಲಿ, ನೀವು ವರ್ಷದಿಂದ ವರ್ಷಕ್ಕೆ ಮತ್ತು ರಾಜ್ಯದಿಂದ ರಾಜ್ಯಕ್ಕೆ yield per colony ಮತ್ತು ಕಾಲೋನಿಗಳ ಸಂಖ್ಯೆಯನ್ನು ಪಕ್ಕಪಕ್ಕವಾಗಿ 3 ಕಾಲಮ್‌ಗಳೊಂದಿಗೆ ಹೋಲಿಸಬಹುದು: -![facet grid](../../../../../translated_images/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.kn.png) +![facet grid](../../../../../translated_images/kn/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.png) ಈ ಡೇಟಾಸೆಟ್‌ಗೆ, ಕಾಲೋನಿಗಳ ಸಂಖ್ಯೆ ಮತ್ತು ಅವರ ಉತ್ಪಾದನೆಯ ಕುರಿತು ವರ್ಷದಿಂದ ವರ್ಷಕ್ಕೆ ಮತ್ತು ರಾಜ್ಯದಿಂದ ರಾಜ್ಯಕ್ಕೆ ವಿಶೇಷವಾಗಿ ಏನೂ ಹೊರಹೊಮ್ಮುವುದಿಲ್ಲ. ಈ ಎರಡು ವ್ಯತ್ಯಾಸಗಳ ನಡುವಿನ ಸಂಬಂಧವನ್ನು ಕಂಡುಹಿಡಿಯಲು ಬೇರೆ ವಿಧಾನವಿದೆಯೇ? @@ -152,7 +152,7 @@ plot(honey$year, honey$yieldpercol, pch = 17, col = 3, axis(side = 4, at = pretty(range(y2))) mtext("colony yield", side = 4, line = 3) ``` -![superimposed plots](../../../../../translated_images/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.kn.png) +![superimposed plots](../../../../../translated_images/kn/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.png) 2003 ರ ಸುತ್ತಲೂ ಏನೂ ವಿಶೇಷವಾಗಿ ಗಮನ ಸೆಳೆಯದಿದ್ದರೂ, ಈ ಪಾಠವನ್ನು ಸ್ವಲ್ಪ ಸಂತೋಷಕರವಾಗಿ ಮುಗಿಸಲು ಇದು ಸಹಾಯ ಮಾಡುತ್ತದೆ: ಒಟ್ಟಾರೆ ಕಾಲೋನಿಗಳ ಸಂಖ್ಯೆ ಕಡಿಮೆಯಾಗುತ್ತಿದೆಯಾದರೂ, ಕಾಲೋನಿಗಳ ಸಂಖ್ಯೆ ಸ್ಥಿರವಾಗುತ್ತಿದೆ ಮತ್ತು ಅವರ yield per colony ಕಡಿಮೆಯಾಗುತ್ತಿದೆ. diff --git a/translations/kn/3-Data-Visualization/R/13-meaningful-vizualizations/README.md b/translations/kn/3-Data-Visualization/R/13-meaningful-vizualizations/README.md index f7221301..2e6a1608 100644 --- a/translations/kn/3-Data-Visualization/R/13-meaningful-vizualizations/README.md +++ b/translations/kn/3-Data-Visualization/R/13-meaningful-vizualizations/README.md @@ -47,25 +47,25 @@ CO_OP_TRANSLATOR_METADATA: ಒಂದು ಡೇಟಾ ವಿಜ್ಞಾನಿ ಸರಿಯಾದ ಡೇಟಾ ಗೆ ಸರಿಯಾದ ಚಾರ್ಟ್ ಆಯ್ಕೆಮಾಡಿದರೂ ಸಹ, ಡೇಟಾವನ್ನು ತೋರಿಸುವ ಹಲವು ಮಾರ್ಗಗಳಿವೆ, ಅವು ಡೇಟಾವನ್ನು ಹಾಳುಮಾಡುವ ಬೆಲೆಗಾಗಿ ಒಂದು ವಿಷಯವನ್ನು ಸಾಬೀತುಪಡಿಸಲು. ಮೋಸಮಯ ಚಾರ್ಟ್‌ಗಳು ಮತ್ತು ಇನ್ಫೋಗ್ರಾಫಿಕ್ಸ್‌ಗಳ ಅನೇಕ ಉದಾಹರಣೆಗಳಿವೆ! -[![ಅಲ್ಬೆರ್ಟೋ ಕೈರೋ ಅವರ "ಹೌ ಚಾರ್ಟ್ಸ್ ಲೈ"](../../../../../translated_images/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.kn.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "ಹೌ ಚಾರ್ಟ್ಸ್ ಲೈ") +[![ಅಲ್ಬೆರ್ಟೋ ಕೈರೋ ಅವರ "ಹೌ ಚಾರ್ಟ್ಸ್ ಲೈ"](../../../../../translated_images/kn/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "ಹೌ ಚಾರ್ಟ್ಸ್ ಲೈ") > 🎥 ಮೇಲಿನ ಚಿತ್ರವನ್ನು ಕ್ಲಿಕ್ ಮಾಡಿ ಮೋಸಮಯ ಚಾರ್ಟ್‌ಗಳ ಬಗ್ಗೆ ಸಮ್ಮೇಳನ ಮಾತುಕತೆ ನೋಡಿರಿ ಈ ಚಾರ್ಟ್ X ಅಕ್ಷವನ್ನು ತಿರುಗಿಸಿ ಸತ್ಯದ ವಿರುದ್ಧ ತೋರಿಸುತ್ತದೆ, ದಿನಾಂಕ ಆಧಾರಿತ: -![ಕೆಟ್ಟ ಚಾರ್ಟ್ 1](../../../../../translated_images/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.kn.png) +![ಕೆಟ್ಟ ಚಾರ್ಟ್ 1](../../../../../translated_images/kn/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.png) [ಈ ಚಾರ್ಟ್](https://media.firstcoastnews.com/assets/WTLV/images/170ae16f-4643-438f-b689-50d66ca6a8d8/170ae16f-4643-438f-b689-50d66ca6a8d8_1140x641.jpg) ಇನ್ನೂ ಹೆಚ್ಚು ಮೋಸಮಯವಾಗಿದೆ, ಕಣ್ಣು ಬಲಕ್ಕೆ ಸೆಳೆಯುತ್ತದೆ ಮತ್ತು ಸಮಯದೊಂದಿಗೆ COVID ಪ್ರಕರಣಗಳು ವಿವಿಧ ಜಿಲ್ಲೆಗಳಲ್ಲಿ ಕಡಿಮೆಯಾದಂತೆ ತೋರುತ್ತದೆ. ನಿಜವಾಗಿ, ದಿನಾಂಕಗಳನ್ನು ಗಮನದಿಂದ ನೋಡಿದರೆ, ಅವು ಮೋಸಮಯ ಇಳಿಜಾರಿನ ಪ್ರವೃತ್ತಿಯನ್ನು ನೀಡಲು ಮರುಕ್ರಮಿಸಲಾಗಿದೆ. -![ಕೆಟ್ಟ ಚಾರ್ಟ್ 2](../../../../../translated_images/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.kn.jpg) +![ಕೆಟ್ಟ ಚಾರ್ಟ್ 2](../../../../../translated_images/kn/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.jpg) ಈ ಪ್ರಸಿದ್ಧ ಉದಾಹರಣೆ ಬಣ್ಣ ಮತ್ತು ತಿರುಗಿದ Y ಅಕ್ಷವನ್ನು ಬಳಸಿಕೊಂಡು ಮೋಸ ಮಾಡುತ್ತದೆ: ಗನ್-ಸ್ನೇಹಿ ಕಾನೂನುಗಳ ಅಂಗೀಕಾರದ ನಂತರ ಗನ್ ಸಾವುಗಳು ಏರಿಕೆಯಾಗಿವೆ ಎಂದು ನಿರ್ಣಯಿಸುವ ಬದಲು, ಕಣ್ಣು ವಿರುದ್ಧವನ್ನು ನಂಬುವಂತೆ ಮಾಡುತ್ತದೆ: -![ಕೆಟ್ಟ ಚಾರ್ಟ್ 3](../../../../../translated_images/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.kn.jpg) +![ಕೆಟ್ಟ ಚಾರ್ಟ್ 3](../../../../../translated_images/kn/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.jpg) ಈ ವಿಚಿತ್ರ ಚಾರ್ಟ್ ಅನುಪಾತವನ್ನು ಹೇಗೆ ಮ್ಯಾನಿಪ್ಯುಲೇಟ್ ಮಾಡಬಹುದು ಎಂಬುದನ್ನು ಹಾಸ್ಯಾಸ್ಪದ ಪರಿಣಾಮದೊಂದಿಗೆ ತೋರಿಸುತ್ತದೆ: -![ಕೆಟ್ಟ ಚಾರ್ಟ್ 4](../../../../../translated_images/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.kn.jpg) +![ಕೆಟ್ಟ ಚಾರ್ಟ್ 4](../../../../../translated_images/kn/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.jpg) ತೂಕಮಾಡಲಾಗದ ವಸ್ತುಗಳನ್ನು ಹೋಲಿಸುವುದು ಇನ್ನೊಂದು ಅನುಮಾನಾಸ್ಪದ ತಂತ್ರ. 'ಸ್ಪ್ಯೂರಿಯಸ್ ಕೊರಿಲೇಶನ್ಸ್' ಬಗ್ಗೆ ಒಂದು [ಅದ್ಭುತ ವೆಬ್ ಸೈಟ್](https://tylervigen.com/spurious-correlations) ಇದೆ, ಇದು ಮೇನ್‌ನ ವಿಚ್ಛೇದನ ದರ ಮತ್ತು ಮಾರ್ಜರಿನ್ ಬಳಕೆಯಂತಹ ವಿಷಯಗಳನ್ನು ಹೊಂದಿರುವ 'ವಾಸ್ತವಗಳನ್ನು' ತೋರಿಸುತ್ತದೆ. ರೆಡ್ಡಿಟ್ ಗುಂಪು ಕೂಡ ಡೇಟಾ ಬಳಕೆಯ [ಕಿರುಕುಳಗಳನ್ನು](https://www.reddit.com/r/dataisugly/top/?t=all) ಸಂಗ್ರಹಿಸುತ್ತದೆ. @@ -100,13 +100,13 @@ CO_OP_TRANSLATOR_METADATA: ನಿಮ್ಮ ಡೇಟಾ ಪಠ್ಯಾತ್ಮಕ ಮತ್ತು X ಅಕ್ಷದಲ್ಲಿ ವಿಸ್ತಾರವಾಗಿದ್ದರೆ, ಓದಲು ಸುಲಭವಾಗಲು ಪಠ್ಯವನ್ನು ತಿರುಗಿಸಬಹುದು. [plot3D](https://cran.r-project.org/web/packages/plot3D/index.html) 3D ಪ್ಲಾಟಿಂಗ್ ಅನ್ನು ಒದಗಿಸುತ್ತದೆ, ನಿಮ್ಮ ಡೇಟಾ ಅದನ್ನು ಬೆಂಬಲಿಸಿದರೆ. ಅದನ್ನು ಬಳಸಿ ಸುಧಾರಿತ ಡೇಟಾ ದೃಶ್ಯೀಕರಣಗಳನ್ನು ಸೃಷ್ಟಿಸಬಹುದು. -![3D ಪ್ಲಾಟ್‌ಗಳು](../../../../../translated_images/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.kn.png) +![3D ಪ್ಲಾಟ್‌ಗಳು](../../../../../translated_images/kn/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.png) ## ಅನಿಮೇಷನ್ ಮತ್ತು 3D ಚಾರ್ಟ್ ಪ್ರದರ್ಶನ ಇಂದಿನ ಅತ್ಯುತ್ತಮ ಡೇಟಾ ದೃಶ್ಯೀಕರಣಗಳಲ್ಲಿ ಕೆಲವು ಅನಿಮೇಟೆಡ್ ಆಗಿವೆ. ಶಿರ್ಲಿ ವು D3 ಬಳಸಿ ಅದ್ಭುತ ಅನಿಮೇಟೆಡ್ ದೃಶ್ಯೀಕರಣಗಳನ್ನು ಮಾಡಿದ್ದಾಳೆ, ಉದಾಹರಣೆಗೆ '[ಫಿಲ್ಮ್ ಫ್ಲವರ್ಸ್](http://bl.ocks.org/sxywu/raw/d612c6c653fb8b4d7ff3d422be164a5d/)', ಇಲ್ಲಿ ಪ್ರತಿ ಹೂವು ಒಂದು ಚಲನಚಿತ್ರದ ದೃಶ್ಯೀಕರಣವಾಗಿದೆ. ಇನ್ನೊಂದು ಉದಾಹರಣೆ ಗಾರ್ಡಿಯನ್‌ನ 'ಬಸ್ಸ್ಡ್ ಔಟ್', ಇದು ಗ್ರೀನ್ಸಾಕ್ ಮತ್ತು D3 ಜೊತೆಗೆ ಇಂಟರಾಕ್ಟಿವ್ ಅನುಭವ ಮತ್ತು ಸ್ಕ್ರೋಲಿಟೆಲ್ಲಿಂಗ್ ಲೇಖನ ರೂಪದಲ್ಲಿ NYC ತನ್ನ ಗೃಹವಿಹೀನ ಸಮಸ್ಯೆಯನ್ನು ಬಸ್ಸಿಂಗ್ ಮೂಲಕ ಹೇಗೆ ನಿರ್ವಹಿಸುತ್ತದೆ ಎಂಬುದನ್ನು ತೋರಿಸುತ್ತದೆ. -![ಬಸ್ಸಿಂಗ್](../../../../../translated_images/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.kn.png) +![ಬಸ್ಸಿಂಗ್](../../../../../translated_images/kn/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.png) > "ಬಸ್ಸ್ಡ್ ಔಟ್: ಅಮೆರಿಕ ಗೃಹವಿಹೀನರನ್ನು ಹೇಗೆ ಸಾಗಿಸುತ್ತದೆ" [ಗಾರ್ಡಿಯನ್](https://www.theguardian.com/us-news/ng-interactive/2017/dec/20/bussed-out-america-moves-homeless-people-country-study) ನಿಂದ. ದೃಶ್ಯೀಕರಣಗಳು ನಾಡಿಯೆ ಬ್ರೆಮರ್ ಮತ್ತು ಶಿರ್ಲಿ ವು ಅವರಿಂದ @@ -116,7 +116,7 @@ CO_OP_TRANSLATOR_METADATA: ನೀವು ಈ ಸಾಮಾಜಿಕ ಜಾಲದೃಶ್ಯೀಕರಣದ ಅನಿಮೇಟೆಡ್ ವೀಕ್ಷಣೆಯನ್ನು ಪ್ರದರ್ಶಿಸುವ ವೆಬ್ ಅಪ್ಲಿಕೇಶನ್ ಅನ್ನು ಪೂರ್ಣಗೊಳಿಸುವಿರಿ. ಇದು Vue.js ಮತ್ತು D3 ಬಳಸಿ [ಜಾಲದೃಶ್ಯ](https://github.com/emiliorizzo/vue-d3-network) ಸೃಷ್ಟಿಸಲು ನಿರ್ಮಿಸಲಾದ ಗ್ರಂಥಾಲಯವನ್ನು ಬಳಸುತ್ತದೆ. ಅಪ್ಲಿಕೇಶನ್ ಚಾಲನೆಯಲ್ಲಿದ್ದಾಗ, ನೀವು ನೊಡ್‌ಗಳನ್ನು ಪರದೆ ಮೇಲೆ ಎಳೆಯಬಹುದು ಮತ್ತು ಡೇಟಾವನ್ನು ಮರುಸಂರಚಿಸಬಹುದು. -![ಲಿಯಾಜನ್ಸ್](../../../../../translated_images/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.kn.png) +![ಲಿಯಾಜನ್ಸ್](../../../../../translated_images/kn/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.png) ## ಪ್ರಾಜೆಕ್ಟ್: D3.js ಬಳಸಿ ಜಾಲವನ್ನು ತೋರಿಸಲು ಚಾರ್ಟ್ ನಿರ್ಮಿಸಿ diff --git a/translations/kn/3-Data-Visualization/README.md b/translations/kn/3-Data-Visualization/README.md index cebdb72e..e692a19b 100644 --- a/translations/kn/3-Data-Visualization/README.md +++ b/translations/kn/3-Data-Visualization/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # ದೃಶ್ಯೀಕರಣಗಳು -![ಲ್ಯಾವೆಂಡರ್ ಹೂವಿನ ಮೇಲೆ ಒಂದು ಜೇನುತುಪ್ಪ](../../../translated_images/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.kn.jpg) +![ಲ್ಯಾವೆಂಡರ್ ಹೂವಿನ ಮೇಲೆ ಒಂದು ಜೇನುತುಪ್ಪ](../../../translated_images/kn/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.jpg) > ಫೋಟೋ ಜೆನ್ನಾ ಲೀ ಅವರಿಂದ ಅನ್ಸ್ಪ್ಲ್ಯಾಶ್ ನಲ್ಲಿ diff --git a/translations/kn/4-Data-Science-Lifecycle/14-Introduction/README.md b/translations/kn/4-Data-Science-Lifecycle/14-Introduction/README.md index 53e75296..8cec84c1 100644 --- a/translations/kn/4-Data-Science-Lifecycle/14-Introduction/README.md +++ b/translations/kn/4-Data-Science-Lifecycle/14-Introduction/README.md @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: ಈ ಪಾಠವು ಜೀವನಚಕ್ರದ 3 ಭಾಗಗಳ ಮೇಲೆ ಕೇಂದ್ರೀಕರಿಸುತ್ತದೆ: ಸೆರೆಹಿಡಿಯುವುದು, ಪ್ರಕ್ರಿಯೆಗೊಳಿಸುವುದು ಮತ್ತು ನಿರ್ವಹಣೆ. -![ಡೇಟಾ ಸೈನ್ಸ್ ಜೀವನಚಕ್ರದ ಚಿತ್ರಣ](../../../../translated_images/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.kn.jpg) +![ಡೇಟಾ ಸೈನ್ಸ್ ಜೀವನಚಕ್ರದ ಚಿತ್ರಣ](../../../../translated_images/kn/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.jpg) > ಚಿತ್ರ: [ಬರ್ಕ್ಲಿ ಮಾಹಿತಿ ಶಾಲೆ](https://ischoolonline.berkeley.edu/data-science/what-is-data-science/) ## ಸೆರೆಹಿಡಿಯುವುದು @@ -97,7 +97,7 @@ CO_OP_TRANSLATOR_METADATA: |ಟೀಮ್ ಡೇಟಾ ಸೈನ್ಸ್ ಪ್ರಕ್ರಿಯೆ (TDSP)|ಕ್ರಾಸ್-ಇಂಡಸ್ಟ್ರಿ ಸ್ಟ್ಯಾಂಡರ್ಡ್ ಪ್ರಕ್ರಿಯೆ ಫಾರ್ ಡೇಟಾ ಮೈನಿಂಗ್ (CRISP-DM)| |--|--| -|![ಟೀಮ್ ಡೇಟಾ ಸೈನ್ಸ್ ಜೀವನಚಕ್ರ](../../../../translated_images/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.kn.png) | ![ಡೇಟಾ ಸೈನ್ಸ್ ಪ್ರಕ್ರಿಯೆ ಅಲಯನ್ಸ್ ಚಿತ್ರ](../../../../translated_images/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.kn.png) | +|![ಟೀಮ್ ಡೇಟಾ ಸೈನ್ಸ್ ಜೀವನಚಕ್ರ](../../../../translated_images/kn/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.png) | ![ಡೇಟಾ ಸೈನ್ಸ್ ಪ್ರಕ್ರಿಯೆ ಅಲಯನ್ಸ್ ಚಿತ್ರ](../../../../translated_images/kn/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.png) | | ಚಿತ್ರ: [Microsoft](https://docs.microsoft.comazure/architecture/data-science-process/lifecycle) | ಚಿತ್ರ: [ಡೇಟಾ ಸೈನ್ಸ್ ಪ್ರಕ್ರಿಯೆ ಅಲಯನ್ಸ್](https://www.datascience-pm.com/crisp-dm-2/) | ## [ಪೋಸ್ಟ್-ಲೇಕ್ಚರ್ ಕ್ವಿಜ್](https://ff-quizzes.netlify.app/en/ds/quiz/27) diff --git a/translations/kn/4-Data-Science-Lifecycle/README.md b/translations/kn/4-Data-Science-Lifecycle/README.md index 2cc27890..d1e9a0d9 100644 --- a/translations/kn/4-Data-Science-Lifecycle/README.md +++ b/translations/kn/4-Data-Science-Lifecycle/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # ಡೇಟಾ ಸೈನ್ಸ್ ಜೀವನಚಕ್ರ -![communication](../../../translated_images/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.kn.jpg) +![communication](../../../translated_images/kn/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.jpg) > ಫೋಟೋ ಹೆಡ್‌ವೇ ಅವರಿಂದ ಅನ್ಸ್ಪ್ಲ್ಯಾಶ್ ನಲ್ಲಿ ಈ ಪಾಠಗಳಲ್ಲಿ, ನೀವು ಡೇಟಾ ಸೈನ್ಸ್ ಜೀವನಚಕ್ರದ ಕೆಲವು ಅಂಶಗಳನ್ನು, ಡೇಟಾ ಸುತ್ತಲೂ ವಿಶ್ಲೇಷಣೆ ಮತ್ತು ಸಂವಹನವನ್ನು ಅನ್ವೇಷಿಸುವಿರಿ. diff --git a/translations/kn/5-Data-Science-In-Cloud/18-Low-Code/README.md b/translations/kn/5-Data-Science-In-Cloud/18-Low-Code/README.md index fded9ad5..de3972e4 100644 --- a/translations/kn/5-Data-Science-In-Cloud/18-Low-Code/README.md +++ b/translations/kn/5-Data-Science-In-Cloud/18-Low-Code/README.md @@ -62,7 +62,7 @@ CO_OP_TRANSLATOR_METADATA: ಯೋಜನೆಗಳನ್ನು ರಚಿಸುವುದು ಮತ್ತು ನಿರ್ಮಿಸುವುದು ನಿಮ್ಮ ಕೌಶಲ್ಯ ಮತ್ತು ಜ್ಞಾನವನ್ನು ಪರೀಕ್ಷಿಸಲು ಅತ್ಯುತ್ತಮ ಮಾರ್ಗವೆಂದು ಯಾವುದೇ ಸಂಶಯವಿಲ್ಲ. ಈ ಪಾಠದಲ್ಲಿ, ನಾವು ಅಜೂರ್ ಎಂಎಲ್ ಸ್ಟುಡಿಯೋದಲ್ಲಿ ಹೃದಯ ವೈಫಲ್ಯ ದಾಳಿಗಳ ಭವಿಷ್ಯವಾಣಿಗಾಗಿ ಡೇಟಾ ಸೈನ್ಸ್ ಯೋಜನೆಯನ್ನು ಎರಡು ವಿಭಿನ್ನ ವಿಧಾನಗಳಲ್ಲಿ ಅನ್ವೇಷಿಸುವೆವು: ಲೋ ಕೋಡ್/ನೋ ಕೋಡ್ ಮತ್ತು ಅಜೂರ್ ಎಂಎಲ್ SDK ಮೂಲಕ, ಕೆಳಗಿನ ಚಿತ್ರಣದಂತೆ: -![project-schema](../../../../translated_images/project-schema.736f6e403f321eb48d10242b3f4334dc6ccf0eabef8ff87daf52b89781389fcb.kn.png) +![project-schema](../../../../translated_images/kn/project-schema.736f6e403f321eb48d10242b3f4334dc6ccf0eabef8ff87daf52b89781389fcb.png) ಪ್ರತಿ ವಿಧಾನಕ್ಕೂ ತನ್ನದೇ ಆದ ಲಾಭ ಮತ್ತು ನಷ್ಟಗಳಿವೆ. ಲೋ ಕೋಡ್/ನೋ ಕೋಡ್ ವಿಧಾನವು GUI (ಗ್ರಾಫಿಕಲ್ ಯೂಸರ್ ಇಂಟರ್ಫೇಸ್) ಜೊತೆಗೆ ಸಂವಹನ ಮಾಡುವುದರಿಂದ ಪ್ರಾರಂಭಿಸಲು ಸುಲಭವಾಗಿದೆ, ಕೋಡ್ ಬಗ್ಗೆ ಪೂರ್ವಜ್ಞಾನ ಅಗತ್ಯವಿಲ್ಲ. ಈ ವಿಧಾನವು ಯೋಜನೆಯ ಸಾಧ್ಯತೆಯನ್ನು ತ್ವರಿತವಾಗಿ ಪರೀಕ್ಷಿಸಲು ಮತ್ತು POC (ಪ್ರೂಫ್ ಆಫ್ ಕಾಂಸೆಪ್ಟ್) ರಚಿಸಲು ಸಹಾಯ ಮಾಡುತ್ತದೆ. ಆದರೆ, ಯೋಜನೆ ವಿಸ್ತಾರವಾಗುತ್ತಾ ಮತ್ತು ಉತ್ಪಾದನೆಗೆ ಸಿದ್ಧವಾಗಬೇಕಾದಾಗ, GUI ಮೂಲಕ ಸಂಪನ್ಮೂಲಗಳನ್ನು ರಚಿಸುವುದು ಸಾಧ್ಯವಿಲ್ಲ. ಸಂಪನ್ಮೂಲಗಳ ರಚನೆ, ಮಾದರಿ ನಿಯೋಜನೆ ಸೇರಿದಂತೆ ಎಲ್ಲವನ್ನೂ ಪ್ರೋಗ್ರಾಮ್ಯಾಟಿಕಾಗಿ ಸ್ವಯಂಚಾಲಿತಗೊಳಿಸಬೇಕಾಗುತ್ತದೆ. ಈ ಸಂದರ್ಭದಲ್ಲಿ ಅಜೂರ್ ಎಂಎಲ್ SDK ಬಳಕೆ ತಿಳಿದುಕೊಳ್ಳುವುದು ಅತ್ಯಂತ ಮುಖ್ಯ. @@ -115,15 +115,15 @@ CO_OP_TRANSLATOR_METADATA: 1. ನಿಮ್ಮ ಅಜೂರ್ ಸಬ್ಸ್ಕ್ರಿಪ್ಷನ್‌ಗೆ ಸಂಬಂಧಿಸಿದ ಮೈಕ್ರೋಸಾಫ್ಟ್ ಕ್ರೆಡೆನ್ಷಿಯಲ್ಸ್ ಬಳಸಿ [ಅಜೂರ್ ಪೋರ್ಟಲ್](https://ms.portal.azure.com/) ಗೆ ಸೈನ್ ಇನ್ ಆಗಿ. 2. **＋ಸಂಪನ್ಮೂಲ ರಚಿಸಿ** ಆಯ್ಕೆಮಾಡಿ - ![workspace-1](../../../../translated_images/workspace-1.ac8694d60b073ed1ae8333d71244dc8a9b3e439d54593724f98f1beefdd27b08.kn.png) + ![workspace-1](../../../../translated_images/kn/workspace-1.ac8694d60b073ed1ae8333d71244dc8a9b3e439d54593724f98f1beefdd27b08.png) ಮೆಷಿನ್ ಲರ್ನಿಂಗ್ ಅನ್ನು ಹುಡುಕಿ ಮತ್ತು ಮೆಷಿನ್ ಲರ್ನಿಂಗ್ ಟೈಲ್ ಆಯ್ಕೆಮಾಡಿ - ![workspace-2](../../../../translated_images/workspace-2.ae7c486db8796147075e4a56566aa819827dd6c4c8d18d64590317c3be625f17.kn.png) + ![workspace-2](../../../../translated_images/kn/workspace-2.ae7c486db8796147075e4a56566aa819827dd6c4c8d18d64590317c3be625f17.png) ರಚನೆ ಬಟನ್ ಕ್ಲಿಕ್ ಮಾಡಿ - ![workspace-3](../../../../translated_images/workspace-3.398ca4a5858132cce584db9df10c5a011cd9075eb182e647a77d5cac01771eea.kn.png) + ![workspace-3](../../../../translated_images/kn/workspace-3.398ca4a5858132cce584db9df10c5a011cd9075eb182e647a77d5cac01771eea.png) ಕೆಳಗಿನಂತೆ ಸೆಟ್ಟಿಂಗ್‌ಗಳನ್ನು ಭರ್ತಿ ಮಾಡಿ: - ಸಬ್ಸ್ಕ್ರಿಪ್ಷನ್: ನಿಮ್ಮ ಅಜೂರ್ ಸಬ್ಸ್ಕ್ರಿಪ್ಷನ್ @@ -135,17 +135,17 @@ CO_OP_TRANSLATOR_METADATA: - ಅಪ್ಲಿಕೇಶನ್ ಇನ್ಸೈಟ್ಸ್: ನಿಮ್ಮ ವರ್ಕ್‌ಸ್ಪೇಸ್‌ಗೆ ಹೊಸ ಡೀಫಾಲ್ಟ್ ಅಪ್ಲಿಕೇಶನ್ ಇನ್ಸೈಟ್ಸ್ ಸಂಪನ್ಮೂಲ ಸೃಷ್ಟಿಸಲಾಗುತ್ತದೆ - ಕಂಟೈನರ್ ರಿಜಿಸ್ಟ್ರಿ: ಇಲ್ಲ (ನೀವು ಮೊದಲ ಬಾರಿಗೆ ಮಾದರಿಯನ್ನು ಕಂಟೈನರ್‌ಗೆ ನಿಯೋಜಿಸುವಾಗ ಸ್ವಯಂಚಾಲಿತವಾಗಿ ಸೃಷ್ಟಿಸಲಾಗುತ್ತದೆ) - ![workspace-4](../../../../translated_images/workspace-4.bac87f6599c4df63e624fc2608990f965887bee551d9dedc71c687b43b986b6a.kn.png) + ![workspace-4](../../../../translated_images/kn/workspace-4.bac87f6599c4df63e624fc2608990f965887bee551d9dedc71c687b43b986b6a.png) - ರಚಿಸಿ + ಪರಿಶೀಲಿಸಿ ಕ್ಲಿಕ್ ಮಾಡಿ ಮತ್ತು ನಂತರ ರಚನೆ ಬಟನ್ ಕ್ಲಿಕ್ ಮಾಡಿ 3. ನಿಮ್ಮ ವರ್ಕ್‌ಸ್ಪೇಸ್ ರಚನೆಗೆ ಕೆಲವು ನಿಮಿಷಗಳು ಬೇಕಾಗಬಹುದು. ನಂತರ ಪೋರ್ಟಲ್‌ನಲ್ಲಿ ಅದಕ್ಕೆ ಹೋಗಿ. ನೀವು ಅದನ್ನು ಮೆಷಿನ್ ಲರ್ನಿಂಗ್ ಅಜೂರ್ ಸೇವೆಯ ಮೂಲಕ ಹುಡುಕಬಹುದು. 4. ನಿಮ್ಮ ವರ್ಕ್‌ಸ್ಪೇಸ್‌ನ ಅವಲೋಕನ ಪುಟದಲ್ಲಿ, ಅಜೂರ್ ಮೆಷಿನ್ ಲರ್ನಿಂಗ್ ಸ್ಟುಡಿಯೋವನ್ನು ಪ್ರಾರಂಭಿಸಿ (ಅಥವಾ ಹೊಸ ಬ್ರೌಸರ್ ಟ್ಯಾಬ್ ತೆರೆಯಿರಿ ಮತ್ತು https://ml.azure.com ಗೆ ಹೋಗಿ), ಮತ್ತು ನಿಮ್ಮ ಮೈಕ್ರೋಸಾಫ್ಟ್ ಖಾತೆ ಬಳಸಿ ಅಜೂರ್ ಮೆಷಿನ್ ಲರ್ನಿಂಗ್ ಸ್ಟುಡಿಯೋದಲ್ಲಿ ಸೈನ್ ಇನ್ ಆಗಿ. ಕೇಳಿದರೆ, ನಿಮ್ಮ ಅಜೂರ್ ಡೈರೆಕ್ಟರಿ ಮತ್ತು ಸಬ್ಸ್ಕ್ರಿಪ್ಷನ್ ಮತ್ತು ಅಜೂರ್ ಮೆಷಿನ್ ಲರ್ನಿಂಗ್ ವರ್ಕ್‌ಸ್ಪೇಸ್ ಆಯ್ಕೆಮಾಡಿ. -![workspace-5](../../../../translated_images/workspace-5.a6eb17e0a5e6420018b08bdaf3755ce977f96f1df3ea363d2476a9dce7e15adb.kn.png) +![workspace-5](../../../../translated_images/kn/workspace-5.a6eb17e0a5e6420018b08bdaf3755ce977f96f1df3ea363d2476a9dce7e15adb.png) 5. ಅಜೂರ್ ಮೆಷಿನ್ ಲರ್ನಿಂಗ್ ಸ್ಟುಡಿಯೋದಲ್ಲಿ, ಎಡಮೇಲೆ ಇರುವ ☰ ಐಕಾನ್ ಅನ್ನು ಟಾಗಲ್ ಮಾಡಿ ಇಂಟರ್ಫೇಸ್‌ನ ವಿವಿಧ ಪುಟಗಳನ್ನು ವೀಕ್ಷಿಸಿ. ನೀವು ಈ ಪುಟಗಳನ್ನು ನಿಮ್ಮ ವರ್ಕ್‌ಸ್ಪೇಸ್‌ನ ಸಂಪನ್ಮೂಲಗಳನ್ನು ನಿರ್ವಹಿಸಲು ಬಳಸಬಹುದು. -![workspace-6](../../../../translated_images/workspace-6.8dd81fe841797ee17f8f73916769576260b16c4e17e850d277a49db35fd74a15.kn.png) +![workspace-6](../../../../translated_images/kn/workspace-6.8dd81fe841797ee17f8f73916769576260b16c4e17e850d277a49db35fd74a15.png) ನೀವು ಅಜೂರ್ ಪೋರ್ಟಲ್ ಬಳಸಿ ನಿಮ್ಮ ವರ್ಕ್‌ಸ್ಪೇಸ್ ಅನ್ನು ನಿರ್ವಹಿಸಬಹುದು, ಆದರೆ ಡೇಟಾ ವಿಜ್ಞಾನಿಗಳು ಮತ್ತು ಮೆಷಿನ್ ಲರ್ನಿಂಗ್ ಕಾರ್ಯಾಚರಣೆ ಇಂಜಿನಿಯರ್‌ಗಳಿಗೆ, ಅಜೂರ್ ಮೆಷಿನ್ ಲರ್ನಿಂಗ್ ಸ್ಟುಡಿಯೋ ವರ್ಕ್‌ಸ್ಪೇಸ್ ಸಂಪನ್ಮೂಲಗಳನ್ನು ನಿರ್ವಹಿಸಲು ಹೆಚ್ಚು ಕೇಂದ್ರೀಕೃತ ಬಳಕೆದಾರ ಇಂಟರ್ಫೇಸ್ ಒದಗಿಸುತ್ತದೆ. @@ -193,18 +193,18 @@ CPU ಮತ್ತು GPU ವಾಸ್ತುಶಿಲ್ಪದ ಮುಖ್ಯ ವ ನಾವು ಮೊದಲು ರಚಿಸಿದ [ಅಜೂರ್ ML ವರ್ಕ್‌ಸ್ಪೇಸ್](https://ml.azure.com/) ನಲ್ಲಿ, ಗಣನೆಗೆ ಹೋಗಿ ನಾವು ಈಗಾಗಲೇ ಚರ್ಚಿಸಿದ ವಿವಿಧ ಗಣನೆ ಸಂಪನ್ಮೂಲಗಳನ್ನು ನೋಡಬಹುದು (ಅಂದರೆ ಗಣನೆ ಉದಾಹರಣೆಗಳು, ಗಣನೆ ಕ್ಲಸ್ಟರ್‌ಗಳು, ಅನುವಾದ ಕ್ಲಸ್ಟರ್‌ಗಳು ಮತ್ತು ಸಂಯೋಜಿತ ಗಣನೆ). ಈ ಯೋಜನೆಗಾಗಿ, ನಮಗೆ ಮಾದರಿ ತರಬೇತಿಗೆ ಗಣನೆ ಕ್ಲಸ್ಟರ್ ಬೇಕಾಗುತ್ತದೆ. ಸ್ಟುಡಿಯೋದಲ್ಲಿ, "Compute" ಮೆನು ಕ್ಲಿಕ್ ಮಾಡಿ, ನಂತರ "Compute cluster" ಟ್ಯಾಬ್ ಕ್ಲಿಕ್ ಮಾಡಿ ಮತ್ತು "+ New" ಬಟನ್ ಕ್ಲಿಕ್ ಮಾಡಿ ಗಣನೆ ಕ್ಲಸ್ಟರ್ ರಚಿಸಲು. -![22](../../../../translated_images/cluster-1.b78cb630bb543729b11f60c34d97110a263f8c27b516ba4dc47807b3cee5579f.kn.png) +![22](../../../../translated_images/kn/cluster-1.b78cb630bb543729b11f60c34d97110a263f8c27b516ba4dc47807b3cee5579f.png) 1. ನಿಮ್ಮ ಆಯ್ಕೆಗಳನ್ನು ಆರಿಸಿ: ನಿರ್ದಿಷ್ಟ ವಿರುದ್ಧ ಕಡಿಮೆ ಪ್ರಾಥಮಿಕತೆ, CPU ಅಥವಾ GPU, VM ಗಾತ್ರ ಮತ್ತು ಕೋರ್ ಸಂಖ್ಯೆ (ಈ ಯೋಜನೆಗಾಗಿ ಡೀಫಾಲ್ಟ್ ಸೆಟ್ಟಿಂಗ್‌ಗಳನ್ನು ಇಡಬಹುದು). 2. ಮುಂದಿನ ಬಟನ್ ಕ್ಲಿಕ್ ಮಾಡಿ. -![23](../../../../translated_images/cluster-2.ea30cdbc9f926bb9e05af3fdbc1f679811c796dc2a6847f935290aec15526e88.kn.png) +![23](../../../../translated_images/kn/cluster-2.ea30cdbc9f926bb9e05af3fdbc1f679811c796dc2a6847f935290aec15526e88.png) 3. ಕ್ಲಸ್ಟರ್‌ಗೆ ಗಣನೆ ಹೆಸರು ನೀಡಿ 4. ನಿಮ್ಮ ಆಯ್ಕೆಗಳನ್ನು ಆರಿಸಿ: ಕನಿಷ್ಠ/ಗರಿಷ್ಠ ನೋಡ್‌ಗಳ ಸಂಖ್ಯೆ, ಸ್ಕೇಲ್ ಡೌನ್ ಮಾಡುವ ಮೊದಲು ನಿರ್ಜೀವ ಸೆಕೆಂಡುಗಳು, SSH ಪ್ರವೇಶ. ಕನಿಷ್ಠ ನೋಡ್‌ಗಳ ಸಂಖ್ಯೆ 0 ಇದ್ದರೆ, ಕ್ಲಸ್ಟರ್ ನಿರ್ಜೀವವಾಗಿರುವಾಗ ನೀವು ಹಣ ಉಳಿಸಬಹುದು. ಗರಿಷ್ಠ ನೋಡ್‌ಗಳ ಸಂಖ್ಯೆ ಹೆಚ್ಚಾದರೆ ತರಬೇತಿ ಕಡಿಮೆ ಸಮಯ ತೆಗೆದುಕೊಳ್ಳುತ್ತದೆ. ಶಿಫಾರಸು ಮಾಡಲಾದ ಗರಿಷ್ಠ ನೋಡ್‌ಗಳ ಸಂಖ್ಯೆ 3. 5. "Create" ಬಟನ್ ಕ್ಲಿಕ್ ಮಾಡಿ. ಈ ಹಂತಕ್ಕೆ ಕೆಲವು ನಿಮಿಷಗಳು ಬೇಕಾಗಬಹುದು. -![29](../../../../translated_images/cluster-3.8a334bc070ec173a329ce5abd2a9d727542e83eb2347676c9af20f2c8870b3e7.kn.png) +![29](../../../../translated_images/kn/cluster-3.8a334bc070ec173a329ce5abd2a9d727542e83eb2347676c9af20f2c8870b3e7.png) ಅದ್ಭುತ! ಈಗ ನಮಗೆ ಗಣನೆ ಕ್ಲಸ್ಟರ್ ಇದೆ, ನಾವು ಡೇಟಾವನ್ನು ಅಜೂರ್ ML ಸ್ಟುಡಿಯೋಗೆ ಲೋಡ್ ಮಾಡಬೇಕಾಗಿದೆ. @@ -212,15 +212,15 @@ CPU ಮತ್ತು GPU ವಾಸ್ತುಶಿಲ್ಪದ ಮುಖ್ಯ ವ 1. ನಾವು ಮೊದಲು ರಚಿಸಿದ [ಅಜೂರ್ ML ವರ್ಕ್‌ಸ್ಪೇಸ್](https://ml.azure.com/) ನಲ್ಲಿ, ಎಡ ಮೆನುದಲ್ಲಿ "Datasets" ಕ್ಲಿಕ್ ಮಾಡಿ ಮತ್ತು "+ Create dataset" ಬಟನ್ ಕ್ಲಿಕ್ ಮಾಡಿ ಡೇಟಾಸೆಟ್ ರಚಿಸಲು. "From local files" ಆಯ್ಕೆ ಮಾಡಿ ಮತ್ತು ನಾವು ಮೊದಲು ಡೌನ್‌ಲೋಡ್ ಮಾಡಿದ ಕಾಗಲ್ ಡೇಟಾಸೆಟ್ ಆಯ್ಕೆಮಾಡಿ. - ![24](../../../../translated_images/dataset-1.e86ab4e10907a6e9c2a72577b51db35f13689cb33702337b8b7032f2ef76dac2.kn.png) + ![24](../../../../translated_images/kn/dataset-1.e86ab4e10907a6e9c2a72577b51db35f13689cb33702337b8b7032f2ef76dac2.png) 2. ನಿಮ್ಮ ಡೇಟಾಸೆಟ್‌ಗೆ ಹೆಸರು, ಪ್ರಕಾರ ಮತ್ತು ವಿವರಣೆ ನೀಡಿ. ಮುಂದಿನ ಬಟನ್ ಕ್ಲಿಕ್ ಮಾಡಿ. ಫೈಲ್‌ಗಳಿಂದ ಡೇಟಾ ಅಪ್‌ಲೋಡ್ ಮಾಡಿ. ಮುಂದಿನ ಬಟನ್ ಕ್ಲಿಕ್ ಮಾಡಿ. - ![25](../../../../translated_images/dataset-2.f58de1c435d5bf9ccb16ccc5f5d4380eb2b50affca85cfbf4f97562bdab99f77.kn.png) + ![25](../../../../translated_images/kn/dataset-2.f58de1c435d5bf9ccb16ccc5f5d4380eb2b50affca85cfbf4f97562bdab99f77.png) 3. ಸ್ಕೀಮಾ‌ನಲ್ಲಿ, ಕೆಳಗಿನ ವೈಶಿಷ್ಟ್ಯಗಳಿಗಾಗಿ ಡೇಟಾ ಪ್ರಕಾರವನ್ನು ಬೂಲಿಯನ್‌ಗೆ ಬದಲಾಯಿಸಿ: ಅನೀಮಿಯಾ, ಡಯಾಬಿಟಿಸ್, ಹೈ ಬ್ಲಡ್ ಪ್ರೆಶರ್, ಲಿಂಗ, ಧೂಮಪಾನ, ಮತ್ತು DEATH_EVENT. ಮುಂದಿನ ಬಟನ್ ಕ್ಲಿಕ್ ಮಾಡಿ ಮತ್ತು ರಚಿಸಿ ಕ್ಲಿಕ್ ಮಾಡಿ. - ![26](../../../../translated_images/dataset-3.58db8c0eb783e89236a02bbce5bb4ba808d081a87d994d5284b1ae59928c95bf.kn.png) + ![26](../../../../translated_images/kn/dataset-3.58db8c0eb783e89236a02bbce5bb4ba808d081a87d994d5284b1ae59928c95bf.png) ಚೆನ್ನಾಗಿದೆ! ಈಗ ಡೇಟಾಸೆಟ್ ಸಿದ್ಧವಾಗಿದೆ ಮತ್ತು ಗಣನೆ ಕ್ಲಸ್ಟರ್ ರಚಿಸಲಾಗಿದೆ, ನಾವು ಮಾದರಿ ತರಬೇತಿಯನ್ನು ಪ್ರಾರಂಭಿಸಬಹುದು! @@ -231,19 +231,19 @@ CPU ಮತ್ತು GPU ವಾಸ್ತುಶಿಲ್ಪದ ಮುಖ್ಯ ವ 1. ನಾವು ಮೊದಲು ರಚಿಸಿದ [ಅಜೂರ್ ML ವರ್ಕ್‌ಸ್ಪೇಸ್](https://ml.azure.com/) ನಲ್ಲಿ ಎಡ ಮೆನುದಲ್ಲಿ "Automated ML" ಕ್ಲಿಕ್ ಮಾಡಿ ಮತ್ತು ನೀವು appena ಅಪ್‌ಲೋಡ್ ಮಾಡಿದ ಡೇಟಾಸೆಟ್ ಆಯ್ಕೆಮಾಡಿ. ಮುಂದಿನ ಬಟನ್ ಕ್ಲಿಕ್ ಮಾಡಿ. - ![27](../../../../translated_images/aml-1.67281a85d3a1e2f34eb367b2d0f74e1039d13396e510f363cd8766632106d1ec.kn.png) + ![27](../../../../translated_images/kn/aml-1.67281a85d3a1e2f34eb367b2d0f74e1039d13396e510f363cd8766632106d1ec.png) 2. ಹೊಸ ಪ್ರಯೋಗದ ಹೆಸರು, ಗುರಿ ಕಾಲಮ್ (DEATH_EVENT) ಮತ್ತು ನಾವು ರಚಿಸಿದ ಗಣನೆ ಕ್ಲಸ್ಟರ್ ನಮೂದಿಸಿ. ಮುಂದಿನ ಬಟನ್ ಕ್ಲಿಕ್ ಮಾಡಿ. - ![28](../../../../translated_images/aml-2.c9fb9cffb39ccbbe21ab9810ae937195d41a489744e15cff2b8477ed4dcae1ec.kn.png) + ![28](../../../../translated_images/kn/aml-2.c9fb9cffb39ccbbe21ab9810ae937195d41a489744e15cff2b8477ed4dcae1ec.png) 3. "Classification" ಆಯ್ಕೆ ಮಾಡಿ ಮತ್ತು Finish ಕ್ಲಿಕ್ ಮಾಡಿ. ಈ ಹಂತವು ನಿಮ್ಮ ಗಣನೆ ಕ್ಲಸ್ಟರ್ ಗಾತ್ರದ ಮೇಲೆ ಅವಲಂಬಿತವಾಗಿ 30 ನಿಮಿಷದಿಂದ 1 ಗಂಟೆ ತೆಗೆದುಕೊಳ್ಳಬಹುದು. - ![30](../../../../translated_images/aml-3.a7952e4295f38cc6cdb0c7ed6dc71ea756b7fb5697ec126bc1220f87c5fa9231.kn.png) + ![30](../../../../translated_images/kn/aml-3.a7952e4295f38cc6cdb0c7ed6dc71ea756b7fb5697ec126bc1220f87c5fa9231.png) 4. ರನ್ ಪೂರ್ಣಗೊಂಡ ನಂತರ, "Automated ML" ಟ್ಯಾಬ್ ಕ್ಲಿಕ್ ಮಾಡಿ, ನಿಮ್ಮ ರನ್ ಕ್ಲಿಕ್ ಮಾಡಿ, ಮತ್ತು "Best model summary" ಕಾರ್ಡ್‌ನಲ್ಲಿನ ಆಲ್ಗೋರಿದಮ್ ಕ್ಲಿಕ್ ಮಾಡಿ. - ![31](../../../../translated_images/aml-4.7a627e09cb6f16d0aa246059d9faee3d1725cc4258d0c8df15e801f73afc7e2c.kn.png) + ![31](../../../../translated_images/kn/aml-4.7a627e09cb6f16d0aa246059d9faee3d1725cc4258d0c8df15e801f73afc7e2c.png) ಇಲ್ಲಿ ನೀವು AutoML ರಚಿಸಿದ ಅತ್ಯುತ್ತಮ ಮಾದರಿಯ ವಿವರವಾದ ವಿವರಣೆಯನ್ನು ನೋಡಬಹುದು. ನೀವು ಮಾದರಿಗಳ ಟ್ಯಾಬ್‌ನಲ್ಲಿ ಇತರ ಮಾದರಿಗಳನ್ನು ಸಹ ಅನ್ವೇಷಿಸಬಹುದು. ವಿವರಣೆಗಳಲ್ಲಿ (ಪೂರ್ವವೀಕ್ಷಣಾ ಬಟನ್) ಕೆಲವು ನಿಮಿಷಗಳನ್ನು ತೆಗೆದು ಮಾದರಿಗಳನ್ನು ಪರಿಶೀಲಿಸಿ. ನೀವು ಬಳಸಲು ಬಯಸುವ ಮಾದರಿಯನ್ನು ಆಯ್ಕೆಮಾಡಿದ ನಂತರ (ಇಲ್ಲಿ ನಾವು AutoML ಆಯ್ಕೆಮಾಡಿದ ಅತ್ಯುತ್ತಮ ಮಾದರಿಯನ್ನು ಆಯ್ಕೆಮಾಡುತ್ತೇವೆ), ನಾವು ಅದನ್ನು ಹೇಗೆ ನಿಯೋಜಿಸಬಹುದು ಎಂದು ನೋಡೋಣ. @@ -254,15 +254,15 @@ CPU ಮತ್ತು GPU ವಾಸ್ತುಶಿಲ್ಪದ ಮುಖ್ಯ ವ ಅತ್ಯುತ್ತಮ ಮಾದರಿ ವಿವರಣೆಯಲ್ಲಿ, "Deploy" ಬಟನ್ ಕ್ಲಿಕ್ ಮಾಡಿ. -![deploy-1](../../../../translated_images/deploy-1.ddad725acadc84e34553c3d09e727160faeb32527a9fb8b904c0f99235a34bb6.kn.png) +![deploy-1](../../../../translated_images/kn/deploy-1.ddad725acadc84e34553c3d09e727160faeb32527a9fb8b904c0f99235a34bb6.png) 15. ಅದಕ್ಕೆ ಹೆಸರು, ವಿವರಣೆ, ಗಣನೆ ಪ್ರಕಾರ (Azure Container Instance), ಪ್ರಮಾಣೀಕರಣ ಸಕ್ರಿಯಗೊಳಿಸಿ ಮತ್ತು Deploy ಕ್ಲಿಕ್ ಮಾಡಿ. ಈ ಹಂತವು ಪೂರ್ಣಗೊಳ್ಳಲು ಸುಮಾರು 20 ನಿಮಿಷ ತೆಗೆದುಕೊಳ್ಳಬಹುದು. ನಿಯೋಜನೆ ಪ್ರಕ್ರಿಯೆಯಲ್ಲಿ ಮಾದರಿಯನ್ನು ನೋಂದಾಯಿಸುವುದು, ಸಂಪನ್ಮೂಲಗಳನ್ನು ರಚಿಸುವುದು ಮತ್ತು ಅವುಗಳನ್ನು ವೆಬ್ ಸೇವೆಗೆ ಸಂರಚಿಸುವುದು ಸೇರಿವೆ. Deploy ಸ್ಥಿತಿಯಡಿ ಸ್ಥಿತಿ ಸಂದೇಶ ಕಾಣಿಸುತ್ತದೆ. ನಿಯೋಜನೆ ಸ್ಥಿತಿಯನ್ನು ಪರಿಶೀಲಿಸಲು Refresh periodically ಆಯ್ಕೆಮಾಡಿ. ಸ್ಥಿತಿ "Healthy" ಆಗಿದ್ದಾಗ ಅದು ನಿಯೋಜಿತ ಮತ್ತು ಚಾಲನೆಯಲ್ಲಿ ಇದೆ. -![deploy-2](../../../../translated_images/deploy-2.94dbb13f239086473aa4bf814342fd40483d136849b080f02bafbb995383940e.kn.png) +![deploy-2](../../../../translated_images/kn/deploy-2.94dbb13f239086473aa4bf814342fd40483d136849b080f02bafbb995383940e.png) 16. ನಿಯೋಜನೆಯಾದ ನಂತರ, Endpoint ಟ್ಯಾಬ್ ಕ್ಲಿಕ್ ಮಾಡಿ ಮತ್ತು ನೀವು appena ನಿಯೋಜಿಸಿದ ಎಂಡ್ಪಾಯಿಂಟ್ ಕ್ಲಿಕ್ ಮಾಡಿ. ಇಲ್ಲಿ ನೀವು ಎಂಡ್ಪಾಯಿಂಟ್ ಬಗ್ಗೆ ಬೇಕಾದ ಎಲ್ಲಾ ವಿವರಗಳನ್ನು ಕಾಣಬಹುದು. -![deploy-3](../../../../translated_images/deploy-3.fecefef070e8ef3b28e802326d107f61ac4e672d20bf82d05f78d025f9e6c611.kn.png) +![deploy-3](../../../../translated_images/kn/deploy-3.fecefef070e8ef3b28e802326d107f61ac4e672d20bf82d05f78d025f9e6c611.png) ಅದ್ಭುತ! ಈಗ ನಮಗೆ ಮಾದರಿ ನಿಯೋಜಿಸಲಾಗಿದೆ, ನಾವು ಎಂಡ್ಪಾಯಿಂಟ್ ಬಳಕೆಯನ್ನು ಪ್ರಾರಂಭಿಸಬಹುದು. @@ -272,7 +272,7 @@ CPU ಮತ್ತು GPU ವಾಸ್ತುಶಿಲ್ಪದ ಮುಖ್ಯ ವ ಈ ಸ್ಕ್ರಿಪ್ಟ್ ನೇರವಾಗಿ ನಿಮ್ಮ ಸ್ಥಳೀಯ ಯಂತ್ರದಿಂದ ಚಾಲನೆ ಮಾಡಬಹುದು ಮತ್ತು ನಿಮ್ಮ ಎಂಡ್ಪಾಯಿಂಟ್ ಅನ್ನು ಬಳಕೆ ಮಾಡುತ್ತದೆ. -![35](../../../../translated_images/consumption-1.700abd196452842a020c7d745908637a6e4c5c50494ad1217be80e283e0de154.kn.png) +![35](../../../../translated_images/kn/consumption-1.700abd196452842a020c7d745908637a6e4c5c50494ad1217be80e283e0de154.png) ಆ ಎರಡು ಸಾಲುಗಳ ಕೋಡ್ ಪರಿಶೀಲಿಸಲು ಒಂದು ಕ್ಷಣ ತೆಗೆದುಕೊಳ್ಳಿ: diff --git a/translations/kn/5-Data-Science-In-Cloud/19-Azure/README.md b/translations/kn/5-Data-Science-In-Cloud/19-Azure/README.md index 9eb00b15..3e71924e 100644 --- a/translations/kn/5-Data-Science-In-Cloud/19-Azure/README.md +++ b/translations/kn/5-Data-Science-In-Cloud/19-Azure/README.md @@ -57,7 +57,7 @@ CO_OP_TRANSLATOR_METADATA: [ಹಿಂದಿನ ಪಾಠದಲ್ಲಿ](../18-Low-Code/README.md), ನಾವು ಕಡಿಮೆ ಕೋಡ್/ಕೋಡ್ ಇಲ್ಲದ ರೀತಿಯಲ್ಲಿ ಮಾದರಿಯನ್ನು ತರಬೇತಿ, ನಿಯೋಜನೆ ಮತ್ತು ಬಳಕೆ ಮಾಡುವುದು ಹೇಗೆ ಎಂದು ನೋಡಿದ್ದೇವೆ. ನಾವು ಹೃದಯ ವೈಫಲ್ಯ ಡೇಟಾಸೆಟ್ ಬಳಸಿ ಹೃದಯ ವೈಫಲ್ಯ ಭವಿಷ್ಯವಾಣಿ ಮಾದರಿಯನ್ನು ರಚಿಸಿದ್ದೇವೆ. ಈ ಪಾಠದಲ್ಲಿ, ನಾವು ಅದೇ ಕಾರ್ಯವನ್ನು ಅಜೂರ್ ಮೆಷಿನ್ ಲರ್ನಿಂಗ್ ಎಸ್‌ಡಿಕೆ ಬಳಸಿ ಮಾಡಲಿದ್ದೇವೆ. -![project-schema](../../../../translated_images/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.kn.png) +![project-schema](../../../../translated_images/kn/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.png) ### 1.2 ಹೃದಯ ವೈಫಲ್ಯ ಭವಿಷ್ಯವಾಣಿ ಯೋಜನೆ ಮತ್ತು ಡೇಟಾಸೆಟ್ ಪರಿಚಯ @@ -74,7 +74,7 @@ CO_OP_TRANSLATOR_METADATA: ನಾವು ಮೊದಲು ರಚಿಸಿದ [ಅಜೂರ್ ಎಂಎಲ್ ವರ್ಕ್‌ಸ್ಪೇಸ್](https://ml.azure.com/) ನಲ್ಲಿ, ಕಂಪ್ಯೂಟ್ ಮೆನುಗೆ ಹೋಗಿ ಲಭ್ಯವಿರುವ ವಿವಿಧ ಕಂಪ್ಯೂಟ್ ಸಂಪನ್ಮೂಲಗಳನ್ನು ನೋಡಬಹುದು -![compute-instance-1](../../../../translated_images/compute-instance-1.dba347cb199ca4996b3e3d649295ed95626ba481479d3986557b9b98e76d8816.kn.png) +![compute-instance-1](../../../../translated_images/kn/compute-instance-1.dba347cb199ca4996b3e3d649295ed95626ba481479d3986557b9b98e76d8816.png) ಜುಪೈಟರ್ ನೋಟ್ಬುಕ್ ಪ್ರೊವಿಷನ್ ಮಾಡಲು ಕಂಪ್ಯೂಟ್ ಇನ್ಸ್ಟಾನ್ಸ್ ರಚಿಸೋಣ. 1. + New ಬಟನ್ ಕ್ಲಿಕ್ ಮಾಡಿ. @@ -97,10 +97,10 @@ CO_OP_TRANSLATOR_METADATA: 1. Applications ವಿಭಾಗದಲ್ಲಿ, Jupyter ಆಯ್ಕೆಯನ್ನು ಕ್ಲಿಕ್ ಮಾಡಿ. 2. "Yes, I understand" ಬಾಕ್ಸ್ ಟಿಕ್ ಮಾಡಿ ಮತ್ತು Continue ಬಟನ್ ಕ್ಲಿಕ್ ಮಾಡಿ. -![notebook-1](../../../../translated_images/notebook-1.12998af7b02c83f536c11b3aeba561be16e0f05e94146600728ec64270ce1105.kn.png) +![notebook-1](../../../../translated_images/kn/notebook-1.12998af7b02c83f536c11b3aeba561be16e0f05e94146600728ec64270ce1105.png) 3. ಇದು ನಿಮ್ಮ ಜುಪೈಟರ್ ನೋಟ್ಬುಕ್ ಇನ್ಸ್ಟಾನ್ಸ್‌ನೊಂದಿಗೆ ಹೊಸ ಬ್ರೌಸರ್ ಟ್ಯಾಬ್ ತೆರೆಯುತ್ತದೆ. ನೋಟ್ಬುಕ್ ರಚಿಸಲು "New" ಬಟನ್ ಕ್ಲಿಕ್ ಮಾಡಿ. -![notebook-2](../../../../translated_images/notebook-2.9a657c037e34f1cf26c0212f5ee9e2da8545b3e107c7682c55114e494167a8aa.kn.png) +![notebook-2](../../../../translated_images/kn/notebook-2.9a657c037e34f1cf26c0212f5ee9e2da8545b3e107c7682c55114e494167a8aa.png) ನಾವು ಈಗ ನೋಟ್ಬುಕ್ ಹೊಂದಿದ್ದೇವೆ, ಅಜೂರ್ ಎಂಎಲ್ ಎಸ್‌ಡಿಕೆ ಬಳಸಿ ಮಾದರಿಯನ್ನು ತರಬೇತಿ ಮಾಡಬಹುದು. diff --git a/translations/kn/5-Data-Science-In-Cloud/README.md b/translations/kn/5-Data-Science-In-Cloud/README.md index 40820757..1d600e6c 100644 --- a/translations/kn/5-Data-Science-In-Cloud/README.md +++ b/translations/kn/5-Data-Science-In-Cloud/README.md @@ -9,13 +9,13 @@ CO_OP_TRANSLATOR_METADATA: --> # ಕ್ಲೌಡ್‌ನಲ್ಲಿ ಡೇಟಾ ಸೈನ್ಸ್ -![cloud-picture](../../../translated_images/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.kn.jpg) +![cloud-picture](../../../translated_images/kn/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.jpg) > ಫೋಟೋ [Jelleke Vanooteghem](https://unsplash.com/@ilumire) ಅವರಿಂದ [Unsplash](https://unsplash.com/s/photos/cloud?orientation=landscape) ಬೃಹತ್ ಡೇಟಾ ಜೊತೆಗೆ ಡೇಟಾ ಸೈನ್ಸ್ ಮಾಡುವಾಗ, ಕ್ಲೌಡ್ ಒಂದು ಆಟ ಬದಲಿಸುವುದಾಗಿ ಇರಬಹುದು. ಮುಂದಿನ ಮೂರು ಪಾಠಗಳಲ್ಲಿ, ನಾವು ಕ್ಲೌಡ್ ಎಂದರೇನು ಮತ್ತು ಅದು ಏಕೆ ಬಹಳ ಸಹಾಯಕವಾಗಬಹುದು ಎಂಬುದನ್ನು ನೋಡಲಿದ್ದೇವೆ. ನಾವು ಹೃದಯ ವೈಫಲ್ಯ ಡೇಟಾಸೆಟ್ ಅನ್ನು ಅನ್ವೇಷಿಸಿ, ಯಾರಿಗಾದರೂ ಹೃದಯ ವೈಫಲ್ಯ ಇರುವ ಸಾಧ್ಯತೆಯನ್ನು ಅಂದಾಜಿಸಲು ಸಹಾಯ ಮಾಡುವ ಮಾದರಿಯನ್ನು ನಿರ್ಮಿಸಲಿದ್ದೇವೆ. ನಾವು ಮಾದರಿಯನ್ನು ತರಬೇತಿ, ನಿಯೋಜನೆ ಮತ್ತು ಬಳಕೆ ಮಾಡಲು ಕ್ಲೌಡ್ ಶಕ್ತಿಯನ್ನು ಎರಡು ವಿಭಿನ್ನ ರೀತಿಗಳಲ್ಲಿ ಬಳಸಲಿದ್ದೇವೆ. ಒಂದು ಮಾರ್ಗವು ಕೇವಲ ಬಳಕೆದಾರ ಇಂಟರ್ಫೇಸ್ ಅನ್ನು ಬಳಸಿಕೊಂಡು ಲೋ ಕೋಡ್/ನೋ ಕೋಡ್ ಶೈಲಿಯಲ್ಲಿ, ಮತ್ತೊಂದು ಮಾರ್ಗವು ಅಜೂರ್ ಮೆಷಿನ್ ಲರ್ನಿಂಗ್ ಸಾಫ್ಟ್‌ವೇರ್ ಡೆವಲಪರ್ ಕಿಟ್ (Azure ML SDK) ಬಳಸಿ. -![project-schema](../../../translated_images/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.kn.png) +![project-schema](../../../translated_images/kn/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.png) ### ವಿಷಯಗಳು diff --git a/translations/kn/6-Data-Science-In-Wild/20-Real-World-Examples/README.md b/translations/kn/6-Data-Science-In-Wild/20-Real-World-Examples/README.md index 8b4abbee..6d33d80f 100644 --- a/translations/kn/6-Data-Science-In-Wild/20-Real-World-Examples/README.md +++ b/translations/kn/6-Data-Science-In-Wild/20-Real-World-Examples/README.md @@ -41,7 +41,7 @@ CO_OP_TRANSLATOR_METADATA: * [ಆರೋಗ್ಯ ಸೇವೆಯಲ್ಲಿ ಡೇಟಾ ಸೈನ್ಸ್](https://data-flair.training/blogs/data-science-in-healthcare/) - ವೈದ್ಯಕೀಯ ಚಿತ್ರಣ (ಉದಾ: MRI, X-ರೇ, CT-ಸ್ಕ್ಯಾನ್), ಜಿನೋಮಿಕ್ಸ್ (DNA ಕ್ರಮಬದ್ಧತೆ), ಔಷಧಿ ಅಭಿವೃದ್ಧಿ (ಅಪಾಯ ಮೌಲ್ಯಮಾಪನ, ಯಶಸ್ಸಿನ ಭವಿಷ್ಯವಾಣಿ), ಭವಿಷ್ಯವಾಣಿ ವಿಶ್ಲೇಷಣೆ (ರೋಗಿ ಆರೈಕೆ ಮತ್ತು ಸರಬರಾಜು ಲಾಜಿಸ್ಟಿಕ್ಸ್), ರೋಗ ಟ್ರ್ಯಾಕಿಂಗ್ ಮತ್ತು ತಡೆ ಮುಂತಾದ ಅನ್ವಯಿಕೆಗಳನ್ನು ಹೈಲೈಟ್ ಮಾಡುತ್ತದೆ. -![ನಿಜಜೀವನದಲ್ಲಿ ಡೇಟಾ ಸೈನ್ಸ್ ಅನ್ವಯಿಕೆಗಳು](../../../../translated_images/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.kn.png) ಚಿತ್ರ ಕ್ರೆಡಿಟ್: [ಡೇಟಾ ಫ್ಲೇರ್: 6 ಅದ್ಭುತ ಡೇಟಾ ಸೈನ್ಸ್ ಅನ್ವಯಿಕೆಗಳು](https://data-flair.training/blogs/data-science-applications/) +![ನಿಜಜೀವನದಲ್ಲಿ ಡೇಟಾ ಸೈನ್ಸ್ ಅನ್ವಯಿಕೆಗಳು](../../../../translated_images/kn/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.png) ಚಿತ್ರ ಕ್ರೆಡಿಟ್: [ಡೇಟಾ ಫ್ಲೇರ್: 6 ಅದ್ಭುತ ಡೇಟಾ ಸೈನ್ಸ್ ಅನ್ವಯಿಕೆಗಳು](https://data-flair.training/blogs/data-science-applications/) ಚಿತ್ರವು ಡೇಟಾ ಸೈನ್ಸ್ ತಂತ್ರಗಳನ್ನು ಅನ್ವಯಿಸುವ ಇತರ ಕ್ಷೇತ್ರಗಳು ಮತ್ತು ಉದಾಹರಣೆಗಳನ್ನು ತೋರಿಸುತ್ತದೆ. ಇತರ ಅನ್ವಯಿಕೆಗಳನ್ನು ಅನ್ವೇಷಿಸಲು ಬಯಸುತ್ತೀರಾ? ಕೆಳಗಿನ [ಪರಿಶೀಲನೆ ಮತ್ತು ಸ್ವಯಂ ಅಧ್ಯಯನ](../../../../6-Data-Science-In-Wild/20-Real-World-Examples) ವಿಭಾಗವನ್ನು ಪರಿಶೀಲಿಸಿ. diff --git a/translations/kn/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md b/translations/kn/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md index 6f932078..5660214d 100644 --- a/translations/kn/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md +++ b/translations/kn/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md @@ -22,7 +22,7 @@ CO_OP_TRANSLATOR_METADATA: 2. ಡೇಟಾಸೆಟ್ [ಕ್ಯಾಟಲಾಗ್](https://planetarycomputer.microsoft.com/catalog) ಅನ್ನು ಅನ್ವೇಷಿಸಿ - ಪ್ರತಿ ಡೇಟಾಸೆಟ್ ಉದ್ದೇಶವನ್ನು ತಿಳಿದುಕೊಳ್ಳಿ. 3. ಎಕ್ಸ್‌ಪ್ಲೋರರ್ ಬಳಸಿ - ಆಸಕ್ತಿಯ ಡೇಟಾಸೆಟ್ ಆಯ್ಕೆಮಾಡಿ, ಸಂಬಂಧಿತ ಪ್ರಶ್ನೆ ಮತ್ತು ರೆಂಡರಿಂಗ್ ಆಯ್ಕೆಯನ್ನು ಆರಿಸಿ. -![ಗ್ರಹಣ ಕಂಪ್ಯೂಟರ್ ಎಕ್ಸ್‌ಪ್ಲೋರರ್](../../../../translated_images/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.kn.png) +![ಗ್ರಹಣ ಕಂಪ್ಯೂಟರ್ ಎಕ್ಸ್‌ಪ್ಲೋರರ್](../../../../translated_images/kn/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.png) `ನಿಮ್ಮ ಕಾರ್ಯ:` ಈಗ ಬ್ರೌಸರ್‌ನಲ್ಲಿ ರೆಂಡರ್ ಆಗಿರುವ ದೃಶ್ಯೀಕರಣವನ್ನು ಅಧ್ಯಯನ ಮಾಡಿ ಮತ್ತು ಕೆಳಗಿನ ಪ್ರಶ್ನೆಗಳಿಗೆ ಉತ್ತರ ನೀಡಿ: diff --git a/translations/kn/CONTRIBUTING.md b/translations/kn/CONTRIBUTING.md index 11e44df8..70ea56bf 100644 --- a/translations/kn/CONTRIBUTING.md +++ b/translations/kn/CONTRIBUTING.md @@ -312,7 +312,7 @@ def calculate_mean(data): import pandas as pd ``` ```` -- ಚಿತ್ರಗಳಿಗೆ alt ಪಠ್ಯ ಸೇರಿಸಿ: `![Alt text](../../translated_images/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.kn.png)` +- ಚಿತ್ರಗಳಿಗೆ alt ಪಠ್ಯ ಸೇರಿಸಿ: `![Alt text](../../translated_images/kn/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.png)` - ಸಾಲಿನ ಉದ್ದವನ್ನು ಯುಕ್ತಮಟ್ಟದಲ್ಲಿ ಇಡಿ (ಸುಮಾರು 80-100 ಅಕ್ಷರಗಳು) ### Python diff --git a/translations/kn/README.md b/translations/kn/README.md index 6b9ad808..f8b76713 100644 --- a/translations/kn/README.md +++ b/translations/kn/README.md @@ -33,7 +33,7 @@ Microsoft ನ Azure Cloud Advocates ಗಳು ಡೇಟಾ ಸೈನ್ಸ್ **🙏 ನಮ್ಮ [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/) ಲೇಖಕರು, ವಿಮರ್ಶಕರು ಮತ್ತು ವಿಷಯದ ದಾನಿಗಳಿಗೆ ವಿಶೇಷ ಧನ್ಯವಾದಗಳು 🙏,** ವಿಶೇಷವಾಗಿ Aaryan Arora, [Aditya Garg](https://github.com/AdityaGarg00), [Alondra Sanchez](https://www.linkedin.com/in/alondra-sanchez-molina/), [Ankita Singh](https://www.linkedin.com/in/ankitasingh007), [Anupam Mishra](https://www.linkedin.com/in/anupam--mishra/), [Arpita Das](https://www.linkedin.com/in/arpitadas01/), ChhailBihari Dubey, [Dibri Nsofor](https://www.linkedin.com/in/dibrinsofor), [Dishita Bhasin](https://www.linkedin.com/in/dishita-bhasin-7065281bb), [Majd Safi](https://www.linkedin.com/in/majd-s/), [Max Blum](https://www.linkedin.com/in/max-blum-6036a1186/), [Miguel Correa](https://www.linkedin.com/in/miguelmque/), [Mohamma Iftekher (Iftu) Ebne Jalal](https://twitter.com/iftu119), [Nawrin Tabassum](https://www.linkedin.com/in/nawrin-tabassum), [Raymond Wangsa Putra](https://www.linkedin.com/in/raymond-wp/), [Rohit Yadav](https://www.linkedin.com/in/rty2423), Samridhi Sharma, [Sanya Sinha](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200), [Sheena Narula](https://www.linkedin.com/in/sheena-narua-n/), [Tauqeer Ahmad](https://www.linkedin.com/in/tauqeerahmad5201/), Yogendrasingh Pawar , [Vidushi Gupta](https://www.linkedin.com/in/vidushi-gupta07/), [Jasleen Sondhi](https://www.linkedin.com/in/jasleen-sondhi/) -|![ಸ್ಕೆಟ್ಚ್‌ನೋಟ್ - @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.kn.png)| +|![ಸ್ಕೆಟ್ಚ್‌ನೋಟ್ - @sketchthedocs https://sketchthedocs.dev](../../translated_images/kn/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.png)| |:---:| | ಡೇಟಾ ಸೈನ್ಸ್ ಆರಂಭಿಕರಿಗೆ - _ಸ್ಕೆಟ್ಚ್‌ನೋಟ್ ಇಂದ [@nitya](https://twitter.com/nitya)_ | @@ -52,7 +52,7 @@ Microsoft ನ Azure Cloud Advocates ಗಳು ಡೇಟಾ ಸೈನ್ಸ್ ನಾವು ಡಿಸ್ಕಾರ್ಡ್‌ನಲ್ಲಿ "Learn with AI" ಸರಣಿಯನ್ನು ನಡೆಸುತ್ತಿದ್ದೇವೆ — ಹೆಚ್ಚಿನ ಮಾಹಿತಿಗಾಗಿ ಮತ್ತು 18 - 30 September, 2025 ರ ನಡುವೆ ನಮ್ಮೊಂದಿಗೆ ಸೇರುವಿರಿ: [Learn with AI Series](https://aka.ms/learnwithai/discord). ನೀವು GitHub Copilot ಅನ್ನು ಡೇಟಾ ಸೈನ್ಸ್ ಕಾರ್ಯಗಳಿಗಾಗಿ ಬಳಸುವ ಸಲಹೆಗಳು ಮತ್ತು ಚತುರ್ತೆಯನ್ನು ಪಡೆಯುತ್ತೀರಿ. -![AI ಜೊತೆಗೆ ಕಲಿಯುವ ಸರಣಿ](../../translated_images/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.kn.jpg) +![AI ಜೊತೆಗೆ ಕಲಿಯುವ ಸರಣಿ](../../translated_images/kn/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.jpg) # ನೀವು ವಿದ್ಯಾರ್ಥಿ? @@ -132,7 +132,7 @@ Microsoft ನ Azure Cloud Advocates ಗಳು ಡೇಟಾ ಸೈನ್ಸ್ ## Lessons -|![ ಸ್ಕೆಚ್‌ನೋಟ್ ರಚನೆ: @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.kn.png)| +|![ ಸ್ಕೆಚ್‌ನೋಟ್ ರಚನೆ: @sketchthedocs https://sketchthedocs.dev](../../translated_images/kn/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png)| |:---:| | ಆರಂಭಿಕರಿಗಾಗಿ ಡೇಟಾ ಸೈನ್ಸ್: ಮಾರ್ಗಸೂಚಿ - _ಸ್ಕೆಚ್‌ನೋಟ್ ರಚನೆ: [@nitya](https://twitter.com/nitya)_ | diff --git a/translations/kn/sketchnotes/README.md b/translations/kn/sketchnotes/README.md index 51e328ae..8a0684d5 100644 --- a/translations/kn/sketchnotes/README.md +++ b/translations/kn/sketchnotes/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: ನಿತ್ಯ ನಾರಸಿಂಹನ್, ಕಲಾವಿದ -![roadmap sketchnote](../../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.kn.png) +![roadmap sketchnote](../../../translated_images/kn/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png) --- diff --git a/translations/ko/1-Introduction/01-defining-data-science/README.md b/translations/ko/1-Introduction/01-defining-data-science/README.md index 3cc46da7..7dcb7508 100644 --- a/translations/ko/1-Introduction/01-defining-data-science/README.md +++ b/translations/ko/1-Introduction/01-defining-data-science/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: --- -[![데이터 과학 정의 비디오](../../../../translated_images/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.ko.png)](https://youtu.be/beZ7Mb_oz9I) +[![데이터 과학 정의 비디오](../../../../translated_images/ko/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.png)](https://youtu.be/beZ7Mb_oz9I) ## [강의 전 퀴즈](https://ff-quizzes.netlify.app/en/ds/quiz/0) @@ -153,7 +153,7 @@ CO_OP_TRANSLATOR_METADATA: 이번 도전 과제에서는 텍스트를 분석하여 데이터 과학 분야와 관련된 개념을 찾아보겠습니다. 데이터 과학에 대한 위키피디아 글을 가져와 텍스트를 처리한 후, 아래와 같은 워드 클라우드를 만들어 보겠습니다: -![데이터 과학 워드 클라우드](../../../../translated_images/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.ko.png) +![데이터 과학 워드 클라우드](../../../../translated_images/ko/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.png) [`notebook.ipynb`](../../../../1-Introduction/01-defining-data-science/notebook.ipynb ':ignore')를 방문하여 코드를 읽어보세요. 코드를 실행하여 모든 데이터 변환이 실시간으로 어떻게 수행되는지 확인할 수도 있습니다. diff --git a/translations/ko/1-Introduction/04-stats-and-probability/README.md b/translations/ko/1-Introduction/04-stats-and-probability/README.md index 06fc1b97..119cd632 100644 --- a/translations/ko/1-Introduction/04-stats-and-probability/README.md +++ b/translations/ko/1-Introduction/04-stats-and-probability/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: 통계와 확률 이론은 데이터 과학에서 매우 중요한 수학의 두 가지 밀접한 분야입니다. 수학에 대한 깊은 지식 없이도 데이터를 다룰 수는 있지만, 최소한 기본 개념은 알고 있는 것이 좋습니다. 여기에서는 시작하는 데 도움이 되는 간단한 소개를 제공합니다. -[![Intro Video](../../../../translated_images/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.ko.png)](https://youtu.be/Z5Zy85g4Yjw) +[![Intro Video](../../../../translated_images/ko/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.png)](https://youtu.be/Z5Zy85g4Yjw) ## [강의 전 퀴즈](https://ff-quizzes.netlify.app/en/ds/quiz/6) @@ -39,7 +39,7 @@ CO_OP_TRANSLATOR_METADATA: 우리는 변수 값이 특정 구간에 속할 확률, 예를 들어 P(t1≤X2)에 대해서만 이야기할 수 있습니다. 이 경우, 확률 분포는 **확률 밀도 함수** p(x)로 설명됩니다. 이 함수는 다음과 같이 정의됩니다: -![P(t_1\le X 상관관계와 공분산에 대한 더 많은 예제는 [첨부된 노트북](notebook.ipynb)에서 확인할 수 있습니다. diff --git a/translations/ko/1-Introduction/README.md b/translations/ko/1-Introduction/README.md index 77a472ed..c95a7477 100644 --- a/translations/ko/1-Introduction/README.md +++ b/translations/ko/1-Introduction/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 데이터 과학 입문 -![데이터 활용](../../../translated_images/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.ko.jpg) +![데이터 활용](../../../translated_images/ko/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.jpg) > 사진 제공: Stephen Dawson / Unsplash 이 강의에서는 데이터 과학이 어떻게 정의되는지 알아보고, 데이터 과학자가 반드시 고려해야 할 윤리적 문제들에 대해 배웁니다. 또한 데이터가 어떻게 정의되는지 배우고, 데이터 과학의 핵심 학문 분야인 통계와 확률에 대해 간단히 살펴볼 것입니다. diff --git a/translations/ko/2-Working-With-Data/07-python/README.md b/translations/ko/2-Working-With-Data/07-python/README.md index c518c03f..52fd7a9b 100644 --- a/translations/ko/2-Working-With-Data/07-python/README.md +++ b/translations/ko/2-Working-With-Data/07-python/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: | :-------------------------------------------------------------------------------------------------------: | | Python 작업하기 - _Sketchnote by [@nitya](https://twitter.com/nitya)_ | -[![Intro Video](../../../../translated_images/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.ko.png)](https://youtu.be/dZjWOGbsN4Y) +[![Intro Video](../../../../translated_images/ko/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.png)](https://youtu.be/dZjWOGbsN4Y) 데이터베이스는 데이터를 저장하고 쿼리 언어를 사용하여 데이터를 조회하는 데 매우 효율적인 방법을 제공하지만, 데이터 처리를 가장 유연하게 수행하는 방법은 직접 프로그램을 작성하여 데이터를 조작하는 것입니다. 많은 경우 데이터베이스 쿼리가 더 효과적인 방법일 수 있습니다. 하지만 더 복잡한 데이터 처리가 필요한 경우 SQL로 쉽게 처리할 수 없는 경우도 있습니다. 데이터 처리는 어떤 프로그래밍 언어로도 작성할 수 있지만, 데이터 작업에 있어 더 높은 수준의 언어들이 있습니다. 데이터 과학자들은 일반적으로 다음 언어들 중 하나를 선호합니다: @@ -73,7 +73,7 @@ print(f"Length of index is {len(idx)}") items_sold = pd.Series(np.random.randint(25,50,size=len(idx)),index=idx) items_sold.plot() ``` -![Time Series Plot](../../../../translated_images/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.ko.png) +![Time Series Plot](../../../../translated_images/ko/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.png) 이제 매주 친구들과 파티를 열고 추가로 아이스크림 10팩을 가져간다고 가정해 봅시다. 이를 나타내는 또 다른 시리즈를 생성할 수 있습니다: ```python @@ -84,7 +84,7 @@ additional_items = pd.Series(10,index=pd.date_range(start_date,end_date,freq="W" total_items = items_sold.add(additional_items,fill_value=0) total_items.plot() ``` -![Time Series Plot](../../../../translated_images/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.ko.png) +![Time Series Plot](../../../../translated_images/ko/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.png) > **참고**: 단순한 문법 `total_items+additional_items`을 사용하지 않았습니다. 그렇게 하면 결과 시리즈에 많은 `NaN`(*Not a Number*) 값이 생깁니다. 이는 `additional_items` 시리즈의 일부 인덱스 포인트에 값이 없기 때문이며, `NaN`을 다른 값에 더하면 결과는 `NaN`이 됩니다. 따라서 덧셈 중에 `fill_value` 매개변수를 지정해야 합니다. @@ -93,7 +93,7 @@ total_items.plot() monthly = total_items.resample("1M").mean() ax = monthly.plot(kind='bar') ``` -![Monthly Time Series Averages](../../../../translated_images/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.ko.png) +![Monthly Time Series Averages](../../../../translated_images/ko/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.png) ### 데이터프레임 (DataFrame) @@ -219,7 +219,7 @@ df = pd.read_csv('file.csv') 데이터를 다루는 방법을 보여주기 위해 [`notebook-covidspread.ipynb`](notebook-covidspread.ipynb)를 열어 처음부터 끝까지 읽어보시길 권장합니다. 셀을 실행하고, 마지막에 남겨둔 몇 가지 도전을 수행할 수도 있습니다. -![COVID 확산](../../../../translated_images/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.ko.png) +![COVID 확산](../../../../translated_images/ko/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.png) > Jupyter Notebook에서 코드를 실행하는 방법을 모른다면 [이 기사](https://soshnikov.com/education/how-to-execute-notebooks-from-github/)를 참고하세요. @@ -241,7 +241,7 @@ df = pd.read_csv('file.csv') [`notebook-papers.ipynb`](notebook-papers.ipynb)를 열어 처음부터 끝까지 읽어보세요. 셀을 실행하고, 마지막에 남겨둔 몇 가지 도전을 수행할 수도 있습니다. -![COVID 의료 치료](../../../../translated_images/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.ko.png) +![COVID 의료 치료](../../../../translated_images/ko/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.png) ## 이미지 데이터 처리 diff --git a/translations/ko/2-Working-With-Data/README.md b/translations/ko/2-Working-With-Data/README.md index 95dfc07b..c975a64a 100644 --- a/translations/ko/2-Working-With-Data/README.md +++ b/translations/ko/2-Working-With-Data/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 데이터 작업하기 -![data love](../../../translated_images/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.ko.jpg) +![data love](../../../translated_images/ko/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.jpg) > 사진 제공: Alexander Sinn on Unsplash 이 강의에서는 데이터를 관리, 조작, 그리고 애플리케이션에서 활용하는 다양한 방법을 배우게 됩니다. 관계형 및 비관계형 데이터베이스에 대해 배우고, 데이터가 어떻게 저장될 수 있는지 알아볼 것입니다. 또한, 데이터를 관리하기 위해 Python을 사용하는 기본 원리를 배우고, Python을 활용하여 데이터를 관리하고 분석하는 다양한 방법을 발견하게 될 것입니다. diff --git a/translations/ko/3-Data-Visualization/12-visualization-relationships/README.md b/translations/ko/3-Data-Visualization/12-visualization-relationships/README.md index 3318f6f9..f4009473 100644 --- a/translations/ko/3-Data-Visualization/12-visualization-relationships/README.md +++ b/translations/ko/3-Data-Visualization/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ honey.head() ```python sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ``` -![scatterplot 1](../../../../translated_images/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.ko.png) +![scatterplot 1](../../../../translated_images/ko/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.png) 이제 꿀 색상 테마를 추가하여 연도별로 가격이 어떻게 변화했는지 보여주세요. 'hue' 매개변수를 추가하여 연도별 변화를 표시할 수 있습니다: @@ -60,7 +60,7 @@ sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ```python sns.relplot(x="priceperlb", y="state", hue="year", palette="YlOrBr", data=honey, height=15, aspect=.5); ``` -![scatterplot 2](../../../../translated_images/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.ko.png) +![scatterplot 2](../../../../translated_images/ko/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.png) 이 색상 테마 변경을 통해 꿀 가격이 연도별로 강한 상승 추세를 보인다는 것을 명확히 알 수 있습니다. 실제로 데이터를 샘플링하여 확인해 보면(예: 애리조나 주) 연도별로 가격이 증가하는 패턴을 확인할 수 있으며, 몇 가지 예외를 제외하고는 그렇습니다: @@ -89,7 +89,7 @@ sns.relplot(x="priceperlb", y="state", size="year", data=honey, height=15, aspec ``` 점의 크기가 점차 증가하는 것을 볼 수 있습니다. -![scatterplot 3](../../../../translated_images/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.ko.png) +![scatterplot 3](../../../../translated_images/ko/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.png) 이것이 단순히 수요와 공급의 문제일까요? 기후 변화와 꿀벌 군집 붕괴와 같은 요인으로 인해 연도별로 구매 가능한 꿀이 줄어들고, 그 결과 가격이 상승하는 것일까요? @@ -104,7 +104,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); ``` 답변: 네, 2003년을 중심으로 몇 가지 예외가 있습니다: -![line chart 1](../../../../translated_images/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.ko.png) +![line chart 1](../../../../translated_images/ko/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.png) ✅ Seaborn은 데이터를 하나의 선으로 집계하며, "각 x 값에서 여러 측정을 평균과 평균 주변의 95% 신뢰 구간을 표시하여 플로팅합니다". [출처](https://seaborn.pydata.org/tutorial/relational.html). 이 시간 소모적인 동작은 `ci=None`을 추가하여 비활성화할 수 있습니다. @@ -114,7 +114,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); sns.relplot(x="year", y="totalprod", kind="line", data=honey); ``` -![line chart 2](../../../../translated_images/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.ko.png) +![line chart 2](../../../../translated_images/ko/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.png) 답변: 그렇지 않습니다. 총 생산량을 보면, 특정 연도에는 실제로 증가한 것으로 보이지만, 일반적으로 꿀 생산량은 이 기간 동안 감소하는 추세입니다. @@ -139,7 +139,7 @@ sns.relplot( ``` 이 시각화에서는 꿀벌 군집당 생산량과 꿀벌 군집 수를 연도별로 나란히 비교할 수 있으며, 열을 3으로 설정하여 랩을 적용합니다: -![facet grid](../../../../translated_images/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.ko.png) +![facet grid](../../../../translated_images/ko/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.png) 이 데이터셋에서는 연도별, 주별 꿀벌 군집 수와 생산량에 관해 특별히 두드러지는 점은 없습니다. 이 두 변수 간의 상관관계를 찾는 다른 방법이 있을까요? @@ -162,7 +162,7 @@ sns.despine(right=False) plt.ylabel('colony yield') ax.figure.legend(); ``` -![superimposed plots](../../../../translated_images/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.ko.png) +![superimposed plots](../../../../translated_images/ko/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.png) 2003년을 중심으로 눈에 띄는 점은 없지만, 이 강의를 조금 더 긍정적인 노트로 마무리할 수 있습니다: 꿀벌 군집 수는 전반적으로 감소하고 있지만, 군집 수는 안정화되고 있으며 군집당 생산량은 감소하고 있습니다. diff --git a/translations/ko/3-Data-Visualization/R/09-visualization-quantities/README.md b/translations/ko/3-Data-Visualization/R/09-visualization-quantities/README.md index 5a4dd8fb..68612620 100644 --- a/translations/ko/3-Data-Visualization/R/09-visualization-quantities/README.md +++ b/translations/ko/3-Data-Visualization/R/09-visualization-quantities/README.md @@ -66,7 +66,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` 여기서는 `ggplot2` 패키지를 설치한 후 `library("ggplot2")` 명령을 사용하여 작업 공간에 가져옵니다. ggplot에서 플롯을 그리려면 `ggplot()` 함수를 사용하며 데이터셋, x 및 y 변수 등을 속성으로 지정합니다. 이 경우 선형 플롯을 그리기 위해 `geom_line()` 함수를 사용합니다. -![MaxWingspan-lineplot](../../../../../translated_images/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.ko.png) +![MaxWingspan-lineplot](../../../../../translated_images/ko/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.png) 즉시 눈에 띄는 점은 무엇인가요? 적어도 하나의 이상치가 있는 것 같습니다. 2000cm 이상의 날개 길이는 20미터가 넘습니다. 미네소타에 프테로닥틸이 살고 있는 걸까요? 조사해 봅시다. @@ -84,7 +84,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` `theme`에서 각도를 지정하고 `xlab()`과 `ylab()`에서 x축과 y축 레이블을 지정합니다. `ggtitle()`은 그래프/플롯에 이름을 부여합니다. -![MaxWingspan-lineplot-improved](../../../../../translated_images/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.ko.png) +![MaxWingspan-lineplot-improved](../../../../../translated_images/ko/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.png) 레이블을 45도 회전시켰음에도 불구하고 읽기에는 너무 많습니다. 다른 전략을 시도해 봅시다: 이상치만 레이블을 지정하고 차트 내에서 레이블을 설정합니다. 산점도를 사용하여 레이블링 공간을 더 확보할 수 있습니다: @@ -100,7 +100,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + 무엇을 발견했나요? -![MaxWingspan-scatterplot](../../../../../translated_images/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.ko.png) +![MaxWingspan-scatterplot](../../../../../translated_images/ko/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.png) ## 데이터 필터링 @@ -119,7 +119,7 @@ ggplot(data=birds_filtered, aes(x=Name, y=MaxWingspan,group=1)) + ``` 새로운 데이터프레임 `birds_filtered`를 만들고 산점도를 그렸습니다. 이상치를 필터링함으로써 데이터가 더 일관되고 이해하기 쉬워졌습니다. -![MaxWingspan-scatterplot-improved](../../../../../translated_images/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.ko.png) +![MaxWingspan-scatterplot-improved](../../../../../translated_images/ko/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.png) 이제 날개 길이에 관한 데이터가 더 깨끗해졌으니, 이 새들에 대해 더 알아봅시다. @@ -159,7 +159,7 @@ birds_filtered %>% group_by(Category) %>% ``` 다음 코드 스니펫에서는 데이터를 조작하고 그룹화하여 누적 막대 차트를 그리기 위해 [dplyr](https://www.rdocumentation.org/packages/dplyr/versions/0.7.8)과 [lubridate](https://www.rdocumentation.org/packages/lubridate/versions/1.8.0) 패키지를 설치합니다. 먼저 새의 `Category`로 데이터를 그룹화한 후 `MinLength`, `MaxLength`, `MinBodyMass`, `MaxBodyMass`, `MinWingspan`, `MaxWingspan` 열을 요약합니다. 그런 다음 `ggplot2` 패키지를 사용하여 막대 차트를 그리고 각 카테고리에 대한 색상과 레이블을 지정합니다. -![Stacked bar chart](../../../../../translated_images/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.ko.png) +![Stacked bar chart](../../../../../translated_images/ko/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.png) 하지만 이 막대 차트는 너무 많은 비그룹화된 데이터로 인해 읽기 어렵습니다. 플롯하려는 데이터만 선택해야 합니다. 새의 카테고리를 기준으로 길이를 살펴봅시다. @@ -174,7 +174,7 @@ ggplot(birds_count,aes(Category,n))+geom_bar(stat="identity")+coord_flip() ``` 먼저 `Category` 열의 고유 값을 계산한 후 이를 새 데이터프레임 `birds_count`로 정렬합니다. 이 정렬된 데이터를 동일한 수준으로 팩터링하여 정렬된 방식으로 플롯됩니다. 그런 다음 `ggplot2`를 사용하여 데이터를 막대 차트로 플롯합니다. `coord_flip()`은 수평 막대를 플롯합니다. -![category-length](../../../../../translated_images/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.ko.png) +![category-length](../../../../../translated_images/ko/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.png) 이 막대 차트는 각 카테고리의 새 수를 잘 보여줍니다. 한눈에 미네소타 지역에서 가장 많은 새가 Ducks/Geese/Waterfowl 카테고리에 속한다는 것을 알 수 있습니다. 미네소타는 '10,000개의 호수의 땅'이므로 놀랍지 않습니다! @@ -197,7 +197,7 @@ ggplot(birds_grouped,aes(Category,MaxLength))+geom_bar(stat="identity")+coord_fl ``` `birds_filtered` 데이터를 `Category`로 그룹화한 후 막대 그래프를 플롯합니다. -![comparing data](../../../../../translated_images/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.ko.png) +![comparing data](../../../../../translated_images/ko/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.png) 여기서 놀라운 점은 없습니다: 벌새는 펠리컨이나 기러기에 비해 MaxLength가 가장 적습니다. 데이터가 논리적으로 맞아떨어지는 것은 좋은 일입니다! @@ -209,7 +209,7 @@ ggplot(data=birds_grouped, aes(x=Category)) + geom_bar(aes(y=MinLength), stat="identity", position="identity", fill='orange')+ coord_flip() ``` -![super-imposed values](../../../../../translated_images/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.ko.png) +![super-imposed values](../../../../../translated_images/ko/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.png) ## 🚀 도전 과제 diff --git a/translations/ko/3-Data-Visualization/R/10-visualization-distributions/README.md b/translations/ko/3-Data-Visualization/R/10-visualization-distributions/README.md index 1772e2ee..7513bcbb 100644 --- a/translations/ko/3-Data-Visualization/R/10-visualization-distributions/README.md +++ b/translations/ko/3-Data-Visualization/R/10-visualization-distributions/README.md @@ -45,7 +45,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + geom_point() + ggtitle("Max Length per order") + coord_flip() ``` -![max length per order](../../../../../translated_images/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.ko.png) +![max length per order](../../../../../translated_images/ko/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.png) 이 그래프는 새의 Order별 몸 길이의 일반적인 분포를 보여주지만, 실제 분포를 표시하기에는 최적의 방법이 아닙니다. 이 작업은 보통 히스토그램을 생성하여 수행됩니다. @@ -57,7 +57,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=10)+ylab('Frequency') ``` -![distribution over entire dataset](../../../../../translated_images/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.ko.png) +![distribution over entire dataset](../../../../../translated_images/ko/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.png) 보시다시피, 이 데이터셋에 포함된 400개 이상의 새들 중 대부분은 Max Body Mass가 2000 이하 범위에 속합니다. `bins` 매개변수를 30과 같은 더 높은 숫자로 변경하여 데이터를 더 자세히 살펴보세요: @@ -65,7 +65,7 @@ ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![distribution-30bins](../../../../../translated_images/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.ko.png) +![distribution-30bins](../../../../../translated_images/ko/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.png) 이 차트는 분포를 조금 더 세부적으로 보여줍니다. 왼쪽으로 덜 치우친 차트를 만들려면 특정 범위 내의 데이터만 선택하도록 필터링하면 됩니다: @@ -77,7 +77,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![filtered histogram](../../../../../translated_images/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.ko.png) +![filtered histogram](../../../../../translated_images/ko/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.png) ✅ 다른 필터와 데이터 포인트를 시도해 보세요. 데이터의 전체 분포를 보려면 `['MaxBodyMass']` 필터를 제거하여 라벨이 있는 분포를 표시하세요. @@ -91,7 +91,7 @@ ggplot(data=birds_filtered_1, aes(x=MaxBodyMass, y=MaxLength) ) + ``` 이 두 요소가 예상 축을 따라 예상되는 상관관계를 가지며, 특히 강한 수렴 지점이 하나 있는 것으로 보입니다: -![2d plot](../../../../../translated_images/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.ko.png) +![2d plot](../../../../../translated_images/ko/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.png) 히스토그램은 기본적으로 숫자 데이터에 잘 작동합니다. 그렇다면 텍스트 데이터에 따라 분포를 확인해야 한다면 어떻게 해야 할까요? @@ -123,7 +123,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + scale_fill_manual(name="Conservation Status",values=c("red","green","blue","pink"),labels=c("Endangered","Near Threathened","Vulnerable","Least Concern")) ``` -![wingspan and conservation collation](../../../../../translated_images/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.ko.png) +![wingspan and conservation collation](../../../../../translated_images/ko/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.png) 최소 날개 길이와 보존 상태 간에 뚜렷한 상관관계는 없어 보입니다. 이 방법을 사용하여 데이터셋의 다른 요소를 테스트해 보세요. 다른 필터도 시도해 보세요. 상관관계를 발견할 수 있나요? @@ -137,7 +137,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + geom_density() ``` -![density plot](../../../../../translated_images/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.ko.png) +![density plot](../../../../../translated_images/ko/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.png) 이 플롯은 이전의 최소 날개 길이 데이터와 유사한 결과를 보여줍니다. 단지 조금 더 부드럽게 표현되었을 뿐입니다. 두 번째로 생성한 MaxBodyMass의 울퉁불퉁한 선을 다시 방문하고 싶다면, 이 방법을 사용하여 매우 부드럽게 만들 수 있습니다: @@ -145,7 +145,7 @@ ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density() ``` -![bodymass density](../../../../../translated_images/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.ko.png) +![bodymass density](../../../../../translated_images/ko/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.png) 너무 부드럽지 않은 선을 원한다면 `adjust` 매개변수를 편집하세요: @@ -153,7 +153,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density(adjust = 1/5) ``` -![less smooth bodymass](../../../../../translated_images/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.ko.png) +![less smooth bodymass](../../../../../translated_images/ko/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.png) ✅ 이 유형의 플롯에 사용할 수 있는 매개변수에 대해 읽어보고 실험해 보세요! @@ -163,7 +163,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + ggplot(data=birds_filtered_1,aes(x = MaxBodyMass, fill = Order)) + geom_density(alpha=0.5) ``` -![bodymass per order](../../../../../translated_images/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.ko.png) +![bodymass per order](../../../../../translated_images/ko/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.png) ## 🚀 도전 과제 diff --git a/translations/ko/3-Data-Visualization/R/11-visualization-proportions/README.md b/translations/ko/3-Data-Visualization/R/11-visualization-proportions/README.md index 33de5892..8a294785 100644 --- a/translations/ko/3-Data-Visualization/R/11-visualization-proportions/README.md +++ b/translations/ko/3-Data-Visualization/R/11-visualization-proportions/README.md @@ -92,7 +92,7 @@ pie(grouped$count,grouped$class, main="Edible?") ``` 짜잔, 이 두 가지 버섯 클래스에 따라 데이터 비율을 보여주는 파이 차트가 완성되었습니다. 레이블 배열을 생성할 때 순서를 올바르게 설정하는 것이 특히 중요하므로 반드시 확인하세요! -![pie chart](../../../../../translated_images/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.ko.png) +![pie chart](../../../../../translated_images/ko/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.png) ## 도넛! @@ -126,7 +126,7 @@ library(webr) PieDonut(habitat, aes(habitat, count=count)) ``` -![donut chart](../../../../../translated_images/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.ko.png) +![donut chart](../../../../../translated_images/ko/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.png) 이 코드는 두 개의 라이브러리 - ggplot2와 webr을 사용합니다. webr 라이브러리의 PieDonut 함수를 사용하면 도넛 차트를 쉽게 만들 수 있습니다! @@ -164,7 +164,7 @@ waffle((cap_color$count/10), rows = 7, title = "Waffle Chart")+scale_fill_manual 와플 차트를 사용하면 이 버섯 데이터셋의 갓 색상 비율을 명확히 볼 수 있습니다. 흥미롭게도 녹색 갓을 가진 버섯이 많이 있습니다! -![waffle chart](../../../../../translated_images/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.ko.png) +![waffle chart](../../../../../translated_images/ko/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.png) 이 강의에서는 비율을 시각화하는 세 가지 방법을 배웠습니다. 먼저 데이터를 카테고리로 그룹화한 후 데이터를 표시하는 가장 적합한 방법 - 파이, 도넛, 또는 와플을 결정해야 합니다. 모두 맛있고 사용자에게 데이터셋의 즉각적인 스냅샷을 제공합니다. diff --git a/translations/ko/3-Data-Visualization/R/12-visualization-relationships/README.md b/translations/ko/3-Data-Visualization/R/12-visualization-relationships/README.md index 6f3f8141..385e4b71 100644 --- a/translations/ko/3-Data-Visualization/R/12-visualization-relationships/README.md +++ b/translations/ko/3-Data-Visualization/R/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ library(ggplot2) ggplot(honey, aes(x = priceperlb, y = state)) + geom_point(colour = "blue") ``` -![scatterplot 1](../../../../../translated_images/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.ko.png) +![scatterplot 1](../../../../../translated_images/ko/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.png) 이제 같은 데이터를 꿀 색상 테마로 표시하여 연도별로 가격이 어떻게 변화했는지 보여주세요. 이를 위해 'scale_color_gradientn' 매개변수를 추가하여 연도별 변화를 표시할 수 있습니다: @@ -61,7 +61,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ggplot(honey, aes(x = priceperlb, y = state, color=year)) + geom_point()+scale_color_gradientn(colours = colorspace::heat_hcl(7)) ``` -![scatterplot 2](../../../../../translated_images/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.ko.png) +![scatterplot 2](../../../../../translated_images/ko/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.png) 이 색상 테마 변경을 통해 꿀의 파운드당 가격이 연도별로 강한 상승 추세를 보이는 것을 명확히 확인할 수 있습니다. 실제로 데이터를 샘플링하여 확인해 보면(예: 애리조나 주) 연도별로 가격이 증가하는 패턴을 확인할 수 있으며, 예외는 거의 없습니다: @@ -92,7 +92,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ``` 점의 크기가 점차 증가하는 것을 확인할 수 있습니다. -![scatterplot 3](../../../../../translated_images/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.ko.png) +![scatterplot 3](../../../../../translated_images/ko/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.png) 이것이 단순히 수요와 공급의 문제일까요? 기후 변화와 꿀벌 군집 붕괴와 같은 요인으로 인해 구매 가능한 꿀이 연도별로 줄어들고, 그 결과 가격이 상승하는 것일까요? @@ -107,7 +107,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab ``` 답변: 네, 2003년을 중심으로 몇 가지 예외가 있습니다: -![line chart 1](../../../../../translated_images/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.ko.png) +![line chart 1](../../../../../translated_images/ko/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.png) 질문: 그렇다면 2003년에 꿀 공급량에서도 급증이 있었나요? 연도별 총 생산량을 살펴보면 어떨까요? @@ -115,7 +115,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab qplot(honey$year,honey$totalprod, geom='smooth', span =0.5, xlab = "year",ylab = "totalprod") ``` -![line chart 2](../../../../../translated_images/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.ko.png) +![line chart 2](../../../../../translated_images/ko/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.png) 답변: 그렇지 않습니다. 총 생산량을 보면, 특정 연도에는 실제로 증가한 것으로 보이지만, 일반적으로 꿀 생산량은 이 기간 동안 감소하는 추세입니다. @@ -135,7 +135,7 @@ ggplot(honey, aes(x=yieldpercol, y = numcol,group = 1)) + ``` 이 시각화에서는 꿀벌 군집당 생산량과 군집 수를 연도별로 나란히 비교할 수 있습니다. 열은 3으로 설정합니다: -![facet grid](../../../../../translated_images/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.ko.png) +![facet grid](../../../../../translated_images/ko/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.png) 이 데이터셋에서는 연도별, 주별로 꿀벌 군집 수와 생산량에 관해 특별히 두드러지는 점은 없습니다. 이 두 변수 간 상관관계를 찾는 다른 방법이 있을까요? @@ -152,7 +152,7 @@ plot(honey$year, honey$yieldpercol, pch = 17, col = 3, axis(side = 4, at = pretty(range(y2))) mtext("colony yield", side = 4, line = 3) ``` -![superimposed plots](../../../../../translated_images/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.ko.png) +![superimposed plots](../../../../../translated_images/ko/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.png) 2003년을 중심으로 눈에 띄는 점은 없지만, 이 강의를 조금 더 긍정적인 노트로 마무리할 수 있습니다: 꿀벌 군집 수는 전반적으로 감소하고 있지만, 군집 수는 안정화되고 있으며 군집당 생산량은 감소하고 있습니다. diff --git a/translations/ko/3-Data-Visualization/R/13-meaningful-vizualizations/README.md b/translations/ko/3-Data-Visualization/R/13-meaningful-vizualizations/README.md index 9033456c..d22652ce 100644 --- a/translations/ko/3-Data-Visualization/R/13-meaningful-vizualizations/README.md +++ b/translations/ko/3-Data-Visualization/R/13-meaningful-vizualizations/README.md @@ -47,25 +47,25 @@ CO_OP_TRANSLATOR_METADATA: 데이터 과학자가 올바른 데이터에 적합한 차트를 신중히 선택하더라도, 데이터를 특정 관점을 증명하기 위해 표시하는 과정에서 데이터 자체를 훼손하는 경우가 많습니다. 기만적인 차트와 인포그래픽의 예는 무수히 많습니다! -[![How Charts Lie by Alberto Cairo](../../../../../translated_images/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.ko.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "How charts lie") +[![How Charts Lie by Alberto Cairo](../../../../../translated_images/ko/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "How charts lie") > 🎥 위 이미지를 클릭하면 기만적인 차트에 대한 컨퍼런스 강연을 볼 수 있습니다. 이 차트는 X축을 반대로 뒤집어 날짜를 기준으로 진실의 반대를 보여줍니다: -![bad chart 1](../../../../../translated_images/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.ko.png) +![bad chart 1](../../../../../translated_images/ko/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.png) [이 차트](https://media.firstcoastnews.com/assets/WTLV/images/170ae16f-4643-438f-b689-50d66ca6a8d8/170ae16f-4643-438f-b689-50d66ca6a8d8_1140x641.jpg)는 더욱 기만적입니다. 눈은 오른쪽으로 향하며 시간이 지남에 따라 COVID 사례가 감소했다고 결론짓게 만듭니다. 하지만 날짜를 자세히 보면, 기만적인 하락 추세를 보여주기 위해 날짜가 재배열된 것을 알 수 있습니다. -![bad chart 2](../../../../../translated_images/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.ko.jpg) +![bad chart 2](../../../../../translated_images/ko/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.jpg) 이 악명 높은 예시는 색상과 뒤집힌 Y축을 사용하여 기만합니다. 총기 친화적 법안 통과 후 총기 사망자가 급증했음에도 불구하고, 눈은 반대의 결론을 내리도록 속습니다: -![bad chart 3](../../../../../translated_images/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.ko.jpg) +![bad chart 3](../../../../../translated_images/ko/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.jpg) 이 이상한 차트는 비율을 조작하여 웃음을 자아냅니다: -![bad chart 4](../../../../../translated_images/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.ko.jpg) +![bad chart 4](../../../../../translated_images/ko/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.jpg) 비교할 수 없는 것을 비교하는 것도 또 다른 기만적인 방법입니다. [멋진 웹사이트](https://tylervigen.com/spurious-correlations)는 '허위 상관관계'를 보여주는 '사실'을 수집하며, 예를 들어 메인주의 이혼율과 마가린 소비를 상관시키는 데이터를 제공합니다. Reddit 그룹은 데이터의 [기만적인 사용](https://www.reddit.com/r/dataisugly/top/?t=all)을 수집합니다. @@ -100,13 +100,13 @@ CO_OP_TRANSLATOR_METADATA: X축에 텍스트 데이터가 길고 자세하다면, 텍스트를 각도로 조정하여 가독성을 높일 수 있습니다. [plot3D](https://cran.r-project.org/web/packages/plot3D/index.html)는 데이터가 지원하는 경우 3D 플로팅을 제공합니다. 이를 사용하여 정교한 데이터 시각화를 생성할 수 있습니다. -![3d plots](../../../../../translated_images/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.ko.png) +![3d plots](../../../../../translated_images/ko/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.png) ## 애니메이션 및 3D 차트 표시 오늘날 최고의 데이터 시각화 중 일부는 애니메이션으로 제작됩니다. Shirley Wu는 D3를 사용하여 '[영화 꽃](http://bl.ocks.org/sxywu/raw/d612c6c653fb8b4d7ff3d422be164a5d/)'과 같은 놀라운 애니메이션을 만들었습니다. 각 꽃은 영화의 시각화를 나타냅니다. 또 다른 예로 Guardian의 'Bussed Out'은 NYC가 노숙자 문제를 해결하기 위해 사람들을 도시 밖으로 버스에 태워 보내는 방식을 보여주는 스크롤텔링 기사 형식과 Greensock 및 D3를 결합한 인터랙티브 경험입니다. -![busing](../../../../../translated_images/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.ko.png) +![busing](../../../../../translated_images/ko/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.png) > "Bussed Out: How America Moves its Homeless" from [the Guardian](https://www.theguardian.com/us-news/ng-interactive/2017/dec/20/bussed-out-america-moves-homeless-people-country-study). Visualizations by Nadieh Bremer & Shirley Wu @@ -116,7 +116,7 @@ X축에 텍스트 데이터가 길고 자세하다면, 텍스트를 각도로 Vue.js와 D3를 사용하여 네트워크 시각화를 표시하는 라이브러리를 사용하여 애니메이션화된 소셜 네트워크를 보여주는 웹 앱을 완성하세요. 앱이 실행되면 화면에서 노드를 끌어 데이터를 재배열할 수 있습니다. -![liaisons](../../../../../translated_images/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.ko.png) +![liaisons](../../../../../translated_images/ko/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.png) ## 프로젝트: D3.js를 사용하여 네트워크를 보여주는 차트 만들기 diff --git a/translations/ko/3-Data-Visualization/README.md b/translations/ko/3-Data-Visualization/README.md index 445fa2b4..817c34e5 100644 --- a/translations/ko/3-Data-Visualization/README.md +++ b/translations/ko/3-Data-Visualization/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 시각화 -![라벤더 꽃 위의 벌](../../../translated_images/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.ko.jpg) +![라벤더 꽃 위의 벌](../../../translated_images/ko/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.jpg) > 사진 제공: Jenna Lee on Unsplash 데이터 시각화는 데이터 과학자에게 가장 중요한 작업 중 하나입니다. "이미지는 천 마디 말보다 강하다"는 말처럼, 시각화는 데이터의 스파이크, 이상치, 그룹화, 경향 등 흥미로운 부분을 식별하는 데 도움을 주며, 데이터가 전달하려는 이야기를 이해하는 데 큰 도움을 줍니다. diff --git a/translations/ko/4-Data-Science-Lifecycle/14-Introduction/README.md b/translations/ko/4-Data-Science-Lifecycle/14-Introduction/README.md index a60f0be0..459dad0a 100644 --- a/translations/ko/4-Data-Science-Lifecycle/14-Introduction/README.md +++ b/translations/ko/4-Data-Science-Lifecycle/14-Introduction/README.md @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 이번 강의에서는 생애 주기의 3가지 부분인 데이터 수집, 처리, 유지 관리에 초점을 맞춥니다. -![데이터 과학 생애 주기 다이어그램](../../../../translated_images/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.ko.jpg) +![데이터 과학 생애 주기 다이어그램](../../../../translated_images/ko/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.jpg) > 사진 출처: [Berkeley School of Information](https://ischoolonline.berkeley.edu/data-science/what-is-data-science/) ## 데이터 수집 @@ -97,7 +97,7 @@ CO_OP_TRANSLATOR_METADATA: |Team Data Science Process (TDSP)|Cross-industry standard process for data mining (CRISP-DM)| |--|--| -|![Team Data Science Lifecycle](../../../../translated_images/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.ko.png) | ![Data Science Process Alliance Image](../../../../translated_images/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.ko.png) | +|![Team Data Science Lifecycle](../../../../translated_images/ko/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.png) | ![Data Science Process Alliance Image](../../../../translated_images/ko/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.png) | | 이미지 출처: [Microsoft](https://docs.microsoft.comazure/architecture/data-science-process/lifecycle) | 이미지 출처: [Data Science Process Alliance](https://www.datascience-pm.com/crisp-dm-2/) | ## [강의 후 퀴즈](https://ff-quizzes.netlify.app/en/ds/quiz/27) diff --git a/translations/ko/4-Data-Science-Lifecycle/README.md b/translations/ko/4-Data-Science-Lifecycle/README.md index b56b4e64..acdfd494 100644 --- a/translations/ko/4-Data-Science-Lifecycle/README.md +++ b/translations/ko/4-Data-Science-Lifecycle/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 데이터 과학 생명주기 -![communication](../../../translated_images/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.ko.jpg) +![communication](../../../translated_images/ko/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.jpg) > 사진 제공: Headway on Unsplash 이 강의에서는 데이터 과학 생명주기의 일부 측면, 특히 데이터 분석과 데이터에 대한 커뮤니케이션을 탐구합니다. diff --git a/translations/ko/5-Data-Science-In-Cloud/README.md b/translations/ko/5-Data-Science-In-Cloud/README.md index 25a01505..5fe6cccf 100644 --- a/translations/ko/5-Data-Science-In-Cloud/README.md +++ b/translations/ko/5-Data-Science-In-Cloud/README.md @@ -9,13 +9,13 @@ CO_OP_TRANSLATOR_METADATA: --> # 클라우드에서의 데이터 과학 -![cloud-picture](../../../translated_images/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.ko.jpg) +![cloud-picture](../../../translated_images/ko/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.jpg) > 사진 제공: [Jelleke Vanooteghem](https://unsplash.com/@ilumire) / [Unsplash](https://unsplash.com/s/photos/cloud?orientation=landscape) 빅데이터를 활용한 데이터 과학을 수행할 때, 클라우드는 게임 체인저가 될 수 있습니다. 다음 세 가지 강의에서 클라우드가 무엇인지, 그리고 왜 유용한지 알아보겠습니다. 또한 심부전 데이터셋을 탐구하고, 심부전 발생 가능성을 평가하는 데 도움을 줄 수 있는 모델을 구축할 것입니다. 클라우드의 강력한 기능을 활용하여 두 가지 방식으로 모델을 학습, 배포 및 활용할 것입니다. 첫 번째는 Low code/No code 방식으로 사용자 인터페이스만을 사용하는 방법이고, 두 번째는 Azure Machine Learning Software Developer Kit (Azure ML SDK)를 사용하는 방법입니다. -![project-schema](../../../translated_images/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.ko.png) +![project-schema](../../../translated_images/ko/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.png) ### 주제 diff --git a/translations/ko/6-Data-Science-In-Wild/20-Real-World-Examples/README.md b/translations/ko/6-Data-Science-In-Wild/20-Real-World-Examples/README.md index c807114c..66b9c4d2 100644 --- a/translations/ko/6-Data-Science-In-Wild/20-Real-World-Examples/README.md +++ b/translations/ko/6-Data-Science-In-Wild/20-Real-World-Examples/README.md @@ -41,7 +41,7 @@ AI의 민주화 덕분에 개발자들은 이제 AI 기반 의사결정과 데 * [헬스케어에서의 데이터 과학](https://data-flair.training/blogs/data-science-in-healthcare/) - 의료 영상 (예: MRI, X-Ray, CT-Scan), 유전체학 (DNA 시퀀싱), 약물 개발 (위험 평가, 성공 예측), 예측 분석 (환자 관리 및 공급 물류), 질병 추적 및 예방 등과 같은 응용 사례를 강조합니다. -![현실 세계에서의 데이터 과학 응용 사례](../../../../translated_images/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.ko.png) 이미지 출처: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) +![현실 세계에서의 데이터 과학 응용 사례](../../../../translated_images/ko/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.png) 이미지 출처: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) 이 그림은 데이터 과학 기술을 적용할 수 있는 다른 도메인과 예제를 보여줍니다. 다른 응용 사례를 탐구하고 싶으신가요? 아래의 [리뷰 및 자기 학습](../../../../6-Data-Science-In-Wild/20-Real-World-Examples) 섹션을 확인하세요. diff --git a/translations/ko/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md b/translations/ko/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md index 5e2afafa..fb0261e4 100644 --- a/translations/ko/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md +++ b/translations/ko/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md @@ -22,7 +22,7 @@ Explorer 인터페이스(아래 스크린샷 참조)는 데이터셋(제공된 2. 데이터셋 [카탈로그](https://planetarycomputer.microsoft.com/catalog)를 탐색하여 각 데이터셋의 목적을 학습하세요. 3. Explorer를 사용하여 관심 있는 데이터셋을 선택하고, 관련 쿼리와 렌더링 옵션을 선택하세요. -![The Planetary Computer Explorer](../../../../translated_images/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.ko.png) +![The Planetary Computer Explorer](../../../../translated_images/ko/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.png) `여러분의 과제:` 이제 브라우저에 렌더링된 시각화를 연구하고 다음 질문에 답하세요: diff --git a/translations/ko/CONTRIBUTING.md b/translations/ko/CONTRIBUTING.md index ee82be39..d4c01607 100644 --- a/translations/ko/CONTRIBUTING.md +++ b/translations/ko/CONTRIBUTING.md @@ -311,7 +311,7 @@ PR 설명에 포함할 내용: import pandas as pd ``` ```` -- 이미지에 대체 텍스트 추가: `![Alt text](../../translated_images/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.ko.png)` +- 이미지에 대체 텍스트 추가: `![Alt text](../../translated_images/ko/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.png)` - 줄 길이는 적당히 유지 (약 80-100자) ### Python diff --git a/translations/ko/README.md b/translations/ko/README.md index aa1124af..cdc9c04f 100644 --- a/translations/ko/README.md +++ b/translations/ko/README.md @@ -33,7 +33,7 @@ CO_OP_TRANSLATOR_METADATA: **🙏 특별 감사 🙏 우리 [Microsoft 학생 홍보대사](https://studentambassadors.microsoft.com/) 저자, 검토자 및 콘텐츠 기여자들에게,** 특히 Aaryan Arora, [Aditya Garg](https://github.com/AdityaGarg00), [Alondra Sanchez](https://www.linkedin.com/in/alondra-sanchez-molina/), [Ankita Singh](https://www.linkedin.com/in/ankitasingh007), [Anupam Mishra](https://www.linkedin.com/in/anupam--mishra/), [Arpita Das](https://www.linkedin.com/in/arpitadas01/), ChhailBihari Dubey, [Dibri Nsofor](https://www.linkedin.com/in/dibrinsofor), [Dishita Bhasin](https://www.linkedin.com/in/dishita-bhasin-7065281bb), [Majd Safi](https://www.linkedin.com/in/majd-s/), [Max Blum](https://www.linkedin.com/in/max-blum-6036a1186/), [Miguel Correa](https://www.linkedin.com/in/miguelmque/), [Mohamma Iftekher (Iftu) Ebne Jalal](https://twitter.com/iftu119), [Nawrin Tabassum](https://www.linkedin.com/in/nawrin-tabassum), [Raymond Wangsa Putra](https://www.linkedin.com/in/raymond-wp/), [Rohit Yadav](https://www.linkedin.com/in/rty2423), Samridhi Sharma, [Sanya Sinha](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200), [Sheena Narula](https://www.linkedin.com/in/sheena-narua-n/), [Tauqeer Ahmad](https://www.linkedin.com/in/tauqeerahmad5201/), Yogendrasingh Pawar , [Vidushi Gupta](https://www.linkedin.com/in/vidushi-gupta07/), [Jasleen Sondhi](https://www.linkedin.com/in/jasleen-sondhi/) -|![스케치노트 작성자 @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.ko.png)| +|![스케치노트 작성자 @sketchthedocs https://sketchthedocs.dev](../../translated_images/ko/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.png)| |:---:| | 초보자를 위한 데이터 사이언스 - _스케치노트 작성자 [@nitya](https://twitter.com/nitya)_ | @@ -52,7 +52,7 @@ CO_OP_TRANSLATOR_METADATA: 우리는 Discord에서 Learn with AI 시리즈를 진행하고 있습니다. 자세한 내용 및 참여는 [AI와 함께 배우기 시리즈](https://aka.ms/learnwithai/discord)에서 확인하세요. 기간: 2025년 9월 18일 - 30일. GitHub Copilot을 데이터 사이언스에 활용하는 팁과 요령을 얻을 수 있습니다. -![AI와 함께 배우기 시리즈](../../translated_images/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.ko.jpg) +![AI와 함께 배우기 시리즈](../../translated_images/ko/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.jpg) # 학생이신가요? @@ -132,7 +132,7 @@ CO_OP_TRANSLATOR_METADATA: ## Lessons -|![ 스케치노트 작성자 @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.ko.png)| +|![ 스케치노트 작성자 @sketchthedocs https://sketchthedocs.dev](../../translated_images/ko/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png)| |:---:| | 초보자를 위한 데이터 과학: 로드맵 - _스케치노트 작성자 [@nitya](https://twitter.com/nitya)_ | diff --git a/translations/ko/sketchnotes/README.md b/translations/ko/sketchnotes/README.md index f0391587..801db96f 100644 --- a/translations/ko/sketchnotes/README.md +++ b/translations/ko/sketchnotes/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: 아티스트: Nitya Narasimhan -![로드맵 스케치노트](../../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.ko.png) +![로드맵 스케치노트](../../../translated_images/ko/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png) **면책 조항**: 이 문서는 AI 번역 서비스 [Co-op Translator](https://github.com/Azure/co-op-translator)를 사용하여 번역되었습니다. 정확성을 위해 최선을 다하고 있지만, 자동 번역에는 오류나 부정확성이 포함될 수 있습니다. 원본 문서의 원어 버전을 권위 있는 출처로 간주해야 합니다. 중요한 정보의 경우, 전문적인 인간 번역을 권장합니다. 이 번역 사용으로 인해 발생하는 오해나 잘못된 해석에 대해 책임을 지지 않습니다. \ No newline at end of file diff --git a/translations/lt/1-Introduction/01-defining-data-science/README.md b/translations/lt/1-Introduction/01-defining-data-science/README.md index 0e82df10..b7f13281 100644 --- a/translations/lt/1-Introduction/01-defining-data-science/README.md +++ b/translations/lt/1-Introduction/01-defining-data-science/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: --- -[![Duomenų mokslas: apibrėžimo vaizdo įrašas](../../../../translated_images/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.lt.png)](https://youtu.be/beZ7Mb_oz9I) +[![Duomenų mokslas: apibrėžimo vaizdo įrašas](../../../../translated_images/lt/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.png)](https://youtu.be/beZ7Mb_oz9I) ## [Prieš paskaitos testas](https://ff-quizzes.netlify.app/en/ds/quiz/0) @@ -149,7 +149,7 @@ Jei norime dar labiau pasigilinti, galime sudaryti grafiką, kuriame būtų pava Šiame iššūkyje bandysime rasti konceptus, susijusius su duomenų mokslo sritimi, analizuodami tekstus. Paimsime Vikipedijos straipsnį apie duomenų mokslą, atsisiųsime ir apdorosime tekstą, o tada sukursime žodžių debesį, panašų į šį: -![Žodžių debesis apie duomenų mokslą](../../../../translated_images/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.lt.png) +![Žodžių debesis apie duomenų mokslą](../../../../translated_images/lt/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.png) Apsilankykite [`notebook.ipynb`](../../../../1-Introduction/01-defining-data-science/notebook.ipynb ':ignore'), kad peržiūrėtumėte kodą. Taip pat galite paleisti kodą ir pamatyti, kaip jis realiu laiku atlieka visus duomenų transformavimus. diff --git a/translations/lt/1-Introduction/04-stats-and-probability/README.md b/translations/lt/1-Introduction/04-stats-and-probability/README.md index 6987a8c0..30bcf730 100644 --- a/translations/lt/1-Introduction/04-stats-and-probability/README.md +++ b/translations/lt/1-Introduction/04-stats-and-probability/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: Statistika ir tikimybių teorija yra dvi glaudžiai susijusios matematikos sritys, kurios yra itin svarbios duomenų mokslui. Nors galima dirbti su duomenimis neturint gilių matematikos žinių, vis tiek verta susipažinti bent su pagrindinėmis sąvokomis. Čia pateiksime trumpą įvadą, kuris padės jums pradėti. -[![Intro Video](../../../../translated_images/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.lt.png)](https://youtu.be/Z5Zy85g4Yjw) +[![Intro Video](../../../../translated_images/lt/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.png)](https://youtu.be/Z5Zy85g4Yjw) ## [Prieš paskaitą atlikite testą](https://ff-quizzes.netlify.app/en/ds/quiz/6) @@ -39,7 +39,7 @@ Sunkiau aprašyti tęstinio kintamojo tikimybių pasiskirstymą, kai reikšmės Galime kalbėti tik apie tikimybę, kad kintamasis pateks į tam tikrą reikšmių intervalą, pvz., P(t1≤X2). Tokiu atveju tikimybių pasiskirstymas aprašomas **tikimybių tankio funkcija** p(x), tokia, kad -![P(t_1\le X Daugiau koreliacijos ir kovariacijos pavyzdžių galite rasti [pridedamoje užrašų knygelėje](notebook.ipynb). diff --git a/translations/lt/1-Introduction/README.md b/translations/lt/1-Introduction/README.md index cc1a06fe..828901bf 100644 --- a/translations/lt/1-Introduction/README.md +++ b/translations/lt/1-Introduction/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Įvadas į Duomenų Mokslą -![duomenys veiksme](../../../translated_images/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.lt.jpg) +![duomenys veiksme](../../../translated_images/lt/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.jpg) > Nuotrauka: Stephen Dawson iš Unsplash Šiose pamokose sužinosite, kaip apibrėžiamas Duomenų Mokslas, ir susipažinsite su etiniais aspektais, kuriuos privalo apsvarstyti duomenų mokslininkas. Taip pat sužinosite, kas yra duomenys, ir šiek tiek apie statistiką bei tikimybes – pagrindines Duomenų Mokslo akademines sritis. diff --git a/translations/lt/2-Working-With-Data/07-python/README.md b/translations/lt/2-Working-With-Data/07-python/README.md index 0ec95422..3d0df946 100644 --- a/translations/lt/2-Working-With-Data/07-python/README.md +++ b/translations/lt/2-Working-With-Data/07-python/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: | :-------------------------------------------------------------------------------------------------------: | | Darbas su Python - _Sketchnote by [@nitya](https://twitter.com/nitya)_ | -[![Intro Video](../../../../translated_images/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.lt.png)](https://youtu.be/dZjWOGbsN4Y) +[![Intro Video](../../../../translated_images/lt/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.png)](https://youtu.be/dZjWOGbsN4Y) Nors duomenų bazės siūlo labai efektyvius būdus saugoti duomenis ir užklausas vykdyti naudojant užklausų kalbas, lankstiausias būdas apdoroti duomenis yra rašyti savo programą, kuri manipuliuoja duomenimis. Daugeliu atvejų duomenų bazės užklausa būtų efektyvesnis sprendimas. Tačiau kai kuriais atvejais, kai reikia sudėtingesnio duomenų apdorojimo, tai negali būti lengvai atlikta naudojant SQL. Duomenų apdorojimas gali būti programuojamas bet kuria programavimo kalba, tačiau yra tam tikrų kalbų, kurios yra aukštesnio lygio dirbant su duomenimis. Duomenų mokslininkai paprastai renkasi vieną iš šių kalbų: @@ -75,7 +75,7 @@ print(f"Length of index is {len(idx)}") items_sold = pd.Series(np.random.randint(25,50,size=len(idx)),index=idx) items_sold.plot() ``` -![Laiko serijos grafikas](../../../../translated_images/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.lt.png) +![Laiko serijos grafikas](../../../../translated_images/lt/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.png) Dabar tarkime, kad kiekvieną savaitę organizuojame vakarėlį draugams ir pasiimame papildomus 10 ledų pakuočių vakarėliui. Galime sukurti kitą seriją, indeksuotą savaitėmis, kad tai parodytume: ```python @@ -86,7 +86,7 @@ Kai sudedame dvi serijas, gauname bendrą skaičių: total_items = items_sold.add(additional_items,fill_value=0) total_items.plot() ``` -![Laiko serijos grafikas](../../../../translated_images/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.lt.png) +![Laiko serijos grafikas](../../../../translated_images/lt/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.png) > **Pastaba**: Mes nenaudojame paprastos sintaksės `total_items+additional_items`. Jei tai darytume, gautume daug `NaN` (*Not a Number*) reikšmių rezultato serijoje. Taip yra todėl, kad kai kuriems indeksų taškams serijoje `additional_items` trūksta reikšmių, o sudėjus `NaN` su bet kuo gaunamas `NaN`. Todėl reikia nurodyti `fill_value` parametrą sudėties metu. @@ -95,7 +95,7 @@ Su laiko serijomis taip pat galime **perdaryti** seriją su skirtingais laiko in monthly = total_items.resample("1M").mean() ax = monthly.plot(kind='bar') ``` -![Mėnesio laiko serijos vidurkiai](../../../../translated_images/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.lt.png) +![Mėnesio laiko serijos vidurkiai](../../../../translated_images/lt/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.png) ### Duomenų rėmelis @@ -221,7 +221,7 @@ Pirmoji problema, į kurią sutelksime dėmesį, yra COVID-19 epidemijos plitimo Kadangi norime parodyti, kaip dirbti su duomenimis, kviečiame atidaryti [`notebook-covidspread.ipynb`](notebook-covidspread.ipynb) ir perskaityti jį nuo pradžios iki pabaigos. Taip pat galite vykdyti langelius ir atlikti keletą iššūkių, kuriuos palikome jums pabaigoje. -![COVID plitimas](../../../../translated_images/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.lt.png) +![COVID plitimas](../../../../translated_images/lt/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.png) > Jei nežinote, kaip vykdyti kodą Jupyter Notebook, peržiūrėkite [šį straipsnį](https://soshnikov.com/education/how-to-execute-notebooks-from-github/). @@ -243,7 +243,7 @@ Pilnas šio duomenų rinkinio analizės pavyzdys naudojant [Text Analytics for H Atidarykite [`notebook-papers.ipynb`](notebook-papers.ipynb) ir perskaitykite jį nuo pradžios iki pabaigos. Taip pat galite vykdyti langelius ir atlikti keletą iššūkių, kuriuos palikome jums pabaigoje. -![COVID medicininis gydymas](../../../../translated_images/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.lt.png) +![COVID medicininis gydymas](../../../../translated_images/lt/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.png) ## Vaizdų duomenų apdorojimas diff --git a/translations/lt/2-Working-With-Data/README.md b/translations/lt/2-Working-With-Data/README.md index 3a12b1fd..ecd06dcf 100644 --- a/translations/lt/2-Working-With-Data/README.md +++ b/translations/lt/2-Working-With-Data/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Darbas su duomenimis -![data love](../../../translated_images/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.lt.jpg) +![data love](../../../translated_images/lt/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.jpg) > Nuotrauka Alexander Sinn iš Unsplash Šiose pamokose sužinosite, kaip galima valdyti, manipuliuoti ir naudoti duomenis programose. Susipažinsite su reliacinėmis ir nereliacinėmis duomenų bazėmis bei kaip jose galima saugoti duomenis. Išmoksite pagrindus, kaip naudotis Python valdant duomenis, ir atrasite daugybę būdų, kaip Python gali būti naudojamas duomenims valdyti ir analizuoti. diff --git a/translations/lt/3-Data-Visualization/12-visualization-relationships/README.md b/translations/lt/3-Data-Visualization/12-visualization-relationships/README.md index 45e5a46a..f8e8286e 100644 --- a/translations/lt/3-Data-Visualization/12-visualization-relationships/README.md +++ b/translations/lt/3-Data-Visualization/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ Sukurkite paprastą sklaidos diagramą, kad parodytumėte ryšį tarp medaus kai ```python sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ``` -![sklaidos diagrama 1](../../../../translated_images/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.lt.png) +![sklaidos diagrama 1](../../../../translated_images/lt/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.png) Dabar parodykite tuos pačius duomenis su medaus spalvų schema, kad pavaizduotumėte, kaip kaina keitėsi per metus. Tai galite padaryti pridėdami 'hue' parametrą, kuris parodys pokyčius metai iš metų: @@ -60,7 +60,7 @@ Dabar parodykite tuos pačius duomenis su medaus spalvų schema, kad pavaizduotu ```python sns.relplot(x="priceperlb", y="state", hue="year", palette="YlOrBr", data=honey, height=15, aspect=.5); ``` -![sklaidos diagrama 2](../../../../translated_images/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.lt.png) +![sklaidos diagrama 2](../../../../translated_images/lt/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.png) Naudodami šią spalvų schemą, galite pastebėti, kad medaus kaina už svarą akivaizdžiai didėja metai iš metų. Iš tiesų, jei patikrinsite duomenų pavyzdį (pavyzdžiui, Arizonos valstiją), galite pastebėti kainų didėjimo tendenciją su keliomis išimtimis: @@ -89,7 +89,7 @@ sns.relplot(x="priceperlb", y="state", size="year", data=honey, height=15, aspec ``` Galite pastebėti, kaip taškų dydis palaipsniui didėja. -![sklaidos diagrama 3](../../../../translated_images/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.lt.png) +![sklaidos diagrama 3](../../../../translated_images/lt/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.png) Ar tai paprastas pasiūlos ir paklausos atvejis? Dėl tokių veiksnių kaip klimato kaita ir kolonijų žlugimas, ar metai iš metų mažėja medaus pasiūla, todėl kaina kyla? @@ -104,7 +104,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); ``` Atsakymas: Taip, su keliomis išimtimis apie 2003 metus: -![linijinė diagrama 1](../../../../translated_images/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.lt.png) +![linijinė diagrama 1](../../../../translated_images/lt/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.png) ✅ Kadangi Seaborn agreguoja duomenis aplink vieną liniją, jis rodo „kelis matavimus kiekvienoje x reikšmėje, braižydamas vidurkį ir 95% pasitikėjimo intervalą aplink vidurkį“. [Šaltinis](https://seaborn.pydata.org/tutorial/relational.html). Šį laikui imlų veiksmą galima išjungti pridėjus `ci=None`. @@ -114,7 +114,7 @@ Klausimas: Na, o 2003 metais, ar taip pat matome medaus pasiūlos šuolį? Ką, sns.relplot(x="year", y="totalprod", kind="line", data=honey); ``` -![linijinė diagrama 2](../../../../translated_images/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.lt.png) +![linijinė diagrama 2](../../../../translated_images/lt/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.png) Atsakymas: Ne visai. Jei pažvelgsite į bendrą gamybą, ji iš tikrųjų atrodo padidėjusi tais metais, nors apskritai medaus gamyba mažėja per šiuos metus. @@ -139,7 +139,7 @@ sns.relplot( ``` Šioje vizualizacijoje galite palyginti derlių vienai kolonijai ir kolonijų skaičių metai iš metų, šalia vienas kito, su wrap nustatytu 3 stulpeliams: -![facet grid](../../../../translated_images/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.lt.png) +![facet grid](../../../../translated_images/lt/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.png) Šiame duomenų rinkinyje niekas ypatingai neišsiskiria, kalbant apie kolonijų skaičių ir jų derlių metai iš metų bei valstija iš valstijos. Ar yra kitas būdas ieškoti koreliacijos tarp šių dviejų kintamųjų? @@ -162,7 +162,7 @@ sns.despine(right=False) plt.ylabel('colony yield') ax.figure.legend(); ``` -![superimposed plots](../../../../translated_images/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.lt.png) +![superimposed plots](../../../../translated_images/lt/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.png) Nors niekas akivaizdžiai neišsiskiria apie 2003 metus, tai leidžia mums užbaigti šią pamoką šiek tiek linksmesne nata: nors kolonijų skaičius apskritai mažėja, jų skaičius stabilizuojasi, net jei derlius vienai kolonijai mažėja. diff --git a/translations/lt/3-Data-Visualization/R/09-visualization-quantities/README.md b/translations/lt/3-Data-Visualization/R/09-visualization-quantities/README.md index ff10ab8f..428f0cd0 100644 --- a/translations/lt/3-Data-Visualization/R/09-visualization-quantities/README.md +++ b/translations/lt/3-Data-Visualization/R/09-visualization-quantities/README.md @@ -66,7 +66,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` Čia įdiegiame `ggplot2` paketą ir importuojame jį į darbo aplinką naudodami komandą `library("ggplot2")`. Norint sukurti bet kokią diagramą su ggplot, naudojama funkcija `ggplot()`, kurioje nurodote duomenų rinkinį, x ir y kintamuosius kaip atributus. Šiuo atveju naudojame funkciją `geom_line()`, nes norime sukurti linijinę diagramą. -![MaxWingspan-lineplot](../../../../../translated_images/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.lt.png) +![MaxWingspan-lineplot](../../../../../translated_images/lt/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.png) Ką pastebite iš karto? Atrodo, kad yra bent vienas išskirtinis atvejis – tai tikrai įspūdingas sparnų plotis! Sparnų plotis, viršijantis 2000 cm, yra daugiau nei 20 metrų – ar Minesotoje skraido pterodaktiliai? Išsiaiškinkime. @@ -84,7 +84,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` Mes nurodome kampą `theme` ir nurodome x ir y ašių etiketes naudodami `xlab()` ir `ylab()` atitinkamai. `ggtitle()` suteikia grafikui/diagramai pavadinimą. -![MaxWingspan-lineplot-improved](../../../../../translated_images/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.lt.png) +![MaxWingspan-lineplot-improved](../../../../../translated_images/lt/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.png) Net ir pasukus etiketes 45 laipsnių kampu, jų per daug, kad būtų galima perskaityti. Pabandykime kitą strategiją: pažymėkime tik tuos išskirtinius atvejus ir nustatykime etiketes pačioje diagramoje. Galite naudoti sklaidos diagramą, kad būtų daugiau vietos etiketėms: @@ -100,7 +100,7 @@ Kas čia vyksta? Naudojote funkciją `geom_point()`, kad pavaizduotumėte sklaid Ką pastebite? -![MaxWingspan-scatterplot](../../../../../translated_images/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.lt.png) +![MaxWingspan-scatterplot](../../../../../translated_images/lt/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.png) ## Filtruokite savo duomenis @@ -119,7 +119,7 @@ ggplot(data=birds_filtered, aes(x=Name, y=MaxWingspan,group=1)) + ``` Sukūrėme naują duomenų rėmelį `birds_filtered` ir tada pavaizdavome sklaidos diagramą. Filtruodami išskirtinius atvejus, jūsų duomenys tampa nuoseklesni ir suprantamesni. -![MaxWingspan-scatterplot-improved](../../../../../translated_images/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.lt.png) +![MaxWingspan-scatterplot-improved](../../../../../translated_images/lt/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.png) Dabar, kai turime švaresnį duomenų rinkinį bent jau sparnų pločio atžvilgiu, sužinokime daugiau apie šiuos paukščius. @@ -160,7 +160,7 @@ birds_filtered %>% group_by(Category) %>% ``` Šiame fragmente įdiegiame [dplyr](https://www.rdocumentation.org/packages/dplyr/versions/0.7.8) ir [lubridate](https://www.rdocumentation.org/packages/lubridate/versions/1.8.0) paketus, kad padėtume manipuliuoti ir grupuoti duomenis, norint pavaizduoti sukrautą stulpelinę diagramą. Pirmiausia grupuojate duomenis pagal paukščių `Category` ir tada apibendrinate `MinLength`, `MaxLength`, `MinBodyMass`, `MaxBodyMass`, `MinWingspan`, `MaxWingspan` stulpelius. Tada, naudodami `ggplot2` paketą, pavaizduojate stulpelinę diagramą, nurodydami spalvas skirtingoms kategorijoms ir etiketes. -![Stacked bar chart](../../../../../translated_images/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.lt.png) +![Stacked bar chart](../../../../../translated_images/lt/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.png) Ši stulpelinė diagrama, tačiau, yra neįskaitoma, nes yra per daug negrupuotų duomenų. Turite pasirinkti tik tuos duomenis, kuriuos norite pavaizduoti, todėl pažvelkime į paukščių ilgį pagal jų kategoriją. @@ -175,7 +175,7 @@ ggplot(birds_count,aes(Category,n))+geom_bar(stat="identity")+coord_flip() ``` Pirmiausia suskaičiuojate unikalių reikšmių skaičių `Category` stulpelyje ir tada surūšiuojate jas į naują duomenų rėmelį `birds_count`. Šie surūšiuoti duomenys tada faktorizuojami tame pačiame lygyje, kad būtų pavaizduoti surūšiuota tvarka. Naudodami `ggplot2` tada pavaizduojate duomenis stulpelinėje diagramoje. Funkcija `coord_flip()` pavaizduoja horizontalius stulpelius. -![category-length](../../../../../translated_images/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.lt.png) +![category-length](../../../../../translated_images/lt/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.png) Ši stulpelinė diagrama suteikia gerą vaizdą apie paukščių skaičių kiekvienoje kategorijoje. Vienu žvilgsniu matote, kad didžiausias paukščių skaičius šiame regione priklauso Antys/Žąsys/Vandens kategorijai. Minesota yra „10 000 ežerų kraštas“, todėl tai nestebina! @@ -198,7 +198,7 @@ ggplot(birds_grouped,aes(Category,MaxLength))+geom_bar(stat="identity")+coord_fl ``` Mes grupuojame `birds_filtered` duomenis pagal `Category` ir tada pavaizduojame stulpelinę diagramą. -![comparing data](../../../../../translated_images/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.lt.png) +![comparing data](../../../../../translated_images/lt/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.png) Čia nieko nestebina: kolibriai turi mažiausią `MaxLength`, palyginti su pelikanais ar žąsimis. Gerai, kai duomenys logiškai atitinka! @@ -210,7 +210,7 @@ ggplot(data=birds_grouped, aes(x=Category)) + geom_bar(aes(y=MinLength), stat="identity", position="identity", fill='orange')+ coord_flip() ``` -![super-imposed values](../../../../../translated_images/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.lt.png) +![super-imposed values](../../../../../translated_images/lt/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.png) ## 🚀 Iššūkis diff --git a/translations/lt/3-Data-Visualization/R/10-visualization-distributions/README.md b/translations/lt/3-Data-Visualization/R/10-visualization-distributions/README.md index 6831b875..d27c5fa4 100644 --- a/translations/lt/3-Data-Visualization/R/10-visualization-distributions/README.md +++ b/translations/lt/3-Data-Visualization/R/10-visualization-distributions/README.md @@ -45,7 +45,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + geom_point() + ggtitle("Max Length per order") + coord_flip() ``` -![maksimalus ilgis pagal būrį](../../../../../translated_images/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.lt.png) +![maksimalus ilgis pagal būrį](../../../../../translated_images/lt/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.png) Tai suteikia bendrą paukščių kūno ilgio pasiskirstymo pagal būrį apžvalgą, tačiau tai nėra optimalus būdas tikriems pasiskirstymams parodyti. Šią užduotį paprastai atlieka histograma. @@ -57,7 +57,7 @@ Tai suteikia bendrą paukščių kūno ilgio pasiskirstymo pagal būrį apžvalg ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=10)+ylab('Frequency') ``` -![pasiskirstymas visame duomenų rinkinyje](../../../../../translated_images/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.lt.png) +![pasiskirstymas visame duomenų rinkinyje](../../../../../translated_images/lt/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.png) Kaip matote, dauguma iš 400+ paukščių šiame duomenų rinkinyje patenka į mažesnę nei 2000 Max Kūno Masės ribą. Gaukite daugiau įžvalgų apie duomenis, pakeisdami `bins` parametrą į didesnį skaičių, pavyzdžiui, 30: @@ -65,7 +65,7 @@ Kaip matote, dauguma iš 400+ paukščių šiame duomenų rinkinyje patenka į m ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![pasiskirstymas su 30 intervalų](../../../../../translated_images/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.lt.png) +![pasiskirstymas su 30 intervalų](../../../../../translated_images/lt/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.png) Ši diagrama rodo pasiskirstymą šiek tiek detaliau. Mažiau į kairę pasvirusią diagramą būtų galima sukurti užtikrinant, kad pasirinktumėte tik duomenis tam tikrame diapazone: @@ -77,7 +77,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![filtruota histograma](../../../../../translated_images/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.lt.png) +![filtruota histograma](../../../../../translated_images/lt/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.png) ✅ Išbandykite kitus filtrus ir duomenų taškus. Norėdami pamatyti visą duomenų pasiskirstymą, pašalinkite `['MaxBodyMass']` filtrą, kad parodytumėte pažymėtus pasiskirstymus. @@ -91,7 +91,7 @@ ggplot(data=birds_filtered_1, aes(x=MaxBodyMass, y=MaxLength) ) + ``` Atrodo, kad tarp šių dviejų elementų yra tikėtinas koreliavimas pagal numatomą ašį, su viena ypač stipria susiliejimo vieta: -![2D diagrama](../../../../../translated_images/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.lt.png) +![2D diagrama](../../../../../translated_images/lt/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.png) Histogramų numatytasis veikimas gerai tinka skaitiniams duomenims. O kas, jei reikia pamatyti pasiskirstymus pagal tekstinius duomenis? ## Tyrinėkite duomenų rinkinį pagal tekstinius duomenis @@ -122,7 +122,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + scale_fill_manual(name="Conservation Status",values=c("red","green","blue","pink"),labels=c("Endangered","Near Threathened","Vulnerable","Least Concern")) ``` -![sparnų amplitudė ir apsaugos statusas](../../../../../translated_images/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.lt.png) +![sparnų amplitudė ir apsaugos statusas](../../../../../translated_images/lt/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.png) Atrodo, kad nėra gero koreliavimo tarp minimalaus sparnų amplitudės dydžio ir apsaugos statuso. Išbandykite kitus duomenų rinkinio elementus naudodami šį metodą. Taip pat galite išbandyti skirtingus filtrus. Ar pastebite kokį nors koreliavimą? @@ -136,7 +136,7 @@ Dabar dirbkime su tankio diagramomis! ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + geom_density() ``` -![tankio diagrama](../../../../../translated_images/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.lt.png) +![tankio diagrama](../../../../../translated_images/lt/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.png) Galite matyti, kaip ši diagrama atspindi ankstesnę minimalaus sparnų amplitudės duomenų diagramą; ji tiesiog šiek tiek sklandesnė. Jei norėtumėte peržiūrėti tą dantytą MaxBodyMass liniją antroje sukurtoje diagramoje, galėtumėte ją labai gerai išlyginti, naudodami šį metodą: @@ -144,7 +144,7 @@ Galite matyti, kaip ši diagrama atspindi ankstesnę minimalaus sparnų amplitud ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density() ``` -![kūno masės tankis](../../../../../translated_images/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.lt.png) +![kūno masės tankis](../../../../../translated_images/lt/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.png) Jei norėtumėte sklandžios, bet ne per daug sklandžios linijos, redaguokite `adjust` parametrą: @@ -152,7 +152,7 @@ Jei norėtumėte sklandžios, bet ne per daug sklandžios linijos, redaguokite ` ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density(adjust = 1/5) ``` -![mažiau sklandi kūno masė](../../../../../translated_images/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.lt.png) +![mažiau sklandi kūno masė](../../../../../translated_images/lt/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.png) ✅ Perskaitykite apie šio tipo diagramos parametrus ir eksperimentuokite! @@ -162,7 +162,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + ggplot(data=birds_filtered_1,aes(x = MaxBodyMass, fill = Order)) + geom_density(alpha=0.5) ``` -![kūno masė pagal būrį](../../../../../translated_images/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.lt.png) +![kūno masė pagal būrį](../../../../../translated_images/lt/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.png) ## 🚀 Iššūkis diff --git a/translations/lt/3-Data-Visualization/R/11-visualization-proportions/README.md b/translations/lt/3-Data-Visualization/R/11-visualization-proportions/README.md index d2ce6a9f..e90d4133 100644 --- a/translations/lt/3-Data-Visualization/R/11-visualization-proportions/README.md +++ b/translations/lt/3-Data-Visualization/R/11-visualization-proportions/README.md @@ -93,7 +93,7 @@ pie(grouped$count,grouped$class, main="Edible?") ``` Štai pyrago diagrama, rodanti šių duomenų proporcijas pagal šias dvi grybų klases. Labai svarbu teisingai nustatyti etikečių tvarką, ypač čia, todėl būtinai patikrinkite, ar etikečių masyvas sudarytas teisinga tvarka! -![pyrago diagrama](../../../../../translated_images/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.lt.png) +![pyrago diagrama](../../../../../translated_images/lt/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.png) ## Žiedai! @@ -127,7 +127,7 @@ library(webr) PieDonut(habitat, aes(habitat, count=count)) ``` -![žiedo diagrama](../../../../../translated_images/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.lt.png) +![žiedo diagrama](../../../../../translated_images/lt/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.png) Šis kodas naudoja dvi bibliotekas - ggplot2 ir webr. Naudodami webr bibliotekos PieDonut funkciją, galite lengvai sukurti žiedo diagramą! @@ -165,7 +165,7 @@ waffle((cap_color$count/10), rows = 7, title = "Waffle Chart")+scale_fill_manual Naudodami vaflio diagramą, galite aiškiai matyti grybų kepurėlių spalvų proporcijas šiame duomenų rinkinyje. Įdomu tai, kad yra daug grybų su žaliomis kepurėlėmis! -![vaflio diagrama](../../../../../translated_images/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.lt.png) +![vaflio diagrama](../../../../../translated_images/lt/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.png) Šioje pamokoje išmokote tris būdus, kaip vizualizuoti proporcijas. Pirmiausia reikia suskirstyti savo duomenis į kategorijas, o tada nuspręsti, kuris būdas geriausiai atspindi duomenis - pyragas, žiedas ar vaflis. Visi jie yra „skanūs“ ir suteikia vartotojui greitą duomenų rinkinio apžvalgą. diff --git a/translations/lt/3-Data-Visualization/R/12-visualization-relationships/README.md b/translations/lt/3-Data-Visualization/R/12-visualization-relationships/README.md index ef7237dd..3a539261 100644 --- a/translations/lt/3-Data-Visualization/R/12-visualization-relationships/README.md +++ b/translations/lt/3-Data-Visualization/R/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ library(ggplot2) ggplot(honey, aes(x = priceperlb, y = state)) + geom_point(colour = "blue") ``` -![sklaidos diagrama 1](../../../../../translated_images/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.lt.png) +![sklaidos diagrama 1](../../../../../translated_images/lt/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.png) Dabar parodykite tuos pačius duomenis su medaus spalvų schema, kad parodytumėte, kaip kaina keičiasi metai iš metų. Tai galite padaryti pridėdami 'scale_color_gradientn' parametrą, kad parodytumėte pokyčius: @@ -61,7 +61,7 @@ Dabar parodykite tuos pačius duomenis su medaus spalvų schema, kad parodytumė ggplot(honey, aes(x = priceperlb, y = state, color=year)) + geom_point()+scale_color_gradientn(colours = colorspace::heat_hcl(7)) ``` -![sklaidos diagrama 2](../../../../../translated_images/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.lt.png) +![sklaidos diagrama 2](../../../../../translated_images/lt/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.png) Naudodami šią spalvų schemą, galite pastebėti, kad per metus medaus kaina už svarą akivaizdžiai kyla. Iš tiesų, jei patikrinsite duomenų pavyzdį (pavyzdžiui, Arizonos valstiją), galite pastebėti kainų kilimo modelį metai iš metų, su keliomis išimtimis: @@ -92,7 +92,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ``` Matote, kaip taškų dydis palaipsniui didėja. -![sklaidos diagrama 3](../../../../../translated_images/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.lt.png) +![sklaidos diagrama 3](../../../../../translated_images/lt/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.png) Ar tai paprastas pasiūlos ir paklausos atvejis? Dėl tokių veiksnių kaip klimato kaita ir kolonijų žlugimas, ar medaus kiekis, kurį galima įsigyti, mažėja metai iš metų, todėl kaina kyla? @@ -107,7 +107,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab ``` Atsakymas: Taip, su keliomis išimtimis apie 2003 metus: -![linijinė diagrama 1](../../../../../translated_images/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.lt.png) +![linijinė diagrama 1](../../../../../translated_images/lt/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.png) Klausimas: Na, o 2003 metais, ar taip pat matome medaus tiekimo šuolį? Ką, jei pažvelgtume į bendrą gamybą metai iš metų? @@ -115,7 +115,7 @@ Klausimas: Na, o 2003 metais, ar taip pat matome medaus tiekimo šuolį? Ką, je qplot(honey$year,honey$totalprod, geom='smooth', span =0.5, xlab = "year",ylab = "totalprod") ``` -![linijinė diagrama 2](../../../../../translated_images/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.lt.png) +![linijinė diagrama 2](../../../../../translated_images/lt/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.png) Atsakymas: Ne visai. Jei pažvelgsite į bendrą gamybą, atrodo, kad ji iš tikrųjų padidėjo tais metais, nors apskritai medaus gamybos kiekis mažėja per šiuos metus. @@ -135,7 +135,7 @@ ggplot(honey, aes(x=yieldpercol, y = numcol,group = 1)) + ``` Šioje vizualizacijoje galite palyginti derlių vienai kolonijai ir kolonijų skaičių metai iš metų, šalia vienas kito, su wrap nustatytu 3 stulpeliams: -![facet grid](../../../../../translated_images/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.lt.png) +![facet grid](../../../../../translated_images/lt/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.png) Šiam duomenų rinkiniui niekas ypatingai neišsiskiria, kalbant apie kolonijų skaičių ir jų derlių, metai iš metų ir valstija po valstijos. Ar yra kitoks būdas ieškoti koreliacijos tarp šių dviejų kintamųjų? @@ -152,7 +152,7 @@ plot(honey$year, honey$yieldpercol, pch = 17, col = 3, axis(side = 4, at = pretty(range(y2))) mtext("colony yield", side = 4, line = 3) ``` -![uždėtos diagramos](../../../../../translated_images/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.lt.png) +![uždėtos diagramos](../../../../../translated_images/lt/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.png) Nors niekas ypatingai neišsiskiria apie 2003 metus, tai leidžia mums užbaigti šią pamoką šiek tiek linksmesne nata: nors kolonijų skaičius apskritai mažėja, jų skaičius stabilizuojasi, net jei jų derlius vienai kolonijai mažėja. diff --git a/translations/lt/3-Data-Visualization/R/13-meaningful-vizualizations/README.md b/translations/lt/3-Data-Visualization/R/13-meaningful-vizualizations/README.md index 33476754..d4b3dad3 100644 --- a/translations/lt/3-Data-Visualization/R/13-meaningful-vizualizations/README.md +++ b/translations/lt/3-Data-Visualization/R/13-meaningful-vizualizations/README.md @@ -47,25 +47,25 @@ Ankstesnėse pamokose eksperimentavote su įvairių tipų duomenų vizualizacijo Net jei duomenų mokslininkas kruopščiai pasirenka tinkamą diagramą tinkamiems duomenims, yra daugybė būdų, kaip duomenys gali būti pateikti taip, kad įrodytų tam tikrą tašką, dažnai pažeidžiant pačius duomenis. Yra daugybė klaidinančių diagramų ir infografikų pavyzdžių! -[![Kaip meluoja diagramos, Alberto Cairo](../../../../../translated_images/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.lt.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "Kaip meluoja diagramos") +[![Kaip meluoja diagramos, Alberto Cairo](../../../../../translated_images/lt/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "Kaip meluoja diagramos") > 🎥 Spustelėkite aukščiau esančią nuotrauką, kad pamatytumėte konferencijos pranešimą apie klaidinančias diagramas Ši diagrama apverčia X ašį, kad parodytų priešingą tiesai, remiantis datomis: -![bloga diagrama 1](../../../../../translated_images/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.lt.png) +![bloga diagrama 1](../../../../../translated_images/lt/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.png) [Ši diagrama](https://media.firstcoastnews.com/assets/WTLV/images/170ae16f-4643-438f-b689-50d66ca6a8d8/170ae16f-4643-438f-b689-50d66ca6a8d8_1140x641.jpg) yra dar labiau klaidinanti, nes akis nukreipiama į dešinę, kad būtų padaryta išvada, jog laikui bėgant COVID atvejų skaičius sumažėjo įvairiose apskrityse. Tačiau, jei atidžiai pažvelgsite į datas, pastebėsite, kad jos buvo pertvarkytos, kad būtų parodyta klaidinanti mažėjimo tendencija. -![bloga diagrama 2](../../../../../translated_images/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.lt.jpg) +![bloga diagrama 2](../../../../../translated_images/lt/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.jpg) Šis liūdnai pagarsėjęs pavyzdys naudoja spalvas IR apverstą Y ašį, kad suklaidintų: vietoj išvados, kad ginklų mirčių skaičius padidėjo po ginklams palankios teisės aktų priėmimo, akis apgaunama manyti, kad tiesa yra priešinga: -![bloga diagrama 3](../../../../../translated_images/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.lt.jpg) +![bloga diagrama 3](../../../../../translated_images/lt/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.jpg) Ši keista diagrama rodo, kaip proporcijos gali būti manipuliuojamos, sukeliant juoką: -![bloga diagrama 4](../../../../../translated_images/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.lt.jpg) +![bloga diagrama 4](../../../../../translated_images/lt/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.jpg) Palyginti nepalyginamus dalykus yra dar vienas abejotinas triukas. Yra [puiki svetainė](https://tylervigen.com/spurious-correlations), skirta „klaidingoms koreliacijoms“, kurioje pateikiami „faktai“, koreliuojantys tokius dalykus kaip skyrybų rodiklis Meino valstijoje ir margarino vartojimas. Reddit grupė taip pat renka [blogus duomenų naudojimo pavyzdžius](https://www.reddit.com/r/dataisugly/top/?t=all). @@ -100,13 +100,13 @@ Pažymėkite ašis, pateikite legendą, jei reikia, ir pasiūlykite užuominas ( Jei jūsų duomenys yra tekstiniai ir ilgi X ašyje, galite pakreipti tekstą, kad jis būtų lengviau skaitomas. [plot3D](https://cran.r-project.org/web/packages/plot3D/index.html) siūlo 3D diagramų kūrimą, jei jūsų duomenys tai palaiko. Naudojant šią biblioteką galima sukurti sudėtingas duomenų vizualizacijas. -![3D diagramos](../../../../../translated_images/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.lt.png) +![3D diagramos](../../../../../translated_images/lt/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.png) ## Animacija ir 3D diagramų rodymas Kai kurios geriausios šių dienų duomenų vizualizacijos yra animuotos. Shirley Wu sukūrė nuostabias vizualizacijas su D3, tokias kaip '[film flowers](http://bl.ocks.org/sxywu/raw/d612c6c653fb8b4d7ff3d422be164a5d/)', kur kiekviena gėlė yra filmo vizualizacija. Kitas pavyzdys, sukurtas Guardian, yra „bussed out“, interaktyvi patirtis, derinanti vizualizacijas su Greensock ir D3 bei pasakojimo straipsnio formatą, kad parodytų, kaip NYC sprendžia benamių problemą, išsiųsdama žmones iš miesto. -![busing](../../../../../translated_images/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.lt.png) +![busing](../../../../../translated_images/lt/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.png) > „Bussed Out: How America Moves its Homeless“ iš [the Guardian](https://www.theguardian.com/us-news/ng-interactive/2017/dec/20/bussed-out-america-moves-homeless-people-country-study). Vizualizacijos: Nadieh Bremer & Shirley Wu @@ -116,7 +116,7 @@ Nors ši pamoka nėra pakankama, kad išsamiai išmokytų šias galingas vizuali Jūs sukursite internetinę programą, kuri parodys animuotą šio socialinio tinklo vaizdą. Ji naudoja biblioteką, sukurtą [tinklo vizualizacijai](https://github.com/emiliorizzo/vue-d3-network) su Vue.js ir D3. Kai programa veikia, galite perkelti mazgus ekrane, kad pertvarkytumėte duomenis. -![liaisons](../../../../../translated_images/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.lt.png) +![liaisons](../../../../../translated_images/lt/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.png) ## Projektas: Sukurkite diagramą tinklui parodyti naudojant D3.js diff --git a/translations/lt/3-Data-Visualization/README.md b/translations/lt/3-Data-Visualization/README.md index 8beffc47..a9b63273 100644 --- a/translations/lt/3-Data-Visualization/README.md +++ b/translations/lt/3-Data-Visualization/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Vizualizacijos -![bitė ant levandos žiedo](../../../translated_images/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.lt.jpg) +![bitė ant levandos žiedo](../../../translated_images/lt/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.jpg) > Nuotrauka Jenna Lee iš Unsplash Duomenų vizualizavimas yra viena svarbiausių duomenų mokslininko užduočių. Vaizdai verti tūkstančio žodžių, o vizualizacija gali padėti atskleisti įvairius įdomius jūsų duomenų aspektus, tokius kaip šuoliai, anomalijos, grupavimai, tendencijos ir dar daugiau, kas padeda suprasti istoriją, kurią jūsų duomenys bando papasakoti. diff --git a/translations/lt/4-Data-Science-Lifecycle/14-Introduction/README.md b/translations/lt/4-Data-Science-Lifecycle/14-Introduction/README.md index 3e8b3284..f015c402 100644 --- a/translations/lt/4-Data-Science-Lifecycle/14-Introduction/README.md +++ b/translations/lt/4-Data-Science-Lifecycle/14-Introduction/README.md @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: Šioje pamokoje daugiausia dėmesio skiriama trims gyvavimo ciklo dalims: duomenų rinkimui, apdorojimui ir priežiūrai. -![Duomenų mokslo gyvavimo ciklo diagrama](../../../../translated_images/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.lt.jpg) +![Duomenų mokslo gyvavimo ciklo diagrama](../../../../translated_images/lt/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.jpg) > Nuotrauka iš [Berkeley School of Information](https://ischoolonline.berkeley.edu/data-science/what-is-data-science/) ## Duomenų rinkimas @@ -101,7 +101,7 @@ Išnagrinėkite [Komandos duomenų mokslo proceso gyvavimo ciklą](https://docs. |Komandos duomenų mokslo procesas (TDSP)|Kryžminės pramonės standartinis duomenų gavybos procesas (CRISP-DM)| |--|--| -|![Komandos duomenų mokslo gyvavimo ciklas](../../../../translated_images/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.lt.png) | ![Duomenų mokslo proceso aljanso vaizdas](../../../../translated_images/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.lt.png) | +|![Komandos duomenų mokslo gyvavimo ciklas](../../../../translated_images/lt/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.png) | ![Duomenų mokslo proceso aljanso vaizdas](../../../../translated_images/lt/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.png) | | Vaizdas iš [Microsoft](https://docs.microsoft.comazure/architecture/data-science-process/lifecycle) | Vaizdas iš [Duomenų mokslo proceso aljanso](https://www.datascience-pm.com/crisp-dm-2/) | ## [Po paskaitos: testas](https://ff-quizzes.netlify.app/en/ds/quiz/27) diff --git a/translations/lt/4-Data-Science-Lifecycle/README.md b/translations/lt/4-Data-Science-Lifecycle/README.md index ffdf8a3e..5aa61573 100644 --- a/translations/lt/4-Data-Science-Lifecycle/README.md +++ b/translations/lt/4-Data-Science-Lifecycle/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Duomenų mokslo gyvavimo ciklas -![communication](../../../translated_images/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.lt.jpg) +![communication](../../../translated_images/lt/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.jpg) > Nuotrauka Headway iš Unsplash Šiose pamokose nagrinėsite kai kuriuos duomenų mokslo gyvavimo ciklo aspektus, įskaitant duomenų analizę ir komunikaciją. diff --git a/translations/lt/5-Data-Science-In-Cloud/README.md b/translations/lt/5-Data-Science-In-Cloud/README.md index 8e699052..5ac7f62b 100644 --- a/translations/lt/5-Data-Science-In-Cloud/README.md +++ b/translations/lt/5-Data-Science-In-Cloud/README.md @@ -9,13 +9,13 @@ CO_OP_TRANSLATOR_METADATA: --> # Duomenų mokslas debesyje -![cloud-picture](../../../translated_images/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.lt.jpg) +![cloud-picture](../../../translated_images/lt/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.jpg) > Nuotrauka [Jelleke Vanooteghem](https://unsplash.com/@ilumire) iš [Unsplash](https://unsplash.com/s/photos/cloud?orientation=landscape) Kalbant apie duomenų mokslą su dideliais duomenų kiekiais, debesis gali būti tikras žaidimo keitiklis. Per artimiausias tris pamokas sužinosime, kas yra debesis ir kodėl jis gali būti labai naudingas. Taip pat išnagrinėsime širdies nepakankamumo duomenų rinkinį ir sukursime modelį, kuris padės įvertinti tikimybę, ar žmogui gresia širdies nepakankamumas. Naudosime debesies galią, kad apmokytume, diegtume ir naudotume modelį dviem skirtingais būdais. Vienas būdas – naudojant tik vartotojo sąsają „Low code/No code“ stiliumi, kitas – naudojant „Azure Machine Learning Software Developer Kit“ (Azure ML SDK). -![project-schema](../../../translated_images/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.lt.png) +![project-schema](../../../translated_images/lt/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.png) ### Temos diff --git a/translations/lt/6-Data-Science-In-Wild/20-Real-World-Examples/README.md b/translations/lt/6-Data-Science-In-Wild/20-Real-World-Examples/README.md index e3c78830..932c1372 100644 --- a/translations/lt/6-Data-Science-In-Wild/20-Real-World-Examples/README.md +++ b/translations/lt/6-Data-Science-In-Wild/20-Real-World-Examples/README.md @@ -41,7 +41,7 @@ Dėl AI demokratizacijos kūrėjams dabar lengviau kurti ir integruoti AI pagrį * [Duomenų mokslas sveikatos apsaugoje](https://data-flair.training/blogs/data-science-in-healthcare/) - pabrėžia taikymą, pvz., medicininį vaizdavimą (pvz., MRT, rentgenas, KT skenavimas), genomiką (DNR sekos nustatymas), vaistų kūrimą (rizikos vertinimas, sėkmės prognozė), prognozavimo analizę (pacientų priežiūra ir tiekimo logistika), ligų stebėjimą ir prevenciją ir kt. -![Duomenų mokslo taikymas realiame pasaulyje](../../../../translated_images/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.lt.png) Vaizdo kreditas: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) +![Duomenų mokslo taikymas realiame pasaulyje](../../../../translated_images/lt/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.png) Vaizdo kreditas: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) Paveikslėlyje parodytos kitos sritys ir pavyzdžiai, kaip taikyti duomenų mokslo technikas. Norite tyrinėti kitus taikymus? Peržiūrėkite [Peržiūra ir savarankiškas mokymasis](../../../../6-Data-Science-In-Wild/20-Real-World-Examples) skyrių žemiau. diff --git a/translations/lt/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md b/translations/lt/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md index a33a92c8..37e5d166 100644 --- a/translations/lt/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md +++ b/translations/lt/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md @@ -22,7 +22,7 @@ Explorer sąsaja (pavaizduota žemiau esančiame ekrano vaizde) leidžia pasirin 2. Išnagrinėti duomenų rinkinių [Katalogą](https://planetarycomputer.microsoft.com/catalog) – sužinoti kiekvieno paskirtį. 3. Naudoti Explorer – pasirinkti jus dominantį duomenų rinkinį, pasirinkti tinkamą užklausą ir atvaizdavimo parinktį. -![Planetary Computer Explorer](../../../../translated_images/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.lt.png) +![Planetary Computer Explorer](../../../../translated_images/lt/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.png) `Jūsų užduotis:` Dabar išanalizuokite naršyklėje sugeneruotą vizualizaciją ir atsakykite į šiuos klausimus: diff --git a/translations/lt/CONTRIBUTING.md b/translations/lt/CONTRIBUTING.md index 1f45f19a..e02c182e 100644 --- a/translations/lt/CONTRIBUTING.md +++ b/translations/lt/CONTRIBUTING.md @@ -312,7 +312,7 @@ Pavyzdžiai: import pandas as pd ``` ```` -- Pridėkite alternatyvų tekstą vaizdams: `![Alt text](../../translated_images/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.lt.png)` +- Pridėkite alternatyvų tekstą vaizdams: `![Alt text](../../translated_images/lt/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.png)` - Išlaikykite pagrįstą eilutės ilgį (apie 80–100 simbolių) ### Python diff --git a/translations/lt/README.md b/translations/lt/README.md index 60623213..73a21f24 100644 --- a/translations/lt/README.md +++ b/translations/lt/README.md @@ -33,7 +33,7 @@ Azure Cloud Advocates komanda Microsoft įmonėje džiaugiasi galėdama pasiūly **🙏 Ypatingas ačiū 🙏 mūsų [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/) autoriams, peržiūrėtojams ir turinio bendradarbiams,** išskirtinai Aaryan Arora, [Aditya Garg](https://github.com/AdityaGarg00), [Alondra Sanchez](https://www.linkedin.com/in/alondra-sanchez-molina/), [Ankita Singh](https://www.linkedin.com/in/ankitasingh007), [Anupam Mishra](https://www.linkedin.com/in/anupam--mishra/), [Arpita Das](https://www.linkedin.com/in/arpitadas01/), ChhailBihari Dubey, [Dibri Nsofor](https://www.linkedin.com/in/dibrinsofor), [Dishita Bhasin](https://www.linkedin.com/in/dishita-bhasin-7065281bb), [Majd Safi](https://www.linkedin.com/in/majd-s/), [Max Blum](https://www.linkedin.com/in/max-blum-6036a1186/), [Miguel Correa](https://www.linkedin.com/in/miguelmque/), [Mohamma Iftekher (Iftu) Ebne Jalal](https://twitter.com/iftu119), [Nawrin Tabassum](https://www.linkedin.com/in/nawrin-tabassum), [Raymond Wangsa Putra](https://www.linkedin.com/in/raymond-wp/), [Rohit Yadav](https://www.linkedin.com/in/rty2423), Samridhi Sharma, [Sanya Sinha](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200), [Sheena Narula](https://www.linkedin.com/in/sheena-narua-n/), [Tauqeer Ahmad](https://www.linkedin.com/in/tauqeerahmad5201/), Yogendrasingh Pawar , [Vidushi Gupta](https://www.linkedin.com/in/vidushi-gupta07/), [Jasleen Sondhi](https://www.linkedin.com/in/jasleen-sondhi/) -|![Sketchnote autorius @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.lt.png)| +|![Sketchnote autorius @sketchthedocs https://sketchthedocs.dev](../../translated_images/lt/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.png)| |:---:| | Duomenų mokslas pradedantiesiems - _Sketchnote autorius [@nitya](https://twitter.com/nitya)_ | @@ -52,7 +52,7 @@ Azure Cloud Advocates komanda Microsoft įmonėje džiaugiasi galėdama pasiūly Mes rengiame Discord „Mokymasis su DI“ seriją — sužinokite daugiau ir prisijunkite prie mūsų [Mokymasis su DI serijos](https://aka.ms/learnwithai/discord) renginio nuo 2025 m. rugsėjo 18 d. iki 30 d. Gavę patarimų ir gudrybių, kaip naudoti GitHub Copilot Duomenų mokslui. -![Mokymasis su DI serija](../../translated_images/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.lt.jpg) +![Mokymasis su DI serija](../../translated_images/lt/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.jpg) # Ar esi studentas? @@ -132,7 +132,7 @@ Kiekvienas pavyzdys turi išsamius komentarus, paaiškinančius kiekvieną žing ## Pamokos -|![ Sketchnote sukūrė @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.lt.png)| +|![ Sketchnote sukūrė @sketchthedocs https://sketchthedocs.dev](../../translated_images/lt/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png)| |:---:| | Duomenų mokslas pradedantiesiems: kelio žemėlapis - _Sketchnote sukūrė [@nitya](https://twitter.com/nitya)_ | diff --git a/translations/lt/sketchnotes/README.md b/translations/lt/sketchnotes/README.md index 7e42758c..27a5f075 100644 --- a/translations/lt/sketchnotes/README.md +++ b/translations/lt/sketchnotes/README.md @@ -13,7 +13,7 @@ Raskite visas sketchnotes čia! Nitya Narasimhan, menininkė -![kelio žemėlapio sketchnote](../../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.lt.png) +![kelio žemėlapio sketchnote](../../../translated_images/lt/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png) --- diff --git a/translations/ml/1-Introduction/01-defining-data-science/README.md b/translations/ml/1-Introduction/01-defining-data-science/README.md index 367db425..a320e071 100644 --- a/translations/ml/1-Introduction/01-defining-data-science/README.md +++ b/translations/ml/1-Introduction/01-defining-data-science/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: --- -[![Defining Data Science Video](../../../../translated_images/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.ml.png)](https://youtu.be/beZ7Mb_oz9I) +[![Defining Data Science Video](../../../../translated_images/ml/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.png)](https://youtu.be/beZ7Mb_oz9I) ## [പ്രീ-ലെക്ചർ ക്വിസ്](https://ff-quizzes.netlify.app/en/ds/quiz/0) @@ -153,7 +153,7 @@ CO_OP_TRANSLATOR_METADATA: ഈ വെല്ലുവിളിയിൽ, ഡാറ്റാ സയൻസ് മേഖലയുമായി ബന്ധപ്പെട്ട ആശയങ്ങൾ വാചകങ്ങൾ പരിശോധിച്ച് കണ്ടെത്താൻ ശ്രമിക്കും. ഡാറ്റാ സയൻസിനെക്കുറിച്ചുള്ള ഒരു വിക്കിപീഡിയ ലേഖനം എടുത്ത്, ടെക്സ്റ്റ് ഡൗൺലോഡ് ചെയ്ത് പ്രോസസ് ചെയ്ത്, താഴെ കാണുന്ന പോലെ ഒരു വാക്ക് ക്ലൗഡ് നിർമ്മിക്കും: -![Word Cloud for Data Science](../../../../translated_images/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.ml.png) +![Word Cloud for Data Science](../../../../translated_images/ml/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.png) കോഡ് വായിക്കാൻ [`notebook.ipynb`](../../../../1-Introduction/01-defining-data-science/notebook.ipynb ':ignore') സന്ദർശിക്കുക. നിങ്ങൾക്ക് കോഡ് പ്രവർത്തിപ്പിച്ച് എല്ലാ ഡാറ്റാ പരിവർത്തനങ്ങളും യഥാർത്ഥ സമയത്ത് എങ്ങനെ നടക്കുന്നതെന്ന് കാണാം. diff --git a/translations/ml/1-Introduction/04-stats-and-probability/README.md b/translations/ml/1-Introduction/04-stats-and-probability/README.md index 755893dd..fefa095a 100644 --- a/translations/ml/1-Introduction/04-stats-and-probability/README.md +++ b/translations/ml/1-Introduction/04-stats-and-probability/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: സാംഖ്യശാസ്ത്രവും സാദ്ധ്യത സിദ്ധാന്തവും ഗണിതശാസ്ത്രത്തിന്റെ രണ്ട് വളരെ ബന്ധപ്പെട്ട മേഖലകളാണ്, ഇവ ഡാറ്റാ സയൻസിനോട് വളരെ ബന്ധപ്പെട്ടവയാണ്. ഗണിതശാസ്ത്രത്തിന്റെ ആഴത്തിലുള്ള അറിവില്ലാതെ ഡാറ്റയുമായി പ്രവർത്തിക്കുന്നത് സാധ്യമാണ്, പക്ഷേ കുറഞ്ഞത് ചില അടിസ്ഥാന ആശയങ്ങൾ അറിയുന്നത് മെച്ചമാണ്. ഇവിടെ നിങ്ങൾക്ക് ആരംഭിക്കാൻ സഹായിക്കുന്ന ഒരു ചെറിയ പരിചയം നൽകുന്നു. -[![Intro Video](../../../../translated_images/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.ml.png)](https://youtu.be/Z5Zy85g4Yjw) +[![Intro Video](../../../../translated_images/ml/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.png)](https://youtu.be/Z5Zy85g4Yjw) ## [പ്രീ-ലെക്ചർ ക്വിസ്](https://ff-quizzes.netlify.app/en/ds/quiz/6) @@ -40,7 +40,7 @@ CO_OP_TRANSLATOR_METADATA: ഒരു ചാരത്രം ഒരു നൽകിയ മൂല്യ പരിധിയിൽപ്പെടുന്ന സാദ്ധ്യതയെക്കുറിച്ച് മാത്രമേ സംസാരിക്കാനാകൂ, ഉദാഹരണത്തിന് P(t1≤X<t2). ഈ സാഹചര്യത്തിൽ, സാദ്ധ്യത വിതരണം **സാദ്ധ്യത സാന്ദ്രത ഫംഗ്ഷൻ** p(x) ഉപയോഗിച്ച് വിവരിക്കപ്പെടുന്നു, അതായത് -![P(t_1\le X +Box Plot Explanation ഇവിടെ **ഇന്റർ-ക്വാർട്ടൈൽ റേഞ്ച്** IQR=Q3-Q1 കണക്കാക്കുന്നു, കൂടാതെ **ഔട്ട്‌ലൈയേഴ്സ്** - [Q1-1.5*IQR, Q3+1.5*IQR] എന്ന പരിധിക്ക് പുറത്തുള്ള മൂല്യങ്ങൾ. @@ -83,11 +83,11 @@ CO_OP_TRANSLATOR_METADATA: ഇവിടെ നമ്മുടെ ഡാറ്റയുടെ ശരാശരി, മീഡിയൻ, ക്വാർട്ടൈൽസ് കാണിക്കുന്ന ബോക്സ് പ്ലോട്ട്: -![Weight Box Plot](../../../../translated_images/weight-boxplot.1dbab1c03af26f8a008fff4e17680082c8ab147d6df646cbac440bbf8f5b9c42.ml.png) +![Weight Box Plot](../../../../translated_images/ml/weight-boxplot.1dbab1c03af26f8a008fff4e17680082c8ab147d6df646cbac440bbf8f5b9c42.png) നമ്മുടെ ഡാറ്റയിൽ വിവിധ കളിക്കാരുടെ **പങ്കുകൾ** സംബന്ധിച്ച വിവരങ്ങൾ ഉള്ളതിനാൽ, പങ്ക് അനുസരിച്ച് ബോക്സ് പ്ലോട്ട് ചെയ്യാം - ഇത് പങ്കുകൾക്കിടയിലെ പാരാമീറ്റർ മൂല്യങ്ങൾ എങ്ങനെ വ്യത്യസ്തമാണെന്ന് മനസ്സിലാക്കാൻ സഹായിക്കും. ഈ തവണ ഉയരം പരിഗണിക്കാം: -![Box plot by role](../../../../translated_images/boxplot_byrole.036b27a1c3f52d42f66fba2324ec5cde0a1bca6a01a619eeb0ce7cd054b2527b.ml.png) +![Box plot by role](../../../../translated_images/ml/boxplot_byrole.036b27a1c3f52d42f66fba2324ec5cde0a1bca6a01a619eeb0ce7cd054b2527b.png) ഈ ചിത്രരൂപം സൂചിപ്പിക്കുന്നത്, ശരാശരിയിൽ, ഫസ്റ്റ് ബേസ്മാന്റെ ഉയരം സെക്കന്റ് ബേസ്മാന്റെ ഉയരത്തേക്കാൾ കൂടുതലാണെന്ന്. ഈ പാഠത്തിൽ പിന്നീട് നാം ഈ ഹിപോത്തസിസ് കൂടുതൽ ഔപചാരികമായി പരിശോധിക്കുന്നതും, നമ്മുടെ ഡാറ്റ സാംഖ്യശാസ്ത്രപരമായി പ്രാധാന്യമുള്ളതാണെന്ന് തെളിയിക്കുന്നതും പഠിക്കും. @@ -95,7 +95,7 @@ CO_OP_TRANSLATOR_METADATA: നമ്മുടെ ഡാറ്റയുടെ വിതരണമോ എന്താണെന്ന് കാണാൻ, **ഹിസ്റ്റോഗ്രാം** എന്ന ഗ്രാഫ് വരച്ചിടാം. X-അക്ഷത്തിൽ വ്യത്യസ്ത ഭാര ഇടവേളകൾ (അഥവാ **ബിൻസ്**) കാണിക്കും, ലംബ അക്ഷത്തിൽ ആ ഇടവേളയിൽ നമ്മുടെ യാദൃച്ഛിക ചാരത്രം എത്ര തവണപ്പെട്ടുവെന്ന് കാണിക്കും. -![Histogram of real world data](../../../../translated_images/weight-histogram.bfd00caf7fc30b145b21e862dba7def41c75635d5280de25d840dd7f0b00545e.ml.png) +![Histogram of real world data](../../../../translated_images/ml/weight-histogram.bfd00caf7fc30b145b21e862dba7def41c75635d5280de25d840dd7f0b00545e.png) ഈ ഹിസ്റ്റോഗ്രാമിൽ നിന്ന് കാണാം എല്ലാ മൂല്യങ്ങളും ഒരു ശരാശരി ഭാരം ചുറ്റും കേന്ദ്രീകരിച്ചിരിക്കുന്നു, ആ ഭാരം വിട്ട് ദൂരെ പോകുമ്പോൾ ആ മൂല്യത്തിന്റെ ഭാരങ്ങൾ കുറവാണ്. അഥവാ, ഒരു ബേസ്ബോൾ കളിക്കാരന്റെ ഭാരം ശരാശരി ഭാരത്തിൽ നിന്ന് വളരെ വ്യത്യസ്തമായിരിക്കാനുള്ള സാധ്യത വളരെ കുറവാണ്. ഭാരങ്ങളുടെ വ്യതിയാനം ശരാശരി മുതൽ വ്യത്യാസം എത്രത്തോളം ഉണ്ടാകാമെന്ന് കാണിക്കുന്നു. @@ -112,7 +112,7 @@ samples = np.random.normal(mean,std,1000) സൃഷ്ടിച്ച സാമ്പിളുകളുടെ ഹിസ്റ്റോഗ്രാം വരച്ചാൽ മുകളിൽ കാണിച്ച ചിത്രത്തിന് സമാനമായ ചിത്രം കാണാം. സാമ്പിളുകളുടെ എണ്ണം കൂടുകയും ബിൻസിന്റെ എണ്ണം കൂടുകയും ചെയ്താൽ, നോർമൽ വിതരണത്തിന്റെ ഒരു കൂടുതൽ ഐഡിയൽ ചിത്രം സൃഷ്ടിക്കാം: -![Normal Distribution with mean=0 and std.dev=1](../../../../translated_images/normal-histogram.dfae0d67c202137d552d0015fb87581eca263925e512404f3c12d8885315432e.ml.png) +![Normal Distribution with mean=0 and std.dev=1](../../../../translated_images/ml/normal-histogram.dfae0d67c202137d552d0015fb87581eca263925e512404f3c12d8885315432e.png) *ശരാശരി=0, സ്റ്റാൻഡേർഡ് ഡിവിയേഷൻ=1 ഉള്ള നോർമൽ വിതരണം* @@ -231,7 +231,7 @@ array([[1. , 0.52959196], നമ്മുടെ കേസിൽ, 0.53 എന്ന മൂല്യം ഒരു വ്യക്തിയുടെ ഭാരം, ഉയരം എന്നിവ തമ്മിൽ ചില കോറിലേഷൻ ഉണ്ടെന്ന് സൂചിപ്പിക്കുന്നു. നാം ഒരു മൂല്യം മറ്റൊന്നിനെതിരെ സ്കാറ്റർ പ്ലോട്ട് ഉണ്ടാക്കുകയും ബന്ധം ദൃശ്യമായി കാണുകയും ചെയ്യാം: -![ഭാരം, ഉയരം തമ്മിലുള്ള ബന്ധം](../../../../translated_images/weight-height-relationship.3f06bde4ca2aba9974182c4ef037ed602acd0fbbbbe2ca91cefd838a9e66bcf9.ml.png) +![ഭാരം, ഉയരം തമ്മിലുള്ള ബന്ധം](../../../../translated_images/ml/weight-height-relationship.3f06bde4ca2aba9974182c4ef037ed602acd0fbbbbe2ca91cefd838a9e66bcf9.png) > കോറിലേഷൻ, കോവേറിയൻസ് എന്നിവയുടെ കൂടുതൽ ഉദാഹരണങ്ങൾ [സഹായക നോട്ട്‌ബുക്കിൽ](notebook.ipynb) കാണാം. diff --git a/translations/ml/1-Introduction/README.md b/translations/ml/1-Introduction/README.md index 07a34ea1..bac643cf 100644 --- a/translations/ml/1-Introduction/README.md +++ b/translations/ml/1-Introduction/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # ഡാറ്റാ സയൻസിലേക്ക് പരിചയം -![data in action](../../../translated_images/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.ml.jpg) +![data in action](../../../translated_images/ml/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.jpg) > ഫോട്ടോ സ്റ്റീഫൻ ഡോസൺ അൺസ്പ്ലാഷിൽ ഈ പാഠങ്ങളിൽ, ഡാറ്റാ സയൻസ് എങ്ങനെ നിർവചിക്കപ്പെടുന്നു എന്ന് നിങ്ങൾ കണ്ടെത്തും, ഒരു ഡാറ്റാ സയന്റിസ്റ്റ് പരിഗണിക്കേണ്ട നൈതിക കാര്യങ്ങളെക്കുറിച്ച് പഠിക്കും. ഡാറ്റ എങ്ങനെ നിർവചിക്കപ്പെടുന്നു എന്നും, ഡാറ്റാ സയൻസിന്റെ മേധാവി അക്കാദമിക് മേഖലകളായ സ്ഥിതിവിവരശാസ്ത്രവും സാദ്ധ്യതയും കുറിച്ച് കുറച്ച് പഠിക്കും. diff --git a/translations/ml/2-Working-With-Data/06-non-relational/README.md b/translations/ml/2-Working-With-Data/06-non-relational/README.md index b91a73bc..4265e4d0 100644 --- a/translations/ml/2-Working-With-Data/06-non-relational/README.md +++ b/translations/ml/2-Working-With-Data/06-non-relational/README.md @@ -21,7 +21,7 @@ CO_OP_TRANSLATOR_METADATA: സ്പ്രെഡ്‌ഷീറ്റുകൾ ഡാറ്റ സംഭരിക്കാനും പരിശോധിക്കാനും പ്രചാരത്തിലുള്ള മാർഗമാണ്, കാരണം സജ്ജീകരിക്കാനും ആരംഭിക്കാനും കുറച്ച് ശ്രമം മാത്രം ആവശ്യമാണ്. ഈ പാഠത്തിൽ നിങ്ങൾ സ്പ്രെഡ്‌ഷീറ്റിന്റെ അടിസ്ഥാന ഘടകങ്ങളും ഫോർമുലകളും ഫംഗ്ഷനുകളും പഠിക്കും. ഉദാഹരണങ്ങൾ മൈക്രോസോഫ്റ്റ് എക്സെലിൽ കാണിക്കും, പക്ഷേ മറ്റ് സ്പ്രെഡ്‌ഷീറ്റ് സോഫ്റ്റ്‌വെയറുകളുമായി താരതമ്യപ്പെടുത്തുമ്പോൾ പല ഭാഗങ്ങളും വിഷയങ്ങളും സമാനമായ പേരുകളും ഘട്ടങ്ങളും ഉണ്ടാകും. -![രണ്ട് വർക്ക്‌ഷീറ്റുകളുള്ള ഒരു ശൂന്യമായ മൈക്രോസോഫ്റ്റ് എക്സെൽ വർക്ക്‌ബുക്ക്](../../../../translated_images/parts-of-spreadsheet.120711c82aa18a45c3e62a491a15bba0a31ab0e9db407ec022702fed8ffd89bf.ml.png) +![രണ്ട് വർക്ക്‌ഷീറ്റുകളുള്ള ഒരു ശൂന്യമായ മൈക്രോസോഫ്റ്റ് എക്സെൽ വർക്ക്‌ബുക്ക്](../../../../translated_images/ml/parts-of-spreadsheet.120711c82aa18a45c3e62a491a15bba0a31ab0e9db407ec022702fed8ffd89bf.png) ഒരു സ്പ്രെഡ്‌ഷീറ്റ് ഒരു ഫയലാണ്, ഇത് കമ്പ്യൂട്ടർ, ഉപകരണം, അല്ലെങ്കിൽ ക്ലൗഡ് അടിസ്ഥാനമാക്കിയ ഫയൽ സിസ്റ്റത്തിൽ ലഭ്യമാണ്. സോഫ്റ്റ്‌വെയർ ബ്രൗസർ അടിസ്ഥാനമോ കമ്പ്യൂട്ടറിൽ ഇൻസ്റ്റാൾ ചെയ്യേണ്ട ആപ്ലിക്കേഷനോ ആപ്പ് ഡൗൺലോഡ് ചെയ്യേണ്ടതായിരിക്കാം. എക്സെലിൽ ഈ ഫയലുകൾ **വർക്ക്‌ബുക്കുകൾ** എന്നറിയപ്പെടുന്നു, ഈ പദം ഈ പാഠത്തിന്റെ ശേഷിക്കുന്ന ഭാഗത്ത് ഉപയോഗിക്കും. @@ -33,11 +33,11 @@ CO_OP_TRANSLATOR_METADATA: "InventoryExample" എന്ന പേരിലുള്ള സ്പ്രെഡ്‌ഷീറ്റ് ഫയൽ ഒരു ഇൻവെന്ററിയിലുള്ള വസ്തുക്കളുടെ ഫോർമാറ്റ് ചെയ്ത സ്പ്രെഡ്‌ഷീറ്റാണ്, ഇതിൽ മൂന്ന് വർക്ക്‌ഷീറ്റുകൾ ഉണ്ട്, ടാബുകൾ "Inventory List", "Inventory Pick List" , "Bin Lookup" എന്നിങ്ങനെയാണ് ലേബൽ ചെയ്തിരിക്കുന്നത്. Inventory List വർക്ക്‌ഷീറ്റിലെ 4-ആം വരി ഹെഡറാണ്, ഹെഡർ കോളത്തിലെ ഓരോ സെല്ലിന്റെ മൂല്യം വിവരിക്കുന്നു. -![മൈക്രോസോഫ്റ്റ് എക്സെലിൽ ഒരു ഉദാഹരണ ഇൻവെന്ററി ലിസ്റ്റിൽ ഹൈലൈറ്റ് ചെയ്ത ഫോർമുല](../../../../translated_images/formula-excel.ad1068c220892f5ead570d12f2394897961d31a5043a1dd4e6fc5d7690c7a14e.ml.png) +![മൈക്രോസോഫ്റ്റ് എക്സെലിൽ ഒരു ഉദാഹരണ ഇൻവെന്ററി ലിസ്റ്റിൽ ഹൈലൈറ്റ് ചെയ്ത ഫോർമുല](../../../../translated_images/ml/formula-excel.ad1068c220892f5ead570d12f2394897961d31a5043a1dd4e6fc5d7690c7a14e.png) ഒരു സെൽ മറ്റൊരു സെല്ലുകളുടെ മൂല്യങ്ങളിൽ ആശ്രയിച്ചിരിക്കാം അതിന്റെ മൂല്യം നിർമിക്കാൻ. Inventory List സ്പ്രെഡ്‌ഷീറ്റ് ഇൻവെന്ററിയിലെ ഓരോ വസ്തുവിന്റെ ചെലവ് ട്രാക്ക് ചെയ്യുന്നു, പക്ഷേ ഇൻവെന്ററിയിലെ എല്ലാം മൂല്യം അറിയേണ്ടതുണ്ടെങ്കിൽ? [**ഫോർമുലകൾ**](https://support.microsoft.com/en-us/office/overview-of-formulas-34519a4e-1e8d-4f4b-84d4-d642c4f63263) സെൽ ഡാറ്റയിൽ പ്രവർത്തനങ്ങൾ നടത്തുന്നു, ഈ ഉദാഹരണത്തിൽ ഇൻവെന്ററിയുടെ ചെലവ് കണക്കാക്കാൻ ഉപയോഗിക്കുന്നു. ഈ സ്പ്രെഡ്‌ഷീറ്റിൽ Inventory Value കോളത്തിൽ ഒരു ഫോർമുല ഉപയോഗിച്ച് ഓരോ വസ്തുവിന്റെ മൂല്യം കണക്കാക്കുന്നു, QTY ഹെഡറിന്റെ കീഴിലുള്ള അളവും COST ഹെഡറിന്റെ കീഴിലുള്ള ചെലവും ഗുണിച്ച്. ഒരു സെൽ ഡബിൾ ക്ലിക്കുചെയ്യുകയോ ഹൈലൈറ്റ് ചെയ്യുകയോ ചെയ്താൽ ഫോർമുല കാണാം. ഫോർമുലകൾ സമം ചിഹ്നത്തോടെ ആരംഭിക്കുന്നു, തുടർന്ന് കണക്കുകൂട്ടൽ അല്ലെങ്കിൽ പ്രവർത്തനം വരുന്നു. -![മൈക്രോസോഫ്റ്റ് എക്സെലിൽ ഒരു ഉദാഹരണ ഇൻവെന്ററി ലിസ്റ്റിൽ ഹൈലൈറ്റ് ചെയ്ത ഫംഗ്ഷൻ](../../../../translated_images/function-excel.be2ae4feddc10ca089f3d4363040d93b7fd046c8d4f83ba975ec46483ee99895.ml.png) +![മൈക്രോസോഫ്റ്റ് എക്സെലിൽ ഒരു ഉദാഹരണ ഇൻവെന്ററി ലിസ്റ്റിൽ ഹൈലൈറ്റ് ചെയ്ത ഫംഗ്ഷൻ](../../../../translated_images/ml/function-excel.be2ae4feddc10ca089f3d4363040d93b7fd046c8d4f83ba975ec46483ee99895.png) നാം മറ്റൊരു ഫോർമുല ഉപയോഗിച്ച് Inventory Value യിലെ എല്ലാ മൂല്യങ്ങളും കൂട്ടിച്ചേർത്ത് ആകെ മൂല്യം കണ്ടെത്താം. ഓരോ സെലും കൂട്ടിച്ചേർക്കുന്നത് ഒരു ബുദ്ധിമുട്ടുള്ള ജോലി ആകാം. എക്സെലിൽ [**ഫംഗ്ഷനുകൾ**](https://support.microsoft.com/en-us/office/sum-function-043e1c7d-7726-4e80-8f32-07b23e057f89) ഉണ്ട്, സെൽ മൂല്യങ്ങളിൽ കണക്കുകൂട്ടലുകൾ നടത്താൻ മുൻകൂട്ടി നിർവചിച്ച ഫോർമുലകൾ. ഫംഗ്ഷനുകൾക്ക് ആർഗ്യുമെന്റുകൾ ആവശ്യമുണ്ട്, കണക്കുകൂട്ടലുകൾ നടത്താൻ ആവശ്യമായ മൂല്യങ്ങൾ. ഫംഗ്ഷനുകൾക്ക് ഒരിലധികം ആർഗ്യുമെന്റുകൾ ആവശ്യമെങ്കിൽ, അവ ഒരു പ്രത്യേക ക്രമത്തിൽ ലിസ്റ്റ് ചെയ്യണം അല്ലെങ്കിൽ ഫംഗ്ഷൻ ശരിയായ മൂല്യം കണക്കാക്കില്ല. ഈ ഉദാഹരണത്തിൽ SUM ഫംഗ്ഷൻ ഉപയോഗിക്കുന്നു, Inventory Value യിലെ മൂല്യങ്ങൾ ആർഗ്യുമെന്റായി ഉപയോഗിച്ച് B3 (3-ആം വരി, B കോളം) ൽ ആകെ മൂല്യം കണക്കാക്കുന്നു. @@ -45,17 +45,17 @@ CO_OP_TRANSLATOR_METADATA: NoSQL എന്നത് നോൺ-റിലേഷണൽ ഡാറ്റ സംഭരിക്കുന്ന വ്യത്യസ്ത മാർഗങ്ങൾക്കുള്ള ഒരു പൊതുവായ പദമാണ്, ഇത് "non-SQL", "non-relational" അല്ലെങ്കിൽ "not only SQL" എന്നർത്ഥത്തിൽ വ്യാഖ്യാനിക്കാം. ഈ തരം ഡാറ്റാബേസ് സിസ്റ്റങ്ങൾ 4 തരം ആയി വർഗ്ഗീകരിക്കാം. -![കീ-വാല്യു ഡാറ്റ സ്റ്റോർ കാണിക്കുന്ന 4 വ്യത്യസ്ത സംഖ്യാത്മക കീകൾ 4 വ്യത്യസ്ത മൂല്യങ്ങളുമായി ബന്ധിപ്പിച്ചിരിക്കുന്ന ഗ്രാഫിക്കൽ പ്രതിനിധാനം](../../../../translated_images/kv-db.e8f2b75686bbdfcba0c827b9272c10ae0821611ea0fe98429b9d13194383afa6.ml.png) +![കീ-വാല്യു ഡാറ്റ സ്റ്റോർ കാണിക്കുന്ന 4 വ്യത്യസ്ത സംഖ്യാത്മക കീകൾ 4 വ്യത്യസ്ത മൂല്യങ്ങളുമായി ബന്ധിപ്പിച്ചിരിക്കുന്ന ഗ്രാഫിക്കൽ പ്രതിനിധാനം](../../../../translated_images/ml/kv-db.e8f2b75686bbdfcba0c827b9272c10ae0821611ea0fe98429b9d13194383afa6.png) > സ്രോതസ്സ്: [Michał Białecki Blog](https://www.michalbialecki.com/2018/03/18/azure-cosmos-db-key-value-database-cloud/) [കീ-വാല്യു](https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/non-relational-data#keyvalue-data-stores) ഡാറ്റാബേസുകൾ വ്യത്യസ്ത മൂല്യങ്ങളുമായി ബന്ധപ്പെട്ട ഒരു പ്രത്യേക കീ (അനന്യ തിരിച്ചറിയൽ) യെ ജോഡിക്കുന്നു. ഈ ജോഡികൾ [ഹാഷ് ടേബിൾ](https://www.hackerearth.com/practice/data-structures/hash-tables/basics-of-hash-tables/tutorial/) ഉപയോഗിച്ച് അനുയോജ്യമായ ഹാഷിംഗ് ഫംഗ്ഷൻ ഉപയോഗിച്ച് സംഭരിക്കുന്നു. -![പേര്, താൽപ്പര്യങ്ങൾ, സ്ഥലങ്ങൾ എന്നിവ തമ്മിലുള്ള ബന്ധങ്ങൾ കാണിക്കുന്ന ഗ്രാഫ് ഡാറ്റ സ്റ്റോർ ഗ്രാഫിക്കൽ പ്രതിനിധാനം](../../../../translated_images/graph-db.d13629152f79a9dac895b20fa7d841d4d4d6f6008b1382227c3bbd200fd4cfa1.ml.png) +![പേര്, താൽപ്പര്യങ്ങൾ, സ്ഥലങ്ങൾ എന്നിവ തമ്മിലുള്ള ബന്ധങ്ങൾ കാണിക്കുന്ന ഗ്രാഫ് ഡാറ്റ സ്റ്റോർ ഗ്രാഫിക്കൽ പ്രതിനിധാനം](../../../../translated_images/ml/graph-db.d13629152f79a9dac895b20fa7d841d4d4d6f6008b1382227c3bbd200fd4cfa1.png) > സ്രോതസ്സ്: [Microsoft](https://docs.microsoft.com/en-us/azure/cosmos-db/graph/graph-introduction#graph-database-by-example) [ഗ്രാഫ്](https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/non-relational-data#graph-data-stores) ഡാറ്റാബേസുകൾ ഡാറ്റയിലെ ബന്ധങ്ങൾ വിവരിക്കുന്നു, ഇത് നോഡുകളും എഡ്ജുകളും അടങ്ങിയ ഒരു ശേഖരമായി പ്രതിനിധീകരിക്കുന്നു. ഒരു നോഡ് ഒരു സত্ত്വത്തെ പ്രതിനിധീകരിക്കുന്നു, ഉദാഹരണത്തിന് ഒരു വിദ്യാർത്ഥി അല്ലെങ്കിൽ ബാങ്ക് സ്റ്റേറ്റ്മെന്റ് പോലുള്ള യാഥാർത്ഥ്യത്തിൽ ഉള്ള ഒന്നും. എഡ്ജുകൾ രണ്ട് സത്ത്വങ്ങൾ തമ്മിലുള്ള ബന്ധം പ്രതിനിധീകരിക്കുന്നു. ഓരോ നോഡിനും എഡ്ജിനും അവയുടെ അധിക വിവരങ്ങൾ നൽകുന്ന പ്രോപ്പർട്ടികൾ ഉണ്ട്. -![ഐഡന്റിറ്റി, കോൺടാക്റ്റ് ഇൻഫോ എന്നീ രണ്ട് കോളം ഫാമിലികൾ ഉള്ള ഒരു കസ്റ്റമർ ഡാറ്റാബേസ് കാണിക്കുന്ന കോളമ്നാർ ഡാറ്റ സ്റ്റോർ ഗ്രാഫിക്കൽ പ്രതിനിധാനം](../../../../translated_images/columnar-db.ffcfe73c3e9063a8c8f93f8ace85e1200863584b1e324eb5159d8ca10f62ec04.ml.png) +![ഐഡന്റിറ്റി, കോൺടാക്റ്റ് ഇൻഫോ എന്നീ രണ്ട് കോളം ഫാമിലികൾ ഉള്ള ഒരു കസ്റ്റമർ ഡാറ്റാബേസ് കാണിക്കുന്ന കോളമ്നാർ ഡാറ്റ സ്റ്റോർ ഗ്രാഫിക്കൽ പ്രതിനിധാനം](../../../../translated_images/ml/columnar-db.ffcfe73c3e9063a8c8f93f8ace85e1200863584b1e324eb5159d8ca10f62ec04.png) [കോളമ്നാർ](https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/non-relational-data#columnar-data-stores) ഡാറ്റ സ്റ്റോറുകൾ ഡാറ്റയെ കോളങ്ങളും വരികളും ആയി ക്രമീകരിക്കുന്നു, റിലേഷണൽ ഡാറ്റ ഘടന പോലെയാണ്, പക്ഷേ ഓരോ കോളവും കോളം ഫാമിലി എന്ന ഗ്രൂപ്പുകളായി വിഭജിച്ചിരിക്കുന്നു, ഒരു കോളത്തിലെ എല്ലാ ഡാറ്റയും ബന്ധപ്പെട്ടതാണ്, ഒറ്റ യൂണിറ്റായി തിരികെ ലഭിക്കാനും മാറ്റാനും കഴിയും. @@ -88,11 +88,11 @@ Cosmos DB ഡാറ്റാബേസ് "Not Only SQL" എന്ന നിർ എമുലേറ്റർ ഒരു ബ്രൗസർ വിൻഡോ തുറക്കും, എക്സ്പ്ലോറർ വ്യൂ ഡോക്യുമെന്റുകൾ പരിശോധിക്കാൻ അനുവദിക്കുന്നു. -![Cosmos DB എമുലേറ്ററിന്റെ എക്സ്പ്ലോറർ വ്യൂ](../../../../translated_images/cosmosdb-emulator-explorer.a1c80b1347206fe2f30f88fc123821636587d04fc5a56a9eb350c7da6b31f361.ml.png) +![Cosmos DB എമുലേറ്ററിന്റെ എക്സ്പ്ലോറർ വ്യൂ](../../../../translated_images/ml/cosmosdb-emulator-explorer.a1c80b1347206fe2f30f88fc123821636587d04fc5a56a9eb350c7da6b31f361.png) നിങ്ങൾ പിന്തുടരുകയാണെങ്കിൽ, "Start with Sample" ക്ലിക്കുചെയ്യുക, SampleDB എന്ന സാമ്പിൾ ഡാറ്റാബേസ് സൃഷ്ടിക്കും. Sample DB-നെ ആർക്കോ ക്ലിക്കുചെയ്ത് വിപുലീകരിച്ചാൽ `Persons` എന്ന കണ്ടെയ്‌നർ കാണാം, കണ്ടെയ്‌നർ ഒരു വസ്തുക്കളുടെ ശേഖരമാണ്, ഇവ ഡോക്യുമെന്റുകളാണ്. `Items` എന്നതിൽ നാല് വ്യക്തിഗത ഡോക്യുമെന്റുകൾ പരിശോധിക്കാം. -![Cosmos DB എമുലേറ്ററിൽ സാമ്പിൾ ഡാറ്റ പരിശോധിക്കൽ](../../../../translated_images/cosmosdb-emulator-persons.bf640586a7077c8985dfd3071946465c8e074c722c7c202d6d714de99a93b90a.ml.png) +![Cosmos DB എമുലേറ്ററിൽ സാമ്പിൾ ഡാറ്റ പരിശോധിക്കൽ](../../../../translated_images/ml/cosmosdb-emulator-persons.bf640586a7077c8985dfd3071946465c8e074c722c7c202d6d714de99a93b90a.png) #### Cosmos DB എമുലേറ്റർ ഉപയോഗിച്ച് ഡോക്യുമെന്റ് ഡാറ്റ ക്വറി ചെയ്യൽ @@ -102,7 +102,7 @@ Cosmos DB ഡാറ്റാബേസ് "Not Only SQL" എന്ന നിർ `SELECT * FROM c where c.age < 40` - ![Cosmos DB എമുലേറ്ററിൽ SELECT ക്വറി പ്രവർത്തിപ്പിച്ച് പ്രായം 40-ൽ താഴെയുള്ള ഡോക്യുമെന്റുകൾ കണ്ടെത്തൽ](../../../../translated_images/cosmosdb-emulator-persons-query.6905ebb497e3cd047cd96e55a0a03f69ce1b91b2b3d8c147e617b746b22b7e33.ml.png) + ![Cosmos DB എമുലേറ്ററിൽ SELECT ക്വറി പ്രവർത്തിപ്പിച്ച് പ്രായം 40-ൽ താഴെയുള്ള ഡോക്യുമെന്റുകൾ കണ്ടെത്തൽ](../../../../translated_images/ml/cosmosdb-emulator-persons-query.6905ebb497e3cd047cd96e55a0a03f69ce1b91b2b3d8c147e617b746b22b7e33.png) ക്വറി രണ്ട് ഡോക്യുമെന്റുകൾ തിരികെ നൽകുന്നു, ഓരോ ഡോക്യുമെന്റിന്റെയും പ്രായം 40-ൽ താഴെയാണ്. diff --git a/translations/ml/2-Working-With-Data/07-python/README.md b/translations/ml/2-Working-With-Data/07-python/README.md index ed7fe8fe..37bb4db5 100644 --- a/translations/ml/2-Working-With-Data/07-python/README.md +++ b/translations/ml/2-Working-With-Data/07-python/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: | :-------------------------------------------------------------------------------------------------------: | | Python ഉപയോഗിച്ച് പ്രവർത്തിക്കൽ - _Sketchnote by [@nitya](https://twitter.com/nitya)_ | -[![Intro Video](../../../../translated_images/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.ml.png)](https://youtu.be/dZjWOGbsN4Y) +[![Intro Video](../../../../translated_images/ml/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.png)](https://youtu.be/dZjWOGbsN4Y) ഡാറ്റാബേസുകൾ ഡാറ്റ സംഭരിക്കാനും ക്വറി ഭാഷകൾ ഉപയോഗിച്ച് അവയെ ക്വറി ചെയ്യാനും വളരെ കാര്യക്ഷമമായ മാർഗങ്ങൾ നൽകുമ്പോഴും, ഡാറ്റ പ്രോസസ്സിംഗിന്റെ ഏറ്റവും ലവച്ഛമായ മാർഗം ഡാറ്റ കൈകാര്യം ചെയ്യാൻ നിങ്ങളുടെ സ്വന്തം പ്രോഗ്രാം എഴുതുകയാണ്. പലപ്പോഴും, ഡാറ്റാബേസ് ക്വറി ചെയ്യുന്നത് കൂടുതൽ ഫലപ്രദമായ മാർഗമായിരിക്കും. എന്നാൽ, കൂടുതൽ സങ്കീർണ്ണമായ ഡാറ്റ പ്രോസസ്സിംഗ് ആവശ്യമായപ്പോൾ, അത് എളുപ്പത്തിൽ SQL ഉപയോഗിച്ച് ചെയ്യാനാകില്ല. ഡാറ്റ പ്രോസസ്സിംഗ് ഏതെങ്കിലും പ്രോഗ്രാമിംഗ് ഭാഷയിൽ പ്രോഗ്രാം ചെയ്യാം, പക്ഷേ ഡാറ്റ കൈകാര്യം ചെയ്യുന്നതിൽ ഉയർന്ന തലത്തിലുള്ള ചില ഭാഷകൾ ഉണ്ട്. ഡാറ്റ സയന്റിസ്റ്റുകൾ സാധാരണയായി താഴെപ്പറയുന്ന ഭാഷകളിൽ ഒന്നിനെ മുൻഗണന നൽകുന്നു: @@ -75,7 +75,7 @@ print(f"Length of index is {len(idx)}") items_sold = pd.Series(np.random.randint(25,50,size=len(idx)),index=idx) items_sold.plot() ``` -![Time Series Plot](../../../../translated_images/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.ml.png) +![Time Series Plot](../../../../translated_images/ml/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.png) ഇപ്പോൾ ഓരോ ആഴ്ചയും നാം സുഹൃത്തുക്കൾക്കായി പാർട്ടി സംഘടിപ്പിക്കുന്നു, പാർട്ടിക്കായി അധികം 10 പാക്ക് ഐസ്‌ക്രീം കൊണ്ടുവരുന്നു എന്ന് കരുതുക. ആ ആഴ്ചയുടെ ഇൻഡക്സ് ഉപയോഗിച്ച് മറ്റൊരു സീരീസ് സൃഷ്ടിക്കാം: ```python @@ -86,7 +86,7 @@ additional_items = pd.Series(10,index=pd.date_range(start_date,end_date,freq="W" total_items = items_sold.add(additional_items,fill_value=0) total_items.plot() ``` -![Time Series Plot](../../../../translated_images/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.ml.png) +![Time Series Plot](../../../../translated_images/ml/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.png) > **കുറിപ്പ്**: നാം ലളിതമായ `total_items+additional_items` സിന്താക്സ് ഉപയോഗിക്കുന്നില്ല. അതുപയോഗിച്ചാൽ, ഫലസീരീസിൽ പല `NaN` (*Not a Number*) മൂല്യങ്ങളും ഉണ്ടാകുമായിരിക്കും. കാരണം `additional_items` സീരീസിൽ ചില ഇൻഡക്സ് പോയിന്റുകൾക്ക് മൂല്യങ്ങൾ ഇല്ല, `NaN` യെ ഏതെങ്കിലും മൂല്യത്തിലേക്ക് കൂട്ടിച്ചേർക്കുമ്പോൾ ഫലം `NaN` ആകുന്നു. അതിനാൽ കൂട്ടിച്ചേർക്കുമ്പോൾ `fill_value` പാരാമീറ്റർ നിർദ്ദേശിക്കേണ്ടതാണ്. @@ -95,7 +95,7 @@ total_items.plot() monthly = total_items.resample("1M").mean() ax = monthly.plot(kind='bar') ``` -![Monthly Time Series Averages](../../../../translated_images/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.ml.png) +![Monthly Time Series Averages](../../../../translated_images/ml/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.png) ### ഡാറ്റാഫ്രെയിം @@ -224,7 +224,7 @@ df = pd.read_csv('file.csv') ഡാറ്റ കൈകാര്യം ചെയ്യുന്നത് എങ്ങനെ എന്ന് കാണിക്കാൻ, നിങ്ങൾക്ക് [`notebook-covidspread.ipynb`](notebook-covidspread.ipynb) തുറന്ന് മുകളിൽ നിന്ന് താഴേക്ക് വായിക്കാൻ ഞങ്ങൾ ക്ഷണിക്കുന്നു. സെല്ലുകൾ പ്രവർത്തിപ്പിക്കാനും, അവസാനം നാം നിങ്ങൾക്കായി വെച്ച ചില ചലഞ്ചുകൾ ചെയ്യാനും കഴിയും. -![COVID Spread](../../../../translated_images/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.ml.png) +![COVID Spread](../../../../translated_images/ml/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.png) > Jupyter Notebook ൽ കോഡ് എങ്ങനെ റൺ ചെയ്യാമെന്ന് അറിയില്ലെങ്കിൽ, [ഈ ലേഖനം](https://soshnikov.com/education/how-to-execute-notebooks-from-github/) കാണുക. @@ -246,7 +246,7 @@ df = pd.read_csv('file.csv') [`notebook-papers.ipynb`](notebook-papers.ipynb) തുറന്ന് മുകളിൽ നിന്ന് താഴേക്ക് വായിക്കുക. സെല്ലുകൾ പ്രവർത്തിപ്പിക്കാനും, അവസാനം നാം നിങ്ങൾക്കായി വെച്ച ചില ചലഞ്ചുകൾ ചെയ്യാനും കഴിയും. -![Covid Medical Treatment](../../../../translated_images/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.ml.png) +![Covid Medical Treatment](../../../../translated_images/ml/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.png) ## ചിത്ര ഡാറ്റ പ്രോസസ്സിംഗ് diff --git a/translations/ml/2-Working-With-Data/README.md b/translations/ml/2-Working-With-Data/README.md index 73344dce..39785444 100644 --- a/translations/ml/2-Working-With-Data/README.md +++ b/translations/ml/2-Working-With-Data/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # ഡാറ്റയുമായി പ്രവർത്തിക്കൽ -![data love](../../../translated_images/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.ml.jpg) +![data love](../../../translated_images/ml/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.jpg) > ഫോട്ടോ Alexander Sinn യുടെ Unsplash ൽ നിന്നാണ് ഈ പാഠങ്ങളിൽ, ഡാറ്റ എങ്ങനെ കൈകാര്യം ചെയ്യാമെന്ന്, മാറ്റം വരുത്താമെന്ന്, ആപ്ലിക്കേഷനുകളിൽ ഉപയോഗിക്കാമെന്ന് നിങ്ങൾ പഠിക്കും. ബന്ധപരമായ (relational) ഡാറ്റാബേസുകളും ബന്ധമില്ലാത്ത (non-relational) ഡാറ്റാബേസുകളും എന്താണെന്ന്, അവയിൽ ഡാറ്റ എങ്ങനെ സൂക്ഷിക്കാമെന്ന് നിങ്ങൾ അറിയും. ഡാറ്റ കൈകാര്യം ചെയ്യാൻ പൈത്തൺ ഉപയോഗിക്കുന്നതിന്റെ അടിസ്ഥാനങ്ങൾ നിങ്ങൾ പഠിക്കും, കൂടാതെ പൈത്തൺ ഉപയോഗിച്ച് ഡാറ്റ കൈകാര്യം ചെയ്യാനും ഡാറ്റയിൽ നിന്ന് വിവരങ്ങൾ കണ്ടെത്താനും ഉള്ള നിരവധി മാർഗങ്ങൾ നിങ്ങൾ കണ്ടെത്തും. diff --git a/translations/ml/3-Data-Visualization/09-visualization-quantities/README.md b/translations/ml/3-Data-Visualization/09-visualization-quantities/README.md index d793dcec..f5d42519 100644 --- a/translations/ml/3-Data-Visualization/09-visualization-quantities/README.md +++ b/translations/ml/3-Data-Visualization/09-visualization-quantities/README.md @@ -61,7 +61,7 @@ birds.head() wingspan = birds['MaxWingspan'] wingspan.plot() ``` -![Max Wingspan](../../../../translated_images/max-wingspan-02.e79fd847b2640b89e21e340a3a9f4c5d4b224c4fcd65f54385e84f1c9ed26d52.ml.png) +![Max Wingspan](../../../../translated_images/ml/max-wingspan-02.e79fd847b2640b89e21e340a3a9f4c5d4b224c4fcd65f54385e84f1c9ed26d52.png) നിങ്ങൾ ഉടൻ ശ്രദ്ധിക്കുന്നതെന്താണ്? കുറഞ്ഞത് ഒരു ഔട്ട്‌ലൈയർ ഉണ്ടെന്ന് തോന്നുന്നു - അത്ര വലിയ വിങ്‌സ്‌പാൻ! 2300 സെന്റീമീറ്റർ വിങ്‌സ്‌പാൻ 23 മീറ്ററിനാണ് തുല്യമായത് - മിന്നസോട്ടയിൽ പ്റ്റെറോഡാക്ടിലുകൾ നടക്കുകയാണോ? പരിശോധിക്കാം. @@ -81,7 +81,7 @@ plt.plot(x, y) plt.show() ``` -![wingspan with labels](../../../../translated_images/max-wingspan-labels-02.aa90e826ca49a9d1dde78075e9755c1849ef56a4e9ec60f7e9f3806daf9283e2.ml.png) +![wingspan with labels](../../../../translated_images/ml/max-wingspan-labels-02.aa90e826ca49a9d1dde78075e9755c1849ef56a4e9ec60f7e9f3806daf9283e2.png) 45 ഡിഗ്രി ലേബലുകളുടെ റൊട്ടേഷൻ സജ്ജമാക്കിയിട്ടും വായിക്കാൻ വളരെ അധികം ലേബലുകൾ ഉണ്ട്. വ്യത്യസ്തമായ ഒരു തന്ത്രം പരീക്ഷിക്കാം: ഔട്ട്‌ലൈയറുകൾ മാത്രം ലേബൽ ചെയ്യുക, ലേബലുകൾ ചാർട്ടിനുള്ളിൽ സജ്ജമാക്കുക. ലേബലിംഗിന് കൂടുതൽ സ്ഥലം നൽകാൻ സ്കാറ്റർ ചാർട്ട് ഉപയോഗിക്കാം: @@ -103,7 +103,7 @@ plt.show() നിങ്ങൾ എന്ത് കണ്ടെത്തി? -![outliers](../../../../translated_images/labeled-wingspan-02.6110e2d2401cd5238ccc24dfb6d04a6c19436101f6cec151e3992e719f9f1e1f.ml.png) +![outliers](../../../../translated_images/ml/labeled-wingspan-02.6110e2d2401cd5238ccc24dfb6d04a6c19436101f6cec151e3992e719f9f1e1f.png) ## നിങ്ങളുടെ ഡാറ്റ ഫിൽട്ടർ ചെയ്യുക ബാൾഡ് ഈഗിൾക്കും പ്രെയറി ഫാൽക്കണിനും, വളരെ വലിയ പക്ഷികളായിരിക്കാം, പക്ഷേ പരമാവധി വിങ്‌സ്‌പാനിൽ ഒരു അധികം `0` ചേർത്തിട്ടുള്ള തെറ്റായ ലേബലിംഗാണ്. 25 മീറ്റർ വിങ്‌സ്‌പാൻ ഉള്ള ബാൾഡ് ഈഗിൾ കാണാൻ സാധ്യത കുറവാണ്, പക്ഷേ ഉണ്ടെങ്കിൽ ദയവായി ഞങ്ങളെ അറിയിക്കുക! ആ രണ്ട് ഔട്ട്‌ലൈയറുകൾ ഇല്ലാതെ പുതിയ ഡാറ്റാഫ്രെയിം സൃഷ്ടിക്കാം: @@ -123,7 +123,7 @@ plt.show() ഔട്ട്‌ലൈയറുകൾ ഫിൽട്ടർ ചെയ്തതോടെ, നിങ്ങളുടെ ഡാറ്റ കൂടുതൽ ഏകീകൃതവും മനസ്സിലാക്കാൻ എളുപ്പവുമാണ്. -![scatterplot of wingspans](../../../../translated_images/scatterplot-wingspan-02.1c33790094ce36a75f5fb45b25ed2cf27f0356ea609e43c11e97a2cedd7011a4.ml.png) +![scatterplot of wingspans](../../../../translated_images/ml/scatterplot-wingspan-02.1c33790094ce36a75f5fb45b25ed2cf27f0356ea609e43c11e97a2cedd7011a4.png) ഇപ്പോൾ വിങ്‌സ്‌പാൻ സംബന്ധിച്ച് കുറഞ്ഞത് ശുദ്ധമായ ഡാറ്റാസെറ്റ് ഉണ്ടാകുമ്പോൾ, ഈ പക്ഷികളെക്കുറിച്ച് കൂടുതൽ കണ്ടെത്താം. @@ -149,7 +149,7 @@ birds.plot(x='Category', title='Birds of Minnesota') ``` -![full data as a bar chart](../../../../translated_images/full-data-bar-02.aaa3fda71c63ed564b917841a1886c177dd9a26424142e510c0c0498fd6ca160.ml.png) +![full data as a bar chart](../../../../translated_images/ml/full-data-bar-02.aaa3fda71c63ed564b917841a1886c177dd9a26424142e510c0c0498fd6ca160.png) എന്നാൽ ഈ ബാർ ചാർട്ട് വായിക്കാൻ കഴിയുന്നില്ല, കാരണം ഗ്രൂപ്പുചെയ്യാത്ത ഡാറ്റ വളരെ കൂടുതലാണ്. നിങ്ങൾ പ്ലോട്ട് ചെയ്യാൻ ആഗ്രഹിക്കുന്ന ഡാറ്റ മാത്രം തിരഞ്ഞെടുക്കണം, അതിനാൽ പക്ഷികളുടെ വിഭാഗം അടിസ്ഥാനമാക്കി നീളം നോക്കാം. @@ -164,7 +164,7 @@ category_count = birds.value_counts(birds['Category'].values, sort=True) plt.rcParams['figure.figsize'] = [6, 12] category_count.plot.barh() ``` -![category and length](../../../../translated_images/category-counts-02.0b9a0a4de42275ae5096d0f8da590d8bf520d9e7e40aad5cc4fc8d276480cc32.ml.png) +![category and length](../../../../translated_images/ml/category-counts-02.0b9a0a4de42275ae5096d0f8da590d8bf520d9e7e40aad5cc4fc8d276480cc32.png) ഈ ബാർ ചാർട്ട് ഓരോ വിഭാഗത്തിലും പക്ഷികളുടെ എണ്ണം നല്ല കാഴ്ച നൽകുന്നു. ഒരു നിമിഷത്തിൽ, ഈ പ്രദേശത്തെ ഏറ്റവും വലിയ പക്ഷി എണ്ണം Ducks/Geese/Waterfowl വിഭാഗത്തിലാണ് എന്ന് കാണാം. മിന്നസോട്ട '10,000 തടാകങ്ങളുടെ നാട്' ആകുന്നതിനാൽ ഇത് അത്ഭുതമല്ല! @@ -180,7 +180,7 @@ plt.barh(y=birds['Category'], width=maxlength) plt.rcParams['figure.figsize'] = [6, 12] plt.show() ``` -![comparing data](../../../../translated_images/category-length-02.7304bf519375c9807d8165cc7ec60dd2a60f7b365b23098538e287d89adb7d76.ml.png) +![comparing data](../../../../translated_images/ml/category-length-02.7304bf519375c9807d8165cc7ec60dd2a60f7b365b23098538e287d89adb7d76.png) ഇവിടെ എന്തും അത്ഭുതപ്പെടുത്തുന്നില്ല: ഹമ്മിംഗ്‌ബേർഡുകൾക്ക് പെലിക്കാനുകളോ ഗീസുകളോ അപേക്ഷിച്ച് ഏറ്റവും കുറഞ്ഞ MaxLength ഉണ്ട്. ഡാറ്റ ലജിക്കൽ ആകുമ്പോൾ നല്ലതാണ്! @@ -198,7 +198,7 @@ plt.show() ``` ഈ പ്ലോട്ടിൽ, ഓരോ പക്ഷി വിഭാഗത്തിന്റെയും കുറഞ്ഞ നീളം, പരമാവധി നീളം കാണാം. ഈ ഡാറ്റ പ്രകാരം, പക്ഷി വലുതായാൽ, അതിന്റെ നീളം വ്യത്യാസവും വലുതാണ് എന്ന് പറയാം. അത്ഭുതകരം! -![superimposed values](../../../../translated_images/superimposed-02.f03058536baeb2ed7864f01102538464d4c2fd7ade881ddd7d5ba74dc5d2fdae.ml.png) +![superimposed values](../../../../translated_images/ml/superimposed-02.f03058536baeb2ed7864f01102538464d4c2fd7ade881ddd7d5ba74dc5d2fdae.png) ## 🚀 ചലഞ്ച് diff --git a/translations/ml/3-Data-Visualization/10-visualization-distributions/README.md b/translations/ml/3-Data-Visualization/10-visualization-distributions/README.md index cdfd4ffa..04002b00 100644 --- a/translations/ml/3-Data-Visualization/10-visualization-distributions/README.md +++ b/translations/ml/3-Data-Visualization/10-visualization-distributions/README.md @@ -49,7 +49,7 @@ plt.xlabel('Max Length') plt.show() ``` -![max length per order](../../../../translated_images/scatter-wb.9d98b0ed7f0388af979441853361a11df5f518f5307938a503ca7913e986111b.ml.png) +![max length per order](../../../../translated_images/ml/scatter-wb.9d98b0ed7f0388af979441853361a11df5f518f5307938a503ca7913e986111b.png) ഇത് ഓരോ പക്ഷി ഓർഡറിനും ശരീര നീളത്തിന്റെ പൊതുവായ വിതരണത്തിന്റെ ഒരു അവലോകനമാണ് നൽകുന്നത്, പക്ഷേ യഥാർത്ഥ വിതരണങ്ങൾ പ്രദർശിപ്പിക്കാൻ ഇത് ഏറ്റവും അനുയോജ്യമായ മാർഗം അല്ല. ആ ജോലി സാധാരണയായി ഹിസ്റ്റോഗ്രാം സൃഷ്ടിച്ച് നിർവഹിക്കുന്നു. ## ഹിസ്റ്റോഗ്രാമുകളുമായി പ്രവർത്തിക്കൽ @@ -60,7 +60,7 @@ Matplotlib ഡാറ്റ വിതരണങ്ങൾ ദൃശ്യവൽക birds['MaxBodyMass'].plot(kind = 'hist', bins = 10, figsize = (12,12)) plt.show() ``` -![distribution over the entire dataset](../../../../translated_images/dist1-wb.0d0cac82e2974fbbec635826fefead401af795f82e2279e2e2678bf2c117d827.ml.png) +![distribution over the entire dataset](../../../../translated_images/ml/dist1-wb.0d0cac82e2974fbbec635826fefead401af795f82e2279e2e2678bf2c117d827.png) നിങ്ങൾക്ക് കാണാമല്ലോ, ഈ ഡാറ്റാസെറ്റിലെ 400+ പക്ഷികളിൽ ഭൂരിഭാഗവും അവരുടെ പരമാവധി ശരീരഭാരം 2000-ൽ താഴെ ഉള്ള പരിധിയിലാണ്. `bins` പാരാമീറ്റർ 30 പോലുള്ള ഉയർന്ന സംഖ്യയാക്കി മാറ്റി ഡാറ്റയിൽ കൂടുതൽ洞察ം നേടാം: @@ -68,7 +68,7 @@ plt.show() birds['MaxBodyMass'].plot(kind = 'hist', bins = 30, figsize = (12,12)) plt.show() ``` -![distribution over the entire dataset with larger bins param](../../../../translated_images/dist2-wb.2c0a7a3499b2fbf561e9f93b69f265dfc538dc78f6de15088ba84a88152e26ba.ml.png) +![distribution over the entire dataset with larger bins param](../../../../translated_images/ml/dist2-wb.2c0a7a3499b2fbf561e9f93b69f265dfc538dc78f6de15088ba84a88152e26ba.png) ഈ ചാർട്ട് കൂടുതൽ സൂക്ഷ്മമായ രീതിയിൽ വിതരണത്തെ കാണിക്കുന്നു. ഇടത്തരം കുറവുള്ള ഒരു ചാർട്ട് സൃഷ്ടിക്കാൻ, നിങ്ങൾക്ക് ഒരു നിശ്ചിത പരിധിയിലുള്ള ഡാറ്റ മാത്രം തിരഞ്ഞെടുക്കണം: @@ -79,7 +79,7 @@ filteredBirds = birds[(birds['MaxBodyMass'] > 1) & (birds['MaxBodyMass'] < 60)] filteredBirds['MaxBodyMass'].plot(kind = 'hist',bins = 40,figsize = (12,12)) plt.show() ``` -![filtered histogram](../../../../translated_images/dist3-wb.64b88db7f9780200bd486a2c2a3252548dd439672dbd3f778193db7f654b100c.ml.png) +![filtered histogram](../../../../translated_images/ml/dist3-wb.64b88db7f9780200bd486a2c2a3252548dd439672dbd3f778193db7f654b100c.png) ✅ മറ്റ് ഫിൽട്ടറുകളും ഡാറ്റ പോയിന്റുകളും പരീക്ഷിക്കുക. ഡാറ്റയുടെ മുഴുവൻ വിതരണവും കാണാൻ, `['MaxBodyMass']` ഫിൽട്ടർ നീക്കം ചെയ്ത് ലേബൽ ചെയ്ത വിതരണങ്ങൾ കാണിക്കുക. @@ -96,7 +96,7 @@ hist = ax.hist2d(x, y) ``` ഈ രണ്ട് ഘടകങ്ങൾ തമ്മിൽ പ്രതീക്ഷിക്കപ്പെട്ട ബന്ധം ഒരു പ്രതീക്ഷിച്ച അക്ഷം അനുസരിച്ച് കാണപ്പെടുന്നു, പ്രത്യേകിച്ച് ശക്തമായ ഒരു സമന്വയ ബിന്ദു: -![2D plot](../../../../translated_images/2D-wb.ae22fdd33936507a41e3af22e11e4903b04a9be973b23a4e05214efaccfd66c8.ml.png) +![2D plot](../../../../translated_images/ml/2D-wb.ae22fdd33936507a41e3af22e11e4903b04a9be973b23a4e05214efaccfd66c8.png) ഹിസ്റ്റോഗ്രാമുകൾ സംഖ്യാത്മക ഡാറ്റയ്ക്ക് സാധാരണയായി നല്ലതാണ്. ടെക്സ്റ്റ് ഡാറ്റ അനുസരിച്ച് വിതരണങ്ങൾ കാണേണ്ടത് എങ്കിൽ? @@ -136,7 +136,7 @@ plt.gca().set(title='Conservation Status', ylabel='Min Wingspan') plt.legend(); ``` -![wingspan and conservation collation](../../../../translated_images/histogram-conservation-wb.3c40450eb072c14de7a1a3ec5c0fcba4995531024760741b392911b567fd8b70.ml.png) +![wingspan and conservation collation](../../../../translated_images/ml/histogram-conservation-wb.3c40450eb072c14de7a1a3ec5c0fcba4995531024760741b392911b567fd8b70.png) കുറഞ്ഞ വിങ്‌സ്‌പാനും സംരക്ഷണ നിലക്കും നല്ല ബന്ധമില്ലെന്ന് തോന്നുന്നു. ഈ രീതിയിൽ ഡാറ്റാസെറ്റിലെ മറ്റ് ഘടകങ്ങളും പരീക്ഷിക്കുക. നിങ്ങൾക്ക് ഏതെങ്കിലും ബന്ധം കണ്ടെത്താമോ? @@ -154,7 +154,7 @@ import matplotlib.pyplot as plt sns.kdeplot(filteredBirds['MinWingspan']) plt.show() ``` -![Density plot](../../../../translated_images/density1.8801043bd4af2567b0f706332b5853c7614e5e4b81b457acc27eb4e092a65cbd.ml.png) +![Density plot](../../../../translated_images/ml/density1.8801043bd4af2567b0f706332b5853c7614e5e4b81b457acc27eb4e092a65cbd.png) മുൻപത്തെ കുറഞ്ഞ വിങ്‌സ്‌പാൻ ഡാറ്റയുടെ പ്ലോട്ടിനെപ്പോലെ ഇത് കാണാം; ഇത് കുറച്ച് മൃദുവാണ്. Seaborn ഡോക്യുമെന്റേഷനുസരിച്ച്, "ഹിസ്റ്റോഗ്രാമിനോട് താരതമ്യപ്പെടുത്തുമ്പോൾ, KDE ഒരു പ്ലോട്ട് കുറച്ച് കുറവുള്ളതും കൂടുതൽ വ്യാഖ്യാനയോഗ്യവുമാകാം, പ്രത്യേകിച്ച് പല വിതരണങ്ങളും വരച്ചപ്പോൾ. എന്നാൽ അടിസ്ഥാന വിതരണത്തിന് അതിരുകൾ ഉണ്ടെങ്കിൽ അല്ലെങ്കിൽ മൃദുവായില്ലെങ്കിൽ, ഇത് വക്രതകൾ സൃഷ്ടിക്കാം. ഹിസ്റ്റോഗ്രാമുപോലെ, പ്രതിനിധാനത്തിന്റെ ഗുണമേന്മയും നല്ല മൃദുവായ പാരാമീറ്ററുകളുടെ തിരഞ്ഞെടുപ്പിൽ ആശ്രയിച്ചിരിക്കുന്നു." [source](https://seaborn.pydata.org/generated/seaborn.kdeplot.html) മറ്റൊരു വാക്കിൽ, ഔട്ട്‌ലൈയർമാർ എപ്പോഴും നിങ്ങളുടെ ചാർട്ടുകൾക്ക് പ്രശ്നങ്ങൾ സൃഷ്ടിക്കും. @@ -164,7 +164,7 @@ plt.show() sns.kdeplot(filteredBirds['MaxBodyMass']) plt.show() ``` -![smooth bodymass line](../../../../translated_images/density2.8e7647257060ff544a1aaded57e8dd1887586bfe340139e9b77ac1e5287f7977.ml.png) +![smooth bodymass line](../../../../translated_images/ml/density2.8e7647257060ff544a1aaded57e8dd1887586bfe340139e9b77ac1e5287f7977.png) മൃദുവായെങ്കിലും അത്ര മൃദുവായില്ലാത്ത ഒരു ലൈനിനായി, `bw_adjust` പാരാമീറ്റർ എഡിറ്റ് ചെയ്യുക: @@ -172,7 +172,7 @@ plt.show() sns.kdeplot(filteredBirds['MaxBodyMass'], bw_adjust=.2) plt.show() ``` -![less smooth bodymass line](../../../../translated_images/density3.84ae27da82f31e6b83ad977646f029a1d21186574d7581facd70123b3eb257ee.ml.png) +![less smooth bodymass line](../../../../translated_images/ml/density3.84ae27da82f31e6b83ad977646f029a1d21186574d7581facd70123b3eb257ee.png) ✅ ഈ തരത്തിലുള്ള പ്ലോട്ടിനുള്ള പാരാമീറ്ററുകൾക്കുറിച്ച് വായിച്ച് പരീക്ഷിക്കുക! @@ -186,7 +186,7 @@ sns.kdeplot( ) ``` -![bodymass per order](../../../../translated_images/density4.e9d6c033f15c500fd33df94cb592b9f5cf1ed2a3d213c448a3f9e97ba39573ce.ml.png) +![bodymass per order](../../../../translated_images/ml/density4.e9d6c033f15c500fd33df94cb592b9f5cf1ed2a3d213c448a3f9e97ba39573ce.png) ഒരേ ചാർട്ടിൽ പല വേരിയബിളുകളുടെ ഡെൻസിറ്റികളും മാപ്പ് ചെയ്യാം. ഒരു പക്ഷിയുടെ MaxLength, MinLength സംരക്ഷണ നിലയുമായി താരതമ്യം ചെയ്യുക: @@ -194,7 +194,7 @@ sns.kdeplot( sns.kdeplot(data=filteredBirds, x="MinLength", y="MaxLength", hue="ConservationStatus") ``` -![multiple densities, superimposed](../../../../translated_images/multi.56548caa9eae8d0fd9012a8586295538c7f4f426e2abc714ba070e2e4b1fc2c1.ml.png) +![multiple densities, superimposed](../../../../translated_images/ml/multi.56548caa9eae8d0fd9012a8586295538c7f4f426e2abc714ba070e2e4b1fc2c1.png) 'Vulnerable' പക്ഷികളുടെ നീളങ്ങളുടെ ക്ലസ്റ്റർ അർത്ഥവത്താണോ അല്ലയോ എന്ന് അന്വേഷിക്കുന്നത് മൂല്യമുള്ളതായിരിക്കാം. diff --git a/translations/ml/3-Data-Visualization/11-visualization-proportions/README.md b/translations/ml/3-Data-Visualization/11-visualization-proportions/README.md index fc42c4b0..414b3cf2 100644 --- a/translations/ml/3-Data-Visualization/11-visualization-proportions/README.md +++ b/translations/ml/3-Data-Visualization/11-visualization-proportions/README.md @@ -93,7 +93,7 @@ plt.show() ``` ഇവിടെ, ഈ ഡാറ്റയുടെ അനുപാതങ്ങൾ ഈ രണ്ട് മഷ്‌റൂം ക്ലാസുകൾ അനുസരിച്ച് കാണിക്കുന്ന ഒരു പൈ ചാർട്ട്. ലേബലുകളുടെ ക്രമം ശരിയാണെന്ന് ഉറപ്പാക്കുന്നത് വളരെ പ്രധാനമാണ്, പ്രത്യേകിച്ച് ഇവിടെ, അതിനാൽ ലേബൽ അറേ എങ്ങനെ നിർമ്മിക്കപ്പെടുന്നു എന്ന് പരിശോധിക്കുക! -![pie chart](../../../../translated_images/pie1-wb.e201f2fcc335413143ce37650fb7f5f0bb21358e7823a327ed8644dfb84be9db.ml.png) +![pie chart](../../../../translated_images/ml/pie1-wb.e201f2fcc335413143ce37650fb7f5f0bb21358e7823a327ed8644dfb84be9db.png) ## ഡോണട്ടുകൾ! @@ -123,7 +123,7 @@ plt.title('Mushroom Habitats') plt.show() ``` -![donut chart](../../../../translated_images/donut-wb.be3c12a22712302b5d10c40014d5389d4a1ae4412fe1655b3cf4af57b64f799a.ml.png) +![donut chart](../../../../translated_images/ml/donut-wb.be3c12a22712302b5d10c40014d5389d4a1ae4412fe1655b3cf4af57b64f799a.png) ഈ കോഡ് ഒരു ചാർട്ട് വരയ്ക്കുകയും മധ്യത്തിൽ ഒരു വൃത്തം വരയ്ക്കുകയും ചെയ്യുന്നു, പിന്നീട് ആ വൃത്തം ചാർട്ടിൽ ചേർക്കുന്നു. മധ്യ വൃത്തത്തിന്റെ വീതി `0.40` എന്ന മൂല്യം മാറ്റി എഡിറ്റ് ചെയ്യാം. @@ -170,7 +170,7 @@ fig = plt.figure( വാഫിൾ ചാർട്ട് ഉപയോഗിച്ച്, ഈ മഷ്‌റൂം ഡാറ്റാസെറ്റിലെ ക്യാപ് നിറങ്ങളുടെ അനുപാതങ്ങൾ വ്യക്തമായി കാണാം. രസകരമായി, നിരവധി പച്ച ക്യാപ് മഷ്‌റൂമുകൾ ഉണ്ട്! -![waffle chart](../../../../translated_images/waffle.5455dbae4ccf17d53bb40ff0a657ecef7b8aa967e27a19cc96325bd81598f65e.ml.png) +![waffle chart](../../../../translated_images/ml/waffle.5455dbae4ccf17d53bb40ff0a657ecef7b8aa967e27a19cc96325bd81598f65e.png) ✅ Pywaffle ചാർട്ടുകളിൽ [Font Awesome](https://fontawesome.com/) ൽ ലഭ്യമായ ഏതെങ്കിലും ഐക്കൺ ഉപയോഗിച്ച് ഐക്കണുകൾ ഉൾപ്പെടുത്താൻ പിന്തുണ നൽകുന്നു. ചതുരങ്ങൾക്കുപകരം ഐക്കണുകൾ ഉപയോഗിച്ച് കൂടുതൽ രസകരമായ വാഫിൾ ചാർട്ട് സൃഷ്ടിക്കാൻ പരീക്ഷണങ്ങൾ നടത്തുക. diff --git a/translations/ml/3-Data-Visualization/12-visualization-relationships/README.md b/translations/ml/3-Data-Visualization/12-visualization-relationships/README.md index 8587a17e..ef7a9218 100644 --- a/translations/ml/3-Data-Visualization/12-visualization-relationships/README.md +++ b/translations/ml/3-Data-Visualization/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ honey.head() ```python sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ``` -![scatterplot 1](../../../../translated_images/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.ml.png) +![scatterplot 1](../../../../translated_images/ml/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.png) ഇപ്പോൾ, വർഷം തോറും വില എങ്ങനെ മാറിയെന്ന് കാണിക്കാൻ തേൻ നിറത്തിലുള്ള കളർ സ്കീം ഉപയോഗിച്ച് സമാന ഡാറ്റ കാണിക്കുക. വർഷം തോറും മാറ്റം കാണിക്കാൻ 'hue' പാരാമീറ്റർ ചേർക്കാം: @@ -60,7 +60,7 @@ sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ```python sns.relplot(x="priceperlb", y="state", hue="year", palette="YlOrBr", data=honey, height=15, aspect=.5); ``` -![scatterplot 2](../../../../translated_images/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.ml.png) +![scatterplot 2](../../../../translated_images/ml/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.png) ഈ കളർ സ്കീം മാറ്റത്തോടെ, തേന്റെ പൗണ്ട് പ്രതി വില വർഷം തോറും ശക്തമായി ഉയരുന്നുവെന്ന് കാണാം. ഡാറ്റയിൽ ഒരു സാമ്പിൾ സെറ്റ് പരിശോധിച്ചാൽ (ഉദാഹരണത്തിന്, അരിസോണ), വില വർഷം തോറും ഉയരുന്ന ഒരു പാറ്റേൺ കാണാം, കുറച്ച് വ്യത്യാസങ്ങൾ ഒഴികെ: @@ -89,7 +89,7 @@ sns.relplot(x="priceperlb", y="state", size="year", data=honey, height=15, aspec ``` ഡോട്ടുകളുടെ വലിപ്പം ക്രമാതീതമായി വർദ്ധിക്കുന്നതായി കാണാം. -![scatterplot 3](../../../../translated_images/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.ml.png) +![scatterplot 3](../../../../translated_images/ml/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.png) ഇത് സപ്ലൈ ആൻഡ് ഡിമാൻഡ് എന്ന ലളിതമായ കേസ് ആണോ? കാലാവസ്ഥ മാറ്റം, കോളനി തകർച്ച തുടങ്ങിയ ഘടകങ്ങൾ കാരണം വർഷം തോറും വാങ്ങാനുളള തേൻ കുറയുന്നുണ്ടോ, അതിനാൽ വില ഉയരുന്നുണ്ടോ? @@ -104,7 +104,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); ``` ഉത്തരം: ചില വ്യത്യാസങ്ങൾ കൂടെ 2003-ൽ ചില ഉയർച്ചകൾ കാണാം: -![line chart 1](../../../../translated_images/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.ml.png) +![line chart 1](../../../../translated_images/ml/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.png) ✅ Seaborn ഒരു ലൈനിൽ ഡാറ്റ സംഗ്രഹിക്കുന്നതിനാൽ, "ഓരോ x മൂല്യത്തിലും പല അളവുകൾ ശരാശരി കണക്കാക്കി ശരാശരിയുടെ ചുറ്റും 95% വിശ്വാസപരിധി കാണിക്കുന്നു". [മൂലം](https://seaborn.pydata.org/tutorial/relational.html). ഈ സമയം കൂടുതലുള്ള പ്രവർത്തനം `ci=None` ചേർത്താൽ ഒഴിവാക്കാം. @@ -114,7 +114,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); sns.relplot(x="year", y="totalprod", kind="line", data=honey); ``` -![line chart 2](../../../../translated_images/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.ml.png) +![line chart 2](../../../../translated_images/ml/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.png) ഉത്തരം: അത്രയുമല്ല. മൊത്തം ഉത്പാദനം ആ വർഷം വർദ്ധിച്ചിട്ടുണ്ടെങ്കിലും, സാധാരണയായി ഈ വർഷങ്ങളിൽ തേൻ ഉത്പാദനം കുറയുകയാണ്. @@ -139,7 +139,7 @@ sns.relplot( ``` ഈ ദൃശ്യവൽക്കരണത്തിൽ, കോളനി പ്രതി ഉത്പാദനവും കോളനികളുടെ എണ്ണവും വർഷം തോറും, 3 കോളങ്ങൾ വച്ച് വശം വശം താരതമ്യം ചെയ്യാം: -![facet grid](../../../../translated_images/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.ml.png) +![facet grid](../../../../translated_images/ml/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.png) ഈ ഡാറ്റാസെറ്റിൽ, കോളനികളുടെ എണ്ണം, അവയുടെ ഉത്പാദനം വർഷം തോറും, സംസ്ഥാനങ്ങൾ തമ്മിൽ പ്രത്യേകമായ വ്യത്യാസം കാണുന്നില്ല. ഈ രണ്ട് വേരിയബിളുകൾ തമ്മിൽ സഹബന്ധം കണ്ടെത്താൻ മറ്റൊരു മാർഗം ഉണ്ടോ? @@ -162,7 +162,7 @@ sns.despine(right=False) plt.ylabel('colony yield') ax.figure.legend(); ``` -![superimposed plots](../../../../translated_images/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.ml.png) +![superimposed plots](../../../../translated_images/ml/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.png) 2003-ൽ പ്രത്യേകമായി ഒന്നും കാണാനില്ലെങ്കിലും, ഈ പാഠം ഒരു സന്തോഷകരമായ നോട്ടിൽ അവസാനിപ്പിക്കാം: കോളനികളുടെ എണ്ണം കുറയുന്നുണ്ടെങ്കിലും, അവയുടെ എണ്ണം സ്ഥിരതയുള്ളതും, കോളനി പ്രതി ഉത്പാദനം കുറയുന്നുണ്ടെങ്കിലും. diff --git a/translations/ml/3-Data-Visualization/13-meaningful-visualizations/README.md b/translations/ml/3-Data-Visualization/13-meaningful-visualizations/README.md index 2fbf0db8..6443048c 100644 --- a/translations/ml/3-Data-Visualization/13-meaningful-visualizations/README.md +++ b/translations/ml/3-Data-Visualization/13-meaningful-visualizations/README.md @@ -47,25 +47,25 @@ CO_OP_TRANSLATOR_METADATA: ഒരു ഡാറ്റ സയന്റിസ്റ്റ് ശരിയായ ഡാറ്റയ്ക്ക് ശരിയായ ചാർട്ട് തിരഞ്ഞെടുക്കാൻ ശ്രദ്ധിച്ചാലും, ഡാറ്റയെ തെളിയിക്കാൻ പലവിധം വഞ്ചനാപരമായി പ്രദർശിപ്പിക്കാനുള്ള മാർഗ്ഗങ്ങൾ ധാരാളമാണ്, പലപ്പോഴും ഡാറ്റയെ തന്നെ അപഹസിക്കുന്ന വിലക്ക് വരുത്തി. വഞ്ചനാപരമായ ചാർട്ടുകളും ഇൻഫോഗ്രാഫിക്സും ധാരാളം ഉദാഹരണങ്ങളുണ്ട്! -[![How Charts Lie by Alberto Cairo](../../../../translated_images/tornado.9f42168791208f970d6faefc11d1226d7ca89518013b14aa66b1c9edcd7678d2.ml.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "How charts lie") +[![How Charts Lie by Alberto Cairo](../../../../translated_images/ml/tornado.9f42168791208f970d6faefc11d1226d7ca89518013b14aa66b1c9edcd7678d2.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "How charts lie") > 🎥 വഞ്ചനാപരമായ ചാർട്ടുകൾക്കുറിച്ചുള്ള കോൺഫറൻസ് ടോക്കിനായി മുകളിൽ ചിത്രത്തിൽ ക്ലിക്ക് ചെയ്യുക ഈ ചാർട്ട് X അക്ഷം മറിച്ച് സത്യം മറിച്ച് കാണിക്കുന്നു, തീയതി അടിസ്ഥാനമാക്കി: -![bad chart 1](../../../../translated_images/bad-chart-1.93130f495b748bedfb3423d91b1e754d9026e17f94ad967aecdc9ca7203373bf.ml.png) +![bad chart 1](../../../../translated_images/ml/bad-chart-1.93130f495b748bedfb3423d91b1e754d9026e17f94ad967aecdc9ca7203373bf.png) [ഈ ചാർട്ട്](https://media.firstcoastnews.com/assets/WTLV/images/170ae16f-4643-438f-b689-50d66ca6a8d8/170ae16f-4643-438f-b689-50d66ca6a8d8_1140x641.jpg) കൂടുതൽ വഞ്ചനാപരമാണ്, കാരണം കണ്ണ് വലത്തേക്ക് ആകർഷിക്കപ്പെടുന്നു, സമയാനുസൃതമായി COVID കേസുകൾ വിവിധ കൗണ്ടികളിൽ കുറയുകയാണെന്ന് നിഗമനം ചെയ്യാൻ. എന്നാൽ, തീയതികൾ ശ്രദ്ധാപൂർവ്വം നോക്കിയാൽ, അവ വഞ്ചനാപരമായ താഴ്ന്ന ട്രെൻഡ് നൽകാൻ പുനഃക്രമീകരിച്ചിട്ടുള്ളതായി കാണാം. -![bad chart 2](../../../../translated_images/bad-chart-2.c20e36dd4e6f617c0c325878dd421a563885bbf30a394884c147438827254e0e.ml.jpg) +![bad chart 2](../../../../translated_images/ml/bad-chart-2.c20e36dd4e6f617c0c325878dd421a563885bbf30a394884c147438827254e0e.jpg) ഈ പ്രശസ്ത ഉദാഹരണം നിറവും മറിച്ച Y അക്ഷവും ഉപയോഗിച്ച് വഞ്ചന ചെയ്യുന്നു: തോക്കു-സഹായക നിയമം പാസായതിന് ശേഷം തോക്ക് മരണങ്ങൾ വർധിച്ചതായി നിഗമനം ചെയ്യേണ്ടതിനു പകരം, കണ്ണ് മറിച്ച് സത്യം മറിച്ച് കാണുന്നു: -![bad chart 3](../../../../translated_images/bad-chart-3.6865d0afac4108d737558d90a61547d23a8722896397ec792264ee51a1be4be5.ml.jpg) +![bad chart 3](../../../../translated_images/ml/bad-chart-3.6865d0afac4108d737558d90a61547d23a8722896397ec792264ee51a1be4be5.jpg) ഈ വിചിത്രമായ ചാർട്ട് അനുപാതം എങ്ങനെ മാനിപ്പുലേറ്റ് ചെയ്യാമെന്ന് ഹാസ്യകരമായി കാണിക്കുന്നു: -![bad chart 4](../../../../translated_images/bad-chart-4.68cfdf4011b454471053ee1231172747e1fbec2403b4443567f1dc678134f4f2.ml.jpg) +![bad chart 4](../../../../translated_images/ml/bad-chart-4.68cfdf4011b454471053ee1231172747e1fbec2403b4443567f1dc678134f4f2.jpg) തുല്യമായവ അല്ലാത്തവ താരതമ്യം ചെയ്യുക മറ്റൊരു സംശയാസ്പദമായ തന്ത്രമാണ്. 'സ്പ്യൂറിയസ് കോറിലേഷൻസ്' എന്ന വിഷയത്തിൽ 'മെയ്നിലെ വിവാഹമോചന നിരക്കും മാർഗറിൻ ഉപഭോഗവും' പോലുള്ള കാര്യങ്ങൾ തമ്മിലുള്ള 'വാസ്തവങ്ങൾ' പ്രദർശിപ്പിക്കുന്ന [അദ്ഭുതകരമായ വെബ്‌സൈറ്റ്](https://tylervigen.com/spurious-correlations) ഉണ്ട്. Reddit ഗ്രൂപ്പ് ഡാറ്റയുടെ [കീഴടക്കം ഉപയോഗങ്ങൾ](https://www.reddit.com/r/dataisugly/top/?t=all) ശേഖരിക്കുന്നു. @@ -100,13 +100,13 @@ CO_OP_TRANSLATOR_METADATA: നിങ്ങളുടെ ഡാറ്റ ടെക്സ്റ്റ് ആണെങ്കിൽ, X അക്ഷ上的 വാചകം വായനാസൗകര്യത്തിനായി കോണിൽ വയ്ക്കാം. [Matplotlib](https://matplotlib.org/stable/tutorials/toolkits/mplot3d.html) 3D പ്ലോട്ടിംഗ് നൽകുന്നു, നിങ്ങളുടെ ഡാറ്റ അത് പിന്തുണയ്ക്കുന്നുവെങ്കിൽ. സങ്കീർണ്ണമായ ഡാറ്റാ ദൃശ്യവത്കരണങ്ങൾ `mpl_toolkits.mplot3d` ഉപയോഗിച്ച് നിർമ്മിക്കാം. -![3d plots](../../../../translated_images/3d.0cec12bcc60f0ce7284c63baed1411a843e24716f7d7425de878715ebad54a15.ml.png) +![3d plots](../../../../translated_images/ml/3d.0cec12bcc60f0ce7284c63baed1411a843e24716f7d7425de878715ebad54a15.png) ## അനിമേഷൻ, 3D ചാർട്ട് പ്രദർശനം ഇന്നത്തെ മികച്ച ഡാറ്റാ ദൃശ്യവത്കരണങ്ങളിൽ ചിലത് അനിമേറ്റഡ് ആണ്. Shirley Wu D3 ഉപയോഗിച്ച് '[film flowers](http://bl.ocks.org/sxywu/raw/d612c6c653fb8b4d7ff3d422be164a5d/)' പോലുള്ള അത്ഭുതകരമായവ ഉണ്ടാക്കി, ഓരോ പൂവും ഒരു സിനിമയുടെ ദൃശ്യവത്കരണമാണ്. Guardian-നായി മറ്റൊരു ഉദാഹരണം 'bussed out' ആണ്, Greensock, D3 എന്നിവയുമായി സംയോജിപ്പിച്ച ഒരു ഇന്ററാക്ടീവ് അനുഭവം, സ്ക്രോൾടെല്ലിംഗ് ലേഖന ഫോർമാറ്റിൽ, NYC എങ്ങനെ നഗരത്തിലെ ഹോമ്ലെസ്സ് പ്രശ്നം ബസ്സിലൂടെ പുറത്തേക്ക് കൊണ്ടുപോകുന്നു എന്ന് കാണിക്കുന്നു. -![busing](../../../../translated_images/busing.7b9e3b41cd4b981c6d63922cd82004cc1cf18895155536c1d98fcc0999bdd23e.ml.png) +![busing](../../../../translated_images/ml/busing.7b9e3b41cd4b981c6d63922cd82004cc1cf18895155536c1d98fcc0999bdd23e.png) > "Bussed Out: How America Moves its Homeless" from [the Guardian](https://www.theguardian.com/us-news/ng-interactive/2017/dec/20/bussed-out-america-moves-homeless-people-country-study). Visualizations by Nadieh Bremer & Shirley Wu @@ -116,7 +116,7 @@ CO_OP_TRANSLATOR_METADATA: നിങ്ങൾ ഒരു വെബ് ആപ്പ് പൂർത്തിയാക്കും, ഇത് ഈ സോഷ്യൽ നെറ്റ്‌വർക്ക് അനിമേറ്റഡ് ദൃശ്യമായി കാണിക്കും. ഇത് Vue.js, D3 ഉപയോഗിച്ച് [നെറ്റ്‌വർക്ക് ദൃശ്യവത്കരണം](https://github.com/emiliorizzo/vue-d3-network) സൃഷ്ടിക്കാൻ നിർമ്മിച്ച ഒരു ലൈബ്രറി ഉപയോഗിക്കുന്നു. ആപ്പ് പ്രവർത്തിക്കുമ്പോൾ, സ്ക്രീനിൽ നോഡുകൾ നീക്കി ഡാറ്റ മാറ്റാം. -![liaisons](../../../../translated_images/liaisons.7b440b28f6d07ea430244fdf1fc4c64ff48f473f143b8e921846eda1c302aeba.ml.png) +![liaisons](../../../../translated_images/ml/liaisons.7b440b28f6d07ea430244fdf1fc4c64ff48f473f143b8e921846eda1c302aeba.png) ## പ്രോജക്ട്: D3.js ഉപയോഗിച്ച് ഒരു നെറ്റ്‌വർക്ക് കാണിക്കുന്ന ചാർട്ട് നിർമ്മിക്കുക diff --git a/translations/ml/3-Data-Visualization/R/09-visualization-quantities/README.md b/translations/ml/3-Data-Visualization/R/09-visualization-quantities/README.md index f3788eda..08747224 100644 --- a/translations/ml/3-Data-Visualization/R/09-visualization-quantities/README.md +++ b/translations/ml/3-Data-Visualization/R/09-visualization-quantities/README.md @@ -66,7 +66,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` ഇവിടെ, `ggplot2` പാക്കേജ് ഇൻസ്റ്റാൾ ചെയ്ത് `library("ggplot2")` കമാൻഡ് ഉപയോഗിച്ച് വർക്ക്‌സ്പേസിലേക്ക് ഇറക്കുമതി ചെയ്യുന്നു. ggplot-ൽ ഏതെങ്കിലും പ്ലോട്ട് ചെയ്യാൻ `ggplot()` ഫംഗ്ഷൻ ഉപയോഗിക്കുന്നു, ഡാറ്റാസെറ്റ്, x, y വേരിയബിളുകൾ ആട്രിബ്യൂട്ടുകളായി നിശ്ചയിക്കുന്നു. ഈ കേസിൽ, ലൈൻ പ്ലോട്ട് സൃഷ്ടിക്കാനാണ് ഉദ്ദേശം, അതിനാൽ `geom_line()` ഫംഗ്ഷൻ ഉപയോഗിക്കുന്നു. -![MaxWingspan-lineplot](../../../../../translated_images/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.ml.png) +![MaxWingspan-lineplot](../../../../../translated_images/ml/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.png) നിങ്ങൾ ഉടൻ ശ്രദ്ധിക്കുന്നതെന്താണ്? കുറഞ്ഞത് ഒരു ഔട്ട്‌ലൈയർ ഉണ്ടെന്ന് തോന്നുന്നു - അത്ര വലിയ വിങ്‌സ്പാൻ! 2000+ സെന്റീമീറ്റർ വിങ്‌സ്പാൻ 20 മീറ്ററിലധികമാണ് - മിന്നസോട്ടയിൽ പ്റ്റെറോഡാക്റ്റിലുകൾ നടക്കുകയാണോ? പരിശോധിക്കാം. @@ -84,7 +84,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` `theme`-ൽ ആംഗിൾ നിശ്ചയിച്ച് `xlab()`-ൽ x അക്ഷ ലേബൽ, `ylab()`-ൽ y അക്ഷ ലേബൽ നിശ്ചയിക്കുന്നു. `ggtitle()` ഗ്രാഫ്/പ്ലോട്ടിന് പേര് നൽകുന്നു. -![MaxWingspan-lineplot-improved](../../../../../translated_images/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.ml.png) +![MaxWingspan-lineplot-improved](../../../../../translated_images/ml/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.png) 45 ഡിഗ്രി ലേബലുകൾ തിരിയിച്ചിട്ടും വായിക്കാൻ വളരെ അധികം ലേബലുകൾ ഉണ്ട്. വ്യത്യസ്തമായ ഒരു തന്ത്രം പരീക്ഷിക്കാം: ആ ഔട്ട്‌ലൈയറുകൾക്ക് മാത്രമേ ലേബലുകൾ നൽകൂ, ലേബലുകൾ ചാർട്ടിനുള്ളിൽ സജ്ജമാക്കൂ. ലേബലിംഗിന് കൂടുതൽ സ്ഥലം നൽകാൻ സ്കാറ്റർ ചാർട്ട് ഉപയോഗിക്കാം: @@ -100,7 +100,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + നിങ്ങൾ എന്ത് കണ്ടെത്തുന്നു? -![MaxWingspan-scatterplot](../../../../../translated_images/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.ml.png) +![MaxWingspan-scatterplot](../../../../../translated_images/ml/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.png) ## നിങ്ങളുടെ ഡാറ്റ ഫിൽട്ടർ ചെയ്യുക @@ -119,7 +119,7 @@ ggplot(data=birds_filtered, aes(x=Name, y=MaxWingspan,group=1)) + ``` പുതിയ ഡാറ്റാഫ്രെയിം `birds_filtered` സൃഷ്ടിച്ച് സ്കാറ്റർ പ്ലോട്ട് ചെയ്തു. ഔട്ട്‌ലൈയറുകൾ ഫിൽട്ടർ ചെയ്തതിനാൽ, നിങ്ങളുടെ ഡാറ്റ കൂടുതൽ ഏകീകൃതവും മനസ്സിലാക്കാൻ എളുപ്പവുമാണ്. -![MaxWingspan-scatterplot-improved](../../../../../translated_images/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.ml.png) +![MaxWingspan-scatterplot-improved](../../../../../translated_images/ml/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.png) ഇപ്പോൾ വിങ്‌സ്പാൻ സംബന്ധിച്ച് കുറഞ്ഞത് ശുദ്ധമായ ഡാറ്റാസെറ്റ് ഉണ്ടെന്ന് കരുതി, ഈ പക്ഷികളെക്കുറിച്ച് കൂടുതൽ കണ്ടെത്താം. @@ -160,7 +160,7 @@ birds_filtered %>% group_by(Category) %>% ``` താഴെ കൊടുത്തിരിക്കുന്ന കോഡിൽ, ഡാറ്റ മാനിപ്പുലേറ്റ് ചെയ്ത് ഗ്രൂപ്പ് ചെയ്യാൻ സഹായിക്കുന്ന [dplyr](https://www.rdocumentation.org/packages/dplyr/versions/0.7.8)യും [lubridate](https://www.rdocumentation.org/packages/lubridate/versions/1.8.0)യും പാക്കേജുകൾ ഇൻസ്റ്റാൾ ചെയ്യുന്നു. ആദ്യം, പക്ഷികളുടെ `Category` പ്രകാരം ഡാറ്റ ഗ്രൂപ്പ് ചെയ്ത് `MinLength`, `MaxLength`, `MinBodyMass`, `MaxBodyMass`, `MinWingspan`, `MaxWingspan` കോളങ്ങൾ സംഗ്രഹിക്കുന്നു. പിന്നെ `ggplot2` ഉപയോഗിച്ച് ബാർ ചാർട്ട് പ്ലോട്ട് ചെയ്യുന്നു, വ്യത്യസ്ത വിഭാഗങ്ങൾക്ക് നിറങ്ങൾ നിശ്ചയിച്ച് ലേബലുകൾ ചേർക്കുന്നു. -![Stacked bar chart](../../../../../translated_images/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.ml.png) +![Stacked bar chart](../../../../../translated_images/ml/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.png) ഈ ബാർ ചാർട്ട് വായിക്കാൻ ബുദ്ധിമുട്ടാണ്, കാരണം ഗ്രൂപ്പ് ചെയ്യാത്ത ഡാറ്റ വളരെ കൂടുതലാണ്. നിങ്ങൾ പ്ലോട്ട് ചെയ്യാൻ ആഗ്രഹിക്കുന്ന ഡാറ്റ മാത്രം തിരഞ്ഞെടുക്കണം, അതിനാൽ പക്ഷികളുടെ നീളം അവരുടെ വിഭാഗം അടിസ്ഥാനമാക്കി നോക്കാം. @@ -175,7 +175,7 @@ ggplot(birds_count,aes(Category,n))+geom_bar(stat="identity")+coord_flip() ``` `Category` കോളത്തിൽ ഉള്ള വ്യത്യസ്ത മൂല്യങ്ങൾ എണ്ണുകയും അവ `birds_count` എന്ന പുതിയ ഡാറ്റാഫ്രെയിമിൽ ക്രമീകരിക്കുകയും ചെയ്യുന്നു. ഈ ക്രമീകരിച്ച ഡാറ്റയെ ഫാക്ടർ ലെവലിൽ ഉൾപ്പെടുത്തി ക്രമീകരിച്ച രീതിയിൽ പ്ലോട്ട് ചെയ്യുന്നു. `ggplot2` ഉപയോഗിച്ച് ബാർ ചാർട്ട് സൃഷ്ടിക്കുന്നു. `coord_flip()` ഉപയോഗിച്ച് ഹോരിസോണ്ടൽ ബാറുകൾ പ്ലോട്ട് ചെയ്യുന്നു. -![category-length](../../../../../translated_images/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.ml.png) +![category-length](../../../../../translated_images/ml/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.png) ഈ ബാർ ചാർട്ട് ഓരോ വിഭാഗത്തിലും പക്ഷികളുടെ എണ്ണം നല്ല രീതിയിൽ കാണിക്കുന്നു. ഒരു നിമിഷത്തിൽ, ഈ പ്രദേശത്തെ ഏറ്റവും വലിയ പക്ഷി എണ്ണം Ducks/Geese/Waterfowl വിഭാഗത്തിലാണ് എന്ന് കാണാം. മിന്നസോട്ട '10,000 തടാകങ്ങളുടെ നാട്' ആകുന്നതിനാൽ ഇത് അത്ഭുതകരമല്ല! @@ -198,7 +198,7 @@ ggplot(birds_grouped,aes(Category,MaxLength))+geom_bar(stat="identity")+coord_fl ``` `birds_filtered` ഡാറ്റ `Category` പ്രകാരം ഗ്രൂപ്പ് ചെയ്ത് ബാർ ഗ്രാഫ് പ്ലോട്ട് ചെയ്യുന്നു. -![comparing data](../../../../../translated_images/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.ml.png) +![comparing data](../../../../../translated_images/ml/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.png) ഇവിടെ അത്ഭുതകരമായ ഒന്നുമില്ല: ഹമ്മിംഗ്‌ബർഡുകൾക്ക് പെലിക്കാനുകളോ ഗീസുകളോ അപേക്ഷിച്ച് ഏറ്റവും കുറഞ്ഞ MaxLength ഉണ്ട്. ഡാറ്റ ലജിക്കൽ ആകുമ്പോൾ നല്ലതാണ്! @@ -210,7 +210,7 @@ ggplot(data=birds_grouped, aes(x=Category)) + geom_bar(aes(y=MinLength), stat="identity", position="identity", fill='orange')+ coord_flip() ``` -![super-imposed values](../../../../../translated_images/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.ml.png) +![super-imposed values](../../../../../translated_images/ml/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.png) ## 🚀 ചലഞ്ച് diff --git a/translations/ml/3-Data-Visualization/R/10-visualization-distributions/README.md b/translations/ml/3-Data-Visualization/R/10-visualization-distributions/README.md index 954b5381..b0f89a79 100644 --- a/translations/ml/3-Data-Visualization/R/10-visualization-distributions/README.md +++ b/translations/ml/3-Data-Visualization/R/10-visualization-distributions/README.md @@ -45,7 +45,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + geom_point() + ggtitle("Max Length per order") + coord_flip() ``` -![max length per order](../../../../../translated_images/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.ml.png) +![max length per order](../../../../../translated_images/ml/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.png) ഇത് ഓരോ പക്ഷി ഓർഡറിനും ശരീര നീളത്തിന്റെ പൊതുവായ വിതരണത്തിന്റെ ഒരു അവലോകനമാണ് നൽകുന്നത്, പക്ഷേ യഥാർത്ഥ വിതരണങ്ങൾ പ്രദർശിപ്പിക്കാൻ ഇത് ഏറ്റവും അനുയോജ്യമായ മാർഗം അല്ല. ആ ജോലി സാധാരണയായി ഹിസ്റ്റോഗ്രാം സൃഷ്ടിച്ച് കൈകാര്യം ചെയ്യുന്നു. ## ഹിസ്റ്റോഗ്രാമുകളുമായി പ്രവർത്തിക്കൽ @@ -56,7 +56,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=10)+ylab('Frequency') ``` -![distribution over entire dataset](../../../../../translated_images/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.ml.png) +![distribution over entire dataset](../../../../../translated_images/ml/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.png) നിങ്ങൾക്ക് കാണാമല്ലോ, ഈ ഡാറ്റാസെറ്റിലെ 400+ പക്ഷികളിൽ ഭൂരിഭാഗവും അവരുടെ പരമാവധി ശരീരഭാരം 2000-ൽ താഴെ വരുന്ന പരിധിയിലാണ്. `bins` പാരാമീറ്റർ ഉയർന്ന ഒരു സംഖ്യയാക്കി, ഉദാഹരണത്തിന് 30 ആക്കി, ഡാറ്റയിൽ കൂടുതൽ洞察ം നേടാം: @@ -64,7 +64,7 @@ ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![distribution-30bins](../../../../../translated_images/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.ml.png) +![distribution-30bins](../../../../../translated_images/ml/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.png) ഈ ചാർട്ട് വിതരണത്തെ കുറച്ച് കൂടുതൽ സൂക്ഷ്മമായി കാണിക്കുന്നു. ഇടതുവശത്തേക്ക് കുറച്ച് കുറവുള്ള ചാർട്ട് സൃഷ്ടിക്കാൻ, നിങ്ങൾക്ക് ഒരു നിശ്ചിത പരിധിയിലുള്ള ഡാറ്റ മാത്രം തിരഞ്ഞെടുക്കണം: @@ -76,7 +76,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![filtered histogram](../../../../../translated_images/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.ml.png) +![filtered histogram](../../../../../translated_images/ml/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.png) ✅ മറ്റ് ഫിൽട്ടറുകളും ഡാറ്റ പോയിന്റുകളും പരീക്ഷിക്കുക. ഡാറ്റയുടെ മുഴുവൻ വിതരണവും കാണാൻ, `['MaxBodyMass']` ഫിൽട്ടർ നീക്കം ചെയ്ത് ലേബൽ ചെയ്ത വിതരണങ്ങൾ കാണിക്കുക. @@ -90,7 +90,7 @@ ggplot(data=birds_filtered_1, aes(x=MaxBodyMass, y=MaxLength) ) + ``` ഈ രണ്ട് ഘടകങ്ങൾ തമ്മിൽ പ്രതീക്ഷിക്കപ്പെട്ട ബന്ധം ഒരു പ്രതീക്ഷിച്ച അക്ഷരേഖയിൽ കാണപ്പെടുന്നു, പ്രത്യേകിച്ച് ശക്തമായ ഒരു സമന്വയ ബിന്ദു: -![2d plot](../../../../../translated_images/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.ml.png) +![2d plot](../../../../../translated_images/ml/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.png) ഹിസ്റ്റോഗ്രാമുകൾ സംഖ്യാത്മക ഡാറ്റയ്ക്ക് സാധാരണയായി നല്ല രീതിയിൽ പ്രവർത്തിക്കുന്നു. എന്നാൽ നിങ്ങൾക്ക് ടെക്സ്റ്റ് ഡാറ്റയുടെ അടിസ്ഥാനത്തിൽ വിതരണങ്ങൾ കാണേണ്ടതുണ്ടെങ്കിൽ? @@ -122,7 +122,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + scale_fill_manual(name="Conservation Status",values=c("red","green","blue","pink"),labels=c("Endangered","Near Threathened","Vulnerable","Least Concern")) ``` -![wingspan and conservation collation](../../../../../translated_images/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.ml.png) +![wingspan and conservation collation](../../../../../translated_images/ml/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.png) കുറഞ്ഞ വിങ്‌സ്‌പാനും സംരക്ഷണ നിലയ്ക്കും ഇടയിൽ നല്ല ബന്ധമില്ലെന്ന് തോന്നുന്നു. ഈ രീതിയിൽ ഡാറ്റാസെറ്റിലെ മറ്റ് ഘടകങ്ങളും പരീക്ഷിക്കുക. നിങ്ങൾക്ക് ഏതെങ്കിലും ബന്ധം കണ്ടെത്താമോ? @@ -136,7 +136,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + geom_density() ``` -![density plot](../../../../../translated_images/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.ml.png) +![density plot](../../../../../translated_images/ml/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.png) മുൻപത്തെ കുറഞ്ഞ വിങ്‌സ്‌പാൻ ഡാറ്റയ്ക്കുള്ള പ്ലോട്ടിനെ പോലെ ഇത് കാണാം; ഇത് കുറച്ച് സ്മൂത്താണ്. നിങ്ങൾ രണ്ടാമത്തെ ചാർട്ടിൽ നിർമ്മിച്ച ജാഗ്ഗഡ് MaxBodyMass ലൈനിനെ സ്മൂത്ത് ആക്കാൻ ആഗ്രഹിക്കുന്നുവെങ്കിൽ, ഈ രീതിയിൽ പുനഃസൃഷ്ടിച്ച് വളരെ നന്നായി സ്മൂത്ത് ആക്കാം: @@ -144,7 +144,7 @@ ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density() ``` -![bodymass density](../../../../../translated_images/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.ml.png) +![bodymass density](../../../../../translated_images/ml/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.png) കുറച്ച് സ്മൂത്ത്, എന്നാൽ വളരെ സ്മൂത്ത് അല്ലാത്ത ഒരു ലൈനിനായി, `adjust` പാരാമീറ്റർ എഡിറ്റ് ചെയ്യുക: @@ -152,7 +152,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density(adjust = 1/5) ``` -![less smooth bodymass](../../../../../translated_images/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.ml.png) +![less smooth bodymass](../../../../../translated_images/ml/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.png) ✅ ഈ തരം പ്ലോട്ടിനുള്ള പാരാമീറ്ററുകൾക്കുറിച്ച് വായിച്ച് പരീക്ഷണങ്ങൾ നടത്തുക! @@ -162,7 +162,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + ggplot(data=birds_filtered_1,aes(x = MaxBodyMass, fill = Order)) + geom_density(alpha=0.5) ``` -![bodymass per order](../../../../../translated_images/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.ml.png) +![bodymass per order](../../../../../translated_images/ml/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.png) ## 🚀 വെല്ലുവിളി diff --git a/translations/ml/3-Data-Visualization/R/11-visualization-proportions/README.md b/translations/ml/3-Data-Visualization/R/11-visualization-proportions/README.md index ba904e05..b3ec0024 100644 --- a/translations/ml/3-Data-Visualization/R/11-visualization-proportions/README.md +++ b/translations/ml/3-Data-Visualization/R/11-visualization-proportions/README.md @@ -97,7 +97,7 @@ pie(grouped$count,grouped$class, main="Edible?") ``` ഇവിടെ, ഈ ഡാറ്റയുടെ അനുപാതങ്ങൾ ഈ രണ്ട് മഷ്‌റൂം ക്ലാസുകൾ അനുസരിച്ച് കാണിക്കുന്ന ഒരു പൈ ചാർട്ട്. ലേബലുകളുടെ ക്രമം ശരിയാണെന്ന് ഉറപ്പാക്കുന്നത് വളരെ പ്രധാനമാണ്, പ്രത്യേകിച്ച് ഇവിടെ, അതിനാൽ ലേബൽ അറേ എങ്ങനെ നിർമ്മിക്കപ്പെടുന്നു എന്ന് പരിശോധിക്കുക! -![pie chart](../../../../../translated_images/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.ml.png) +![pie chart](../../../../../translated_images/ml/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.png) ## ഡോണട്ടുകൾ! @@ -132,7 +132,7 @@ library(webr) PieDonut(habitat, aes(habitat, count=count)) ``` -![donut chart](../../../../../translated_images/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.ml.png) +![donut chart](../../../../../translated_images/ml/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.png) ഈ കോഡ് രണ്ട് ലൈബ്രറികൾ ഉപയോഗിക്കുന്നു - ggplot2, webr. webr ലൈബ്രറിയുടെ PieDonut ഫംഗ്ഷൻ ഉപയോഗിച്ച്, ഡോണട്ട് ചാർട്ട് എളുപ്പത്തിൽ സൃഷ്ടിക്കാം! @@ -169,7 +169,7 @@ waffle((cap_color$count/10), rows = 7, title = "Waffle Chart")+scale_fill_manual വാഫിൾ ചാർട്ട് ഉപയോഗിച്ച്, ഈ മഷ്‌റൂം dataset-ലെ ക്യാപ് നിറങ്ങളുടെ അനുപാതങ്ങൾ വ്യക്തമായി കാണാം. രസകരമായി, പച്ച ക്യാപ് ഉള്ള മഷ്‌റൂമുകൾ വളരെ കൂടുതലാണ്! -![waffle chart](../../../../../translated_images/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.ml.png) +![waffle chart](../../../../../translated_images/ml/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.png) ഈ പാഠത്തിൽ, നിങ്ങൾക്ക് അനുപാതങ്ങൾ ദൃശ്യവൽക്കരിക്കുന്ന മൂന്ന് മാർഗങ്ങൾ പഠിച്ചു. ആദ്യം, ഡാറ്റയെ വിഭാഗങ്ങളായി ഗ്രൂപ്പ് ചെയ്യണം, പിന്നെ ഡാറ്റ പ്രദർശിപ്പിക്കാൻ ഏറ്റവും നല്ല മാർഗം - പൈ, ഡോണട്ട്, അല്ലെങ്കിൽ വാഫിൾ - തിരഞ്ഞെടുക്കണം. എല്ലാം രുചികരവും ഉപയോക്താവിന് dataset-ന്റെ ഉടൻ ഒരു ദൃശ്യസംഗ്രഹം നൽകുന്നതുമാണ്. diff --git a/translations/ml/3-Data-Visualization/R/12-visualization-relationships/README.md b/translations/ml/3-Data-Visualization/R/12-visualization-relationships/README.md index f5fa8c1d..e3d926cd 100644 --- a/translations/ml/3-Data-Visualization/R/12-visualization-relationships/README.md +++ b/translations/ml/3-Data-Visualization/R/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ library(ggplot2) ggplot(honey, aes(x = priceperlb, y = state)) + geom_point(colour = "blue") ``` -![scatterplot 1](../../../../../translated_images/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.ml.png) +![scatterplot 1](../../../../../translated_images/ml/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.png) ഇപ്പോൾ, വർഷം തോറും വില എങ്ങനെ മാറിയെന്ന് കാണിക്കാൻ തേൻ നിറത്തിലുള്ള കളർ സ്കീം ഉപയോഗിച്ച് സമാന ഡാറ്റ കാണിക്കുക. വർഷം തോറും മാറ്റം കാണിക്കാൻ 'scale_color_gradientn' പാരാമീറ്റർ ചേർക്കാം: @@ -61,7 +61,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ggplot(honey, aes(x = priceperlb, y = state, color=year)) + geom_point()+scale_color_gradientn(colours = colorspace::heat_hcl(7)) ``` -![scatterplot 2](../../../../../translated_images/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.ml.png) +![scatterplot 2](../../../../../translated_images/ml/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.png) ഈ കളർ സ്കീം മാറ്റത്തോടെ, പൗണ്ട് പ്രതി തേൻ വില വർഷം തോറും ശക്തമായി ഉയരുന്നുവെന്ന് വ്യക്തമായി കാണാം. ഡാറ്റയിൽ ഒരു സാമ്പിൾ സെറ്റ് പരിശോധിച്ചാൽ (ഉദാഹരണത്തിന്, അരിസോണ), വില വർഷം തോറും ഉയരുന്ന ഒരു പാറ്റേൺ കാണാം, കുറച്ച് ഒഴിവുകൾ കൂടെ: @@ -92,7 +92,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ``` ഡോട്ടുകളുടെ വലിപ്പം ക്രമാനുസൃതമായി വർദ്ധിക്കുന്നതായി കാണാം. -![scatterplot 3](../../../../../translated_images/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.ml.png) +![scatterplot 3](../../../../../translated_images/ml/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.png) ഇത് സപ്ലൈ-ഡിമാൻഡ് സാദ്ധ്യതയുള്ള ഒരു ലളിതമായ കേസ് ആണോ? കാലാവസ്ഥ മാറ്റം, കോളനി തകർച്ച തുടങ്ങിയ ഘടകങ്ങൾ കാരണം, വർഷം തോറും വാങ്ങാനുളള തേൻ കുറയുന്നുണ്ടോ, അതുകൊണ്ട് വില ഉയരുന്നുണ്ടോ? @@ -107,7 +107,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab ``` ഉത്തരം: ചില ഒഴിവുകൾ കൂടെ, 2003-ൽ ചില വ്യത്യാസങ്ങൾ കാണാം: -![line chart 1](../../../../../translated_images/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.ml.png) +![line chart 1](../../../../../translated_images/ml/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.png) ചോദ്യം: 2003-ൽ തേൻ സപ്ലൈയിൽ ഒരു പീക്ക് കാണാമോ? മൊത്തം ഉത്പാദനം വർഷം തോറും നോക്കിയാൽ? @@ -115,7 +115,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab qplot(honey$year,honey$totalprod, geom='smooth', span =0.5, xlab = "year",ylab = "totalprod") ``` -![line chart 2](../../../../../translated_images/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.ml.png) +![line chart 2](../../../../../translated_images/ml/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.png) ഉത്തരം: അത്രയുമല്ല. മൊത്തം ഉത്പാദനം ആ വർഷം വർദ്ധിച്ചിട്ടുണ്ടെന്ന് തോന്നുന്നു, എങ്കിലും സാധാരണയായി ഈ വർഷങ്ങളിൽ തേൻ ഉത്പാദനം കുറയുകയാണ്. @@ -135,7 +135,7 @@ ggplot(honey, aes(x=yieldpercol, y = numcol,group = 1)) + ``` ഈ ദൃശ്യവൽക്കരണത്തിൽ, കോളനി പ്രതി ഉത്പാദനവും കോളനികളുടെ എണ്ണവും വർഷം തോറും, 3 കോളങ്ങൾ വച്ച് വച്ച് താരതമ്യം ചെയ്യാം: -![facet grid](../../../../../translated_images/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.ml.png) +![facet grid](../../../../../translated_images/ml/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.png) ഈ ഡാറ്റാസെറ്റിൽ, കോളനികളുടെ എണ്ണം, അവയുടെ ഉത്പാദനം വർഷം തോറും, സംസ്ഥാനങ്ങൾ അനുസരിച്ച് പ്രത്യേകമായ ഒന്നും കാണാനില്ല. ഈ രണ്ട് വേരിയബിളുകൾ തമ്മിൽ സഹബന്ധം കണ്ടെത്താൻ മറ്റൊരു മാർഗ്ഗമുണ്ടോ? @@ -152,7 +152,7 @@ plot(honey$year, honey$yieldpercol, pch = 17, col = 3, axis(side = 4, at = pretty(range(y2))) mtext("colony yield", side = 4, line = 3) ``` -![superimposed plots](../../../../../translated_images/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.ml.png) +![superimposed plots](../../../../../translated_images/ml/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.png) 2003-ൽ പ്രത്യേകമായി ഒന്നും കാണാനില്ലെങ്കിലും, ഈ പാഠം ഒരു സന്തോഷകരമായ നോട്ടിൽ അവസാനിപ്പിക്കാൻ സഹായിക്കുന്നു: മൊത്തം കോളനികളുടെ എണ്ണം കുറയുന്നുണ്ടെങ്കിലും, അവയുടെ എണ്ണം സ്ഥിരതയുള്ളതും, കോളനി പ്രതി ഉത്പാദനം കുറയുന്നുണ്ടെങ്കിലും. diff --git a/translations/ml/3-Data-Visualization/R/13-meaningful-vizualizations/README.md b/translations/ml/3-Data-Visualization/R/13-meaningful-vizualizations/README.md index 4259a8d0..f2a96fbb 100644 --- a/translations/ml/3-Data-Visualization/R/13-meaningful-vizualizations/README.md +++ b/translations/ml/3-Data-Visualization/R/13-meaningful-vizualizations/README.md @@ -47,25 +47,25 @@ CO_OP_TRANSLATOR_METADATA: ഒരു ഡാറ്റ സയന്റിസ്റ്റ് ശരിയായ ഡാറ്റയ്ക്ക് ശരിയായ ചാർട്ട് തിരഞ്ഞെടുക്കാൻ ശ്രദ്ധിച്ചാലും, ഡാറ്റയെ തന്നെ തകർക്കുന്ന വിധത്തിൽ ഒരു കാര്യം തെളിയിക്കാൻ ഡാറ്റ പ്രദർശിപ്പിക്കുന്ന നിരവധി മാർഗ്ഗങ്ങൾ ഉണ്ട്. വഞ്ചനാപരമായ ചാർട്ടുകളും ഇൻഫോഗ്രാഫിക്സും നിരവധി ഉദാഹരണങ്ങളുണ്ട്! -[![How Charts Lie by Alberto Cairo](../../../../../translated_images/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.ml.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "How charts lie") +[![How Charts Lie by Alberto Cairo](../../../../../translated_images/ml/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "How charts lie") > 🎥 വഞ്ചനാപരമായ ചാർട്ടുകൾക്കുറിച്ചുള്ള കോൺഫറൻസ് ടോക്കിനായി മുകളിൽ ചിത്രത്തിൽ ക്ലിക്ക് ചെയ്യുക ഈ ചാർട്ട് X അക്ഷം മറിച്ച് സത്യം മറിച്ച് കാണിക്കുന്നു, തീയതി അടിസ്ഥാനമാക്കി: -![bad chart 1](../../../../../translated_images/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.ml.png) +![bad chart 1](../../../../../translated_images/ml/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.png) [ഈ ചാർട്ട്](https://media.firstcoastnews.com/assets/WTLV/images/170ae16f-4643-438f-b689-50d66ca6a8d8/170ae16f-4643-438f-b689-50d66ca6a8d8_1140x641.jpg) കൂടുതൽ വഞ്ചനാപരമാണ്, കാരണം കണ്ണ് വലത്തേക്ക് ആകർഷിക്കപ്പെടുന്നു, കാലക്രമത്തിൽ COVID കേസുകൾ വിവിധ കൗണ്ടികളിൽ കുറയുകയാണെന്ന് നിഗമനം ചെയ്യാൻ. എന്നാൽ, തീയതികൾ സൂക്ഷ്മമായി നോക്കിയാൽ, അവ വഞ്ചനാപരമായ താഴ്ന്ന ട്രെൻഡ് നൽകാൻ പുനഃക്രമീകരിച്ചിട്ടുള്ളതായി കാണാം. -![bad chart 2](../../../../../translated_images/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.ml.jpg) +![bad chart 2](../../../../../translated_images/ml/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.jpg) ഈ പ്രശസ്ത ഉദാഹരണം നിറവും മറിച്ച Y അക്ഷവും ഉപയോഗിച്ച് വഞ്ചന ചെയ്യുന്നു: തോക്കു-സഹായക നിയമം പാസായതിന് ശേഷം തോക്ക് മരണങ്ങൾ ഉയർന്നതായി നിഗമനം ചെയ്യേണ്ടതിനു പകരം, കണ്ണ് മറിച്ച് സത്യം തെറ്റായി കാണുന്നു: -![bad chart 3](../../../../../translated_images/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.ml.jpg) +![bad chart 3](../../../../../translated_images/ml/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.jpg) ഈ വിചിത്രമായ ചാർട്ട് അനുപാതം എങ്ങനെ മാനിപ്പുലേറ്റ് ചെയ്യാമെന്ന് ഹാസ്യകരമായി കാണിക്കുന്നു: -![bad chart 4](../../../../../translated_images/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.ml.jpg) +![bad chart 4](../../../../../translated_images/ml/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.jpg) തുല്യമായവയെ താരതമ്യം ചെയ്യുന്നത് മറ്റൊരു സംശയാസ്പദമായ തന്ത്രമാണ്. 'സ്പ്യൂറിയസ് കോറിലേഷൻസ്' എന്ന വിഷയത്തെക്കുറിച്ച് മുഴുവൻ വെബ്‌സൈറ്റ് [wonderful web site](https://tylervigen.com/spurious-correlations) ഉണ്ട്, മെയ്ൻയിലെ വിവാഹമോചന നിരക്കും മാർഗറിൻ ഉപഭോഗവും തമ്മിലുള്ള 'തथ്യങ്ങൾ' correlated ആയി പ്രദർശിപ്പിക്കുന്നു. Reddit ഗ്രൂപ്പ് ഡാറ്റയുടെ [അസൗന്ദര്യ ഉപയോഗങ്ങൾ](https://www.reddit.com/r/dataisugly/top/?t=all) ശേഖരിക്കുന്നു. @@ -100,13 +100,13 @@ CO_OP_TRANSLATOR_METADATA: നിങ്ങളുടെ ഡാറ്റ ടെക്സ്റ്റ് ആണെങ്കിൽ, X അക്ഷ上的 വാചകം വായനാസൗകര്യത്തിനായി കോണിൽ കാണിക്കാം. [plot3D](https://cran.r-project.org/web/packages/plot3D/index.html) 3D പ്ലോട്ടിംഗ് നൽകുന്നു, നിങ്ങളുടെ ഡാറ്റ അത് പിന്തുണച്ചാൽ. അതുപയോഗിച്ച് സങ്കീർണ്ണമായ ഡാറ്റാ ദൃശ്യവത്കരണങ്ങൾ നിർമ്മിക്കാം. -![3d plots](../../../../../translated_images/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.ml.png) +![3d plots](../../../../../translated_images/ml/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.png) ## അനിമേഷൻ, 3D ചാർട്ട് പ്രദർശനം ഇന്നത്തെ മികച്ച ഡാറ്റാ ദൃശ്യവത്കരണങ്ങളിൽ ചിലത് അനിമേറ്റഡ് ആണ്. Shirley Wu D3 ഉപയോഗിച്ച് '[film flowers](http://bl.ocks.org/sxywu/raw/d612c6c653fb8b4d7ff3d422be164a5d/)' പോലുള്ള അത്ഭുതകരമായവ ഉണ്ടാക്കി, ഓരോ പൂവും ഒരു സിനിമയുടെ ദൃശ്യവത്കരണമാണ്. Guardian-നായി മറ്റൊരു ഉദാഹരണം 'bussed out' ആണ്, Greensock, D3 എന്നിവയുമായി സംയോജിപ്പിച്ച ഇന്ററാക്ടീവ് അനുഭവം, സ്ക്രോൾടെല്ലിംഗ് ലേഖന ഫോർമാറ്റിൽ NYC എങ്ങനെ നഗരത്തിലെ ഹോമ്ലെസ്സ് പ്രശ്നം ബസ്സിലൂടെ പുറത്തേക്ക് കൊണ്ടുപോകുന്നു എന്ന് കാണിക്കുന്നു. -![busing](../../../../../translated_images/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.ml.png) +![busing](../../../../../translated_images/ml/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.png) > "Bussed Out: How America Moves its Homeless" from [the Guardian](https://www.theguardian.com/us-news/ng-interactive/2017/dec/20/bussed-out-america-moves-homeless-people-country-study). Visualizations by Nadieh Bremer & Shirley Wu @@ -116,7 +116,7 @@ CO_OP_TRANSLATOR_METADATA: നിങ്ങൾ ഒരു വെബ് ആപ്പ് പൂർത്തിയാക്കും, ഇത് ഈ സോഷ്യൽ നെറ്റ്‌വർക്ക് അനിമേറ്റഡ് ദൃശ്യവത്കരണം പ്രദർശിപ്പിക്കും. ഇത് Vue.js, D3 ഉപയോഗിച്ച് [നെറ്റ്‌വർക്ക് ദൃശ്യവത്കരണം](https://github.com/emiliorizzo/vue-d3-network) സൃഷ്ടിക്കാൻ നിർമ്മിച്ച ഒരു ലൈബ്രറി ഉപയോഗിക്കുന്നു. ആപ്പ് പ്രവർത്തിക്കുമ്പോൾ, സ്ക്രീനിൽ നോഡുകൾ നീക്കി ഡാറ്റ മാറ്റാം. -![liaisons](../../../../../translated_images/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.ml.png) +![liaisons](../../../../../translated_images/ml/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.png) ## പ്രോജക്ട്: D3.js ഉപയോഗിച്ച് ഒരു നെറ്റ്‌വർക്ക് കാണിക്കുന്ന ചാർട്ട് നിർമ്മിക്കുക diff --git a/translations/ml/3-Data-Visualization/README.md b/translations/ml/3-Data-Visualization/README.md index 88110985..2cc22a7e 100644 --- a/translations/ml/3-Data-Visualization/README.md +++ b/translations/ml/3-Data-Visualization/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # ദൃശ്യവത്കരണങ്ങൾ -![a bee on a lavender flower](../../../translated_images/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.ml.jpg) +![a bee on a lavender flower](../../../translated_images/ml/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.jpg) > ഫോട്ടോ ജെന്ന ലി യുടെ അൺസ്പ്ലാഷിൽ നിന്നാണ് diff --git a/translations/ml/4-Data-Science-Lifecycle/14-Introduction/README.md b/translations/ml/4-Data-Science-Lifecycle/14-Introduction/README.md index 7eed28bb..0b57cdfb 100644 --- a/translations/ml/4-Data-Science-Lifecycle/14-Introduction/README.md +++ b/translations/ml/4-Data-Science-Lifecycle/14-Introduction/README.md @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: ഈ പാഠം ലൈഫ്‌സൈക്കിളിന്റെ 3 ഭാഗങ്ങളായ പിടിച്ചെടുക്കൽ, പ്രോസസ്സിംഗ്, പരിപാലനം എന്നിവയിൽ കേന്ദ്രീകരിക്കുന്നു. -![Diagram of the data science lifecycle](../../../../translated_images/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.ml.jpg) +![Diagram of the data science lifecycle](../../../../translated_images/ml/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.jpg) > ഫോട്ടോ: [ബർക്ക്ലി സ്കൂൾ ഓഫ് ഇൻഫർമേഷൻ](https://ischoolonline.berkeley.edu/data-science/what-is-data-science/) ## പിടിച്ചെടുക്കൽ @@ -97,7 +97,7 @@ CO_OP_TRANSLATOR_METADATA: |ടീം ഡാറ്റാ സയൻസ് പ്രോസസ് (TDSP)|ക്രോസ്-ഇൻഡസ്ട്രി സ്റ്റാൻഡേർഡ് പ്രോസസ് ഫോർ ഡാറ്റ മൈനിംഗ് (CRISP-DM)| |--|--| -|![Team Data Science Lifecycle](../../../../translated_images/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.ml.png) | ![Data Science Process Alliance Image](../../../../translated_images/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.ml.png) | +|![Team Data Science Lifecycle](../../../../translated_images/ml/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.png) | ![Data Science Process Alliance Image](../../../../translated_images/ml/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.png) | | ചിത്രം: [Microsoft](https://docs.microsoft.comazure/architecture/data-science-process/lifecycle) | ചിത്രം: [Data Science Process Alliance](https://www.datascience-pm.com/crisp-dm-2/) | ## [പോസ്റ്റ്-ലെക്ചർ ക്വിസ്](https://ff-quizzes.netlify.app/en/ds/quiz/27) diff --git a/translations/ml/4-Data-Science-Lifecycle/README.md b/translations/ml/4-Data-Science-Lifecycle/README.md index 1c2294e2..dfa27b09 100644 --- a/translations/ml/4-Data-Science-Lifecycle/README.md +++ b/translations/ml/4-Data-Science-Lifecycle/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # ഡാറ്റ സയൻസ് ലൈഫ്‌സൈക്കിൾ -![communication](../../../translated_images/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.ml.jpg) +![communication](../../../translated_images/ml/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.jpg) > ഫോട്ടോ ഹെഡ്‌വേ യുടെ അൺസ്പ്ലാഷിൽ ഈ പാഠങ്ങളിൽ, ഡാറ്റ സയൻസ് ലൈഫ്‌സൈക്കിളിന്റെ ചില ഭാഗങ്ങൾ, ഡാറ്റ ചുറ്റിപ്പറ്റിയുള്ള വിശകലനവും ആശയവിനിമയവും ഉൾപ്പെടെ, നിങ്ങൾ അന്വേഷിക്കും. diff --git a/translations/ml/5-Data-Science-In-Cloud/18-Low-Code/README.md b/translations/ml/5-Data-Science-In-Cloud/18-Low-Code/README.md index fdf6ba03..5be0ce4d 100644 --- a/translations/ml/5-Data-Science-In-Cloud/18-Low-Code/README.md +++ b/translations/ml/5-Data-Science-In-Cloud/18-Low-Code/README.md @@ -62,7 +62,7 @@ CO_OP_TRANSLATOR_METADATA: പ്രോജക്ടുകൾ നിർമ്മിച്ച് പരീക്ഷണങ്ങൾ നടത്തുന്നത് നിങ്ങളുടെ കഴിവുകളും അറിവുകളും പരീക്ഷിക്കാൻ ഏറ്റവും നല്ല മാർഗമാണ്. ഈ പാഠത്തിൽ, ആസ്യൂർ ML സ്റ്റുഡിയോയിൽ ഹാർട്ട് ഫെയില്യർ ആക്രമണങ്ങൾ പ്രവചിക്കുന്ന ഡാറ്റാ സയൻസ് പ്രോജക്ട് നിർമ്മിക്കുന്ന രണ്ട് വ്യത്യസ്ത മാർഗങ്ങൾ പരിശോധിക്കാം: ലോ കോഡ്/നോ കോഡ് വഴി, ആസ്യൂർ ML SDK വഴി, താഴെ കാണുന്ന സ്കീമ പ്രകാരം: -![project-schema](../../../../translated_images/project-schema.736f6e403f321eb48d10242b3f4334dc6ccf0eabef8ff87daf52b89781389fcb.ml.png) +![project-schema](../../../../translated_images/ml/project-schema.736f6e403f321eb48d10242b3f4334dc6ccf0eabef8ff87daf52b89781389fcb.png) ഓരോ മാർഗത്തിനും സ്വന്തം ഗുണങ്ങളും ദോഷങ്ങളും ഉണ്ട്. ലോ കോഡ്/നോ കോഡ് വഴി GUI (ഗ്രാഫിക്കൽ യൂസർ ഇന്റർഫേസ്) ഉപയോഗിച്ച് ആരംഭിക്കാൻ എളുപ്പമാണ്, കോഡ് അറിവ് ആവശ്യമില്ല. ഈ രീതിയിൽ പ്രോജക്ടിന്റെ സാധുത വേഗത്തിൽ പരിശോധിക്കാനും POC (പ്രൂഫ് ഓഫ് കോൺസെപ്റ്റ്) സൃഷ്ടിക്കാനും കഴിയും. എന്നാൽ, പ്രോജക്ട് വളരുമ്പോൾ പ്രൊഡക്ഷൻ റെഡിയാക്കേണ്ടതുണ്ടെങ്കിൽ GUI വഴി റിസോഴ്‌സുകൾ സൃഷ്ടിക്കുന്നത് പ്രായോഗികമല്ല. റിസോഴ്‌സുകൾ സൃഷ്ടിക്കുന്നതിൽ നിന്നും മോഡൽ ഡിപ്ലോയ്മെന്റിലേക്കുള്ള എല്ലാ കാര്യങ്ങളും പ്രോഗ്രാമാറ്റിക്കായി ഓട്ടോമേറ്റ് ചെയ്യേണ്ടതുണ്ട്. ആസ്യൂർ ML SDK ഉപയോഗിക്കുന്നതിന്റെ പ്രാധാന്യം ഇവിടെ വരുന്നു. @@ -115,15 +115,15 @@ CO_OP_TRANSLATOR_METADATA: 1. നിങ്ങളുടെ ആസ്യൂർ സബ്സ്ക്രിപ്ഷനുമായി ബന്ധപ്പെട്ട മൈക്രോസോഫ്റ്റ് ക്രെഡൻഷ്യലുകൾ ഉപയോഗിച്ച് [ആസ്യൂർ പോർട്ടലിൽ](https://ms.portal.azure.com/) സൈൻ ഇൻ ചെയ്യുക. 2. **＋Create a resource** തിരഞ്ഞെടുക്കുക - ![workspace-1](../../../../translated_images/workspace-1.ac8694d60b073ed1ae8333d71244dc8a9b3e439d54593724f98f1beefdd27b08.ml.png) + ![workspace-1](../../../../translated_images/ml/workspace-1.ac8694d60b073ed1ae8333d71244dc8a9b3e439d54593724f98f1beefdd27b08.png) മെഷീൻ ലേണിംഗ് തിരയുക, മെഷീൻ ലേണിംഗ് ടൈൽ തിരഞ്ഞെടുക്കുക - ![workspace-2](../../../../translated_images/workspace-2.ae7c486db8796147075e4a56566aa819827dd6c4c8d18d64590317c3be625f17.ml.png) + ![workspace-2](../../../../translated_images/ml/workspace-2.ae7c486db8796147075e4a56566aa819827dd6c4c8d18d64590317c3be625f17.png) സൃഷ്ടിക്കുക ബട്ടൺ ക്ലിക്ക് ചെയ്യുക - ![workspace-3](../../../../translated_images/workspace-3.398ca4a5858132cce584db9df10c5a011cd9075eb182e647a77d5cac01771eea.ml.png) + ![workspace-3](../../../../translated_images/ml/workspace-3.398ca4a5858132cce584db9df10c5a011cd9075eb182e647a77d5cac01771eea.png) ക്രമീകരണങ്ങൾ താഴെപോലെ പൂരിപ്പിക്കുക: - സബ്സ്ക്രിപ്ഷൻ: നിങ്ങളുടെ ആസ്യൂർ സബ്സ്ക്രിപ്ഷൻ @@ -135,17 +135,17 @@ CO_OP_TRANSLATOR_METADATA: - അപ്ലിക്കേഷൻ ഇൻസൈറ്റ്സ്: നിങ്ങളുടെ വർക്ക്സ്പേസിനായി സൃഷ്ടിക്കപ്പെടുന്ന ഡിഫോൾട്ട് പുതിയ അപ്ലിക്കേഷൻ ഇൻസൈറ്റ്സ് റിസോഴ്‌സ് ശ്രദ്ധിക്കുക - കണ്ടെയ്‌നർ രജിസ്ട്രി: ഇല്ല (മോഡൽ ആദ്യമായി കണ്ടെയ്‌നറിൽ ഡിപ്ലോയ് ചെയ്യുമ്പോൾ സ്വയം സൃഷ്ടിക്കും) - ![workspace-4](../../../../translated_images/workspace-4.bac87f6599c4df63e624fc2608990f965887bee551d9dedc71c687b43b986b6a.ml.png) + ![workspace-4](../../../../translated_images/ml/workspace-4.bac87f6599c4df63e624fc2608990f965887bee551d9dedc71c687b43b986b6a.png) - സൃഷ്ടിക്കുക + അവലോകനം ക്ലിക്ക് ചെയ്ത് പിന്നീട് സൃഷ്ടിക്കുക ബട്ടൺ ക്ലിക്ക് ചെയ്യുക 3. നിങ്ങളുടെ വർക്ക്സ്പേസ് സൃഷ്ടിക്കപ്പെടുന്നത് കാത്തിരിക്കുക (ചില മിനിറ്റുകൾ എടുക്കാം). ശേഷം പോർട്ടലിൽ അതിലേക്ക് പോകുക. ഇത് മെഷീൻ ലേണിംഗ് ആസ്യൂർ സേവനത്തിലൂടെ കണ്ടെത്താം. 4. നിങ്ങളുടെ വർക്ക്സ്പേസ് ഓവർവ്യൂ പേജിൽ, ആസ്യൂർ മെഷീൻ ലേണിംഗ് സ്റ്റുഡിയോ ആരംഭിക്കുക (അല്ലെങ്കിൽ പുതിയ ബ്രൗസർ ടാബ് തുറന്ന് https://ml.azure.com-ൽ പോകുക), നിങ്ങളുടെ മൈക്രോസോഫ്റ്റ് അക്കൗണ്ട് ഉപയോഗിച്ച് സൈൻ ഇൻ ചെയ്യുക. ആവശ്യമായാൽ, നിങ്ങളുടെ ആസ്യൂർ ഡയറക്ടറി, സബ്സ്ക്രിപ്ഷൻ, ആസ്യൂർ ML വർക്ക്സ്പേസ് തിരഞ്ഞെടുക്കുക. -![workspace-5](../../../../translated_images/workspace-5.a6eb17e0a5e6420018b08bdaf3755ce977f96f1df3ea363d2476a9dce7e15adb.ml.png) +![workspace-5](../../../../translated_images/ml/workspace-5.a6eb17e0a5e6420018b08bdaf3755ce977f96f1df3ea363d2476a9dce7e15adb.png) 5. ആസ്യൂർ ML സ്റ്റുഡിയോയിൽ, ഇടത് മുകളിൽ ☰ ഐക്കൺ ടോഗിൾ ചെയ്ത് ഇന്റർഫേസ് ഉള്ള വിവിധ പേജുകൾ കാണുക. ഈ പേജുകൾ ഉപയോഗിച്ച് നിങ്ങളുടെ വർക്ക്സ്പേസ് റിസോഴ്‌സുകൾ നിയന്ത്രിക്കാം. -![workspace-6](../../../../translated_images/workspace-6.8dd81fe841797ee17f8f73916769576260b16c4e17e850d277a49db35fd74a15.ml.png) +![workspace-6](../../../../translated_images/ml/workspace-6.8dd81fe841797ee17f8f73916769576260b16c4e17e850d277a49db35fd74a15.png) നിങ്ങൾ ആസ്യൂർ പോർട്ടൽ ഉപയോഗിച്ച് വർക്ക്സ്പേസ് നിയന്ത്രിക്കാം, എന്നാൽ ഡാറ്റാ സയന്റിസ്റ്റുകൾക്കും മെഷീൻ ലേണിംഗ് ഓപ്പറേഷൻസ് എഞ്ചിനീയർമാർക്കും, ആസ്യൂർ ML സ്റ്റുഡിയോ വർക്ക്സ്പേസ് റിസോഴ്‌സുകൾ നിയന്ത്രിക്കാൻ കൂടുതൽ കേന്ദ്രീകൃതമായ യൂസർ ഇന്റർഫേസ് നൽകുന്നു. @@ -193,18 +193,18 @@ Low-priority instance എന്നത് ഇടപെടാവുന്നതാ മുൻപ് സൃഷ്ടിച്ച [Azure ML workspace](https://ml.azure.com/) ൽ, compute ലേക്ക് പോകുക, നിങ്ങൾ ഇപ്പോൾ ചർച്ച ചെയ്ത വിവിധ കംപ്യൂട്ട് റിസോഴ്‌സുകൾ കാണാൻ കഴിയും (ഉദാ: compute instances, compute clusters, inference clusters, attached compute). ഈ പ്രോജക്ടിനായി, മോഡൽ പരിശീലനത്തിനായി ഒരു compute cluster ആവശ്യമുണ്ട്. സ്റ്റുഡിയോയിൽ, "Compute" മെനുവിൽ ക്ലിക്ക് ചെയ്യുക, തുടർന്ന് "Compute cluster" ടാബിൽ ക്ലിക്ക് ചെയ്ത് "+ New" ബട്ടൺ ക്ലിക്ക് ചെയ്ത് compute cluster സൃഷ്ടിക്കുക. -![22](../../../../translated_images/cluster-1.b78cb630bb543729b11f60c34d97110a263f8c27b516ba4dc47807b3cee5579f.ml.png) +![22](../../../../translated_images/ml/cluster-1.b78cb630bb543729b11f60c34d97110a263f8c27b516ba4dc47807b3cee5579f.png) 1. നിങ്ങളുടെ ഓപ്ഷനുകൾ തിരഞ്ഞെടുക്കുക: Dedicated vs Low priority, CPU അല്ലെങ്കിൽ GPU, VM വലുപ്പം, കോർ എണ്ണം (ഈ പ്രോജക്ടിനായി ഡിഫോൾട്ട് സെറ്റിംഗുകൾ നിലനിർത്താം). 2. Next ബട്ടൺ ക്ലിക്ക് ചെയ്യുക. -![23](../../../../translated_images/cluster-2.ea30cdbc9f926bb9e05af3fdbc1f679811c796dc2a6847f935290aec15526e88.ml.png) +![23](../../../../translated_images/ml/cluster-2.ea30cdbc9f926bb9e05af3fdbc1f679811c796dc2a6847f935290aec15526e88.png) 3. ക്ലസ്റ്ററിന് ഒരു കംപ്യൂട്ട് പേര് നൽകുക 4. നിങ്ങളുടെ ഓപ്ഷനുകൾ തിരഞ്ഞെടുക്കുക: കുറഞ്ഞ/പരമാവധി നോഡുകളുടെ എണ്ണം, സ്കെയിൽ ഡൗൺ ചെയ്യുന്നതിന് മുമ്പുള്ള ഐഡിൽ സെക്കൻഡുകൾ, SSH ആക്‌സസ്. കുറഞ്ഞ നോഡുകളുടെ എണ്ണം 0 ആണെങ്കിൽ, ക്ലസ്റ്റർ ഐഡിൽ ആയപ്പോൾ പണം ലാഭിക്കും. പരമാവധി നോഡുകളുടെ എണ്ണം കൂടുതലായാൽ പരിശീലനം കുറച്ച് സമയം എടുക്കും. പരമാവധി നോഡുകളുടെ ശുപാർശ 3 ആണ്. 5. "Create" ബട്ടൺ ക്ലിക്ക് ചെയ്യുക. ഈ ഘട്ടം കുറച്ച് മിനിറ്റുകൾ എടുക്കാം. -![29](../../../../translated_images/cluster-3.8a334bc070ec173a329ce5abd2a9d727542e83eb2347676c9af20f2c8870b3e7.ml.png) +![29](../../../../translated_images/ml/cluster-3.8a334bc070ec173a329ce5abd2a9d727542e83eb2347676c9af20f2c8870b3e7.png) അദ്ഭുതം! ഇപ്പോൾ ഒരു Compute cluster ഉണ്ടാകുമ്പോൾ, ഡാറ്റ Azure ML Studio-യിൽ ലോഡ് ചെയ്യേണ്ടതാണ്. @@ -212,15 +212,15 @@ Low-priority instance എന്നത് ഇടപെടാവുന്നതാ 1. മുൻപ് സൃഷ്ടിച്ച [Azure ML workspace](https://ml.azure.com/) ൽ, ഇടത് മെനുവിൽ "Datasets" ക്ലിക്ക് ചെയ്ത് "+ Create dataset" ബട്ടൺ ക്ലിക്ക് ചെയ്ത് ഒരു dataset സൃഷ്ടിക്കുക. "From local files" ഓപ്ഷൻ തിരഞ്ഞെടുക്കുക, മുൻപ് ഡൗൺലോഡ് ചെയ്ത Kaggle dataset തിരഞ്ഞെടുക്കുക. - ![24](../../../../translated_images/dataset-1.e86ab4e10907a6e9c2a72577b51db35f13689cb33702337b8b7032f2ef76dac2.ml.png) + ![24](../../../../translated_images/ml/dataset-1.e86ab4e10907a6e9c2a72577b51db35f13689cb33702337b8b7032f2ef76dac2.png) 2. നിങ്ങളുടെ dataset-ന് ഒരു പേര്, തരം, വിവരണം നൽകുക. Next ക്ലിക്ക് ചെയ്യുക. ഫയലുകളിൽ നിന്ന് ഡാറ്റ അപ്‌ലോഡ് ചെയ്യുക. Next ക്ലിക്ക് ചെയ്യുക. - ![25](../../../../translated_images/dataset-2.f58de1c435d5bf9ccb16ccc5f5d4380eb2b50affca85cfbf4f97562bdab99f77.ml.png) + ![25](../../../../translated_images/ml/dataset-2.f58de1c435d5bf9ccb16ccc5f5d4380eb2b50affca85cfbf4f97562bdab99f77.png) 3. Schema-യിൽ, താഴെപ്പറയുന്ന ഫീച്ചറുകൾക്ക് ഡാറ്റ ടൈപ്പ് Boolean ആയി മാറ്റുക: anaemia, diabetes, high blood pressure, sex, smoking, DEATH_EVENT. Next ക്ലിക്ക് ചെയ്ത് Create ക്ലിക്ക് ചെയ്യുക. - ![26](../../../../translated_images/dataset-3.58db8c0eb783e89236a02bbce5bb4ba808d081a87d994d5284b1ae59928c95bf.ml.png) + ![26](../../../../translated_images/ml/dataset-3.58db8c0eb783e89236a02bbce5bb4ba808d081a87d994d5284b1ae59928c95bf.png) ശ്രേഷ്ഠം! Dataset സജ്ജമായപ്പോൾ, compute cluster സൃഷ്ടിച്ചതിനുശേഷം, മോഡൽ പരിശീലനം ആരംഭിക്കാം! @@ -231,19 +231,19 @@ Automated machine learning (AutoML) മെഷീൻ ലേണിംഗ് മോ 1. മുൻപ് സൃഷ്ടിച്ച [Azure ML workspace](https://ml.azure.com/) ൽ, ഇടത് മെനുവിൽ "Automated ML" ക്ലിക്ക് ചെയ്ത് നിങ്ങൾ അപ്‌ലോഡ് ചെയ്ത dataset തിരഞ്ഞെടുക്കുക. Next ക്ലിക്ക് ചെയ്യുക. - ![27](../../../../translated_images/aml-1.67281a85d3a1e2f34eb367b2d0f74e1039d13396e510f363cd8766632106d1ec.ml.png) + ![27](../../../../translated_images/ml/aml-1.67281a85d3a1e2f34eb367b2d0f74e1039d13396e510f363cd8766632106d1ec.png) 2. പുതിയ experiment പേര് നൽകുക, target column (DEATH_EVENT) തിരഞ്ഞെടുക്കുക, സൃഷ്ടിച്ച compute cluster തിരഞ്ഞെടുക്കുക. Next ക്ലിക്ക് ചെയ്യുക. - ![28](../../../../translated_images/aml-2.c9fb9cffb39ccbbe21ab9810ae937195d41a489744e15cff2b8477ed4dcae1ec.ml.png) + ![28](../../../../translated_images/ml/aml-2.c9fb9cffb39ccbbe21ab9810ae937195d41a489744e15cff2b8477ed4dcae1ec.png) 3. "Classification" തിരഞ്ഞെടുക്കുക, Finish ക്ലിക്ക് ചെയ്യുക. ഈ ഘട്ടം compute cluster വലുപ്പം അനുസരിച്ച് 30 മിനിറ്റിൽ 1 മണിക്കൂർ വരെ എടുക്കാം. - ![30](../../../../translated_images/aml-3.a7952e4295f38cc6cdb0c7ed6dc71ea756b7fb5697ec126bc1220f87c5fa9231.ml.png) + ![30](../../../../translated_images/ml/aml-3.a7952e4295f38cc6cdb0c7ed6dc71ea756b7fb5697ec126bc1220f87c5fa9231.png) 4. റൺ പൂർത്തിയായ ശേഷം, "Automated ML" ടാബിൽ ക്ലിക്ക് ചെയ്യുക, നിങ്ങളുടെ റൺ തിരഞ്ഞെടുക്കുക, "Best model summary" കാർഡിലെ Algorithm ക്ലിക്ക് ചെയ്യുക. - ![31](../../../../translated_images/aml-4.7a627e09cb6f16d0aa246059d9faee3d1725cc4258d0c8df15e801f73afc7e2c.ml.png) + ![31](../../../../translated_images/ml/aml-4.7a627e09cb6f16d0aa246059d9faee3d1725cc4258d0c8df15e801f73afc7e2c.png) ഇവിടെ AutoML സൃഷ്ടിച്ച മികച്ച മോഡലിന്റെ വിശദമായ വിവരണം കാണാം. Models ടാബിൽ മറ്റ് മോഡലുകളും പരിശോധിക്കാം. Explanations (preview ബട്ടൺ) ൽ മോഡലുകൾ പരിശോധിക്കാൻ കുറച്ച് സമയം ചെലവഴിക്കുക. നിങ്ങൾ ഉപയോഗിക്കാൻ ആഗ്രഹിക്കുന്ന മോഡൽ തിരഞ്ഞെടുക്കുമ്പോൾ (ഇവിടെ AutoML തിരഞ്ഞെടുത്ത മികച്ച മോഡൽ തിരഞ്ഞെടുക്കും), അതിനെ എങ്ങനെ വിന്യസിക്കാമെന്ന് കാണാം. @@ -254,15 +254,15 @@ Automated machine learning ഇന്റർഫേസ് മികച്ച മോ മികച്ച മോഡൽ വിവരണത്തിൽ "Deploy" ബട്ടൺ ക്ലിക്ക് ചെയ്യുക. -![deploy-1](../../../../translated_images/deploy-1.ddad725acadc84e34553c3d09e727160faeb32527a9fb8b904c0f99235a34bb6.ml.png) +![deploy-1](../../../../translated_images/ml/deploy-1.ddad725acadc84e34553c3d09e727160faeb32527a9fb8b904c0f99235a34bb6.png) 15. ഒരു പേര്, വിവരണം, കംപ്യൂട്ട് തരം (Azure Container Instance), authentication സജ്ജമാക്കുക, Deploy ക്ലിക്ക് ചെയ്യുക. ഈ ഘട്ടം പൂർത്തിയാകാൻ ഏകദേശം 20 മിനിറ്റ് എടുക്കാം. വിന്യാസ പ്രക്രിയയിൽ മോഡൽ രജിസ്റ്റർ ചെയ്യൽ, റിസോഴ്‌സുകൾ സൃഷ്ടിക്കൽ, വെബ് സേവനത്തിനായി അവ ക്രമീകരിക്കൽ ഉൾപ്പെടുന്നു. Deploy status കീഴിൽ സ്റ്റാറ്റസ് സന്ദേശം കാണാം. സ്റ്റാറ്റസ് "Healthy" ആണെങ്കിൽ വിന്യസിച്ചും പ്രവർത്തിക്കുന്നുമാണ്. -![deploy-2](../../../../translated_images/deploy-2.94dbb13f239086473aa4bf814342fd40483d136849b080f02bafbb995383940e.ml.png) +![deploy-2](../../../../translated_images/ml/deploy-2.94dbb13f239086473aa4bf814342fd40483d136849b080f02bafbb995383940e.png) 16. വിന്യസിച്ച ശേഷം, Endpoint ടാബിൽ ക്ലിക്ക് ചെയ്ത് നിങ്ങൾ വിന്യസിച്ച എൻഡ്‌പോയിന്റ് തിരഞ്ഞെടുക്കുക. ഇവിടെ എൻഡ്‌പോയിന്റ് സംബന്ധിച്ച എല്ലാ വിവരങ്ങളും കാണാം. -![deploy-3](../../../../translated_images/deploy-3.fecefef070e8ef3b28e802326d107f61ac4e672d20bf82d05f78d025f9e6c611.ml.png) +![deploy-3](../../../../translated_images/ml/deploy-3.fecefef070e8ef3b28e802326d107f61ac4e672d20bf82d05f78d025f9e6c611.png) അദ്ഭുതം! ഇപ്പോൾ ഒരു മോഡൽ വിന്യസിച്ചിരിക്കുന്നു, എൻഡ്‌പോയിന്റ് ഉപഭോഗം ആരംഭിക്കാം. @@ -272,7 +272,7 @@ Automated machine learning ഇന്റർഫേസ് മികച്ച മോ ഈ സ്ക്രിപ്റ്റ് നിങ്ങളുടെ ലോക്കൽ മെഷീനിൽ നിന്ന് നേരിട്ട് പ്രവർത്തിപ്പിക്കാം, നിങ്ങളുടെ എൻഡ്‌പോയിന്റ് ഉപഭോഗിക്കും. -![35](../../../../translated_images/consumption-1.700abd196452842a020c7d745908637a6e4c5c50494ad1217be80e283e0de154.ml.png) +![35](../../../../translated_images/ml/consumption-1.700abd196452842a020c7d745908637a6e4c5c50494ad1217be80e283e0de154.png) ഈ 2 കോഡ് ലൈൻ പരിശോധിക്കുക: diff --git a/translations/ml/5-Data-Science-In-Cloud/19-Azure/README.md b/translations/ml/5-Data-Science-In-Cloud/19-Azure/README.md index db9351b7..76ac415b 100644 --- a/translations/ml/5-Data-Science-In-Cloud/19-Azure/README.md +++ b/translations/ml/5-Data-Science-In-Cloud/19-Azure/README.md @@ -57,7 +57,7 @@ SDKയുടെ പ്രധാന മേഖലകൾ: [മുൻപത്തെ പാഠത്തിൽ](../18-Low-Code/README.md) നാം ലോ കോഡ്/നോ കോഡ് രീതിയിൽ മോഡൽ പരിശീലിപ്പിക്കുകയും വിന്യസിക്കുകയും ഉപയോഗിക്കുകയും ചെയ്തിരുന്നു. ഹാർട്ട് ഫെയില്യർ ഡാറ്റാസെറ്റ് ഉപയോഗിച്ച് ഹാർട്ട് ഫെയില്യർ പ്രവചന മോഡൽ സൃഷ്ടിച്ചു. ഈ പാഠത്തിൽ, നാം അതേ പ്രവർത്തി Azure Machine Learning SDK ഉപയോഗിച്ച് ചെയ്യാൻ പോകുന്നു. -![project-schema](../../../../translated_images/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.ml.png) +![project-schema](../../../../translated_images/ml/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.png) ### 1.2 ഹാർട്ട് ഫെയില്യർ പ്രവചനം പ്രോജക്ടും ഡാറ്റാസെറ്റും പരിചയം @@ -74,7 +74,7 @@ SDKയുടെ പ്രധാന മേഖലകൾ: മുൻപ് സൃഷ്ടിച്ച [Azure ML വർക്ക്‌സ്‌പേസിൽ](https://ml.azure.com/) കംപ്യൂട്ട് മെനുവിലേക്ക് പോകുക, അവിടെ ലഭ്യമായ വിവിധ കംപ്യൂട്ട് വിഭവങ്ങൾ കാണാം -![compute-instance-1](../../../../translated_images/compute-instance-1.dba347cb199ca4996b3e3d649295ed95626ba481479d3986557b9b98e76d8816.ml.png) +![compute-instance-1](../../../../translated_images/ml/compute-instance-1.dba347cb199ca4996b3e3d649295ed95626ba481479d3986557b9b98e76d8816.png) Jupyter നോട്ട്‌ബുക്ക് പ്രൊവിഷൻ ചെയ്യാൻ ഒരു കംപ്യൂട്ട് ഇൻസ്റ്റൻസ് സൃഷ്ടിക്കാം. 1. + New ബട്ടൺ ക്ലിക്ക് ചെയ്യുക. @@ -97,10 +97,10 @@ Jupyter നോട്ട്‌ബുക്ക് പ്രൊവിഷൻ ചെ 1. Applications വിഭാഗത്തിൽ Jupyter ഓപ്ഷൻ ക്ലിക്ക് ചെയ്യുക. 2. "Yes, I understand" ബോക്സ് ടിക്ക് ചെയ്ത് Continue ബട്ടൺ ക്ലിക്ക് ചെയ്യുക. -![notebook-1](../../../../translated_images/notebook-1.12998af7b02c83f536c11b3aeba561be16e0f05e94146600728ec64270ce1105.ml.png) +![notebook-1](../../../../translated_images/ml/notebook-1.12998af7b02c83f536c11b3aeba561be16e0f05e94146600728ec64270ce1105.png) 3. ഇത് പുതിയ ബ്രൗസർ ടാബിൽ നിങ്ങളുടെ ജുപിറ്റർ നോട്ട്‌ബുക്ക് ഇൻസ്റ്റൻസ് തുറക്കും. "New" ബട്ടൺ ക്ലിക്ക് ചെയ്ത് നോട്ട്‌ബുക്ക് സൃഷ്ടിക്കുക. -![notebook-2](../../../../translated_images/notebook-2.9a657c037e34f1cf26c0212f5ee9e2da8545b3e107c7682c55114e494167a8aa.ml.png) +![notebook-2](../../../../translated_images/ml/notebook-2.9a657c037e34f1cf26c0212f5ee9e2da8545b3e107c7682c55114e494167a8aa.png) ഇപ്പോൾ നോട്ട്‌ബുക്ക് ഉണ്ടാകുമ്പോൾ, Azure ML SDK ഉപയോഗിച്ച് മോഡൽ പരിശീലനം ആരംഭിക്കാം. diff --git a/translations/ml/5-Data-Science-In-Cloud/README.md b/translations/ml/5-Data-Science-In-Cloud/README.md index c8f1f51c..98377029 100644 --- a/translations/ml/5-Data-Science-In-Cloud/README.md +++ b/translations/ml/5-Data-Science-In-Cloud/README.md @@ -9,13 +9,13 @@ CO_OP_TRANSLATOR_METADATA: --> # ക്ലൗഡിലെ ഡാറ്റാ സയൻസ് -![cloud-picture](../../../translated_images/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.ml.jpg) +![cloud-picture](../../../translated_images/ml/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.jpg) > ഫോട്ടോ [Jelleke Vanooteghem](https://unsplash.com/@ilumire) യുടെ [Unsplash](https://unsplash.com/s/photos/cloud?orientation=landscape) ൽ നിന്നാണ് വലിയ ഡാറ്റയുമായി ഡാറ്റാ സയൻസ് ചെയ്യുമ്പോൾ, ക്ലൗഡ് ഒരു ഗെയിം ചേഞ്ചർ ആകാം. അടുത്ത മൂന്ന് പാഠങ്ങളിൽ, ക്ലൗഡ് എന്താണെന്നും അത് എങ്ങനെ സഹായകരമാകാമെന്നും നാം കാണാൻ പോകുന്നു. ഹൃദയ പരാജയ ഡാറ്റാസെറ്റ് പരിശോധിച്ച്, ആരെങ്കിലും ഹൃദയ പരാജയ സാധ്യതയുള്ളതെന്ന് വിലയിരുത്താൻ സഹായിക്കുന്ന ഒരു മോഡൽ നിർമ്മിക്കാനും നാം പോകുന്നു. മോഡൽ പരിശീലിപ്പിക്കാൻ, വിന്യസിക്കാൻ, ഉപയോഗിക്കാൻ ക്ലൗഡിന്റെ ശക്തി ഉപയോഗിക്കും. ഒരു വഴി കുറഞ്ഞ കോഡ്/കോഡ് ഇല്ലാത്ത രീതിയിൽ മാത്രം ഉപയോക്തൃ ഇന്റർഫേസ് ഉപയോഗിച്ച്, മറ്റൊരു വഴി Azure മെഷീൻ ലേണിംഗ് സോഫ്റ്റ്വെയർ ഡെവലപ്പർ കിറ്റ് (Azure ML SDK) ഉപയോഗിച്ച്. -![project-schema](../../../translated_images/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.ml.png) +![project-schema](../../../translated_images/ml/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.png) ### വിഷയങ്ങൾ diff --git a/translations/ml/6-Data-Science-In-Wild/20-Real-World-Examples/README.md b/translations/ml/6-Data-Science-In-Wild/20-Real-World-Examples/README.md index e134aa31..196155db 100644 --- a/translations/ml/6-Data-Science-In-Wild/20-Real-World-Examples/README.md +++ b/translations/ml/6-Data-Science-In-Wild/20-Real-World-Examples/README.md @@ -41,7 +41,7 @@ AI-യുടെ ജനാധിപത്യവൽക്കരണത്തിന * [Data Science in Healthcare](https://data-flair.training/blogs/data-science-in-healthcare/) - മെഡിക്കൽ ഇമേജിംഗ് (MRI, X-Ray, CT-സ്കാൻ), ജീനോമിക്സ് (DNA സീക്വൻസിംഗ്), മരുന്ന് വികസനം (റിസ്‌ക് വിലയിരുത്തൽ, വിജയ പ്രവചന), പ്രവചനാത്മക വിശകലനം (രോഗി പരിചരണം & വിതരണ ലജിസ്റ്റിക്സ്), രോഗം ട്രാക്കിംഗ് & പ്രതിരോധം തുടങ്ങിയ പ്രയോഗങ്ങൾ ഹൈലൈറ്റ് ചെയ്യുന്നു. -![Data Science Applications in The Real World](../../../../translated_images/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.ml.png) ചിത്രം ക്രെഡിറ്റ്: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) +![Data Science Applications in The Real World](../../../../translated_images/ml/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.png) ചിത്രം ക്രെഡിറ്റ്: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) ചിത്രം ഡാറ്റാ സയൻസ് സാങ്കേതിക വിദ്യകൾ പ്രയോഗിക്കാവുന്ന മറ്റ് മേഖലകളും ഉദാഹരണങ്ങളും കാണിക്കുന്നു. മറ്റ് പ്രയോഗങ്ങൾ അന്വേഷിക്കണോ? താഴെയുള്ള [Review & Self Study](../../../../6-Data-Science-In-Wild/20-Real-World-Examples) വിഭാഗം പരിശോധിക്കുക. diff --git a/translations/ml/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md b/translations/ml/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md index f9259fe4..e35dc9e9 100644 --- a/translations/ml/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md +++ b/translations/ml/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md @@ -22,7 +22,7 @@ Explorer ഇന്റർഫേസ് (താഴെ സ്ക്രീൻഷോ 2. ഡാറ്റാസെറ്റ് [കാറ്റലോഗ്](https://planetarycomputer.microsoft.com/catalog) പരിശോധിക്കുക - ഓരോന്നിന്റെയും ഉദ്ദേശ്യം പഠിക്കുക. 3. Explorer ഉപയോഗിക്കുക - താൽപ്പര്യമുള്ള ഒരു ഡാറ്റാസെറ്റ് തിരഞ്ഞെടുക്കുക, പ്രസക്തമായ ക്വറി & റെൻഡറിംഗ് ഓപ്ഷൻ തിരഞ്ഞെടുക്കുക. -![The Planetary Computer Explorer](../../../../translated_images/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.ml.png) +![The Planetary Computer Explorer](../../../../translated_images/ml/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.png) `നിങ്ങളുടെ ജോലി:` ഇപ്പോൾ ബ്രൗസറിൽ റെൻഡർ ചെയ്ത വിസ്വലൈസേഷൻ പഠിച്ച് താഴെ പറയുന്നവയ്ക്ക് ഉത്തരം നൽകുക: diff --git a/translations/ml/CONTRIBUTING.md b/translations/ml/CONTRIBUTING.md index b167427d..0ae9e832 100644 --- a/translations/ml/CONTRIBUTING.md +++ b/translations/ml/CONTRIBUTING.md @@ -310,7 +310,7 @@ def calculate_mean(data): import pandas as pd ``` ```` -- ചിത്രങ്ങൾക്ക് alt ടെക്സ്റ്റ് ചേർക്കുക: `![Alt text](../../translated_images/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.ml.png)` +- ചിത്രങ്ങൾക്ക് alt ടെക്സ്റ്റ് ചേർക്കുക: `![Alt text](../../translated_images/ml/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.png)` - വരി നീളം യുക്തമായിരിക്കണം (ഏകദേശം 80-100 അക്ഷരങ്ങൾ) ### Python diff --git a/translations/ml/README.md b/translations/ml/README.md index 8810ed1e..84052b1f 100644 --- a/translations/ml/README.md +++ b/translations/ml/README.md @@ -28,7 +28,7 @@ Microsoft-യിലെ Azure Cloud Advocates-കൾ ഡാറ്റാ സയൻ **🙏 പ്രത്യേക നന്ദി 🙏 ഞങ്ങളുടെ [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/) എഴുത്തുകാർക്കും, അവലോകനക്കാരും ഉള്ളടക്ക സംഭാവകർക്കും,** പ്രത്യേകിച്ച് Aaryan Arora, [Aditya Garg](https://github.com/AdityaGarg00), [Alondra Sanchez](https://www.linkedin.com/in/alondra-sanchez-molina/), [Ankita Singh](https://www.linkedin.com/in/ankitasingh007), [Anupam Mishra](https://www.linkedin.com/in/anupam--mishra/), [Arpita Das](https://www.linkedin.com/in/arpitadas01/), ChhailBihari Dubey, [Dibri Nsofor](https://www.linkedin.com/in/dibrinsofor), [Dishita Bhasin](https://www.linkedin.com/in/dishita-bhasin-7065281bb), [Majd Safi](https://www.linkedin.com/in/majd-s/), [Max Blum](https://www.linkedin.com/in/max-blum-6036a1186/), [Miguel Correa](https://www.linkedin.com/in/miguelmque/), [Mohamma Iftekher (Iftu) Ebne Jalal](https://twitter.com/iftu119), [Nawrin Tabassum](https://www.linkedin.com/in/nawrin-tabassum), [Raymond Wangsa Putra](https://www.linkedin.com/in/raymond-wp/), [Rohit Yadav](https://www.linkedin.com/in/rty2423), Samridhi Sharma, [Sanya Sinha](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200), [Sheena Narula](https://www.linkedin.com/in/sheena-narua-n/), [Tauqeer Ahmad](https://www.linkedin.com/in/tauqeerahmad5201/), Yogendrasingh Pawar , [Vidushi Gupta](https://www.linkedin.com/in/vidushi-gupta07/), [Jasleen Sondhi](https://www.linkedin.com/in/jasleen-sondhi/) -|![സ്കെച്നോട്ട് - @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.ml.png)| +|![സ്കെച്നോട്ട് - @sketchthedocs https://sketchthedocs.dev](../../translated_images/ml/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.png)| |:---:| | ബെഗിന്നർമാർക്കുള്ള ഡാറ്റാ സയൻസ് - _സ്കെച്നോട്ട് — [@nitya](https://twitter.com/nitya)_ | @@ -47,7 +47,7 @@ Microsoft-യിലെ Azure Cloud Advocates-കൾ ഡാറ്റാ സയൻ ഞങ്ങളുടേത് Discord-ൽ "Learn with AI" സീരീസ ongoing ആണ്, കൂടുതൽ അറിയാനും പങ്കുചേരാനുമായി [Learn with AI Series](https://aka.ms/learnwithai/discord)ൽ 2025 സെപ്റ്റംബർ 18 മുതൽ 30 വരെ. നിങ്ങള്ക്ക് GitHub Copilot ഡാറ്റാ സയൻസിന് ഉപയോഗിക്കുന്നതിന്റെ ടിപ്പുകൾക്കും ട്രിക്കുകൾക്കും ലഭിക്കും. -![AI-യുമായി പഠനം പരമ്പരം](../../translated_images/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.ml.jpg) +![AI-യുമായി പഠനം പരമ്പരം](../../translated_images/ml/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.jpg) # നിങ്ങൾ വിദ്യാർത്ഥിയാണോ? @@ -127,7 +127,7 @@ Microsoft-യിലെ Azure Cloud Advocates-കൾ ഡാറ്റാ സയൻ ## പാഠങ്ങൾ -|![ സ്കെച്ച്നോട്ട്: @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.ml.png)| +|![ സ്കെച്ച്നോട്ട്: @sketchthedocs https://sketchthedocs.dev](../../translated_images/ml/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png)| |:---:| | ഡേറ്റാ സയൻസ് ഫോർ ബിഗിന്നേഴ്സ്: റോഡ്‌മാപ് - _സ്കെച്ച്നോട്ട് രചിച്ചത് [@nitya](https://twitter.com/nitya)_ | diff --git a/translations/ml/sketchnotes/README.md b/translations/ml/sketchnotes/README.md index cf8ca4d1..0421633e 100644 --- a/translations/ml/sketchnotes/README.md +++ b/translations/ml/sketchnotes/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: നിത്യ നരസിംഹൻ, കലാകാരൻ -![roadmap sketchnote](../../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.ml.png) +![roadmap sketchnote](../../../translated_images/ml/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png) --- diff --git a/translations/mo/1-Introduction/01-defining-data-science/README.md b/translations/mo/1-Introduction/01-defining-data-science/README.md index 3fa4d576..a1292dc2 100644 --- a/translations/mo/1-Introduction/01-defining-data-science/README.md +++ b/translations/mo/1-Introduction/01-defining-data-science/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: --- -[![定義數據科學影片](../../../../translated_images/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.mo.png)](https://youtu.be/beZ7Mb_oz9I) +[![定義數據科學影片](../../../../translated_images/mo/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.png)](https://youtu.be/beZ7Mb_oz9I) ## [課前測驗](https://ff-quizzes.netlify.app/en/ds/quiz/0) @@ -153,7 +153,7 @@ CO_OP_TRANSLATOR_METADATA: 在這個挑戰中，我們將透過分析文本來尋找與資料科學領域相關的概念。我們會選取一篇關於資料科學的維基百科文章，下載並處理文本，然後建立一個像這樣的文字雲： -![資料科學文字雲](../../../../translated_images/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.mo.png) +![資料科學文字雲](../../../../translated_images/mo/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.png) 請訪問 [`notebook.ipynb`](../../../../1-Introduction/01-defining-data-science/notebook.ipynb ':ignore') 來閱讀程式碼。你也可以執行程式碼，並即時查看它如何進行所有的資料轉換。 diff --git a/translations/mo/1-Introduction/04-stats-and-probability/README.md b/translations/mo/1-Introduction/04-stats-and-probability/README.md index c48d9351..6d944906 100644 --- a/translations/mo/1-Introduction/04-stats-and-probability/README.md +++ b/translations/mo/1-Introduction/04-stats-and-probability/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: 統計學與機率論是數學中兩個密切相關的領域，對於數據科學非常重要。雖然在沒有深入數學知識的情況下也可以處理數據，但了解一些基本概念仍然是有益的。在這裡，我們將提供一個簡短的介紹，幫助你入門。 -[![介紹影片](../../../../translated_images/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.mo.png)](https://youtu.be/Z5Zy85g4Yjw) +[![介紹影片](../../../../translated_images/mo/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.png)](https://youtu.be/Z5Zy85g4Yjw) ## [課前測驗](https://ff-quizzes.netlify.app/en/ds/quiz/6) @@ -39,7 +39,7 @@ CO_OP_TRANSLATOR_METADATA: 我們只能討論變數落在某個值區間內的機率，例如 P(t1≤X2)。在這種情況下，機率分佈由 **機率密度函數** p(x) 描述，其公式如下： -![P(t_1\le X 更多關於相關性和協方差的例子可以在 [配套筆記本](notebook.ipynb) 中找到。 diff --git a/translations/mo/1-Introduction/README.md b/translations/mo/1-Introduction/README.md index ee32de57..24682751 100644 --- a/translations/mo/1-Introduction/README.md +++ b/translations/mo/1-Introduction/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 資料科學簡介 -![數據運作](../../../translated_images/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.mo.jpg) +![數據運作](../../../translated_images/mo/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.jpg) > 照片由 Stephen Dawson 提供，來源於 Unsplash 在這些課程中，您將了解資料科學的定義，並學習作為資料科學家必須考慮的倫理問題。此外，您還會學習資料的定義，並簡單了解統計學和機率，這些是資料科學的核心學術領域。 diff --git a/translations/mo/2-Working-With-Data/07-python/README.md b/translations/mo/2-Working-With-Data/07-python/README.md index 84960272..15bd8867 100644 --- a/translations/mo/2-Working-With-Data/07-python/README.md +++ b/translations/mo/2-Working-With-Data/07-python/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: | :-------------------------------------------------------------------------------------------------------: | | 使用 Python - _由 [@nitya](https://twitter.com/nitya) 繪製的速記筆記_ | -[![介紹影片](../../../../translated_images/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.mo.png)](https://youtu.be/dZjWOGbsN4Y) +[![介紹影片](../../../../translated_images/mo/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.png)](https://youtu.be/dZjWOGbsN4Y) 雖然資料庫提供了非常高效的方式來存儲數據並使用查詢語言進行查詢，但最靈活的數據處理方式是編寫自己的程式來操作數據。在許多情況下，使用資料庫查詢可能更有效。然而，當需要更複雜的數據處理時，使用 SQL 可能不容易完成。 @@ -74,7 +74,7 @@ print(f"Length of index is {len(idx)}") items_sold = pd.Series(np.random.randint(25,50,size=len(idx)),index=idx) items_sold.plot() ``` -![時間序列圖](../../../../translated_images/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.mo.png) +![時間序列圖](../../../../translated_images/mo/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.png) 假設每週我們都會為朋友舉辦派對，並額外準備 10 盒冰淇淋。我們可以創建另一個以週為索引的 Series 來展示這一點： ```python @@ -85,7 +85,7 @@ additional_items = pd.Series(10,index=pd.date_range(start_date,end_date,freq="W" total_items = items_sold.add(additional_items,fill_value=0) total_items.plot() ``` -![時間序列圖](../../../../translated_images/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.mo.png) +![時間序列圖](../../../../translated_images/mo/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.png) > **注意**：我們沒有使用簡單的語法 `total_items+additional_items`。如果使用該語法，我們會在結果 Series 中得到許多 `NaN`（*非數值*）值。這是因為在 `additional_items` Series 的某些索引點缺少值，並且將 `NaN` 與任何值相加都會得到 `NaN`。因此，我們需要在相加時指定 `fill_value` 參數。 @@ -94,7 +94,7 @@ total_items.plot() monthly = total_items.resample("1M").mean() ax = monthly.plot(kind='bar') ``` -![每月時間序列平均值](../../../../translated_images/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.mo.png) +![每月時間序列平均值](../../../../translated_images/mo/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.png) ### DataFrame @@ -220,7 +220,7 @@ df = pd.read_csv('file.csv') 由於我們想展示如何處理數據，我們邀請你打開 [`notebook-covidspread.ipynb`](notebook-covidspread.ipynb) 並從頭到尾閱讀。你也可以執行單元格，並完成我們在最後留下的一些挑戰。 -![COVID 傳播](../../../../translated_images/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.mo.png) +![COVID 傳播](../../../../translated_images/mo/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.png) > 如果你不知道如何在 Jupyter Notebook 中運行代碼，可以查看 [這篇文章](https://soshnikov.com/education/how-to-execute-notebooks-from-github/)。 @@ -242,7 +242,7 @@ df = pd.read_csv('file.csv') 打開 [`notebook-papers.ipynb`](notebook-papers.ipynb) 並從頭到尾閱讀。你也可以執行單元格，並完成我們在最後留下的一些挑戰。 -![COVID 醫療處理](../../../../translated_images/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.mo.png) +![COVID 醫療處理](../../../../translated_images/mo/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.png) ## 處理圖像數據 diff --git a/translations/mo/2-Working-With-Data/README.md b/translations/mo/2-Working-With-Data/README.md index a71cd58f..97ee9800 100644 --- a/translations/mo/2-Working-With-Data/README.md +++ b/translations/mo/2-Working-With-Data/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 資料處理 -![data love](../../../translated_images/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.mo.jpg) +![data love](../../../translated_images/mo/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.jpg) > 照片由 Alexander Sinn 提供，來自 Unsplash 在這些課程中，您將學習一些管理、操作和應用資料的方法。您將了解關聯式和非關聯式資料庫，以及資料如何存儲於其中。您還會學習使用 Python 管理資料的基礎知識，並探索使用 Python 管理和挖掘資料的多種方式。 diff --git a/translations/mo/3-Data-Visualization/12-visualization-relationships/README.md b/translations/mo/3-Data-Visualization/12-visualization-relationships/README.md index 3f5a0ee4..4bebf280 100644 --- a/translations/mo/3-Data-Visualization/12-visualization-relationships/README.md +++ b/translations/mo/3-Data-Visualization/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ honey.head() ```python sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ``` -![scatterplot 1](../../../../translated_images/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.mo.png) +![scatterplot 1](../../../../translated_images/mo/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.png) 接下來，使用蜂蜜色系展示價格如何隨年份演變。您可以通過添加 'hue' 參數來顯示年份的變化： @@ -60,7 +60,7 @@ sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ```python sns.relplot(x="priceperlb", y="state", hue="year", palette="YlOrBr", data=honey, height=15, aspect=.5); ``` -![scatterplot 2](../../../../translated_images/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.mo.png) +![scatterplot 2](../../../../translated_images/mo/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.png) 使用這種色彩方案，您可以清楚地看到蜂蜜每磅價格在多年來的明顯增長趨勢。事實上，如果您查看數據中的樣本集（例如選擇一個州，亞利桑那州），您會發現價格每年都有增長，只有少數例外： @@ -89,7 +89,7 @@ sns.relplot(x="priceperlb", y="state", size="year", data=honey, height=15, aspec ``` 您可以看到點的大小逐漸增大。 -![scatterplot 3](../../../../translated_images/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.mo.png) +![scatterplot 3](../../../../translated_images/mo/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.png) 這是否只是供需的簡單案例？由於氣候變化和蜂群崩潰等因素，是否每年可供購買的蜂蜜減少，因此價格上漲？ @@ -104,7 +104,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); ``` 答案：是的，除了2003年左右有一些例外： -![line chart 1](../../../../translated_images/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.mo.png) +![line chart 1](../../../../translated_images/mo/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.png) ✅ 由於 Seaborn 將數據聚合到一條線上，它通過繪製均值和均值周圍的95%置信區間來顯示「每個 x 值的多個測量值」。[來源](https://seaborn.pydata.org/tutorial/relational.html)。這種耗時的行為可以通過添加 `ci=None` 禁用。 @@ -114,7 +114,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); sns.relplot(x="year", y="totalprod", kind="line", data=honey); ``` -![line chart 2](../../../../translated_images/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.mo.png) +![line chart 2](../../../../translated_images/mo/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.png) 答案：並不完全。如果您查看總產量，實際上在那一年似乎有所增加，儘管總體而言，蜂蜜的生產量在這些年中呈下降趨勢。 @@ -139,7 +139,7 @@ sns.relplot( ``` 在這個視覺化中，您可以比較每年的每群產量和蜂群數量，並將列的包裹設置為3： -![facet grid](../../../../translated_images/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.mo.png) +![facet grid](../../../../translated_images/mo/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.png) 對於這個數據集，關於蜂群數量和每群產量，按年份和州比較並沒有特別突出的地方。是否有其他方式來尋找這兩個變數之間的相關性？ @@ -162,7 +162,7 @@ sns.despine(right=False) plt.ylabel('colony yield') ax.figure.legend(); ``` -![superimposed plots](../../../../translated_images/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.mo.png) +![superimposed plots](../../../../translated_images/mo/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.png) 雖然在2003年沒有明顯的異常，但這讓我們以一個稍微樂觀的結論結束這節課：儘管蜂群數量總體上在下降，但蜂群數量正在穩定，即使每群產量在減少。 diff --git a/translations/mo/3-Data-Visualization/R/09-visualization-quantities/README.md b/translations/mo/3-Data-Visualization/R/09-visualization-quantities/README.md index d5227608..cf65a976 100644 --- a/translations/mo/3-Data-Visualization/R/09-visualization-quantities/README.md +++ b/translations/mo/3-Data-Visualization/R/09-visualization-quantities/README.md @@ -67,7 +67,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` 在這裡，您安裝了 `ggplot2` 套件，然後使用 `library("ggplot2")` 命令將其導入工作區。要在 ggplot 中繪製任何圖表，使用 `ggplot()` 函數並指定數據集、x 和 y 變數作為屬性。在此情況下，我們使用 `geom_line()` 函數，因為我們的目標是繪製折線圖。 -![MaxWingspan-lineplot](../../../../../translated_images/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.mo.png) +![MaxWingspan-lineplot](../../../../../translated_images/mo/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.png) 您立即注意到什麼？似乎至少有一個異常值——那是一個相當大的翼展！2000+ 公分的翼展超過了 20 公尺——明尼蘇達州有翼龍在飛嗎？讓我們調查一下。 @@ -85,7 +85,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` 我們在 `theme` 中指定了角度，並在 `xlab()` 和 `ylab()` 中分別指定了 x 和 y 軸標籤。`ggtitle()` 為圖表/圖形命名。 -![MaxWingspan-lineplot-improved](../../../../../translated_images/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.mo.png) +![MaxWingspan-lineplot-improved](../../../../../translated_images/mo/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.png) 即使將標籤的旋轉設置為 45 度，仍然有太多標籤難以閱讀。讓我們嘗試另一種策略：僅標記那些異常值並在圖表內設置標籤。您可以使用散點圖來為標籤留出更多空間： @@ -101,7 +101,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + 您發現了什麼？ -![MaxWingspan-scatterplot](../../../../../translated_images/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.mo.png) +![MaxWingspan-scatterplot](../../../../../translated_images/mo/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.png) ## 篩選數據 @@ -120,7 +120,7 @@ ggplot(data=birds_filtered, aes(x=Name, y=MaxWingspan,group=1)) + ``` 我們創建了一個新的數據框 `birds_filtered`，然後繪製了一個散點圖。通過篩選掉異常值，您的數據現在更加一致且易於理解。 -![MaxWingspan-scatterplot-improved](../../../../../translated_images/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.mo.png) +![MaxWingspan-scatterplot-improved](../../../../../translated_images/mo/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.png) 現在我們至少在翼展方面有了一個更乾淨的數據集，讓我們進一步探索這些鳥類。 @@ -163,7 +163,7 @@ birds_filtered %>% group_by(Category) %>% ``` 在以下代碼片段中，我們安裝了 [dplyr](https://www.rdocumentation.org/packages/dplyr/versions/0.7.8) 和 [lubridate](https://www.rdocumentation.org/packages/lubridate/versions/1.8.0) 套件，以幫助操作和分組數據以繪製堆疊條形圖。首先，您按鳥類的 `Category` 分組數據，然後總結 `MinLength`、`MaxLength`、`MinBodyMass`、`MaxBodyMass`、`MinWingspan`、`MaxWingspan` 列。接著，使用 `ggplot2` 套件繪製條形圖並指定不同類別的顏色和標籤。 -![Stacked bar chart](../../../../../translated_images/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.mo.png) +![Stacked bar chart](../../../../../translated_images/mo/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.png) 然而，這個條形圖因為有太多未分組的數據而難以閱讀。您需要選擇僅想要繪製的數據，因此讓我們看看基於鳥類類別的鳥類長度。 @@ -178,7 +178,7 @@ ggplot(birds_count,aes(Category,n))+geom_bar(stat="identity")+coord_flip() ``` 您首先計算 `Category` 列中的唯一值，然後將它們排序到新的數據框 `birds_count` 中。這些排序後的數據在相同層次中進行分級，以便按排序方式繪製。使用 `ggplot2`，您接著繪製條形圖。`coord_flip()` 則繪製水平條形圖。 -![category-length](../../../../../translated_images/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.mo.png) +![category-length](../../../../../translated_images/mo/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.png) 這個條形圖很好地展示了每個類別中鳥類的數量。一眼就能看出，在這個地區最多的鳥類是鴨/鵝/水禽類別。明尼蘇達州是“萬湖之地”，所以這並不令人驚訝！ @@ -201,7 +201,7 @@ ggplot(birds_grouped,aes(Category,MaxLength))+geom_bar(stat="identity")+coord_fl ``` 我們按 `Category` 分組 `birds_filtered` 數據，然後繪製條形圖。 -![comparing data](../../../../../translated_images/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.mo.png) +![comparing data](../../../../../translated_images/mo/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.png) 這裡沒有什麼令人驚訝的：蜂鳥的最大長度比鵜鶘或鵝要小得多。當數據符合邏輯時，這是件好事！ @@ -213,7 +213,7 @@ ggplot(data=birds_grouped, aes(x=Category)) + geom_bar(aes(y=MinLength), stat="identity", position="identity", fill='orange')+ coord_flip() ``` -![super-imposed values](../../../../../translated_images/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.mo.png) +![super-imposed values](../../../../../translated_images/mo/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.png) ## 🚀 挑戰 diff --git a/translations/mo/3-Data-Visualization/R/10-visualization-distributions/README.md b/translations/mo/3-Data-Visualization/R/10-visualization-distributions/README.md index 9a4278ab..c893a479 100644 --- a/translations/mo/3-Data-Visualization/R/10-visualization-distributions/README.md +++ b/translations/mo/3-Data-Visualization/R/10-visualization-distributions/README.md @@ -45,7 +45,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + geom_point() + ggtitle("Max Length per order") + coord_flip() ``` -![每目最大長度](../../../../../translated_images/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.mo.png) +![每目最大長度](../../../../../translated_images/mo/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.png) 這提供了每個鳥類目的一般身體長度分佈概況，但這並不是顯示真實分佈的最佳方式。通常使用直方圖來完成這項任務。 ## 使用直方圖 @@ -56,7 +56,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=10)+ylab('Frequency') ``` -![整個數據集的分佈](../../../../../translated_images/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.mo.png) +![整個數據集的分佈](../../../../../translated_images/mo/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.png) 如你所見，這個數據集中的 400 多種鳥類大多數最大體重都低於 2000。通過將 `bins` 參數更改為更高的數字，例如 30，可以獲得更多的洞察： @@ -64,7 +64,7 @@ ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![30個區間的分佈](../../../../../translated_images/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.mo.png) +![30個區間的分佈](../../../../../translated_images/mo/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.png) 此圖表以更細緻的方式顯示了分佈。通過確保僅選擇特定範圍內的數據，可以創建一個不那麼偏向左側的圖表： @@ -76,7 +76,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![篩選後的直方圖](../../../../../translated_images/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.mo.png) +![篩選後的直方圖](../../../../../translated_images/mo/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.png) ✅ 嘗試其他篩選條件和數據點。要查看數據的完整分佈，移除 `['MaxBodyMass']` 篩選器以顯示標籤分佈。 @@ -90,7 +90,7 @@ ggplot(data=birds_filtered_1, aes(x=MaxBodyMass, y=MaxLength) ) + ``` 沿著預期的軸，這兩個元素之間似乎存在預期的相關性，其中有一個特別強的收斂點： -![2D 圖表](../../../../../translated_images/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.mo.png) +![2D 圖表](../../../../../translated_images/mo/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.png) 直方圖默認適用於數值型數據。如果你需要查看基於文本數據的分佈該怎麼辦？ ## 使用文本數據探索數據集的分佈 @@ -121,7 +121,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + scale_fill_manual(name="Conservation Status",values=c("red","green","blue","pink"),labels=c("Endangered","Near Threathened","Vulnerable","Least Concern")) ``` -![翼展與保育狀態的對比](../../../../../translated_images/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.mo.png) +![翼展與保育狀態的對比](../../../../../translated_images/mo/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.png) 最小翼展和保育狀態之間似乎沒有良好的相關性。使用此方法測試數據集的其他元素。你可以嘗試不同的篩選條件。你是否發現了任何相關性？ @@ -135,7 +135,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + geom_density() ``` -![密度圖](../../../../../translated_images/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.mo.png) +![密度圖](../../../../../translated_images/mo/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.png) 你可以看到該圖表反映了之前的最小翼展數據；它只是稍微平滑了一些。如果你想重新訪問第二個圖表中那條鋸齒狀的最大體重線，可以通過使用此方法非常好地將其平滑化： @@ -143,7 +143,7 @@ ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density() ``` -![體重密度](../../../../../translated_images/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.mo.png) +![體重密度](../../../../../translated_images/mo/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.png) 如果你想要一條平滑但不過於平滑的線，可以編輯 `adjust` 參數： @@ -151,7 +151,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density(adjust = 1/5) ``` -![較不平滑的體重密度](../../../../../translated_images/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.mo.png) +![較不平滑的體重密度](../../../../../translated_images/mo/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.png) ✅ 閱讀此類圖表可用的參數並進行實驗！ @@ -161,7 +161,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + ggplot(data=birds_filtered_1,aes(x = MaxBodyMass, fill = Order)) + geom_density(alpha=0.5) ``` -![每目體重密度](../../../../../translated_images/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.mo.png) +![每目體重密度](../../../../../translated_images/mo/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.png) ## 🚀 挑戰 diff --git a/translations/mo/3-Data-Visualization/R/11-visualization-proportions/README.md b/translations/mo/3-Data-Visualization/R/11-visualization-proportions/README.md index 599788a7..c084b92a 100644 --- a/translations/mo/3-Data-Visualization/R/11-visualization-proportions/README.md +++ b/translations/mo/3-Data-Visualization/R/11-visualization-proportions/README.md @@ -93,7 +93,7 @@ pie(grouped$count,grouped$class, main="Edible?") ``` 完成了，一個圓餅圖展示了根據這兩類蘑菇的數據比例。正確排列標籤的順序非常重要，尤其是在這裡，因此請務必確認標籤數組的構建順序！ -![圓餅圖](../../../../../translated_images/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.mo.png) +![圓餅圖](../../../../../translated_images/mo/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.png) ## 甜甜圈圖！ @@ -128,7 +128,7 @@ library(webr) PieDonut(habitat, aes(habitat, count=count)) ``` -![甜甜圈圖](../../../../../translated_images/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.mo.png) +![甜甜圈圖](../../../../../translated_images/mo/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.png) 這段代碼使用了兩個庫——ggplot2 和 webr。使用 webr 庫的 PieDonut 函數，我們可以輕鬆創建甜甜圈圖！ @@ -166,7 +166,7 @@ waffle((cap_color$count/10), rows = 7, title = "Waffle Chart")+scale_fill_manual 使用華夫圖，你可以清楚地看到這個蘑菇數據集中帽顏色的比例。有趣的是，有許多綠色帽子的蘑菇！ -![華夫圖](../../../../../translated_images/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.mo.png) +![華夫圖](../../../../../translated_images/mo/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.png) 在這節課中，你學到了三種視覺化比例的方法。首先，你需要將數據分組到分類中，然後決定哪種方式最適合展示數據——圓餅圖、甜甜圈圖或華夫圖。這些方法都很有趣，並能讓用戶快速了解數據集。 diff --git a/translations/mo/3-Data-Visualization/R/12-visualization-relationships/README.md b/translations/mo/3-Data-Visualization/R/12-visualization-relationships/README.md index f882d25d..db91edac 100644 --- a/translations/mo/3-Data-Visualization/R/12-visualization-relationships/README.md +++ b/translations/mo/3-Data-Visualization/R/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ library(ggplot2) ggplot(honey, aes(x = priceperlb, y = state)) + geom_point(colour = "blue") ``` -![scatterplot 1](../../../../../translated_images/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.mo.png) +![scatterplot 1](../../../../../translated_images/mo/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.png) 現在，使用蜂蜜色彩方案展示價格如何隨年份演變。您可以通過添加 'scale_color_gradientn' 參數來展示年份的變化： @@ -61,7 +61,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ggplot(honey, aes(x = priceperlb, y = state, color=year)) + geom_point()+scale_color_gradientn(colours = colorspace::heat_hcl(7)) ``` -![scatterplot 2](../../../../../translated_images/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.mo.png) +![scatterplot 2](../../../../../translated_images/mo/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.png) 通過這種色彩方案的改變，您可以明顯看到蜂蜜每磅價格在多年來的強烈增長趨勢。事實上，如果您查看數據中的樣本集（例如選擇亞利桑那州），您可以看到價格逐年上漲的模式，僅有少數例外： @@ -92,7 +92,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ``` 您可以看到點的大小逐漸增大。 -![scatterplot 3](../../../../../translated_images/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.mo.png) +![scatterplot 3](../../../../../translated_images/mo/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.png) 這是否是一個簡單的供需問題？由於氣候變化和蜂群崩潰等因素，是否每年可供購買的蜂蜜減少，導致價格上漲？ @@ -107,7 +107,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab ``` 答案：是的，但在2003年左右有一些例外： -![line chart 1](../../../../../translated_images/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.mo.png) +![line chart 1](../../../../../translated_images/mo/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.png) 問題：那麼在2003年，我們是否也能看到蜂蜜供應的激增？如果您查看總產量逐年變化呢？ @@ -115,7 +115,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab qplot(honey$year,honey$totalprod, geom='smooth', span =0.5, xlab = "year",ylab = "totalprod") ``` -![line chart 2](../../../../../translated_images/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.mo.png) +![line chart 2](../../../../../translated_images/mo/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.png) 答案：並不完全。如果您查看總產量，實際上在那一年似乎有所增加，儘管總體而言蜂蜜的生產量在這些年中呈下降趨勢。 @@ -135,7 +135,7 @@ ggplot(honey, aes(x=yieldpercol, y = numcol,group = 1)) + ``` 在此視覺化中，您可以比較每群產量和蜂群數量逐年變化，並將列數設置為3： -![facet grid](../../../../../translated_images/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.mo.png) +![facet grid](../../../../../translated_images/mo/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.png) 對於此數據集，逐年和逐州比較蜂群數量和每群產量，並未顯示出特別突出的情況。是否有其他方式來尋找這兩個變量之間的相關性？ @@ -152,7 +152,7 @@ plot(honey$year, honey$yieldpercol, pch = 17, col = 3, axis(side = 4, at = pretty(range(y2))) mtext("colony yield", side = 4, line = 3) ``` -![superimposed plots](../../../../../translated_images/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.mo.png) +![superimposed plots](../../../../../translated_images/mo/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.png) 雖然在2003年並未有明顯的異常，但這讓我們可以以一個稍微樂觀的結論結束本課：儘管蜂群數量總體上在下降，但蜂群數量正在穩定，即使每群產量在減少。 diff --git a/translations/mo/3-Data-Visualization/R/13-meaningful-vizualizations/README.md b/translations/mo/3-Data-Visualization/R/13-meaningful-vizualizations/README.md index 21ce9a25..487bb79e 100644 --- a/translations/mo/3-Data-Visualization/R/13-meaningful-vizualizations/README.md +++ b/translations/mo/3-Data-Visualization/R/13-meaningful-vizualizations/README.md @@ -47,25 +47,25 @@ CO_OP_TRANSLATOR_METADATA: 即使數據科學家謹慎選擇了合適的圖表類型，數據仍然可能以某種方式被展示來支持某種觀點，往往以犧牲數據本身為代價。有許多誤導性圖表和信息圖的例子！ -[![Alberto Cairo 的《How Charts Lie》](../../../../../translated_images/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.mo.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "How charts lie") +[![Alberto Cairo 的《How Charts Lie》](../../../../../translated_images/mo/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "How charts lie") > 🎥 點擊上方圖片觀看有關誤導性圖表的會議演講 這張圖表反轉了 X 軸，根據日期顯示了與事實相反的內容： -![糟糕的圖表 1](../../../../../translated_images/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.mo.png) +![糟糕的圖表 1](../../../../../translated_images/mo/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.png) [這張圖表](https://media.firstcoastnews.com/assets/WTLV/images/170ae16f-4643-438f-b689-50d66ca6a8d8/170ae16f-4643-438f-b689-50d66ca6a8d8_1140x641.jpg) 更具誤導性，因為人們的目光會被吸引到右側，得出隨時間推移各縣的 COVID 病例數下降的結論。事實上，如果仔細查看日期，你會發現日期被重新排列以製造出誤導性的下降趨勢。 -![糟糕的圖表 2](../../../../../translated_images/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.mo.jpg) +![糟糕的圖表 2](../../../../../translated_images/mo/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.jpg) 這個臭名昭著的例子使用顏色和翻轉的 Y 軸來誤導：原本應該得出槍支友好立法通過後槍支死亡率激增的結論，事實上卻讓人誤以為情況正好相反： -![糟糕的圖表 3](../../../../../translated_images/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.mo.jpg) +![糟糕的圖表 3](../../../../../translated_images/mo/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.jpg) 這張奇怪的圖表展示了比例如何被操控，效果令人捧腹： -![糟糕的圖表 4](../../../../../translated_images/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.mo.jpg) +![糟糕的圖表 4](../../../../../translated_images/mo/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.jpg) 比較不可比的事物是另一種不正當的手段。有一個[精彩的網站](https://tylervigen.com/spurious-correlations)專門展示「虛假的相關性」，例如緬因州的離婚率與人造奶油的消耗量之間的「事實」相關性。一個 Reddit 群組也收集了[糟糕的數據使用](https://www.reddit.com/r/dataisugly/top/?t=all)。 @@ -100,13 +100,13 @@ CO_OP_TRANSLATOR_METADATA: 如果你的數據在 X 軸上是文本且冗長，可以將文本角度調整以提高可讀性。[plot3D](https://cran.r-project.org/web/packages/plot3D/index.html) 提供了 3D 繪圖功能，如果你的數據支持它，可以使用它製作更高級的數據視覺化。 -![3D 圖表](../../../../../translated_images/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.mo.png) +![3D 圖表](../../../../../translated_images/mo/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.png) ## 動畫和 3D 圖表展示 如今一些最好的數據視覺化是動畫化的。Shirley Wu 使用 D3 創作了令人驚嘆的作品，例如「[電影之花](http://bl.ocks.org/sxywu/raw/d612c6c653fb8b4d7ff3d422be164a5d/)」，每朵花都是一部電影的視覺化。另一個例子是《衛報》的「Bussed Out」，這是一個結合 Greensock 和 D3 的視覺化和滾動敘事文章格式的互動體驗，展示了紐約市如何通過將無家可歸者送出城市來處理其無家可歸問題。 -![Bussed Out](../../../../../translated_images/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.mo.png) +![Bussed Out](../../../../../translated_images/mo/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.png) > 「Bussed Out: How America Moves its Homeless」來自[衛報](https://www.theguardian.com/us-news/ng-interactive/2017/dec/20/bussed-out-america-moves-homeless-people-country-study)。視覺化由 Nadieh Bremer 和 Shirley Wu 創作 @@ -116,7 +116,7 @@ CO_OP_TRANSLATOR_METADATA: 你將完成一個網頁應用，展示這個社交網絡的動畫化視圖。它使用了一個庫，該庫旨在使用 Vue.js 和 D3 創建[網絡視覺化](https://github.com/emiliorizzo/vue-d3-network)。當應用運行時，你可以在屏幕上拖動節點來重新排列數據。 -![危險關係](../../../../../translated_images/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.mo.png) +![危險關係](../../../../../translated_images/mo/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.png) ## 專案：使用 D3.js 建立一個展示網絡的圖表 diff --git a/translations/mo/3-Data-Visualization/README.md b/translations/mo/3-Data-Visualization/README.md index e4865f4f..b7736f08 100644 --- a/translations/mo/3-Data-Visualization/README.md +++ b/translations/mo/3-Data-Visualization/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 視覺化 -![一隻蜜蜂停在薰衣草花上](../../../translated_images/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.mo.jpg) +![一隻蜜蜂停在薰衣草花上](../../../translated_images/mo/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.jpg) > 照片由 Jenna Lee 提供，來源於 Unsplash 視覺化數據是數據科學家最重要的任務之一。圖片勝過千言萬語，視覺化可以幫助你識別數據中的各種有趣部分，例如峰值、異常值、分組、趨勢等，這些都能幫助你理解數據背後的故事。 diff --git a/translations/mo/4-Data-Science-Lifecycle/14-Introduction/README.md b/translations/mo/4-Data-Science-Lifecycle/14-Introduction/README.md index dad46ae5..7add034d 100644 --- a/translations/mo/4-Data-Science-Lifecycle/14-Introduction/README.md +++ b/translations/mo/4-Data-Science-Lifecycle/14-Introduction/README.md @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 本課程將重點放在生命週期的三個部分：資料捕捉、資料處理和資料維護。 -![資料科學生命週期圖示](../../../../translated_images/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.mo.jpg) +![資料科學生命週期圖示](../../../../translated_images/mo/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.jpg) > 圖片來源：[Berkeley School of Information](https://ischoolonline.berkeley.edu/data-science/what-is-data-science/) ## 資料捕捉 @@ -98,7 +98,7 @@ CO_OP_TRANSLATOR_METADATA: |團隊資料科學過程 (TDSP)|跨行業標準資料挖掘過程 (CRISP-DM)| |--|--| -|![團隊資料科學生命週期](../../../../translated_images/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.mo.png) | ![資料科學過程聯盟圖示](../../../../translated_images/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.mo.png) | +|![團隊資料科學生命週期](../../../../translated_images/mo/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.png) | ![資料科學過程聯盟圖示](../../../../translated_images/mo/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.png) | | 圖片來源：[Microsoft](https://docs.microsoft.comazure/architecture/data-science-process/lifecycle) | 圖片來源：[Data Science Process Alliance](https://www.datascience-pm.com/crisp-dm-2/) | ## [課後測驗](https://ff-quizzes.netlify.app/en/ds/quiz/27) diff --git a/translations/mo/4-Data-Science-Lifecycle/README.md b/translations/mo/4-Data-Science-Lifecycle/README.md index 366f5497..fa6704ef 100644 --- a/translations/mo/4-Data-Science-Lifecycle/README.md +++ b/translations/mo/4-Data-Science-Lifecycle/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 數據科學生命週期 -![communication](../../../translated_images/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.mo.jpg) +![communication](../../../translated_images/mo/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.jpg) > 圖片由 Headway 提供，來自 Unsplash 在這些課程中，您將探索數據科學生命週期的一些方面，包括數據的分析和溝通。 diff --git a/translations/mo/5-Data-Science-In-Cloud/README.md b/translations/mo/5-Data-Science-In-Cloud/README.md index 1644c838..a94c7faf 100644 --- a/translations/mo/5-Data-Science-In-Cloud/README.md +++ b/translations/mo/5-Data-Science-In-Cloud/README.md @@ -9,13 +9,13 @@ CO_OP_TRANSLATOR_METADATA: --> # 雲端中的數據科學 -![cloud-picture](../../../translated_images/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.mo.jpg) +![cloud-picture](../../../translated_images/mo/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.jpg) > 圖片來源：[Jelleke Vanooteghem](https://unsplash.com/@ilumire) 來自 [Unsplash](https://unsplash.com/s/photos/cloud?orientation=landscape) 當涉及到使用大數據進行數據科學時，雲端可以成為改變遊戲規則的關鍵。在接下來的三節課中，我們將了解什麼是雲端以及為什麼它非常有用。我們還將探索一個心臟衰竭數據集，並建立一個模型來幫助評估某人發生心臟衰竭的可能性。我們將利用雲端的強大功能來訓練、部署和以兩種不同的方式使用模型。一種方式是僅使用用戶界面，以低代碼/無代碼的方式進行；另一種方式是使用 Azure Machine Learning Software Developer Kit (Azure ML SDK)。 -![project-schema](../../../translated_images/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.mo.png) +![project-schema](../../../translated_images/mo/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.png) ### 主題 diff --git a/translations/mo/6-Data-Science-In-Wild/20-Real-World-Examples/README.md b/translations/mo/6-Data-Science-In-Wild/20-Real-World-Examples/README.md index b6bcc282..3cc4a835 100644 --- a/translations/mo/6-Data-Science-In-Wild/20-Real-World-Examples/README.md +++ b/translations/mo/6-Data-Science-In-Wild/20-Real-World-Examples/README.md @@ -41,7 +41,7 @@ CO_OP_TRANSLATOR_METADATA: * [醫療保健中的數據科學](https://data-flair.training/blogs/data-science-in-healthcare/) - 強調應用如醫學影像（例如 MRI、X光、CT掃描）、基因組學（DNA測序）、藥物開發（風險評估、成功預測）、預測分析（患者護理和供應物流）、疾病追蹤和預防等。 -![數據科學在現實世界中的應用](../../../../translated_images/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.mo.png) 圖片來源：[Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) +![數據科學在現實世界中的應用](../../../../translated_images/mo/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.png) 圖片來源：[Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) 該圖展示了其他領域和應用數據科學技術的例子。想探索更多應用？請查看下面的[回顧與自學](../../../../6-Data-Science-In-Wild/20-Real-World-Examples)部分。 diff --git a/translations/mo/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md b/translations/mo/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md index c0e2a511..88522f95 100644 --- a/translations/mo/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md +++ b/translations/mo/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md @@ -22,7 +22,7 @@ Explorer界面（如下圖所示）允許您選擇一個數據集（從提供的 2. 探索數據集[目錄](https://planetarycomputer.microsoft.com/catalog)——了解每個數據集的用途。 3. 使用Explorer——選擇一個您感興趣的數據集，選擇相關的查詢和渲染選項。 -![行星電腦Explorer](../../../../translated_images/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.mo.png) +![行星電腦Explorer](../../../../translated_images/mo/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.png) `您的任務：` 現在，研究瀏覽器中渲染的可視化，並回答以下問題： diff --git a/translations/mo/CONTRIBUTING.md b/translations/mo/CONTRIBUTING.md index 221314a0..3ec0e899 100644 --- a/translations/mo/CONTRIBUTING.md +++ b/translations/mo/CONTRIBUTING.md @@ -311,7 +311,7 @@ def calculate_mean(data): import pandas as pd ``` ```` -- 為圖片添加替代文字：`![Alt text](../../translated_images/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.mo.png)` +- 為圖片添加替代文字：`![Alt text](../../translated_images/mo/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.png)` - 保持合理的行長度（約 80-100 字元） ### Python diff --git a/translations/mo/README.md b/translations/mo/README.md index 2bde4180..0a1e1dea 100644 --- a/translations/mo/README.md +++ b/translations/mo/README.md @@ -33,7 +33,7 @@ Azure Cloud Advocates at Microsoft 很高興提供一個為期 10 週、共 20 **🙏 特別感謝 🙏 我們的 [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/) 作者、審閱者與內容貢獻者，** 特別是 Aaryan Arora、[Aditya Garg](https://github.com/AdityaGarg00)、[Alondra Sanchez](https://www.linkedin.com/in/alondra-sanchez-molina/)、[Ankita Singh](https://www.linkedin.com/in/ankitasingh007)、[Anupam Mishra](https://www.linkedin.com/in/anupam--mishra/)、[Arpita Das](https://www.linkedin.com/in/arpitadas01/)、ChhailBihari Dubey、[Dibri Nsofor](https://www.linkedin.com/in/dibrinsofor)、[Dishita Bhasin](https://www.linkedin.com/in/dishita-bhasin-7065281bb)、[Majd Safi](https://www.linkedin.com/in/majd-s/)、[Max Blum](https://www.linkedin.com/in/max-blum-6036a1186/)、[Miguel Correa](https://www.linkedin.com/in/miguelmque/)、[Mohamma Iftekher (Iftu) Ebne Jalal](https://twitter.com/iftu119)、[Nawrin Tabassum](https://www.linkedin.com/in/nawrin-tabassum)、[Raymond Wangsa Putra](https://www.linkedin.com/in/raymond-wp/)、[Rohit Yadav](https://www.linkedin.com/in/rty2423)、Samridhi Sharma、[Sanya Sinha](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200), [Sheena Narula](https://www.linkedin.com/in/sheena-narua-n/)、[Tauqeer Ahmad](https://www.linkedin.com/in/tauqeerahmad5201/)、Yogendrasingh Pawar 、[Vidushi Gupta](https://www.linkedin.com/in/vidushi-gupta07/)、[Jasleen Sondhi](https://www.linkedin.com/in/jasleen-sondhi/) -|![Sketchnote by @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.mo.png)| +|![Sketchnote by @sketchthedocs https://sketchthedocs.dev](../../translated_images/mo/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.png)| |:---:| | Data Science For Beginners - _速寫筆記（作者：[ @nitya ](https://twitter.com/nitya)）_ | @@ -52,7 +52,7 @@ Azure Cloud Advocates at Microsoft 很高興提供一個為期 10 週、共 20 我們正在舉辦 Discord 的 Learn with AI 系列活動，了解更多並於 2025 年 9 月 18 日至 30 日加入我們，請參閱 [Learn with AI Series](https://aka.ms/learnwithai/discord)。你將會獲得使用 GitHub Copilot 進行資料科學的祕訣與技巧。 -![Learn with AI 系列](../../translated_images/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.mo.jpg) +![Learn with AI 系列](../../translated_images/mo/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.jpg) # 你是學生嗎？ @@ -132,7 +132,7 @@ Azure Cloud Advocates at Microsoft 很高興提供一個為期 10 週、共 20 ## 課程列表 -|![ 手繪筆記由 @sketchthedocs 提供 https://sketchthedocs.dev](../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.mo.png)| +|![ 手繪筆記由 @sketchthedocs 提供 https://sketchthedocs.dev](../../translated_images/mo/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png)| |:---:| | 初學者資料科學：路線圖 - _手繪筆記作者 [@nitya](https://twitter.com/nitya)_ | diff --git a/translations/mo/sketchnotes/README.md b/translations/mo/sketchnotes/README.md index f834eaf6..8334424b 100644 --- a/translations/mo/sketchnotes/README.md +++ b/translations/mo/sketchnotes/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: Nitya Narasimhan，藝術家 -![roadmap sketchnote](../../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.mo.png) +![roadmap sketchnote](../../../translated_images/mo/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png) --- diff --git a/translations/mr/1-Introduction/01-defining-data-science/README.md b/translations/mr/1-Introduction/01-defining-data-science/README.md index 2b6f324e..9c0e3e61 100644 --- a/translations/mr/1-Introduction/01-defining-data-science/README.md +++ b/translations/mr/1-Introduction/01-defining-data-science/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: --- -[![डेटा सायन्सची व्याख्या व्हिडिओ](../../../../translated_images/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.mr.png)](https://youtu.be/beZ7Mb_oz9I) +[![डेटा सायन्सची व्याख्या व्हिडिओ](../../../../translated_images/mr/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.png)](https://youtu.be/beZ7Mb_oz9I) ## [पूर्व-व्याख्यान प्रश्नमंजुषा](https://ff-quizzes.netlify.app/en/ds/quiz/0) @@ -127,7 +127,7 @@ CO_OP_TRANSLATOR_METADATA: या आव्हानात, आपण डेटा सायन्स क्षेत्राशी संबंधित संकल्पना शोधण्याचा प्रयत्न करू, ते मजकूर पाहून. आपण डेटा सायन्सवरील विकिपीडिया लेख घेऊ, मजकूर डाउनलोड आणि प्रक्रिया करू, आणि नंतर खालीलप्रमाणे एक वर्ड क्लाउड तयार करू: -![डेटा सायन्ससाठी वर्ड क्लाउड](../../../../translated_images/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.mr.png) +![डेटा सायन्ससाठी वर्ड क्लाउड](../../../../translated_images/mr/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.png) कोड वाचण्यासाठी [`notebook.ipynb`](../../../../1-Introduction/01-defining-data-science/notebook.ipynb ':ignore') ला भेट द्या. तुम्ही कोड चालवू शकता आणि तो प्रत्यक्षात डेटा ट्रान्सफॉर्मेशन कसे करतो ते पाहू शकता. diff --git a/translations/mr/1-Introduction/04-stats-and-probability/README.md b/translations/mr/1-Introduction/04-stats-and-probability/README.md index 2d0b0146..0a897311 100644 --- a/translations/mr/1-Introduction/04-stats-and-probability/README.md +++ b/translations/mr/1-Introduction/04-stats-and-probability/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: सांख्यिकी आणि संभाव्यता सिद्धांत हे गणिताचे दोन परस्पर संबंधित क्षेत्र आहेत जे डेटा सायन्ससाठी अत्यंत महत्त्वाचे आहेत. गणिताचा सखोल अभ्यास न करता डेटा हाताळणे शक्य आहे, परंतु किमान काही मूलभूत संकल्पना जाणून घेणे चांगले आहे. येथे आम्ही एक छोटा परिचय सादर करू जो तुम्हाला सुरुवात करण्यात मदत करेल. -[![Intro Video](../../../../translated_images/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.mr.png)](https://youtu.be/Z5Zy85g4Yjw) +[![Intro Video](../../../../translated_images/mr/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.png)](https://youtu.be/Z5Zy85g4Yjw) ## [पूर्व-व्याख्यान प्रश्नमंजूषा](https://ff-quizzes.netlify.app/en/ds/quiz/6) @@ -39,7 +39,7 @@ CO_OP_TRANSLATOR_METADATA: आपण फक्त एखाद्या व्हेरिएबलने दिलेल्या मूल्यांच्या अंतरालात पडण्याच्या संभाव्यतेबद्दल बोलू शकतो, उदा. P(t1≤X2). या प्रकरणात, संभाव्यता वितरण **संभाव्यता घनता फंक्शन** p(x) द्वारे वर्णन केले जाते, ज्यामुळे -![P(t_1\le X सहसंबंध आणि परस्पर संबंधाचे अधिक उदाहरणे [संबंधित नोटबुक](notebook.ipynb) मध्ये सापडू शकतात. diff --git a/translations/mr/1-Introduction/README.md b/translations/mr/1-Introduction/README.md index ffc4c990..55572cc5 100644 --- a/translations/mr/1-Introduction/README.md +++ b/translations/mr/1-Introduction/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # डेटा सायन्सची ओळख -![डेटा अॅक्शनमध्ये](../../../translated_images/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.mr.jpg) +![डेटा अॅक्शनमध्ये](../../../translated_images/mr/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.jpg) > फोटो स्टीफन डॉसन यांनी Unsplash वर प्रकाशित केला. या धड्यांमध्ये, तुम्हाला डेटा सायन्स कसे परिभाषित केले जाते हे समजेल आणि डेटा सायंटिस्टने विचारात घ्याव्या लागणाऱ्या नैतिक बाबींबद्दल शिकायला मिळेल. तुम्हाला डेटा कसा परिभाषित केला जातो हे समजेल आणि डेटा सायन्सच्या मुख्य शैक्षणिक शाखा असलेल्या सांख्यिकी आणि संभाव्यता याबद्दल थोडेसे शिकायला मिळेल. diff --git a/translations/mr/2-Working-With-Data/07-python/README.md b/translations/mr/2-Working-With-Data/07-python/README.md index d6c84215..f2468e54 100644 --- a/translations/mr/2-Working-With-Data/07-python/README.md +++ b/translations/mr/2-Working-With-Data/07-python/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: | :-------------------------------------------------------------------------------------------------------: | | Python सोबत काम करणे - _Sketchnote by [@nitya](https://twitter.com/nitya)_ | -[![Intro Video](../../../../translated_images/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.mr.png)](https://youtu.be/dZjWOGbsN4Y) +[![Intro Video](../../../../translated_images/mr/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.png)](https://youtu.be/dZjWOGbsN4Y) डेटाबेस डेटा साठवण्यासाठी आणि क्वेरी भाषांचा वापर करून त्यावर क्वेरी करण्यासाठी अत्यंत कार्यक्षम पद्धती प्रदान करतात, परंतु डेटा प्रक्रिया करण्याचा सर्वात लवचिक मार्ग म्हणजे स्वतःचा प्रोग्राम लिहून डेटा हाताळणे. अनेक वेळा, डेटाबेस क्वेरी करणे अधिक प्रभावी ठरते. परंतु काही वेळा जेव्हा अधिक जटिल डेटा प्रक्रिया आवश्यक असते, तेव्हा ती SQL वापरून सहजपणे करता येत नाही. डेटा प्रक्रिया कोणत्याही प्रोग्रामिंग भाषेत प्रोग्राम केली जाऊ शकते, परंतु काही भाषा डेटा सोबत काम करण्याच्या दृष्टीने उच्च स्तरावर असतात. डेटा वैज्ञानिक सामान्यतः खालील भाषांपैकी एक निवडतात: @@ -73,7 +73,7 @@ print(f"Length of index is {len(idx)}") items_sold = pd.Series(np.random.randint(25,50,size=len(idx)),index=idx) items_sold.plot() ``` -![Time Series Plot](../../../../translated_images/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.mr.png) +![Time Series Plot](../../../../translated_images/mr/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.png) आता समजा प्रत्येक आठवड्यात आम्ही मित्रांसाठी पार्टी आयोजित करतो आणि पार्टीसाठी अतिरिक्त 10 ice-cream पॅक्स घेतो. आम्ही हे दाखवण्यासाठी आठवड्याने index केलेली आणखी एक series तयार करू शकतो: ```python @@ -84,7 +84,7 @@ additional_items = pd.Series(10,index=pd.date_range(start_date,end_date,freq="W" total_items = items_sold.add(additional_items,fill_value=0) total_items.plot() ``` -![Time Series Plot](../../../../translated_images/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.mr.png) +![Time Series Plot](../../../../translated_images/mr/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.png) > **Note** की आम्ही साधी syntax `total_items+additional_items` वापरत नाही. जर आम्ही तसे केले असते, तर आम्हाला resulting series मध्ये अनेक `NaN` (*Not a Number*) मूल्ये मिळाली असती. कारण `additional_items` series मध्ये काही index point साठी missing values आहेत, आणि `NaN` कशाशीही जोडल्यास `NaN` मिळते. त्यामुळे addition दरम्यान `fill_value` parameter specify करणे आवश्यक आहे. @@ -93,7 +93,7 @@ Time series सोबत, आपण वेगवेगळ्या time interval monthly = total_items.resample("1M").mean() ax = monthly.plot(kind='bar') ``` -![Monthly Time Series Averages](../../../../translated_images/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.mr.png) +![Monthly Time Series Averages](../../../../translated_images/mr/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.png) ### DataFrame @@ -219,7 +219,7 @@ df = pd.read_csv('file.csv') आम्ही डेटा कसा हाताळायचा हे दाखवायचे असल्याने, आम्ही तुम्हाला [`notebook-covidspread.ipynb`](notebook-covidspread.ipynb) उघडून वरपासून खालपर्यंत वाचण्याचे आमंत्रण देतो. तुम्ही सेल्स चालवू शकता आणि शेवटी आम्ही तुमच्यासाठी ठेवलेल्या काही आव्हाने पूर्ण करू शकता. -![COVID Spread](../../../../translated_images/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.mr.png) +![COVID Spread](../../../../translated_images/mr/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.png) > जर तुम्हाला Jupyter Notebook मध्ये कोड कसा चालवायचा माहित नसेल, तर [या लेखाचा](https://soshnikov.com/education/how-to-execute-notebooks-from-github/) अभ्यास करा. @@ -241,7 +241,7 @@ df = pd.read_csv('file.csv') [`notebook-papers.ipynb`](notebook-papers.ipynb) उघडा आणि वरपासून खालपर्यंत वाचा. तुम्ही सेल्स चालवू शकता आणि शेवटी आम्ही तुमच्यासाठी ठेवलेल्या काही आव्हाने पूर्ण करू शकता. -![Covid Medical Treatment](../../../../translated_images/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.mr.png) +![Covid Medical Treatment](../../../../translated_images/mr/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.png) ## प्रतिमा डेटाचे प्रोसेसिंग diff --git a/translations/mr/2-Working-With-Data/README.md b/translations/mr/2-Working-With-Data/README.md index 3e5a9118..62c22c0a 100644 --- a/translations/mr/2-Working-With-Data/README.md +++ b/translations/mr/2-Working-With-Data/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # डेटा सोबत काम करणे -![डेटा प्रेम](../../../translated_images/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.mr.jpg) +![डेटा प्रेम](../../../translated_images/mr/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.jpg) > फोटो अलेक्झांडर सिन यांनी Unsplash वर Unsplash वर दिला आहे या धड्यांमध्ये, तुम्ही डेटा व्यवस्थापित करणे, बदलणे आणि अनुप्रयोगांमध्ये वापरण्याचे काही मार्ग शिकाल. तुम्ही रिलेशनल आणि नॉन-रिलेशनल डेटाबेसबद्दल शिकाल आणि त्यामध्ये डेटा कसा संग्रहित केला जाऊ शकतो हे समजून घ्याल. तुम्ही डेटा व्यवस्थापित करण्यासाठी Python सोबत काम करण्याचे मूलभूत तत्त्व शिकाल आणि Python चा वापर करून डेटा व्यवस्थापित आणि शोधण्यासाठी असलेल्या अनेक पद्धती शोधाल. diff --git a/translations/mr/3-Data-Visualization/12-visualization-relationships/README.md b/translations/mr/3-Data-Visualization/12-visualization-relationships/README.md index 41154d74..bb9ca727 100644 --- a/translations/mr/3-Data-Visualization/12-visualization-relationships/README.md +++ b/translations/mr/3-Data-Visualization/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ honey.head() ```python sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ``` -![scatterplot 1](../../../../translated_images/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.mr.png) +![scatterplot 1](../../../../translated_images/mr/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.png) आता, मधाच्या रंगसंगतीसह वर्षानुवर्षे किंमत कशी बदलते हे दाखवा. तुम्ही 'hue' पॅरामीटर जोडून वर्षानुसार बदल दाखवू शकता: @@ -60,7 +60,7 @@ sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ```python sns.relplot(x="priceperlb", y="state", hue="year", palette="YlOrBr", data=honey, height=15, aspect=.5); ``` -![scatterplot 2](../../../../translated_images/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.mr.png) +![scatterplot 2](../../../../translated_images/mr/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.png) या रंगसंगती बदलासह, तुम्हाला स्पष्टपणे दिसते की मधाच्या प्रति पाउंड किंमतीत वर्षानुवर्षे मजबूत प्रगती आहे. खरंच, जर तुम्ही डेटामधील नमुना सेट तपासला (उदाहरणार्थ, एरिझोना राज्य निवडा) तर तुम्हाला किंमतीत वर्षानुवर्षे वाढ होण्याचा नमुना दिसतो, काही अपवाद वगळता: @@ -89,7 +89,7 @@ sns.relplot(x="priceperlb", y="state", size="year", data=honey, height=15, aspec ``` तुम्हाला डॉट्सचा आकार हळूहळू वाढताना दिसतो. -![scatterplot 3](../../../../translated_images/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.mr.png) +![scatterplot 3](../../../../translated_images/mr/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.png) हे साध्या पुरवठा आणि मागणीचे प्रकरण आहे का? हवामान बदल आणि वसाहतींचा नाश यासारख्या घटकांमुळे, वर्षानुवर्षे खरेदीसाठी कमी मध उपलब्ध आहे का, आणि त्यामुळे किंमती वाढत आहेत? @@ -104,7 +104,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); ``` उत्तर: होय, काही अपवादांसह, विशेषतः 2003 च्या सुमारास: -![line chart 1](../../../../translated_images/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.mr.png) +![line chart 1](../../../../translated_images/mr/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.png) ✅ कारण Seaborn डेटा एका रेषेभोवती एकत्रित करत आहे, तो "प्रत्येक x मूल्यावर एकाधिक मोजमापांचे सरासरी आणि सरासरीभोवती 95% विश्वास अंतर प्लॉट करून" दाखवतो. [Source](https://seaborn.pydata.org/tutorial/relational.html). ही वेळखाऊ प्रक्रिया `ci=None` जोडून अक्षम केली जाऊ शकते. @@ -114,7 +114,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); sns.relplot(x="year", y="totalprod", kind="line", data=honey); ``` -![line chart 2](../../../../translated_images/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.mr.png) +![line chart 2](../../../../translated_images/mr/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.png) उत्तर: खरं तर नाही. जर तुम्ही एकूण उत्पादन पाहिले तर, त्या विशिष्ट वर्षात ते वाढलेले दिसते, जरी सामान्यतः या वर्षांमध्ये मध उत्पादन कमी होत आहे. @@ -139,7 +139,7 @@ sns.relplot( ``` या दृश्यांकनात, तुम्ही प्रति वसाहती उत्पादन आणि वसाहतींची संख्या वर्षानुवर्षे, राज्यानुसार बाजूने तुलना करू शकता, 3 कॉलम्ससाठी रॅप सेट करून: -![facet grid](../../../../translated_images/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.mr.png) +![facet grid](../../../../translated_images/mr/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.png) या डेटासेटसाठी, राज्यानुसार आणि वर्षानुसार वसाहतींची संख्या आणि त्यांचे उत्पादन याबाबत काहीही विशेषतः वेगळे दिसत नाही. या दोन व्हेरिएबल्समधील नातेसंबंध शोधण्यासाठी वेगळ्या प्रकारे पाहण्याचा प्रयत्न करता येईल का? @@ -162,7 +162,7 @@ sns.despine(right=False) plt.ylabel('colony yield') ax.figure.legend(); ``` -![superimposed plots](../../../../translated_images/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.mr.png) +![superimposed plots](../../../../translated_images/mr/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.png) 2003 च्या सुमारास डोळ्यांना काहीही वेगळे दिसत नाही, परंतु हे आपल्याला थोड्या आनंददायक नोटवर धडा संपवण्याची परवानगी देते: जरी वसाहतींची संख्या एकूण कमी होत असली तरी, वसाहतींची संख्या स्थिर होत आहे जरी त्यांचे प्रति वसाहती उत्पादन कमी होत आहे. diff --git a/translations/mr/3-Data-Visualization/R/09-visualization-quantities/README.md b/translations/mr/3-Data-Visualization/R/09-visualization-quantities/README.md index 80c1f447..42280fc6 100644 --- a/translations/mr/3-Data-Visualization/R/09-visualization-quantities/README.md +++ b/translations/mr/3-Data-Visualization/R/09-visualization-quantities/README.md @@ -67,7 +67,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` येथे, तुम्ही `ggplot2` पॅकेज इन्स्टॉल करता आणि नंतर `library("ggplot2")` कमांड वापरून ते वर्कस्पेसमध्ये आयात करता. ggplot मध्ये कोणताही प्लॉट तयार करण्यासाठी `ggplot()` फंक्शन वापरले जाते आणि तुम्ही डेटासेट, x आणि y व्हेरिएबल्स अॅट्रिब्युट्स म्हणून निर्दिष्ट करता. या प्रकरणात, आम्ही लाइन प्लॉट तयार करण्यासाठी `geom_line()` फंक्शन वापरतो. -![MaxWingspan-lineplot](../../../../../translated_images/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.mr.png) +![MaxWingspan-lineplot](../../../../../translated_images/mr/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.png) तुम्हाला लगेच काय दिसते? किमान एक आउटलाईअर आहे - हा पंखांचा विस्तार खूपच मोठा आहे! 2000+ सेंटीमीटर पंखांचा विस्तार म्हणजे 20 मीटरपेक्षा जास्त - मिनेसोटामध्ये पॅटरोडॅक्टाइल्स आहेत का? चला तपास करूया. @@ -85,7 +85,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` आम्ही `theme` मध्ये कोन निर्दिष्ट करतो आणि `xlab()` आणि `ylab()` मध्ये x आणि y अक्ष लेबल्स निर्दिष्ट करतो. `ggtitle()` ग्राफ/प्लॉटला नाव देते. -![MaxWingspan-lineplot-improved](../../../../../translated_images/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.mr.png) +![MaxWingspan-lineplot-improved](../../../../../translated_images/mr/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.png) लेबल्स 45 अंशांवर फिरवूनही वाचण्यासाठी खूप जास्त आहेत. वेगळी रणनीती वापरूया: फक्त आउटलाईअर्सना लेबल करा आणि लेबल्स चार्टमध्ये सेट करा. अधिक जागा मिळवण्यासाठी तुम्ही स्कॅटर चार्ट वापरू शकता: @@ -101,7 +101,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + तुम्हाला काय सापडते? -![MaxWingspan-scatterplot](../../../../../translated_images/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.mr.png) +![MaxWingspan-scatterplot](../../../../../translated_images/mr/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.png) ## तुमचा डेटा फिल्टर करा @@ -120,7 +120,7 @@ ggplot(data=birds_filtered, aes(x=Name, y=MaxWingspan,group=1)) + ``` आम्ही नवीन डेटाफ्रेम `birds_filtered` तयार केला आणि नंतर स्कॅटर प्लॉट तयार केला. आउटलाईअर्स फिल्टर करून, तुमचा डेटा आता अधिक सुसंगत आणि समजण्यास सोपा आहे. -![MaxWingspan-scatterplot-improved](../../../../../translated_images/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.mr.png) +![MaxWingspan-scatterplot-improved](../../../../../translated_images/mr/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.png) आता पंखांच्या विस्ताराच्या बाबतीत तरी आपल्याकडे स्वच्छ डेटासेट आहे, चला या पक्ष्यांबद्दल अधिक शोधूया. @@ -162,7 +162,7 @@ birds_filtered %>% group_by(Category) %>% ``` खालील कोडमध्ये, आम्ही [dplyr](https://www.rdocumentation.org/packages/dplyr/versions/0.7.8) आणि [lubridate](https://www.rdocumentation.org/packages/lubridate/versions/1.8.0) पॅकेजेस इन्स्टॉल करतो जे डेटा हाताळण्यासाठी आणि गटबद्ध करण्यासाठी मदत करतात, जेणेकरून स्टॅक्ड बार चार्ट प्लॉट करता येईल. प्रथम, तुम्ही पक्ष्यांच्या `Category` नुसार डेटा गटबद्ध करता आणि नंतर `MinLength`, `MaxLength`, `MinBodyMass`, `MaxBodyMass`, `MinWingspan`, `MaxWingspan` स्तंभांचे सारांश तयार करता. नंतर, `ggplot2` पॅकेज वापरून बार चार्ट प्लॉट करा आणि विविध श्रेणींसाठी रंग आणि लेबल्स निर्दिष्ट करा. -![Stacked bar chart](../../../../../translated_images/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.mr.png) +![Stacked bar chart](../../../../../translated_images/mr/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.png) हा बार चार्ट, तथापि, वाचण्यायोग्य नाही कारण खूप जास्त न गटबद्ध डेटा आहे. तुम्हाला फक्त प्लॉट करायचा डेटा निवडण्याची गरज आहे, म्हणून पक्ष्यांच्या श्रेणीवर आधारित लांबी पाहूया. @@ -177,7 +177,7 @@ ggplot(birds_count,aes(Category,n))+geom_bar(stat="identity")+coord_flip() ``` तुम्ही `Category` स्तंभातील अद्वितीय मूल्ये मोजता आणि त्यांना नवीन डेटाफ्रेम `birds_count` मध्ये क्रमवारी लावता. ही क्रमवारी लावलेली डेटा नंतर त्याच स्तरावर फॅक्टर केली जाते जेणेकरून ती क्रमवारीत प्लॉट केली जाईल. नंतर, `ggplot2` वापरून तुम्ही डेटा बार चार्टमध्ये प्लॉट करता. `coord_flip()` क्षैतिज बार्स प्लॉट करते. -![category-length](../../../../../translated_images/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.mr.png) +![category-length](../../../../../translated_images/mr/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.png) हा बार चार्ट प्रत्येक श्रेणीतील पक्ष्यांची संख्या चांगल्या प्रकारे दाखवतो. एका झटक्यात, तुम्हाला दिसते की या प्रदेशातील सर्वाधिक पक्षी बदके/हंस/पाणपक्षी या श्रेणीत आहेत. मिनेसोटा '10,000 तलावांचे प्रदेश' असल्याने हे आश्चर्यकारक नाही! @@ -200,7 +200,7 @@ ggplot(birds_grouped,aes(Category,MaxLength))+geom_bar(stat="identity")+coord_fl ``` आम्ही `birds_filtered` डेटा `Category` नुसार गटबद्ध करतो आणि नंतर बार ग्राफ प्लॉट करतो. -![comparing data](../../../../../translated_images/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.mr.png) +![comparing data](../../../../../translated_images/mr/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.png) येथे काहीही आश्चर्यकारक नाही: हुमिंगबर्ड्सचे `MaxLength` पेलिकन्स किंवा गीसेच्या तुलनेत सर्वात कमी आहे. जेव्हा डेटा तार्किक अर्थ लावतो तेव्हा ते चांगले असते! @@ -212,7 +212,7 @@ ggplot(data=birds_grouped, aes(x=Category)) + geom_bar(aes(y=MinLength), stat="identity", position="identity", fill='orange')+ coord_flip() ``` -![super-imposed values](../../../../../translated_images/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.mr.png) +![super-imposed values](../../../../../translated_images/mr/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.png) ## 🚀 आव्हान diff --git a/translations/mr/3-Data-Visualization/R/10-visualization-distributions/README.md b/translations/mr/3-Data-Visualization/R/10-visualization-distributions/README.md index db18df77..e6a3a3fd 100644 --- a/translations/mr/3-Data-Visualization/R/10-visualization-distributions/README.md +++ b/translations/mr/3-Data-Visualization/R/10-visualization-distributions/README.md @@ -45,7 +45,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + geom_point() + ggtitle("Max Length per order") + coord_flip() ``` -![प्रत्येक ऑर्डरनुसार जास्तीत जास्त लांबी](../../../../../translated_images/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.mr.png) +![प्रत्येक ऑर्डरनुसार जास्तीत जास्त लांबी](../../../../../translated_images/mr/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.png) हे प्रत्येक पक्ष्याच्या ऑर्डरनुसार शरीराच्या लांबीचे सामान्य वितरण दर्शवते, परंतु खऱ्या वितरणाचे प्रदर्शन करण्यासाठी हा सर्वोत्तम मार्ग नाही. ही जबाबदारी सहसा हिस्टोग्राम तयार करून हाताळली जाते. @@ -57,7 +57,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=10)+ylab('Frequency') ``` -![संपूर्ण डेटासेटवरील वितरण](../../../../../translated_images/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.mr.png) +![संपूर्ण डेटासेटवरील वितरण](../../../../../translated_images/mr/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.png) जसे तुम्ही पाहू शकता, या डेटासेटमधील 400+ पक्ष्यांपैकी बहुतेक पक्षी त्यांच्या जास्तीत जास्त शरीर वजनासाठी 2000 च्या श्रेणीत येतात. `bins` पॅरामीटरचा आकडा जास्त, जसे की 30, करून डेटाबद्दल अधिक अंतर्दृष्टी मिळवा: @@ -65,7 +65,7 @@ ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![30 बिन्ससह वितरण](../../../../../translated_images/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.mr.png) +![30 बिन्ससह वितरण](../../../../../translated_images/mr/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.png) हा चार्ट वितरण थोड्या अधिक तपशीलवार पद्धतीने दर्शवतो. डावीकडे कमी झुकलेला चार्ट तयार केला जाऊ शकतो जर तुम्ही फक्त दिलेल्या श्रेणीतील डेटा निवडला: @@ -77,7 +77,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![फिल्टर केलेला हिस्टोग्राम](../../../../../translated_images/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.mr.png) +![फिल्टर केलेला हिस्टोग्राम](../../../../../translated_images/mr/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.png) ✅ इतर फिल्टर्स आणि डेटा पॉइंट्स वापरून पहा. डेटाचे पूर्ण वितरण पाहण्यासाठी, `['MaxBodyMass']` फिल्टर काढून टाका आणि लेबल केलेले वितरण दर्शवा. @@ -91,7 +91,7 @@ ggplot(data=birds_filtered_1, aes(x=MaxBodyMass, y=MaxLength) ) + ``` या दोन घटकांमध्ये अपेक्षित अक्षावर एक अपेक्षित संबंध दिसतो, ज्यामध्ये अभिसरणाचा एक विशेषतः मजबूत बिंदू आहे: -![2D प्लॉट](../../../../../translated_images/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.mr.png) +![2D प्लॉट](../../../../../translated_images/mr/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.png) हिस्टोग्राम संख्यात्मक डेटासाठी चांगले कार्य करतात. जर तुम्हाला मजकूर डेटानुसार वितरण पाहायचे असेल तर काय कराल? ## मजकूर डेटाचा वापर करून वितरणांचा अभ्यास करा @@ -122,7 +122,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + scale_fill_manual(name="Conservation Status",values=c("red","green","blue","pink"),labels=c("Endangered","Near Threathened","Vulnerable","Least Concern")) ``` -![पंखांचा विस्तार आणि संवर्धन स्थितीचे संकलन](../../../../../translated_images/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.mr.png) +![पंखांचा विस्तार आणि संवर्धन स्थितीचे संकलन](../../../../../translated_images/mr/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.png) किमान पंखांचा विस्तार आणि संवर्धन स्थिती यामध्ये चांगला संबंध दिसत नाही. या पद्धतीचा वापर करून डेटासेटमधील इतर घटकांची चाचणी घ्या. तुम्ही वेगवेगळे फिल्टर्स देखील वापरू शकता. तुम्हाला काही संबंध सापडतो का? @@ -136,7 +136,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + geom_density() ``` -![घनता प्लॉट](../../../../../translated_images/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.mr.png) +![घनता प्लॉट](../../../../../translated_images/mr/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.png) तुम्ही पाहू शकता की हा प्लॉट किमान पंखांचा विस्तार डेटा दर्शवणाऱ्या मागील प्लॉटसारखाच आहे; तो फक्त थोडा गुळगुळीत आहे. जर तुम्हाला दुसऱ्या चार्टमधील जास्तीत जास्त शरीर वजनाच्या खडबडीत रेषेला गुळगुळीत करायचे असेल, तर तुम्ही ही पद्धत वापरून ती खूप चांगल्या प्रकारे गुळगुळीत करू शकता: @@ -144,7 +144,7 @@ ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density() ``` -![शरीर वजनाची घनता](../../../../../translated_images/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.mr.png) +![शरीर वजनाची घनता](../../../../../translated_images/mr/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.png) जर तुम्हाला गुळगुळीत, पण खूप गुळगुळीत नसलेली रेषा हवी असेल, तर `adjust` पॅरामीटर संपादित करा: @@ -152,7 +152,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density(adjust = 1/5) ``` -![कमी गुळगुळीत शरीर वजन](../../../../../translated_images/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.mr.png) +![कमी गुळगुळीत शरीर वजन](../../../../../translated_images/mr/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.png) ✅ या प्रकारच्या प्लॉटसाठी उपलब्ध पॅरामीटर्सबद्दल वाचा आणि प्रयोग करा! @@ -162,7 +162,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + ggplot(data=birds_filtered_1,aes(x = MaxBodyMass, fill = Order)) + geom_density(alpha=0.5) ``` -![प्रत्येक ऑर्डरनुसार शरीर वजन](../../../../../translated_images/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.mr.png) +![प्रत्येक ऑर्डरनुसार शरीर वजन](../../../../../translated_images/mr/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.png) ## 🚀 आव्हान diff --git a/translations/mr/3-Data-Visualization/R/11-visualization-proportions/README.md b/translations/mr/3-Data-Visualization/R/11-visualization-proportions/README.md index 8067c435..11e1c720 100644 --- a/translations/mr/3-Data-Visualization/R/11-visualization-proportions/README.md +++ b/translations/mr/3-Data-Visualization/R/11-visualization-proportions/README.md @@ -92,7 +92,7 @@ pie(grouped$count,grouped$class, main="Edible?") ``` पाई चार्ट तयार झाला आहे, जो या दोन मशरूम्स वर्गांनुसार डेटाचे प्रमाण दर्शवतो. लेबल्सचा क्रम योग्य असणे खूप महत्त्वाचे आहे, विशेषतः येथे, त्यामुळे लेबल अ‍ॅरे तयार करताना क्रम सत्यापित करा! -![पाई चार्ट](../../../../../translated_images/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.mr.png) +![पाई चार्ट](../../../../../translated_images/mr/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.png) ## डोनट्स! @@ -126,7 +126,7 @@ library(webr) PieDonut(habitat, aes(habitat, count=count)) ``` -![डोनट चार्ट](../../../../../translated_images/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.mr.png) +![डोनट चार्ट](../../../../../translated_images/mr/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.png) या कोडमध्ये दोन लायब्ररी वापरल्या जातात - ggplot2 आणि webr. webr लायब्ररीच्या PieDonut फंक्शनचा वापर करून आपण सहजपणे डोनट चार्ट तयार करू शकतो! @@ -164,7 +164,7 @@ waffle((cap_color$count/10), rows = 7, title = "Waffle Chart")+scale_fill_manual वाफल चार्ट वापरून, तुम्ही मशरूम्स डेटासेटमधील टोपीच्या रंगांचे प्रमाण स्पष्टपणे पाहू शकता. आश्चर्यकारकपणे, अनेक हिरव्या टोपी असलेल्या मशरूम्स आहेत! -![वाफल चार्ट](../../../../../translated_images/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.mr.png) +![वाफल चार्ट](../../../../../translated_images/mr/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.png) या धड्यात, तुम्ही प्रमाणांचे दृश्यांकन करण्याचे तीन मार्ग शिकले. प्रथम, तुम्हाला तुमचा डेटा श्रेणींमध्ये गटबद्ध करावा लागतो आणि नंतर डेटा प्रदर्शित करण्याचा सर्वोत्तम मार्ग ठरवावा लागतो - पाई, डोनट, किंवा वाफल. सर्व प्रकार स्वादिष्ट आहेत आणि वापरकर्त्याला डेटासेटचा त्वरित स्नॅपशॉट देतात. diff --git a/translations/mr/3-Data-Visualization/R/12-visualization-relationships/README.md b/translations/mr/3-Data-Visualization/R/12-visualization-relationships/README.md index 2de7ba32..c5508c0e 100644 --- a/translations/mr/3-Data-Visualization/R/12-visualization-relationships/README.md +++ b/translations/mr/3-Data-Visualization/R/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ library(ggplot2) ggplot(honey, aes(x = priceperlb, y = state)) + geom_point(colour = "blue") ``` -![scatterplot 1](../../../../../translated_images/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.mr.png) +![scatterplot 1](../../../../../translated_images/mr/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.png) आता, मधाच्या रंगसंगतीसह वर्षानुवर्षे किंमत कशी बदलत आहे हे दाखवा. तुम्ही 'scale_color_gradientn' पॅरामीटर जोडून वर्षानुवर्षे बदल दाखवू शकता: @@ -61,7 +61,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ggplot(honey, aes(x = priceperlb, y = state, color=year)) + geom_point()+scale_color_gradientn(colours = colorspace::heat_hcl(7)) ``` -![scatterplot 2](../../../../../translated_images/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.mr.png) +![scatterplot 2](../../../../../translated_images/mr/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.png) या रंगसंगती बदलासह, तुम्ही पाहू शकता की मधाच्या प्रति पाउंड किंमतीत वर्षानुवर्षे स्पष्टपणे प्रगती होत आहे. खरंच, जर तुम्ही डेटामधील नमुना सेट तपासला (उदाहरणार्थ, एरिझोना राज्य निवडा) तर तुम्ही वर्षानुवर्षे किंमती वाढण्याचा नमुना पाहू शकता, काही अपवादांसह: @@ -92,7 +92,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ``` तुम्ही पाहू शकता की डॉट्सचा आकार हळूहळू वाढत आहे. -![scatterplot 3](../../../../../translated_images/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.mr.png) +![scatterplot 3](../../../../../translated_images/mr/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.png) हे साधे पुरवठा आणि मागणीचे प्रकरण आहे का? हवामान बदल आणि वसाहतींचा नाश यासारख्या घटकांमुळे, वर्षानुवर्षे खरेदीसाठी कमी मध उपलब्ध आहे का, आणि त्यामुळे किंमत वाढत आहे का? @@ -107,7 +107,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab ``` उत्तर: होय, काही अपवादांसह, विशेषतः 2003 च्या सुमारास: -![line chart 1](../../../../../translated_images/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.mr.png) +![line chart 1](../../../../../translated_images/mr/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.png) प्रश्न: ठीक आहे, 2003 मध्ये मधाच्या पुरवठ्यातही वाढ दिसते का? जर तुम्ही वर्षानुवर्षे एकूण उत्पादन पाहिले तर काय? @@ -115,7 +115,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab qplot(honey$year,honey$totalprod, geom='smooth', span =0.5, xlab = "year",ylab = "totalprod") ``` -![line chart 2](../../../../../translated_images/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.mr.png) +![line chart 2](../../../../../translated_images/mr/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.png) उत्तर: तसे नाही. जर तुम्ही एकूण उत्पादन पाहिले तर, त्या विशिष्ट वर्षात ते प्रत्यक्षात वाढलेले दिसते, जरी सामान्यतः मधाचे उत्पादन या वर्षांमध्ये घटत आहे. @@ -135,7 +135,7 @@ ggplot(honey, aes(x=yieldpercol, y = numcol,group = 1)) + ``` या दृश्यांकनात, तुम्ही वसाहतीप्रति उत्पादन आणि वसाहतींची संख्या वर्षानुवर्षे, राज्यानुसार बाजूने तुलना करू शकता, 3 कॉलम्ससाठी रॅप सेट करून: -![facet grid](../../../../../translated_images/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.mr.png) +![facet grid](../../../../../translated_images/mr/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.png) या डेटासेटसाठी, वसाहतींची संख्या आणि त्यांचे उत्पादन, वर्षानुवर्षे आणि राज्यानुसार काहीही विशेषतः वेगळे दिसत नाही. या दोन व्हेरिएबल्समधील नातेसंबंध शोधण्यासाठी वेगळ्या प्रकारे पाहण्याचा प्रयत्न करता येईल का? @@ -152,7 +152,7 @@ plot(honey$year, honey$yieldpercol, pch = 17, col = 3, axis(side = 4, at = pretty(range(y2))) mtext("colony yield", side = 4, line = 3) ``` -![superimposed plots](../../../../../translated_images/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.mr.png) +![superimposed plots](../../../../../translated_images/mr/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.png) 2003 च्या सुमारास डोळ्याला काहीही वेगळे दिसत नाही, परंतु हे आपल्याला थोड्या आनंददायक नोटवर धडा संपवण्याची परवानगी देते: जरी वसाहतींची संख्या एकूण घटत आहे, तरीही वसाहतींची संख्या स्थिर होत आहे जरी त्यांचे वसाहतीप्रति उत्पादन कमी होत आहे. diff --git a/translations/mr/3-Data-Visualization/R/13-meaningful-vizualizations/README.md b/translations/mr/3-Data-Visualization/R/13-meaningful-vizualizations/README.md index faf4f2f2..42e587d9 100644 --- a/translations/mr/3-Data-Visualization/R/13-meaningful-vizualizations/README.md +++ b/translations/mr/3-Data-Visualization/R/13-meaningful-vizualizations/README.md @@ -47,25 +47,25 @@ CO_OP_TRANSLATOR_METADATA: जरी डेटा वैज्ञानिक योग्य डेटा साठी योग्य चार्ट निवडण्यात काळजी घेत असेल, तरीही डेटा अशा प्रकारे प्रदर्शित केला जाऊ शकतो ज्यामुळे मुद्दा सिद्ध होतो, परंतु अनेकदा डेटा स्वतःच कमजोर होतो. फसव्या चार्ट्स आणि इन्फोग्राफिक्सचे अनेक उदाहरणे आहेत! -[![How Charts Lie by Alberto Cairo](../../../../../translated_images/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.mr.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "How charts lie") +[![How Charts Lie by Alberto Cairo](../../../../../translated_images/mr/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "How charts lie") > 🎥 वरील प्रतिमेवर क्लिक करा फसव्या चार्ट्सबद्दलच्या परिषदेतल्या चर्चेसाठी हा चार्ट X अक्ष उलटवतो, जेणेकरून तारीख आधारित सत्याच्या उलट दाखवले जाते: -![bad chart 1](../../../../../translated_images/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.mr.png) +![bad chart 1](../../../../../translated_images/mr/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.png) [हा चार्ट](https://media.firstcoastnews.com/assets/WTLV/images/170ae16f-4643-438f-b689-50d66ca6a8d8/170ae16f-4643-438f-b689-50d66ca6a8d8_1140x641.jpg) आणखी फसवणूक करणारा आहे, कारण डोळा उजवीकडे जातो आणि असा निष्कर्ष काढतो की, कालांतराने, विविध काउंटीजमध्ये COVID प्रकरणे कमी झाली आहेत. प्रत्यक्षात, जर तुम्ही तारखांकडे बारकाईने पाहिले, तर तुम्हाला आढळेल की त्या फसव्या उतरणाऱ्या ट्रेंडसाठी पुन्हा व्यवस्था करण्यात आल्या आहेत. -![bad chart 2](../../../../../translated_images/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.mr.jpg) +![bad chart 2](../../../../../translated_images/mr/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.jpg) हा कुप्रसिद्ध उदाहरण रंग आणि उलटवलेल्या Y अक्षाचा वापर करून फसवणूक करतो: बंदूक-संबंधित कायदे लागू झाल्यानंतर बंदूक मृत्यू वाढले, असे निष्कर्ष काढण्याऐवजी, प्रत्यक्षात डोळा उलट विचार करतो: -![bad chart 3](../../../../../translated_images/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.mr.jpg) +![bad chart 3](../../../../../translated_images/mr/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.jpg) हा विचित्र चार्ट दाखवतो की प्रमाण कसे हसण्यासारखे परिणाम देण्यासाठी बदलले जाऊ शकते: -![bad chart 4](../../../../../translated_images/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.mr.jpg) +![bad chart 4](../../../../../translated_images/mr/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.jpg) अतुलनीय गोष्टींची तुलना करणे हा आणखी एक छुपा युक्ती आहे. [एक अप्रतिम वेबसाइट](https://tylervigen.com/spurious-correlations) 'स्प्युरियस करिलेशन्स' बद्दल 'तथ्ये' प्रदर्शित करते, जसे की मेनमधील घटस्फोट दर आणि मार्जरीनचा वापर. रेडिट ग्रुप देखील डेटाच्या [अगदी वाईट उपयोग](https://www.reddit.com/r/dataisugly/top/?t=all) गोळा करतो. @@ -100,13 +100,13 @@ CO_OP_TRANSLATOR_METADATA: जर तुमचा डेटा X अक्षावर टेक्स्ट स्वरूपात आणि विस्तृत असेल, तर वाचनीयतेसाठी टेक्स्ट अँगल करा. [plot3D](https://cran.r-project.org/web/packages/plot3D/index.html) 3D प्लॉटिंग ऑफर करते, जर तुमचा डेटा त्याला समर्थन देत असेल. त्याचा वापर करून प्रगत डेटा दृश्यांकन तयार करता येते. -![3d plots](../../../../../translated_images/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.mr.png) +![3d plots](../../../../../translated_images/mr/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.png) ## अॅनिमेशन आणि 3D चार्ट प्रदर्शन आजकाल काही सर्वोत्तम डेटा दृश्यांकन अॅनिमेटेड आहेत. Shirley Wu ने D3 वापरून केलेली अप्रतिम उदाहरणे आहेत, जसे '[film flowers](http://bl.ocks.org/sxywu/raw/d612c6c653fb8b4d7ff3d422be164a5d/)', जिथे प्रत्येक फूल एका चित्रपटाचे दृश्यांकन आहे. गार्डियनसाठी आणखी एक उदाहरण आहे 'bussed out', एक परस्पर अनुभव जो दृश्यांकन Greensock आणि D3 सह स्क्रोलिटेलिंग लेख स्वरूपात NYC कसे त्याच्या बेघर लोकांना शहराबाहेर पाठवते हे दाखवतो. -![busing](../../../../../translated_images/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.mr.png) +![busing](../../../../../translated_images/mr/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.png) > "Bussed Out: How America Moves its Homeless" [गार्डियन](https://www.theguardian.com/us-news/ng-interactive/2017/dec/20/bussed-out-america-moves-homeless-people-country-study) कडून. दृश्यांकन Nadieh Bremer & Shirley Wu यांनी. @@ -116,7 +116,7 @@ CO_OP_TRANSLATOR_METADATA: तुम्ही एक वेब अॅप पूर्ण कराल जे या सामाजिक नेटवर्कचे अॅनिमेटेड दृश्य प्रदर्शित करेल. Vue.js आणि D3 वापरून नेटवर्कचे [दृश्य](https://github.com/emiliorizzo/vue-d3-network) तयार करण्यासाठी एक लायब्ररी वापरली जाते. अॅप चालू असताना, तुम्ही स्क्रीनवर नोड्स खेचून डेटा हलवू शकता. -![liaisons](../../../../../translated_images/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.mr.png) +![liaisons](../../../../../translated_images/mr/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.png) ## प्रकल्प: D3.js वापरून नेटवर्क दाखवणारा चार्ट तयार करा diff --git a/translations/mr/3-Data-Visualization/README.md b/translations/mr/3-Data-Visualization/README.md index d0e6fcf1..67ba23d2 100644 --- a/translations/mr/3-Data-Visualization/README.md +++ b/translations/mr/3-Data-Visualization/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # दृश्यचित्रण -![लॅव्हेंडर फुलावर मधमाशी](../../../translated_images/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.mr.jpg) +![लॅव्हेंडर फुलावर मधमाशी](../../../translated_images/mr/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.jpg) > फोटो जेनना ली यांनी Unsplash वरून घेतला आहे. Unsplash डेटा सायंटिस्टसाठी डेटा दृश्यचित्रण करणे ही एक महत्त्वाची जबाबदारी आहे. एक चित्र हजार शब्दांपेक्षा जास्त सांगते, आणि दृश्यचित्रण तुम्हाला तुमच्या डेटामधील अनेक मनोरंजक गोष्टी शोधण्यात मदत करू शकते, जसे की स्पाइक्स, आउटलाईयर्स, गट, प्रवृत्ती, आणि बरेच काही, ज्यामुळे तुम्हाला तुमचा डेटा काय सांगू इच्छित आहे हे समजण्यास मदत होते. diff --git a/translations/mr/4-Data-Science-Lifecycle/14-Introduction/README.md b/translations/mr/4-Data-Science-Lifecycle/14-Introduction/README.md index 8994ec20..c25cabeb 100644 --- a/translations/mr/4-Data-Science-Lifecycle/14-Introduction/README.md +++ b/translations/mr/4-Data-Science-Lifecycle/14-Introduction/README.md @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: या धड्यात जीवनचक्राच्या 3 भागांवर लक्ष केंद्रित केले आहे: डेटा संकलन, प्रक्रिया आणि देखभाल. -![डेटा सायन्स जीवनचक्राचे चित्र](../../../../translated_images/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.mr.jpg) +![डेटा सायन्स जीवनचक्राचे चित्र](../../../../translated_images/mr/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.jpg) > फोटो [Berkeley School of Information](https://ischoolonline.berkeley.edu/data-science/what-is-data-science/) यांच्याकडून ## डेटा संकलन @@ -101,7 +101,7 @@ CO_OP_TRANSLATOR_METADATA: |Team Data Science Process (TDSP)|Cross-industry standard process for data mining (CRISP-DM)| |--|--| -|![Team Data Science Lifecycle](../../../../translated_images/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.mr.png) | ![Data Science Process Alliance Image](../../../../translated_images/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.mr.png) | +|![Team Data Science Lifecycle](../../../../translated_images/mr/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.png) | ![Data Science Process Alliance Image](../../../../translated_images/mr/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.png) | | प्रतिमा [Microsoft](https://docs.microsoft.comazure/architecture/data-science-process/lifecycle) यांच्याकडून | प्रतिमा [Data Science Process Alliance](https://www.datascience-pm.com/crisp-dm-2/) यांच्याकडून | ## [व्याख्यानानंतरची प्रश्नमंजूषा](https://ff-quizzes.netlify.app/en/ds/quiz/27) diff --git a/translations/mr/4-Data-Science-Lifecycle/README.md b/translations/mr/4-Data-Science-Lifecycle/README.md index 2268bff5..74ea4c0a 100644 --- a/translations/mr/4-Data-Science-Lifecycle/README.md +++ b/translations/mr/4-Data-Science-Lifecycle/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # डेटा सायन्स जीवनचक्र -![communication](../../../translated_images/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.mr.jpg) +![communication](../../../translated_images/mr/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.jpg) > फोटो हेडवे यांनी अनस्प्लॅश वर घेतला आहे या धड्यांमध्ये, तुम्ही डेटा सायन्स जीवनचक्राच्या काही पैलूंवर चर्चा कराल, ज्यामध्ये डेटाचे विश्लेषण आणि संवाद यांचा समावेश आहे. diff --git a/translations/mr/5-Data-Science-In-Cloud/README.md b/translations/mr/5-Data-Science-In-Cloud/README.md index 9587479e..12d9bc22 100644 --- a/translations/mr/5-Data-Science-In-Cloud/README.md +++ b/translations/mr/5-Data-Science-In-Cloud/README.md @@ -9,13 +9,13 @@ CO_OP_TRANSLATOR_METADATA: --> # क्लाउडमधील डेटा सायन्स -![cloud-picture](../../../translated_images/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.mr.jpg) +![cloud-picture](../../../translated_images/mr/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.jpg) > फोटो [Jelleke Vanooteghem](https://unsplash.com/@ilumire) यांनी [Unsplash](https://unsplash.com/s/photos/cloud?orientation=landscape) वरून घेतला आहे मोठ्या डेटासह डेटा सायन्स करताना, क्लाउड एक गेम चेंजर ठरू शकतो. पुढील तीन धड्यांमध्ये, आपण क्लाउड म्हणजे काय आणि ते कसे उपयुक्त ठरू शकते हे पाहणार आहोत. तसेच, आपण हृदय विकाराचा डेटा सेट एक्सप्लोर करणार आहोत आणि कोणाला हृदय विकार होण्याची शक्यता किती आहे हे ठरवण्यासाठी एक मॉडेल तयार करणार आहोत. क्लाउडची ताकद वापरून आपण मॉडेल ट्रेन, डिप्लॉय आणि दोन वेगवेगळ्या पद्धतींनी वापरणार आहोत. एक पद्धत फक्त यूजर इंटरफेस वापरून Low code/No code प्रकारात, आणि दुसरी पद्धत Azure Machine Learning Software Developer Kit (Azure ML SDK) वापरून. -![project-schema](../../../translated_images/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.mr.png) +![project-schema](../../../translated_images/mr/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.png) ### विषय diff --git a/translations/mr/6-Data-Science-In-Wild/20-Real-World-Examples/README.md b/translations/mr/6-Data-Science-In-Wild/20-Real-World-Examples/README.md index d132c0c4..3af15045 100644 --- a/translations/mr/6-Data-Science-In-Wild/20-Real-World-Examples/README.md +++ b/translations/mr/6-Data-Science-In-Wild/20-Real-World-Examples/README.md @@ -41,7 +41,7 @@ AI च्या लोकशाहीकरणामुळे, विकसक * [Data Science in Healthcare](https://data-flair.training/blogs/data-science-in-healthcare/) - वैद्यकीय प्रतिमा (उदा., एमआरआय, एक्स-रे, सीटी-स्कॅन), जीनोमिक्स (डीएनए अनुक्रमण), औषध विकास (जोखीम मूल्यांकन, यशाचा अंदाज), प्रेडिक्टिव अॅनालिटिक्स (रुग्णांची काळजी आणि पुरवठा लॉजिस्टिक्स), रोग ट्रॅकिंग आणि प्रतिबंध यासारख्या अनुप्रयोगांवर प्रकाश टाकतो. -![वास्तविक जगातील डेटा सायन्स अनुप्रयोग](../../../../translated_images/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.mr.png) प्रतिमा क्रेडिट: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) +![वास्तविक जगातील डेटा सायन्स अनुप्रयोग](../../../../translated_images/mr/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.png) प्रतिमा क्रेडिट: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) आकृतीत डेटा सायन्स तंत्र लागू करण्यासाठी इतर डोमेन आणि उदाहरणे दर्शविली आहेत. इतर अनुप्रयोगांचा शोध घ्यायचा आहे? खालील [पुनरावलोकन आणि स्व-अभ्यास](../../../../6-Data-Science-In-Wild/20-Real-World-Examples) विभाग तपासा. diff --git a/translations/mr/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md b/translations/mr/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md index 974601b4..13ee5c3d 100644 --- a/translations/mr/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md +++ b/translations/mr/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md @@ -22,7 +22,7 @@ Explorer इंटरफेस (खालील स्क्रीनशॉट 2. [Catalog](https://planetarycomputer.microsoft.com/catalog) डेटासेटचा अभ्यास करा - प्रत्येकाचा उद्देश जाणून घ्या. 3. Explorer वापरा - तुमच्या आवडीचा डेटासेट निवडा, संबंधित क्वेरी आणि रेंडरिंग पर्याय निवडा. -![ग्रह संगणक Explorer](../../../../translated_images/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.mr.png) +![ग्रह संगणक Explorer](../../../../translated_images/mr/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.png) `तुमचे काम:` आता ब्राउझरमध्ये तयार झालेले व्हिज्युअलायझेशन अभ्यासा आणि खालील प्रश्नांची उत्तरे द्या: diff --git a/translations/mr/CONTRIBUTING.md b/translations/mr/CONTRIBUTING.md index 201109e2..278eaff6 100644 --- a/translations/mr/CONTRIBUTING.md +++ b/translations/mr/CONTRIBUTING.md @@ -311,7 +311,7 @@ def calculate_mean(data): import pandas as pd ``` ```` -- प्रतिमांसाठी alt टेक्स्ट जोडा: `![Alt text](../../translated_images/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.mr.png)` +- प्रतिमांसाठी alt टेक्स्ट जोडा: `![Alt text](../../translated_images/mr/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.png)` - ओळींची लांबी वाजवी ठेवा (सुमारे 80-100 वर्ण) ### Python diff --git a/translations/mr/README.md b/translations/mr/README.md index e74847c7..26ca7a78 100644 --- a/translations/mr/README.md +++ b/translations/mr/README.md @@ -33,7 +33,7 @@ Microsoft मधील Azure Cloud Advocates हे डेटा सायन् **🙏 विशेष धन्यवाद 🙏 आमच्या [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/) लेखक, पुनरावलोकक आणि सामग्री योगदानकर्त्यांना,** विशेषतः Aaryan Arora, [Aditya Garg](https://github.com/AdityaGarg00), [Alondra Sanchez](https://www.linkedin.com/in/alondra-sanchez-molina/), [Ankita Singh](https://www.linkedin.com/in/ankitasingh007), [Anupam Mishra](https://www.linkedin.com/in/anupam--mishra/), [Arpita Das](https://www.linkedin.com/in/arpitadas01/), ChhailBihari Dubey, [Dibri Nsofor](https://www.linkedin.com/in/dibrinsofor), [Dishita Bhasin](https://www.linkedin.com/in/dishita-bhasin-7065281bb), [Majd Safi](https://www.linkedin.com/in/majd-s/), [Max Blum](https://www.linkedin.com/in/max-blum-6036a1186/), [Miguel Correa](https://www.linkedin.com/in/miguelmque/), [Mohamma Iftekher (Iftu) Ebne Jalal](https://twitter.com/iftu119), [Nawrin Tabassum](https://www.linkedin.com/in/nawrin-tabassum), [Raymond Wangsa Putra](https://www.linkedin.com/in/raymond-wp/), [Rohit Yadav](https://www.linkedin.com/in/rty2423), Samridhi Sharma, [Sanya Sinha](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200), [Sheena Narula](https://www.linkedin.com/in/sheena-narua-n/), [Tauqeer Ahmad](https://www.linkedin.com/in/tauqeerahmad5201/), Yogendrasingh Pawar , [Vidushi Gupta](https://www.linkedin.com/in/vidushi-gupta07/), [Jasleen Sondhi](https://www.linkedin.com/in/jasleen-sondhi/) -|![स्केचनोट द्वारा @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.mr.png)| +|![स्केचनोट द्वारा @sketchthedocs https://sketchthedocs.dev](../../translated_images/mr/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.png)| |:---:| | नवशिक्यांसाठी डेटा सायन्स - _स्केचनोट by [@nitya](https://twitter.com/nitya)_ | @@ -52,7 +52,7 @@ Microsoft मधील Azure Cloud Advocates हे डेटा सायन् आम्ही एक Discord "AI सोबत शिकण्याची" मालिका आयोजित करत आहोत, अधिक जाणून घेण्यासाठी आणि 18 - 30 सप्टेंबर, 2025 रोजी आम्हाला या ठिकाणी सामील व्हा: [Learn with AI Series](https://aka.ms/learnwithai/discord). येथे तुम्हाला डेटा सायन्ससाठी GitHub Copilot वापरण्याचे टिप्स आणि तंत्र मिळतील. -![AI सह शिकण्याची मालिका](../../translated_images/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.mr.jpg) +![AI सह शिकण्याची मालिका](../../translated_images/mr/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.jpg) # तुम्ही विद्यार्थी आहात का? @@ -132,7 +132,7 @@ Microsoft मधील Azure Cloud Advocates हे डेटा सायन् ## धडे -|![ स्केचनोट द्वारा @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.mr.png)| +|![ स्केचनोट द्वारा @sketchthedocs https://sketchthedocs.dev](../../translated_images/mr/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png)| |:---:| | शिकण्याऱ्यांसाठी डेटा सायन्स: रोडमॅप - _स्केचनोट द्वारा [@nitya](https://twitter.com/nitya)_ | diff --git a/translations/mr/sketchnotes/README.md b/translations/mr/sketchnotes/README.md index b75df2de..4bf6cbdb 100644 --- a/translations/mr/sketchnotes/README.md +++ b/translations/mr/sketchnotes/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: नित्या नरसिंहन, कलाकार -![रोडमॅप स्केच नोट](../../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.mr.png) +![रोडमॅप स्केच नोट](../../../translated_images/mr/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png) --- diff --git a/translations/ms/1-Introduction/01-defining-data-science/README.md b/translations/ms/1-Introduction/01-defining-data-science/README.md index 6845bea0..61cc3162 100644 --- a/translations/ms/1-Introduction/01-defining-data-science/README.md +++ b/translations/ms/1-Introduction/01-defining-data-science/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: --- -[![Video Mendefinisikan Sains Data](../../../../translated_images/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.ms.png)](https://youtu.be/beZ7Mb_oz9I) +[![Video Mendefinisikan Sains Data](../../../../translated_images/ms/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.png)](https://youtu.be/beZ7Mb_oz9I) ## [Kuiz pra-kuliah](https://ff-quizzes.netlify.app/en/ds/quiz/0) @@ -153,7 +153,7 @@ Jika kita ingin menjadi lebih rumit, kita boleh memplot masa yang diambil untuk Dalam cabaran ini, kita akan cuba mencari konsep yang relevan dengan bidang Sains Data dengan melihat teks. Kita akan mengambil artikel Wikipedia tentang Sains Data, memuat turun dan memproses teks tersebut, dan kemudian membina awan kata seperti ini: -![Awan Kata untuk Sains Data](../../../../translated_images/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.ms.png) +![Awan Kata untuk Sains Data](../../../../translated_images/ms/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.png) Lawati [`notebook.ipynb`](../../../../1-Introduction/01-defining-data-science/notebook.ipynb ':ignore') untuk membaca kodnya. Anda juga boleh menjalankan kod tersebut, dan melihat bagaimana ia melaksanakan semua transformasi data secara langsung. diff --git a/translations/ms/1-Introduction/04-stats-and-probability/README.md b/translations/ms/1-Introduction/04-stats-and-probability/README.md index b5ece8a8..96eeeb11 100644 --- a/translations/ms/1-Introduction/04-stats-and-probability/README.md +++ b/translations/ms/1-Introduction/04-stats-and-probability/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: Teori Statistik dan Kebarangkalian adalah dua cabang Matematik yang sangat berkait rapat dan amat relevan dalam Sains Data. Walaupun mungkin untuk bekerja dengan data tanpa pengetahuan matematik yang mendalam, adalah lebih baik untuk memahami sekurang-kurangnya konsep asas. Di sini, kami akan memberikan pengenalan ringkas untuk membantu anda bermula. -[![Video Pengenalan](../../../../translated_images/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.ms.png)](https://youtu.be/Z5Zy85g4Yjw) +[![Video Pengenalan](../../../../translated_images/ms/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.png)](https://youtu.be/Z5Zy85g4Yjw) ## [Kuiz Pra-Kuliah](https://ff-quizzes.netlify.app/en/ds/quiz/6) @@ -39,7 +39,7 @@ Lebih sukar untuk menerangkan taburan kebarangkalian pemboleh ubah berterusan, d Kita hanya boleh bercakap tentang kebarangkalian pemboleh ubah jatuh dalam julat nilai tertentu, contohnya P(t1≤X2). Dalam kes ini, taburan kebarangkalian diterangkan oleh **fungsi ketumpatan kebarangkalian** p(x), di mana -![P(t_1\le X Lebih banyak contoh korelasi dan kovarians boleh didapati dalam [notebook yang disertakan](notebook.ipynb). diff --git a/translations/ms/1-Introduction/README.md b/translations/ms/1-Introduction/README.md index 5a9d787c..793b56e1 100644 --- a/translations/ms/1-Introduction/README.md +++ b/translations/ms/1-Introduction/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Pengenalan kepada Sains Data -![data in action](../../../translated_images/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.ms.jpg) +![data in action](../../../translated_images/ms/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.jpg) > Foto oleh Stephen Dawson di Unsplash Dalam pelajaran ini, anda akan meneroka bagaimana Sains Data didefinisikan dan mempelajari tentang pertimbangan etika yang perlu diambil kira oleh seorang saintis data. Anda juga akan mempelajari bagaimana data didefinisikan serta sedikit tentang statistik dan kebarangkalian, bidang akademik teras dalam Sains Data. diff --git a/translations/ms/2-Working-With-Data/07-python/README.md b/translations/ms/2-Working-With-Data/07-python/README.md index 7c8ad88f..6fa2b377 100644 --- a/translations/ms/2-Working-With-Data/07-python/README.md +++ b/translations/ms/2-Working-With-Data/07-python/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: | :-------------------------------------------------------------------------------------------------------: | | Bekerja dengan Python - _Sketchnote oleh [@nitya](https://twitter.com/nitya)_ | -[![Video Pengenalan](../../../../translated_images/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.ms.png)](https://youtu.be/dZjWOGbsN4Y) +[![Video Pengenalan](../../../../translated_images/ms/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.png)](https://youtu.be/dZjWOGbsN4Y) Walaupun pangkalan data menawarkan cara yang sangat efisien untuk menyimpan data dan membuat pertanyaan menggunakan bahasa pertanyaan, cara yang paling fleksibel untuk memproses data adalah dengan menulis program anda sendiri untuk memanipulasi data. Dalam banyak kes, membuat pertanyaan pangkalan data mungkin lebih berkesan. Namun, dalam beberapa kes apabila pemprosesan data yang lebih kompleks diperlukan, ia tidak dapat dilakukan dengan mudah menggunakan SQL. Pemprosesan data boleh diprogramkan dalam mana-mana bahasa pengaturcaraan, tetapi terdapat beberapa bahasa yang lebih tinggi tahapnya dalam bekerja dengan data. Saintis data biasanya memilih salah satu daripada bahasa berikut: @@ -75,7 +75,7 @@ print(f"Length of index is {len(idx)}") items_sold = pd.Series(np.random.randint(25,50,size=len(idx)),index=idx) items_sold.plot() ``` -![Plot Siri Masa](../../../../translated_images/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.ms.png) +![Plot Siri Masa](../../../../translated_images/ms/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.png) Sekarang katakan setiap minggu kita menganjurkan pesta untuk rakan-rakan, dan kita mengambil tambahan 10 pek ais krim untuk pesta. Kita boleh mencipta siri lain, diindeks mengikut minggu, untuk menunjukkan itu: ```python @@ -86,7 +86,7 @@ Apabila kita menambah dua siri bersama-sama, kita mendapat jumlah keseluruhan: total_items = items_sold.add(additional_items,fill_value=0) total_items.plot() ``` -![Plot Siri Masa](../../../../translated_images/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.ms.png) +![Plot Siri Masa](../../../../translated_images/ms/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.png) > **Nota** bahawa kita tidak menggunakan sintaks mudah `total_items+additional_items`. Jika kita melakukannya, kita akan menerima banyak nilai `NaN` (*Not a Number*) dalam siri hasil. Ini kerana terdapat nilai yang hilang untuk beberapa titik indeks dalam siri `additional_items`, dan menambah `NaN` kepada apa-apa menghasilkan `NaN`. Oleh itu, kita perlu menentukan parameter `fill_value` semasa penambahan. @@ -95,7 +95,7 @@ Dengan siri masa, kita juga boleh **menyampel semula** siri dengan selang masa y monthly = total_items.resample("1M").mean() ax = monthly.plot(kind='bar') ``` -![Purata Siri Masa Bulanan](../../../../translated_images/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.ms.png) +![Purata Siri Masa Bulanan](../../../../translated_images/ms/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.png) ### DataFrame @@ -221,7 +221,7 @@ Masalah pertama yang akan kita fokuskan ialah pemodelan penyebaran wabak COVID-1 Oleh kerana kita ingin menunjukkan cara menangani data, kami mengajak anda untuk membuka [`notebook-covidspread.ipynb`](notebook-covidspread.ipynb) dan membacanya dari atas ke bawah. Anda juga boleh melaksanakan sel-sel, dan melakukan beberapa cabaran yang telah kami tinggalkan untuk anda di penghujungnya. -![COVID Spread](../../../../translated_images/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.ms.png) +![COVID Spread](../../../../translated_images/ms/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.png) > Jika anda tidak tahu cara menjalankan kod dalam Jupyter Notebook, lihat [artikel ini](https://soshnikov.com/education/how-to-execute-notebooks-from-github/). @@ -243,7 +243,7 @@ Contoh penuh menganalisis dataset ini menggunakan perkhidmatan kognitif [Text An Buka [`notebook-papers.ipynb`](notebook-papers.ipynb) dan bacalah dari atas ke bawah. Anda juga boleh melaksanakan sel-sel, dan melakukan beberapa cabaran yang telah kami tinggalkan untuk anda di penghujungnya. -![Covid Medical Treatment](../../../../translated_images/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.ms.png) +![Covid Medical Treatment](../../../../translated_images/ms/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.png) ## Memproses Data Imej diff --git a/translations/ms/2-Working-With-Data/README.md b/translations/ms/2-Working-With-Data/README.md index 23e98f25..be0f478b 100644 --- a/translations/ms/2-Working-With-Data/README.md +++ b/translations/ms/2-Working-With-Data/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Bekerja dengan Data -![data love](../../../translated_images/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.ms.jpg) +![data love](../../../translated_images/ms/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.jpg) > Foto oleh Alexander Sinn di Unsplash Dalam pelajaran ini, anda akan mempelajari beberapa cara data boleh diuruskan, dimanipulasi, dan digunakan dalam aplikasi. Anda akan belajar tentang pangkalan data relasi dan bukan relasi serta bagaimana data boleh disimpan di dalamnya. Anda akan mempelajari asas-asas bekerja dengan Python untuk mengurus data, dan anda akan menemui beberapa cara yang pelbagai untuk bekerja dengan Python dalam mengurus dan melombong data. diff --git a/translations/ms/3-Data-Visualization/12-visualization-relationships/README.md b/translations/ms/3-Data-Visualization/12-visualization-relationships/README.md index b47eea95..842f7da3 100644 --- a/translations/ms/3-Data-Visualization/12-visualization-relationships/README.md +++ b/translations/ms/3-Data-Visualization/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ Buat scatterplot asas untuk menunjukkan hubungan antara harga per paun madu dan ```python sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ``` -![scatterplot 1](../../../../translated_images/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.ms.png) +![scatterplot 1](../../../../translated_images/ms/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.png) Sekarang, tunjukkan data yang sama dengan skema warna madu untuk menunjukkan bagaimana harga berkembang dari tahun ke tahun. Anda boleh melakukannya dengan menambah parameter 'hue' untuk menunjukkan perubahan dari tahun ke tahun: @@ -60,7 +60,7 @@ Sekarang, tunjukkan data yang sama dengan skema warna madu untuk menunjukkan bag ```python sns.relplot(x="priceperlb", y="state", hue="year", palette="YlOrBr", data=honey, height=15, aspect=.5); ``` -![scatterplot 2](../../../../translated_images/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.ms.png) +![scatterplot 2](../../../../translated_images/ms/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.png) Dengan perubahan skema warna ini, anda dapat melihat dengan jelas perkembangan yang kuat dari tahun ke tahun dalam harga madu per paun. Malah, jika anda melihat set sampel dalam data untuk mengesahkan (pilih negeri tertentu, contohnya Arizona), anda dapat melihat pola kenaikan harga dari tahun ke tahun, dengan beberapa pengecualian: @@ -89,7 +89,7 @@ sns.relplot(x="priceperlb", y="state", size="year", data=honey, height=15, aspec ``` Anda dapat melihat saiz titik semakin besar. -![scatterplot 3](../../../../translated_images/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.ms.png) +![scatterplot 3](../../../../translated_images/ms/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.png) Adakah ini kes mudah permintaan dan penawaran? Disebabkan faktor seperti perubahan iklim dan keruntuhan koloni, adakah madu semakin kurang tersedia untuk dibeli dari tahun ke tahun, dan oleh itu harga meningkat? @@ -104,7 +104,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); ``` Jawapan: Ya, dengan beberapa pengecualian sekitar tahun 2003: -![line chart 1](../../../../translated_images/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.ms.png) +![line chart 1](../../../../translated_images/ms/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.png) ✅ Oleh kerana Seaborn mengagregatkan data di sekitar satu garis, ia memaparkan "pengukuran berganda pada setiap nilai x dengan memplotkan purata dan selang keyakinan 95% di sekitar purata". [Sumber](https://seaborn.pydata.org/tutorial/relational.html). Tingkah laku yang memakan masa ini boleh dilumpuhkan dengan menambah `ci=None`. @@ -114,7 +114,7 @@ Soalan: Nah, pada tahun 2003 adakah kita juga melihat lonjakan dalam bekalan mad sns.relplot(x="year", y="totalprod", kind="line", data=honey); ``` -![line chart 2](../../../../translated_images/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.ms.png) +![line chart 2](../../../../translated_images/ms/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.png) Jawapan: Tidak begitu. Jika anda melihat jumlah pengeluaran, ia sebenarnya kelihatan meningkat pada tahun tersebut, walaupun secara amnya jumlah madu yang dihasilkan menurun sepanjang tahun-tahun ini. @@ -139,7 +139,7 @@ sns.relplot( ``` Dalam visualisasi ini, anda boleh membandingkan hasil per koloni dan bilangan koloni dari tahun ke tahun, bersebelahan dengan susunan kolum sebanyak 3: -![facet grid](../../../../translated_images/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.ms.png) +![facet grid](../../../../translated_images/ms/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.png) Untuk dataset ini, tiada apa yang benar-benar menonjol berkaitan dengan bilangan koloni dan hasilnya, dari tahun ke tahun dan negeri ke negeri. Adakah terdapat cara lain untuk mencari korelasi antara dua pemboleh ubah ini? @@ -162,7 +162,7 @@ sns.despine(right=False) plt.ylabel('colony yield') ax.figure.legend(); ``` -![superimposed plots](../../../../translated_images/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.ms.png) +![superimposed plots](../../../../translated_images/ms/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.png) Walaupun tiada apa yang menonjol di mata sekitar tahun 2003, ia membolehkan kita mengakhiri pelajaran ini dengan nota yang sedikit lebih gembira: walaupun terdapat penurunan bilangan koloni secara keseluruhan, bilangan koloni semakin stabil walaupun hasil per koloni semakin menurun. diff --git a/translations/ms/3-Data-Visualization/R/09-visualization-quantities/README.md b/translations/ms/3-Data-Visualization/R/09-visualization-quantities/README.md index 96124fff..85bd006e 100644 --- a/translations/ms/3-Data-Visualization/R/09-visualization-quantities/README.md +++ b/translations/ms/3-Data-Visualization/R/09-visualization-quantities/README.md @@ -66,7 +66,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` Di sini, anda memasang pakej `ggplot2` dan kemudian mengimportnya ke dalam workspace menggunakan arahan `library("ggplot2")`. Untuk memplot sebarang plot dalam ggplot, fungsi `ggplot()` digunakan dan anda menentukan set data, pemboleh ubah x dan y sebagai atribut. Dalam kes ini, kita menggunakan fungsi `geom_line()` kerana kita ingin memplot plot garis. -![MaxWingspan-lineplot](../../../../../translated_images/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.ms.png) +![MaxWingspan-lineplot](../../../../../translated_images/ms/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.png) Apa yang anda perhatikan dengan segera? Nampaknya terdapat sekurang-kurangnya satu outlier - itu rentang sayap yang sangat besar! Rentang sayap lebih dari 2000 sentimeter bersamaan lebih dari 20 meter - adakah terdapat Pterodaktil berkeliaran di Minnesota? Mari kita siasat. @@ -84,7 +84,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` Kita menentukan sudut dalam `theme` dan menentukan label paksi x dan y dalam `xlab()` dan `ylab()` masing-masing. `ggtitle()` memberikan nama kepada graf/plot. -![MaxWingspan-lineplot-improved](../../../../../translated_images/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.ms.png) +![MaxWingspan-lineplot-improved](../../../../../translated_images/ms/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.png) Walaupun dengan putaran label ditetapkan kepada 45 darjah, masih terlalu banyak untuk dibaca. Mari cuba strategi lain: label hanya outlier tersebut dan tetapkan label dalam carta. Anda boleh menggunakan carta scatter untuk memberikan lebih ruang kepada pelabelan: @@ -100,7 +100,7 @@ Apa yang berlaku di sini? Anda menggunakan fungsi `geom_point()` untuk memplot t Apa yang anda temui? -![MaxWingspan-scatterplot](../../../../../translated_images/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.ms.png) +![MaxWingspan-scatterplot](../../../../../translated_images/ms/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.png) ## Tapis data anda @@ -119,7 +119,7 @@ ggplot(data=birds_filtered, aes(x=Name, y=MaxWingspan,group=1)) + ``` Kami mencipta dataframe baru `birds_filtered` dan kemudian memplot scatter plot. Dengan menapis outlier, data anda kini lebih koheren dan mudah difahami. -![MaxWingspan-scatterplot-improved](../../../../../translated_images/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.ms.png) +![MaxWingspan-scatterplot-improved](../../../../../translated_images/ms/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.png) Sekarang kita mempunyai set data yang lebih bersih sekurang-kurangnya dari segi rentang sayap, mari kita temui lebih banyak tentang burung-burung ini. @@ -160,7 +160,7 @@ birds_filtered %>% group_by(Category) %>% ``` Dalam snippet berikut, kami memasang pakej [dplyr](https://www.rdocumentation.org/packages/dplyr/versions/0.7.8) dan [lubridate](https://www.rdocumentation.org/packages/lubridate/versions/1.8.0) untuk membantu memanipulasi dan mengelompokkan data bagi memplot carta bar bertindan. Pertama, anda mengelompokkan data berdasarkan `Category` burung dan kemudian meringkaskan lajur `MinLength`, `MaxLength`, `MinBodyMass`, `MaxBodyMass`, `MinWingspan`, `MaxWingspan`. Kemudian, plot carta bar menggunakan pakej `ggplot2` dan tentukan warna untuk kategori yang berbeza serta labelnya. -![Stacked bar chart](../../../../../translated_images/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.ms.png) +![Stacked bar chart](../../../../../translated_images/ms/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.png) Namun, carta bar ini sukar dibaca kerana terdapat terlalu banyak data yang tidak dikelompokkan. Anda perlu memilih hanya data yang ingin anda plot, jadi mari kita lihat panjang burung berdasarkan kategori mereka. @@ -175,7 +175,7 @@ ggplot(birds_count,aes(Category,n))+geom_bar(stat="identity")+coord_flip() ``` Anda mula-mula mengira nilai unik dalam lajur `Category` dan kemudian menyusunnya ke dalam dataframe baru `birds_count`. Data yang disusun ini kemudian difaktorkan pada tahap yang sama supaya ia diplotkan dalam cara yang disusun. Menggunakan `ggplot2` anda kemudian memplot data dalam carta bar. `coord_flip()` memplot bar mendatar. -![category-length](../../../../../translated_images/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.ms.png) +![category-length](../../../../../translated_images/ms/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.png) Carta bar ini menunjukkan pandangan yang baik tentang bilangan burung dalam setiap kategori. Sekilas pandang, anda dapat melihat bahawa bilangan burung terbesar di rantau ini adalah dalam kategori Itik/Angsa/BurungAir. Minnesota adalah 'tanah 10,000 tasik' jadi ini tidak mengejutkan! @@ -198,7 +198,7 @@ ggplot(birds_grouped,aes(Category,MaxLength))+geom_bar(stat="identity")+coord_fl ``` Kami mengelompokkan data `birds_filtered` mengikut `Category` dan kemudian memplot graf bar. -![comparing data](../../../../../translated_images/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.ms.png) +![comparing data](../../../../../translated_images/ms/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.png) Tiada yang mengejutkan di sini: burung kolibri mempunyai MaxLength paling kecil berbanding Pelikan atau Angsa. Data yang masuk akal secara logik adalah sesuatu yang baik! @@ -210,7 +210,7 @@ ggplot(data=birds_grouped, aes(x=Category)) + geom_bar(aes(y=MinLength), stat="identity", position="identity", fill='orange')+ coord_flip() ``` -![super-imposed values](../../../../../translated_images/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.ms.png) +![super-imposed values](../../../../../translated_images/ms/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.png) ## 🚀 Cabaran diff --git a/translations/ms/3-Data-Visualization/R/10-visualization-distributions/README.md b/translations/ms/3-Data-Visualization/R/10-visualization-distributions/README.md index ede4bab4..e0e80de2 100644 --- a/translations/ms/3-Data-Visualization/R/10-visualization-distributions/README.md +++ b/translations/ms/3-Data-Visualization/R/10-visualization-distributions/README.md @@ -45,7 +45,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + geom_point() + ggtitle("Max Length per order") + coord_flip() ``` -![panjang maksimum per order](../../../../../translated_images/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.ms.png) +![panjang maksimum per order](../../../../../translated_images/ms/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.png) Ini memberikan gambaran umum tentang taburan panjang badan per Order burung, tetapi ini bukan cara terbaik untuk memaparkan taburan sebenar. Tugas ini biasanya dilakukan dengan mencipta Histogram. ## Bekerja dengan histogram @@ -56,7 +56,7 @@ Ini memberikan gambaran umum tentang taburan panjang badan per Order burung, tet ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=10)+ylab('Frequency') ``` -![taburan seluruh dataset](../../../../../translated_images/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.ms.png) +![taburan seluruh dataset](../../../../../translated_images/ms/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.png) Seperti yang anda lihat, kebanyakan daripada 400+ burung dalam dataset ini berada dalam julat di bawah 2000 untuk Jisim Badan Maksimum mereka. Dapatkan lebih banyak wawasan tentang data dengan menukar parameter `bins` kepada nombor yang lebih tinggi, seperti 30: @@ -64,7 +64,7 @@ Seperti yang anda lihat, kebanyakan daripada 400+ burung dalam dataset ini berad ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![taburan-30bins](../../../../../translated_images/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.ms.png) +![taburan-30bins](../../../../../translated_images/ms/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.png) Carta ini menunjukkan taburan dengan cara yang lebih terperinci. Carta yang kurang condong ke kiri boleh dibuat dengan memastikan anda hanya memilih data dalam julat tertentu: @@ -76,7 +76,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![histogram ditapis](../../../../../translated_images/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.ms.png) +![histogram ditapis](../../../../../translated_images/ms/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.png) ✅ Cuba beberapa penapis dan titik data lain. Untuk melihat taburan penuh data, keluarkan penapis `['MaxBodyMass']` untuk menunjukkan taburan berlabel. @@ -90,7 +90,7 @@ ggplot(data=birds_filtered_1, aes(x=MaxBodyMass, y=MaxLength) ) + ``` Nampaknya terdapat korelasi yang dijangka antara kedua-dua elemen ini sepanjang paksi yang dijangka, dengan satu titik pertemuan yang sangat kuat: -![plot 2d](../../../../../translated_images/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.ms.png) +![plot 2d](../../../../../translated_images/ms/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.png) Histogram berfungsi dengan baik secara lalai untuk data berangka. Bagaimana jika anda perlu melihat taburan mengikut data teks? ## Terokai dataset untuk taburan menggunakan data teks @@ -121,7 +121,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + scale_fill_manual(name="Conservation Status",values=c("red","green","blue","pink"),labels=c("Endangered","Near Threathened","Vulnerable","Least Concern")) ``` -![lebar sayap dan konservasi](../../../../../translated_images/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.ms.png) +![lebar sayap dan konservasi](../../../../../translated_images/ms/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.png) Nampaknya tidak ada korelasi yang baik antara lebar sayap minimum dan status konservasi. Uji elemen lain dalam dataset menggunakan kaedah ini. Anda juga boleh mencuba penapis yang berbeza. Adakah anda menemui sebarang korelasi? @@ -135,7 +135,7 @@ Mari kita bekerja dengan plot ketumpatan sekarang! ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + geom_density() ``` -![plot ketumpatan](../../../../../translated_images/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.ms.png) +![plot ketumpatan](../../../../../translated_images/ms/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.png) Anda dapat melihat bagaimana plot ini mencerminkan yang sebelumnya untuk data LebarSayap Minimum; ia hanya sedikit lebih lancar. Jika anda ingin melihat garis JisimBadanMax yang bergerigi dalam carta kedua yang anda bina, anda boleh melicinkannya dengan baik dengan menciptanya semula menggunakan kaedah ini: @@ -143,7 +143,7 @@ Anda dapat melihat bagaimana plot ini mencerminkan yang sebelumnya untuk data Le ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density() ``` -![ketumpatan jisim badan](../../../../../translated_images/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.ms.png) +![ketumpatan jisim badan](../../../../../translated_images/ms/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.png) Jika anda mahukan garis yang lancar, tetapi tidak terlalu lancar, edit parameter `adjust`: @@ -151,7 +151,7 @@ Jika anda mahukan garis yang lancar, tetapi tidak terlalu lancar, edit parameter ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density(adjust = 1/5) ``` -![jisim badan kurang lancar](../../../../../translated_images/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.ms.png) +![jisim badan kurang lancar](../../../../../translated_images/ms/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.png) ✅ Baca tentang parameter yang tersedia untuk jenis plot ini dan bereksperimen! @@ -161,7 +161,7 @@ Jenis carta ini menawarkan visualisasi yang sangat jelas. Dengan beberapa baris ggplot(data=birds_filtered_1,aes(x = MaxBodyMass, fill = Order)) + geom_density(alpha=0.5) ``` -![jisim badan per order](../../../../../translated_images/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.ms.png) +![jisim badan per order](../../../../../translated_images/ms/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.png) ## 🚀 Cabaran diff --git a/translations/ms/3-Data-Visualization/R/11-visualization-proportions/README.md b/translations/ms/3-Data-Visualization/R/11-visualization-proportions/README.md index 960b6fbd..b923f129 100644 --- a/translations/ms/3-Data-Visualization/R/11-visualization-proportions/README.md +++ b/translations/ms/3-Data-Visualization/R/11-visualization-proportions/README.md @@ -97,7 +97,7 @@ pie(grouped$count,grouped$class, main="Edible?") ``` Voila, sebuah carta pai yang menunjukkan peratusan data ini mengikut dua kelas cendawan ini. Sangat penting untuk mendapatkan susunan label dengan betul, terutamanya di sini, jadi pastikan anda mengesahkan susunan dengan cara array label dibina! -![carta pai](../../../../../translated_images/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.ms.png) +![carta pai](../../../../../translated_images/ms/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.png) ## Donat! @@ -132,7 +132,7 @@ library(webr) PieDonut(habitat, aes(habitat, count=count)) ``` -![carta donat](../../../../../translated_images/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.ms.png) +![carta donat](../../../../../translated_images/ms/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.png) Kod ini menggunakan dua perpustakaan - ggplot2 dan webr. Dengan menggunakan fungsi PieDonut dari perpustakaan webr, kita boleh mencipta carta donat dengan mudah! @@ -169,7 +169,7 @@ waffle((cap_color$count/10), rows = 7, title = "Waffle Chart")+scale_fill_manual Menggunakan carta waffle, anda dapat melihat dengan jelas peratusan warna tudung dalam dataset cendawan ini. Menariknya, terdapat banyak cendawan bertudung hijau! -![carta waffle](../../../../../translated_images/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.ms.png) +![carta waffle](../../../../../translated_images/ms/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.png) Dalam pelajaran ini, anda belajar tiga cara untuk memvisualkan peratusan. Pertama, anda perlu mengelompokkan data anda ke dalam kategori dan kemudian memutuskan cara terbaik untuk memaparkan data - pai, donat, atau waffle. Semua ini menarik dan memberikan pengguna gambaran segera tentang dataset. diff --git a/translations/ms/3-Data-Visualization/R/12-visualization-relationships/README.md b/translations/ms/3-Data-Visualization/R/12-visualization-relationships/README.md index 5fcbc104..b1d5d507 100644 --- a/translations/ms/3-Data-Visualization/R/12-visualization-relationships/README.md +++ b/translations/ms/3-Data-Visualization/R/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ library(ggplot2) ggplot(honey, aes(x = priceperlb, y = state)) + geom_point(colour = "blue") ``` -![scatterplot 1](../../../../../translated_images/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.ms.png) +![scatterplot 1](../../../../../translated_images/ms/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.png) Sekarang, tunjukkan data yang sama dengan skema warna madu untuk menunjukkan bagaimana harga berkembang dari tahun ke tahun. Anda boleh melakukannya dengan menambah parameter 'scale_color_gradientn' untuk menunjukkan perubahan dari tahun ke tahun: @@ -61,7 +61,7 @@ Sekarang, tunjukkan data yang sama dengan skema warna madu untuk menunjukkan bag ggplot(honey, aes(x = priceperlb, y = state, color=year)) + geom_point()+scale_color_gradientn(colours = colorspace::heat_hcl(7)) ``` -![scatterplot 2](../../../../../translated_images/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.ms.png) +![scatterplot 2](../../../../../translated_images/ms/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.png) Dengan perubahan skema warna ini, anda dapat melihat dengan jelas perkembangan yang kuat dari tahun ke tahun dalam hal harga madu per paun. Malah, jika anda melihat set sampel dalam data untuk mengesahkan (pilih negeri tertentu, contohnya Arizona), anda dapat melihat pola kenaikan harga dari tahun ke tahun, dengan beberapa pengecualian: @@ -92,7 +92,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ``` Anda dapat melihat saiz titik semakin besar secara beransur-ansur. -![scatterplot 3](../../../../../translated_images/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.ms.png) +![scatterplot 3](../../../../../translated_images/ms/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.png) Adakah ini kes mudah permintaan dan penawaran? Disebabkan faktor seperti perubahan iklim dan keruntuhan koloni, adakah madu semakin kurang tersedia untuk dibeli dari tahun ke tahun, dan oleh itu harga meningkat? @@ -107,7 +107,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab ``` Jawapan: Ya, dengan beberapa pengecualian sekitar tahun 2003: -![line chart 1](../../../../../translated_images/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.ms.png) +![line chart 1](../../../../../translated_images/ms/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.png) Soalan: Baiklah, pada tahun 2003 adakah kita juga melihat lonjakan dalam bekalan madu? Bagaimana jika anda melihat jumlah pengeluaran dari tahun ke tahun? @@ -115,7 +115,7 @@ Soalan: Baiklah, pada tahun 2003 adakah kita juga melihat lonjakan dalam bekalan qplot(honey$year,honey$totalprod, geom='smooth', span =0.5, xlab = "year",ylab = "totalprod") ``` -![line chart 2](../../../../../translated_images/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.ms.png) +![line chart 2](../../../../../translated_images/ms/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.png) Jawapan: Tidak begitu. Jika anda melihat jumlah pengeluaran, ia sebenarnya kelihatan meningkat pada tahun tersebut, walaupun secara amnya jumlah madu yang dihasilkan menurun sepanjang tahun-tahun ini. @@ -135,7 +135,7 @@ ggplot(honey, aes(x=yieldpercol, y = numcol,group = 1)) + ``` Dalam visualisasi ini, anda boleh membandingkan hasil per koloni dan bilangan koloni dari tahun ke tahun, bersebelahan dengan wrap yang ditetapkan pada 3 untuk kolum: -![facet grid](../../../../../translated_images/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.ms.png) +![facet grid](../../../../../translated_images/ms/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.png) Untuk dataset ini, tiada apa-apa yang benar-benar menonjol berkaitan dengan bilangan koloni dan hasilnya, dari tahun ke tahun dan negeri ke negeri. Adakah terdapat cara lain untuk mencari korelasi antara dua pemboleh ubah ini? @@ -152,7 +152,7 @@ plot(honey$year, honey$yieldpercol, pch = 17, col = 3, axis(side = 4, at = pretty(range(y2))) mtext("colony yield", side = 4, line = 3) ``` -![superimposed plots](../../../../../translated_images/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.ms.png) +![superimposed plots](../../../../../translated_images/ms/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.png) Walaupun tiada apa-apa yang menonjol sekitar tahun 2003, ia membolehkan kita mengakhiri pelajaran ini dengan nota yang sedikit lebih gembira: walaupun terdapat penurunan bilangan koloni secara keseluruhan, bilangan koloni semakin stabil walaupun hasil per koloni semakin berkurangan. diff --git a/translations/ms/3-Data-Visualization/R/13-meaningful-vizualizations/README.md b/translations/ms/3-Data-Visualization/R/13-meaningful-vizualizations/README.md index e4b48b55..1c238b94 100644 --- a/translations/ms/3-Data-Visualization/R/13-meaningful-vizualizations/README.md +++ b/translations/ms/3-Data-Visualization/R/13-meaningful-vizualizations/README.md @@ -47,25 +47,25 @@ Dalam pelajaran sebelumnya, anda telah bereksperimen dengan membina pelbagai jen Walaupun seorang saintis data berhati-hati memilih carta yang sesuai untuk data yang betul, terdapat banyak cara data boleh dipaparkan untuk membuktikan sesuatu, sering kali dengan mengorbankan integriti data itu sendiri. Terdapat banyak contoh carta dan infografik yang mengelirukan! -[![Bagaimana Carta Menipu oleh Alberto Cairo](../../../../../translated_images/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.ms.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "Bagaimana carta menipu") +[![Bagaimana Carta Menipu oleh Alberto Cairo](../../../../../translated_images/ms/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "Bagaimana carta menipu") > 🎥 Klik imej di atas untuk ceramah persidangan tentang carta yang mengelirukan Carta ini membalikkan paksi X untuk menunjukkan kebalikan daripada kebenaran, berdasarkan tarikh: -![carta buruk 1](../../../../../translated_images/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.ms.png) +![carta buruk 1](../../../../../translated_images/ms/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.png) [Carta ini](https://media.firstcoastnews.com/assets/WTLV/images/170ae16f-4643-438f-b689-50d66ca6a8d8/170ae16f-4643-438f-b689-50d66ca6a8d8_1140x641.jpg) lebih mengelirukan, kerana mata tertarik ke kanan untuk menyimpulkan bahawa, dari masa ke masa, kes COVID telah menurun di pelbagai daerah. Sebenarnya, jika anda melihat dengan teliti pada tarikh, anda akan mendapati bahawa ia telah disusun semula untuk memberikan trend menurun yang mengelirukan. -![carta buruk 2](../../../../../translated_images/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.ms.jpg) +![carta buruk 2](../../../../../translated_images/ms/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.jpg) Contoh terkenal ini menggunakan warna DAN paksi Y yang terbalik untuk menipu: bukannya menyimpulkan bahawa kematian akibat senjata meningkat selepas penggubalan undang-undang mesra senjata, mata sebenarnya tertipu untuk berfikir sebaliknya: -![carta buruk 3](../../../../../translated_images/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.ms.jpg) +![carta buruk 3](../../../../../translated_images/ms/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.jpg) Carta pelik ini menunjukkan bagaimana perkadaran boleh dimanipulasi, dengan kesan yang lucu: -![carta buruk 4](../../../../../translated_images/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.ms.jpg) +![carta buruk 4](../../../../../translated_images/ms/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.jpg) Membandingkan perkara yang tidak sebanding adalah satu lagi helah yang tidak jujur. Terdapat [laman web yang hebat](https://tylervigen.com/spurious-correlations) tentang 'korelasi palsu' yang memaparkan 'fakta' yang mengaitkan perkara seperti kadar perceraian di Maine dan penggunaan marjerin. Sebuah kumpulan Reddit juga mengumpulkan [penggunaan data yang buruk](https://www.reddit.com/r/dataisugly/top/?t=all). @@ -100,13 +100,13 @@ Labelkan paksi anda, sediakan legenda jika perlu, dan tawarkan tooltip untuk pem Jika data anda berbentuk teks dan panjang pada paksi X, anda boleh memiringkan teks untuk kebolehbacaan yang lebih baik. [plot3D](https://cran.r-project.org/web/packages/plot3D/index.html) menawarkan pemplotan 3D, jika data anda menyokongnya. Visualisasi data yang canggih boleh dihasilkan menggunakan ini. -![plot 3d](../../../../../translated_images/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.ms.png) +![plot 3d](../../../../../translated_images/ms/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.png) ## Paparan carta animasi dan 3D Beberapa visualisasi data terbaik hari ini adalah animasi. Shirley Wu mempunyai visualisasi yang menakjubkan menggunakan D3, seperti '[film flowers](http://bl.ocks.org/sxywu/raw/d612c6c653fb8b4d7ff3d422be164a5d/)', di mana setiap bunga adalah visualisasi sebuah filem. Contoh lain untuk Guardian ialah 'bussed out', pengalaman interaktif yang menggabungkan visualisasi dengan Greensock dan D3 serta format artikel scrollytelling untuk menunjukkan bagaimana NYC menangani masalah gelandangan dengan menghantar orang keluar dari bandar. -![busing](../../../../../translated_images/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.ms.png) +![busing](../../../../../translated_images/ms/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.png) > "Bussed Out: How America Moves its Homeless" daripada [the Guardian](https://www.theguardian.com/us-news/ng-interactive/2017/dec/20/bussed-out-america-moves-homeless-people-country-study). Visualisasi oleh Nadieh Bremer & Shirley Wu @@ -116,7 +116,7 @@ Walaupun pelajaran ini tidak mencukupi untuk mengajar perpustakaan visualisasi y Anda akan melengkapkan aplikasi web yang akan memaparkan pandangan animasi rangkaian sosial ini. Ia menggunakan perpustakaan yang dibina untuk mencipta [visual rangkaian](https://github.com/emiliorizzo/vue-d3-network) menggunakan Vue.js dan D3. Apabila aplikasi berjalan, anda boleh menarik nod di skrin untuk menyusun semula data. -![liaisons](../../../../../translated_images/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.ms.png) +![liaisons](../../../../../translated_images/ms/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.png) ## Projek: Bina carta untuk menunjukkan rangkaian menggunakan D3.js diff --git a/translations/ms/3-Data-Visualization/README.md b/translations/ms/3-Data-Visualization/README.md index cbb92691..6ed6b891 100644 --- a/translations/ms/3-Data-Visualization/README.md +++ b/translations/ms/3-Data-Visualization/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Visualisasi -![seekor lebah di atas bunga lavender](../../../translated_images/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.ms.jpg) +![seekor lebah di atas bunga lavender](../../../translated_images/ms/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.jpg) > Foto oleh Jenna Lee di Unsplash Memvisualkan data adalah salah satu tugas paling penting bagi seorang saintis data. Gambar bernilai 1000 perkataan, dan visualisasi dapat membantu anda mengenal pasti pelbagai aspek menarik dalam data anda seperti lonjakan, nilai luar biasa, pengelompokan, kecenderungan, dan banyak lagi, yang dapat membantu anda memahami cerita yang ingin disampaikan oleh data anda. diff --git a/translations/ms/4-Data-Science-Lifecycle/14-Introduction/README.md b/translations/ms/4-Data-Science-Lifecycle/14-Introduction/README.md index 852633d8..dcb4b293 100644 --- a/translations/ms/4-Data-Science-Lifecycle/14-Introduction/README.md +++ b/translations/ms/4-Data-Science-Lifecycle/14-Introduction/README.md @@ -25,7 +25,7 @@ Pada tahap ini, anda mungkin telah menyedari bahawa sains data adalah satu prose Pelajaran ini memberi tumpuan kepada 3 bahagian dalam kitaran hayat: pengumpulan, pemprosesan, dan penyelenggaraan. -![Rajah kitaran hayat sains data](../../../../translated_images/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.ms.jpg) +![Rajah kitaran hayat sains data](../../../../translated_images/ms/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.jpg) > Foto oleh [Berkeley School of Information](https://ischoolonline.berkeley.edu/data-science/what-is-data-science/) ## Pengumpulan @@ -101,7 +101,7 @@ Terokai [Kitaran Hayat Proses Sains Data Pasukan](https://docs.microsoft.com/en- |Proses Sains Data Pasukan (TDSP)|Proses standard industri untuk perlombongan data (CRISP-DM)| |--|--| -|![Kitaran Hayat Proses Sains Data Pasukan](../../../../translated_images/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.ms.png) | ![Imej Proses Sains Data Alliance](../../../../translated_images/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.ms.png) | +|![Kitaran Hayat Proses Sains Data Pasukan](../../../../translated_images/ms/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.png) | ![Imej Proses Sains Data Alliance](../../../../translated_images/ms/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.png) | | Imej oleh [Microsoft](https://docs.microsoft.comazure/architecture/data-science-process/lifecycle) | Imej oleh [Data Science Process Alliance](https://www.datascience-pm.com/crisp-dm-2/) | ## [Kuiz Pasca-Kuliah](https://ff-quizzes.netlify.app/en/ds/quiz/27) diff --git a/translations/ms/4-Data-Science-Lifecycle/README.md b/translations/ms/4-Data-Science-Lifecycle/README.md index 984c1a6b..e7e83454 100644 --- a/translations/ms/4-Data-Science-Lifecycle/README.md +++ b/translations/ms/4-Data-Science-Lifecycle/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Kitaran Hayat Sains Data -![communication](../../../translated_images/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.ms.jpg) +![communication](../../../translated_images/ms/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.jpg) > Foto oleh Headway di Unsplash Dalam pelajaran ini, anda akan meneroka beberapa aspek kitaran hayat Sains Data, termasuk analisis dan komunikasi berkaitan data. diff --git a/translations/ms/5-Data-Science-In-Cloud/README.md b/translations/ms/5-Data-Science-In-Cloud/README.md index d42bb2a7..8d8b0a8b 100644 --- a/translations/ms/5-Data-Science-In-Cloud/README.md +++ b/translations/ms/5-Data-Science-In-Cloud/README.md @@ -9,13 +9,13 @@ CO_OP_TRANSLATOR_METADATA: --> # Sains Data di Awan -![cloud-picture](../../../translated_images/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.ms.jpg) +![cloud-picture](../../../translated_images/ms/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.jpg) > Foto oleh [Jelleke Vanooteghem](https://unsplash.com/@ilumire) dari [Unsplash](https://unsplash.com/s/photos/cloud?orientation=landscape) Apabila melibatkan sains data dengan data besar, awan boleh menjadi pengubah permainan. Dalam tiga pelajaran seterusnya, kita akan melihat apa itu awan dan mengapa ia sangat berguna. Kita juga akan meneroka dataset kegagalan jantung dan membina model untuk membantu menilai kebarangkalian seseorang mengalami kegagalan jantung. Kita akan menggunakan kuasa awan untuk melatih, menyebarkan, dan menggunakan model dengan dua cara berbeza. Satu cara menggunakan antara muka pengguna sahaja dalam pendekatan Low code/No code, dan cara lain menggunakan Azure Machine Learning Software Developer Kit (Azure ML SDK). -![project-schema](../../../translated_images/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.ms.png) +![project-schema](../../../translated_images/ms/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.png) ### Topik diff --git a/translations/ms/6-Data-Science-In-Wild/20-Real-World-Examples/README.md b/translations/ms/6-Data-Science-In-Wild/20-Real-World-Examples/README.md index 4a662c72..ac4643bc 100644 --- a/translations/ms/6-Data-Science-In-Wild/20-Real-World-Examples/README.md +++ b/translations/ms/6-Data-Science-In-Wild/20-Real-World-Examples/README.md @@ -41,7 +41,7 @@ Terima kasih kepada pendemokrasian AI, pembangun kini lebih mudah untuk mereka b * [Sains Data dalam Penjagaan Kesihatan](https://data-flair.training/blogs/data-science-in-healthcare/) - menonjolkan aplikasi seperti pengimejan perubatan (contohnya, MRI, X-Ray, CT-Scan), genomik (penjujukan DNA), pembangunan ubat (penilaian risiko, ramalan kejayaan), analitik ramalan (penjagaan pesakit & logistik bekalan), pengesanan & pencegahan penyakit dll. -![Aplikasi Sains Data di Dunia Sebenar](../../../../translated_images/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.ms.png) Kredit Imej: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) +![Aplikasi Sains Data di Dunia Sebenar](../../../../translated_images/ms/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.png) Kredit Imej: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) Rajah ini menunjukkan domain lain dan contoh untuk menerapkan teknik sains data. Mahu meneroka aplikasi lain? Lihat bahagian [Kajian & Pembelajaran Kendiri](../../../../6-Data-Science-In-Wild/20-Real-World-Examples) di bawah. diff --git a/translations/ms/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md b/translations/ms/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md index 9b026838..c50a60b0 100644 --- a/translations/ms/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md +++ b/translations/ms/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md @@ -22,7 +22,7 @@ Antara muka Explorer (seperti yang ditunjukkan dalam tangkapan skrin di bawah) m 2. Terokai [Katalog dataset](https://planetarycomputer.microsoft.com/catalog) - pelajari tujuan setiap dataset. 3. Gunakan Explorer - pilih dataset yang menarik, pilih pertanyaan dan pilihan rendering yang relevan. -![The Planetary Computer Explorer](../../../../translated_images/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.ms.png) +![The Planetary Computer Explorer](../../../../translated_images/ms/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.png) `Tugas Anda:` Sekarang kaji visualisasi yang dipaparkan dalam pelayar dan jawab soalan berikut: diff --git a/translations/ms/CONTRIBUTING.md b/translations/ms/CONTRIBUTING.md index e9d59873..d9ab4eac 100644 --- a/translations/ms/CONTRIBUTING.md +++ b/translations/ms/CONTRIBUTING.md @@ -315,7 +315,7 @@ Sertakan dalam penerangan PR anda: ``` ```` -- Tambah teks alt pada imej: `![Alt text](../../translated_images/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.ms.png)` +- Tambah teks alt pada imej: `![Alt text](../../translated_images/ms/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.png)` - Kekalkan panjang baris yang munasabah (sekitar 80-100 aksara) ### Python diff --git a/translations/ms/README.md b/translations/ms/README.md index 12bc9938..1ea7324e 100644 --- a/translations/ms/README.md +++ b/translations/ms/README.md @@ -33,7 +33,7 @@ Azure Cloud Advocates di Microsoft dengan sukacitanya menawarkan kurikulum 10-mi **🙏 Terima kasih istimewa 🙏 kepada pengarang, penilai dan penyumbang kandungan [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/),** khususnya Aaryan Arora, [Aditya Garg](https://github.com/AdityaGarg00), [Alondra Sanchez](https://www.linkedin.com/in/alondra-sanchez-molina/), [Ankita Singh](https://www.linkedin.com/in/ankitasingh007), [Anupam Mishra](https://www.linkedin.com/in/anupam--mishra/), [Arpita Das](https://www.linkedin.com/in/arpitadas01/), ChhailBihari Dubey, [Dibri Nsofor](https://www.linkedin.com/in/dibrinsofor), [Dishita Bhasin](https://www.linkedin.com/in/dishita-bhasin-7065281bb), [Majd Safi](https://www.linkedin.com/in/majd-s/), [Max Blum](https://www.linkedin.com/in/max-blum-6036a1186/), [Miguel Correa](https://www.linkedin.com/in/miguelmque/), [Mohamma Iftekher (Iftu) Ebne Jalal](https://twitter.com/iftu119), [Nawrin Tabassum](https://www.linkedin.com/in/nawrin-tabassum), [Raymond Wangsa Putra](https://www.linkedin.com/in/raymond-wp/), [Rohit Yadav](https://www.linkedin.com/in/rty2423), Samridhi Sharma, [Sanya Sinha](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200), [Sheena Narula](https://www.linkedin.com/in/sheena-narua-n/), [Tauqeer Ahmad](https://www.linkedin.com/in/tauqeerahmad5201/), Yogendrasingh Pawar , [Vidushi Gupta](https://www.linkedin.com/in/vidushi-gupta07/), [Jasleen Sondhi](https://www.linkedin.com/in/jasleen-sondhi/) -|![Sketchnote oleh @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.ms.png)| +|![Sketchnote oleh @sketchthedocs https://sketchthedocs.dev](../../translated_images/ms/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.png)| |:---:| | Sains Data untuk Pemula - _Sketchnote oleh [@nitya](https://twitter.com/nitya)_ | @@ -52,7 +52,7 @@ Azure Cloud Advocates di Microsoft dengan sukacitanya menawarkan kurikulum 10-mi Kami sedang mengendalikan siri Discord "Belajar dengan AI", ketahui lebih lanjut dan sertai kami di [Learn with AI Series](https://aka.ms/learnwithai/discord) dari 18 - 30 September, 2025. Anda akan mendapat petua dan trik menggunakan GitHub Copilot untuk Sains Data. -![Siri Belajar dengan AI](../../translated_images/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.ms.jpg) +![Siri Belajar dengan AI](../../translated_images/ms/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.jpg) # Adakah anda seorang pelajar? @@ -132,7 +132,7 @@ Setiap contoh merangkumi komen terperinci yang menerangkan setiap langkah, menja ## Pelajaran -|![ Sketchnote oleh @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.ms.png)| +|![ Sketchnote oleh @sketchthedocs https://sketchthedocs.dev](../../translated_images/ms/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png)| |:---:| | Sains Data Untuk Pemula: Peta Jalan - _Sketchnote oleh [@nitya](https://twitter.com/nitya)_ | diff --git a/translations/ms/sketchnotes/README.md b/translations/ms/sketchnotes/README.md index 8fa3928b..552fcb45 100644 --- a/translations/ms/sketchnotes/README.md +++ b/translations/ms/sketchnotes/README.md @@ -13,7 +13,7 @@ Cari semua sketchnote di sini! Nitya Narasimhan, artis -![sketchnote peta jalan](../../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.ms.png) +![sketchnote peta jalan](../../../translated_images/ms/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png) --- diff --git a/translations/my/1-Introduction/01-defining-data-science/README.md b/translations/my/1-Introduction/01-defining-data-science/README.md index 263e5b0c..fbfa362f 100644 --- a/translations/my/1-Introduction/01-defining-data-science/README.md +++ b/translations/my/1-Introduction/01-defining-data-science/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: --- -[![ဒေတာသိပ္ပံကို သတ်မှတ်ခြင်း ဗီဒီယို](../../../../translated_images/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.my.png)](https://youtu.be/beZ7Mb_oz9I) +[![ဒေတာသိပ္ပံကို သတ်မှတ်ခြင်း ဗီဒီယို](../../../../translated_images/my/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.png)](https://youtu.be/beZ7Mb_oz9I) ## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ds/quiz/0) @@ -120,7 +120,7 @@ Machine Learning နယ်ပယ်တစ်ခုဖြစ်တဲ့ Artifici ဤစိန်ခေါ်မှုတွင် ကျွန်ုပ်တို့သည် Data Science နယ်ပယ်နှင့်ဆက်စပ်သောအယူအဆများကို စာသားများကိုကြည့်ခြင်းဖြင့်ရှာဖွေရန်ကြိုးစားမည်။ ကျွန်ုပ်တို့သည် Data Science အကြောင်း Wikipedia ဆောင်းပါးတစ်ခုကိုယူပြီး၊ စာသားကိုဒေါင်းလုပ်လုပ်ပြီး အဆင့်ဆင့်လုပ်ဆောင်ပြီးနောက်၊ အောက်ပါပုံကဲ့သို့သော စကားလုံးတိမ်တစ်ခုကိုတည်ဆောက်မည်။ -![Word Cloud for Data Science](../../../../translated_images/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.my.png) +![Word Cloud for Data Science](../../../../translated_images/my/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.png) [`notebook.ipynb`](../../../../1-Introduction/01-defining-data-science/notebook.ipynb ':ignore') သို့သွားပြီး ကုဒ်ကိုဖတ်ရှုပါ။ သင်သည်ကုဒ်ကိုလည်း run လုပ်နိုင်ပြီး၊ အချက်အလက်ပြောင်းလဲမှုများကို အချိန်နှင့်တပြေးညီလုပ်ဆောင်ပုံကိုကြည့်ရှုနိုင်သည်။ diff --git a/translations/my/1-Introduction/04-stats-and-probability/README.md b/translations/my/1-Introduction/04-stats-and-probability/README.md index ec6e13db..1989af77 100644 --- a/translations/my/1-Introduction/04-stats-and-probability/README.md +++ b/translations/my/1-Introduction/04-stats-and-probability/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: စာရင်းအင်းနှင့် အလားအလာ သီအိုရီသည် သင်္ချာ၏ အလွန်နီးစပ်သော နယ်ပယ်နှစ်ခုဖြစ်ပြီး ဒေတာသိပ္ပံတွင် အလွန်အရေးပါသည်။ သင်္ချာအကြောင်း အနက်ရှိုင်းစွာ မသိဘဲ ဒေတာနှင့် လုပ်ဆောင်နိုင်သော်လည်း အခြေခံအယူအဆအချို့ကို သိထားခြင်းက ပိုမိုကောင်းမွန်ပါသည်။ ဒီမှာ သင်စတင်နိုင်ရန် အကျိုးရှိမည့် အကျဉ်းချုပ်တစ်ခုကို တင်ပြပါမည်။ -[![Intro Video](../../../../translated_images/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.my.png)](https://youtu.be/Z5Zy85g4Yjw) +[![Intro Video](../../../../translated_images/my/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.png)](https://youtu.be/Z5Zy85g4Yjw) ## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ds/quiz/6) @@ -39,7 +39,7 @@ Continuous variable တစ်ခု၏ အလားအလာဖြန့်ဝ ကျွန်ုပ်တို့သည် variable တစ်ခုသည် တန်ဖိုးများ၏ interval တစ်ခုတွင် ကျရောက်သော အလားအလာအကြောင်းသာ ပြောနိုင်သည်၊ ဥပမာအားဖြင့် P(t1≤X2)။ ဒီအခြေအနေတွင် အလားအလာဖြန့်ဝေမှုကို **probability density function** p(x) ဖြင့် ဖော်ပြသည်၊ ၎င်းသည် -![P(t_1\le X Correlation နှင့် covariance ၏ နောက်ထပ်ဥပမာများကို [accompanying notebook](notebook.ipynb) တွင် ရှာဖွေနိုင်သည်။ diff --git a/translations/my/1-Introduction/README.md b/translations/my/1-Introduction/README.md index b1e0dbb8..10e13844 100644 --- a/translations/my/1-Introduction/README.md +++ b/translations/my/1-Introduction/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # ဒေတာသိပ္ပံအကျဉ်းချုပ် -![ဒေတာအလုပ်လုပ်နေမှု](../../../translated_images/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.my.jpg) +![ဒေတာအလုပ်လုပ်နေမှု](../../../translated_images/my/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.jpg) > ဓာတ်ပုံကို Stephen Dawson မှ Unsplash တွင် ရိုက်ထားသည် ဒီသင်ခန်းစာများတွင် ဒေတာသိပ္ပံကို ဘယ်လိုအဓိပ္ပာယ်ဖွင့်ဆိုရမယ်ဆိုတာကို ရှင်းလင်းသွားမှာဖြစ်ပြီး ဒေတာသိပ္ပံပညာရှင်တစ်ဦးအနေနဲ့ လိုက်နာရမယ့် ကျင့်ဝတ်ဆိုင်ရာအချက်များကိုလည်း သင်ယူရမယ်။ ဒေတာကို ဘယ်လိုအဓိပ္ပာယ်ဖွင့်ဆိုရမယ်ဆိုတာကိုလည်း သင်ယူရမယ်။ ဒါ့အပြင် ဒေတာသိပ္ပံရဲ့ အဓိကပညာရပ်များဖြစ်တဲ့ စာရင်းအင်းနှင့် အလားအလာဆိုင်ရာအခြေခံအချက်များကိုလည်း နည်းနည်းလေး သင်ယူရမယ်။ diff --git a/translations/my/2-Working-With-Data/07-python/README.md b/translations/my/2-Working-With-Data/07-python/README.md index 0249fa81..0421a241 100644 --- a/translations/my/2-Working-With-Data/07-python/README.md +++ b/translations/my/2-Working-With-Data/07-python/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: | :-------------------------------------------------------------------------------------------------------: | | Python နှင့်အလုပ်လုပ်ခြင်း - _Sketchnote by [@nitya](https://twitter.com/nitya)_ | -[![Intro Video](../../../../translated_images/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.my.png)](https://youtu.be/dZjWOGbsN4Y) +[![Intro Video](../../../../translated_images/my/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.png)](https://youtu.be/dZjWOGbsN4Y) ဒေတာများကို သိမ်းဆည်းရန်နှင့် query languages အသုံးပြု၍ ရှာဖွေရန်အတွက် databases သည် အလွန်ထိရောက်သောနည်းလမ်းများပေးနိုင်သော်လည်း၊ ဒေတာကို ကိုယ်တိုင်ရေးသားထားသော program ဖြင့် ပြုပြင်ရန် flexibility အများဆုံးရှိသည်။ အချို့သောအခြေအနေများတွင် database query သည် ပိုထိရောက်နိုင်သော်လည်း၊ SQL ဖြင့် လွယ်ကူစွာလုပ်ဆောင်၍မရသော ဒေတာကို ရှုပ်ထွေးစွာ ပြုပြင်ရန်လိုအပ်သောအခါများရှိသည်။ ဒေတာကို programming language မည်သည့်အမျိုးအစားဖြင့်မဆို ပြုပြင်နိုင်သော်လည်း၊ ဒေတာနှင့်အလုပ်လုပ်ရန်အတွက် အဆင့်မြင့်သော programming languages ရှိသည်။ ဒေတာသိပ္ပံပညာရှင်များသည် အောက်ပါဘာသာစကားများကို အများအားဖြင့်နှစ်သက်ကြသည်- @@ -74,7 +74,7 @@ print(f"Length of index is {len(idx)}") items_sold = pd.Series(np.random.randint(25,50,size=len(idx)),index=idx) items_sold.plot() ``` -![Time Series Plot](../../../../translated_images/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.my.png) +![Time Series Plot](../../../../translated_images/my/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.png) အပတ်စဉ်တွင် party အတွက် ice-cream packs 10 ခုကို ထပ်မံယူသည့်အခါ series တစ်ခုကို week အဖြစ် index လုပ်၍ ဖော်ပြနိုင်သည်: ```python @@ -85,7 +85,7 @@ Series နှစ်ခုကို ပေါင်းလိုက်သောအ total_items = items_sold.add(additional_items,fill_value=0) total_items.plot() ``` -![Time Series Plot](../../../../translated_images/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.my.png) +![Time Series Plot](../../../../translated_images/my/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.png) > **Note**: `total_items+additional_items` syntax ကို ရိုးရှင်းစွာမသုံးပါ။ သုံးပါက `NaN` (*Not a Number*) values များကို ရရှိမည်။ ဒါကြောင့် `fill_value` parameter ကို addition အတွင်း specify လုပ်ရန်လိုအပ်သည်။ @@ -94,7 +94,7 @@ Time series တွင် **resample** လုပ်၍ time interval များ monthly = total_items.resample("1M").mean() ax = monthly.plot(kind='bar') ``` -![Monthly Time Series Averages](../../../../translated_images/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.my.png) +![Monthly Time Series Averages](../../../../translated_images/my/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.png) ### DataFrame @@ -198,7 +198,7 @@ Data Scientist တစ်ဦးအနေနဲ့ အချက်အလက်တ အချက်အလက်တွေကို ဘယ်လိုကိုင်တွယ်ရမလဲဆိုတာကို ပြသဖို့ [`notebook-covidspread.ipynb`](notebook-covidspread.ipynb) ကို ဖွင့်ပြီး အပေါ်မှ အောက်သို့ ဖတ်ရှုပါ။ Cell တွေကို run လုပ်နိုင်ပြီး အဆုံးမှာ ကျွန်တော်တို့ထားခဲ့တဲ့ challenge တွေကို လုပ်နိုင်ပါတယ်။ -![COVID Spread](../../../../translated_images/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.my.png) +![COVID Spread](../../../../translated_images/my/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.png) > Jupyter Notebook မှာ code ကို ဘယ်လို run လုပ်ရမလဲ မသိရင် [ဒီဆောင်းပါး](https://soshnikov.com/education/how-to-execute-notebooks-from-github/) ကို ကြည့်ပါ။ @@ -220,7 +220,7 @@ Data Scientist တစ်ဦးအနေနဲ့ အချက်အလက်တ [`notebook-papers.ipynb`](notebook-papers.ipynb) ကို ဖွင့်ပြီး အပေါ်မှ အောက်သို့ ဖတ်ရှုပါ။ Cell တွေကို run လုပ်နိုင်ပြီး အဆုံးမှာ ကျွန်တော်တို့ထားခဲ့တဲ့ challenge တွေကို လုပ်နိုင်ပါတယ်။ -![Covid Medical Treatment](../../../../translated_images/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.my.png) +![Covid Medical Treatment](../../../../translated_images/my/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.png) ## Image Data ကို ကိုင်တွယ်ခြင်း diff --git a/translations/my/2-Working-With-Data/README.md b/translations/my/2-Working-With-Data/README.md index bc559c40..2c17ca18 100644 --- a/translations/my/2-Working-With-Data/README.md +++ b/translations/my/2-Working-With-Data/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # ဒေတာနှင့်အလုပ်လုပ်ခြင်း -![data love](../../../translated_images/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.my.jpg) +![data love](../../../translated_images/my/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.jpg) > ဓာတ်ပုံကို Alexander Sinn မှ Unsplash တွင် ရိုက်ထားသည် ဒီသင်ခန်းစာများတွင် ဒေတာကို စီမံခြင်း၊ ပြောင်းလဲခြင်းနှင့် အက်ပလီကေးရှင်းများတွင် အသုံးပြုခြင်းနည်းလမ်းများကို သင်ယူနိုင်ပါမည်။ သင် relational databases နှင့် non-relational databases အကြောင်းကို သင်ယူပြီး ဒေတာကို ဘယ်လိုသိုလှောင်နိုင်မလဲဆိုတာကိုလည်း သိရှိနိုင်ပါမည်။ Python ကို အသုံးပြု၍ ဒေတာကို စီမံရန် အခြေခံအချက်များကို သင်ယူပြီး Python ကို အသုံးပြု၍ ဒေတာကို စီမံခြင်းနှင့် ရှာဖွေခြင်းနည်းလမ်းများစွာကို ရှာဖွေတွေ့ရှိနိုင်ပါမည်။ diff --git a/translations/my/3-Data-Visualization/12-visualization-relationships/README.md b/translations/my/3-Data-Visualization/12-visualization-relationships/README.md index fe63026b..aa9d8086 100644 --- a/translations/my/3-Data-Visualization/12-visualization-relationships/README.md +++ b/translations/my/3-Data-Visualization/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ honey.head() ```python sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ``` -![scatterplot 1](../../../../translated_images/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.my.png) +![scatterplot 1](../../../../translated_images/my/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.png) ယခုနှစ်အလိုက် ပျားရည်စျေးနှုန်းသည် ဘယ်လိုပြောင်းလဲလာသည်ကို ပျားရည်အရောင်စနစ်ဖြင့် ပြသပါ။ ၎င်းကို 'hue' parameter ကို ထည့်သွင်းခြင်းဖြင့် ပြုလုပ်နိုင်ပြီး နှစ်အလိုက် ပြောင်းလဲမှုကို ပြသနိုင်သည်: @@ -60,7 +60,7 @@ sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ```python sns.relplot(x="priceperlb", y="state", hue="year", palette="YlOrBr", data=honey, height=15, aspect=.5); ``` -![scatterplot 2](../../../../translated_images/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.my.png) +![scatterplot 2](../../../../translated_images/my/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.png) ဤအရောင်စနစ်ပြောင်းလဲမှုဖြင့် ပျားရည်တစ်ပေါင်စျေးနှုန်းသည် နှစ်အလိုက် အားကောင်းစွာတိုးတက်လာသည်ကို မြင်နိုင်ပါသည်။ အမှန်တကယ်၊ ဒေတာထဲမှ နမူနာတစ်ခုကို စစ်ဆေးပါက (ဥပမာအားဖြင့် Arizona) နှစ်အလိုက် စျေးနှုန်းတိုးတက်မှုပုံစံကို အချို့သောကိစ္စများမှလွဲ၍ မြင်နိုင်ပါသည်: @@ -89,7 +89,7 @@ sns.relplot(x="priceperlb", y="state", size="year", data=honey, height=15, aspec ``` dot size များသည် တဖြည်းဖြည်းကြီးလာသည်ကို မြင်နိုင်ပါသည်။ -![scatterplot 3](../../../../translated_images/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.my.png) +![scatterplot 3](../../../../translated_images/my/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.png) ဤသည်မှာ supply နှင့် demand ရိုးရှင်းသောအကြောင်းအရာဖြစ်ပါသလား။ ရာသီဥတုပြောင်းလဲမှုနှင့် colony collapse ကဲ့သို့သောအကြောင်းအရာများကြောင့် နှစ်အလိုက် ဝယ်ယူနိုင်သော ပျားရည်ပမာဏလျော့နည်းလာပြီး စျေးနှုန်းတိုးတက်လာပါသလား။ @@ -104,7 +104,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); ``` အဖြေ- ဟုတ်ပါသည်၊ ၂၀၀၃ ခုနှစ်အနီးတွင် အချို့သောကွဲလွဲမှုများရှိသည်။ -![line chart 1](../../../../translated_images/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.my.png) +![line chart 1](../../../../translated_images/my/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.png) ✅ Seaborn သည် တစ်ခုတည်းသောလိုင်းပေါ်တွင် ဒေတာကို စုစည်းထားပြီး "x value တစ်ခုစီတွင် အမျိုးမျိုးသောတိုင်းတာမှုများကို mean နှင့် mean အပေါ် 95% confidence interval ကို ပြသခြင်းဖြင့်" ဖော်ပြသည်။ [အရင်းအမြစ်](https://seaborn.pydata.org/tutorial/relational.html)။ ဤအချိန်စားသောအပြုအမူကို `ci=None` ထည့်သွင်းခြင်းဖြင့် ပယ်ဖျက်နိုင်သည်။ @@ -114,7 +114,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); sns.relplot(x="year", y="totalprod", kind="line", data=honey); ``` -![line chart 2](../../../../translated_images/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.my.png) +![line chart 2](../../../../translated_images/my/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.png) အဖြေ- အမှန်တကယ်မဟုတ်ပါ။ စုစုပေါင်းထုတ်လုပ်မှုကို ကြည့်ပါက ထိုနှစ်တွင် တိုးတက်လာသလိုပုံရပြီး၊ ယေဘူယျအားဖြင့် ထိုနှစ်များအတွင်း ပျားရည်ထုတ်လုပ်မှုပမာဏသည် လျော့နည်းလာသည်ကို မြင်နိုင်ပါသည်။ @@ -139,7 +139,7 @@ sns.relplot( ``` ဤ visualization တွင် yield per colony နှင့် number of colonies ကို နှစ်အလိုက်၊ ပြည်နယ်အလိုက် wrap ကို 3 column အဖြစ်ထားပြီး ဘက်ဘက်ကွဲကွဲနှိုင်းယှဉ်နိုင်သည်။ -![facet grid](../../../../translated_images/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.my.png) +![facet grid](../../../../translated_images/my/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.png) ဤဒေတာအတွက်၊ နှစ်အလိုက်၊ ပြည်နယ်အလိုက် number of colonies နှင့် yield အကြား ထူးထူးခြားခြားသောအရာများမရှိပါ။ ဤ variable နှစ်ခုအကြား correlation ရှာဖွေခြင်းအတွက် အခြားနည်းလမ်းတစ်ခုရှိပါသလား။ @@ -162,7 +162,7 @@ sns.despine(right=False) plt.ylabel('colony yield') ax.figure.legend(); ``` -![superimposed plots](../../../../translated_images/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.my.png) +![superimposed plots](../../../../translated_images/my/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.png) ၂၀၀၃ ခုနှစ်အနီးတွင် ထူးထူးခြားခြားသောအရာများ မမြင်ရသော်လည်း၊ lesson ၏အဆုံးကို ပျော်ရွှင်စွာပြီးမြောက်စေသောအချက်တစ်ခုကို ပြသနိုင်သည်- colonies အရေအတွက်သည် လျော့နည်းနေသော်လည်း၊ ၎င်းတို့၏ yield per colony လျော့နည်းနေသည့်အချိန်တွင် colonies အရေအတွက်သည် တည်ငြိမ်နေသည်။ diff --git a/translations/my/3-Data-Visualization/R/09-visualization-quantities/README.md b/translations/my/3-Data-Visualization/R/09-visualization-quantities/README.md index 6502827a..34a9d835 100644 --- a/translations/my/3-Data-Visualization/R/09-visualization-quantities/README.md +++ b/translations/my/3-Data-Visualization/R/09-visualization-quantities/README.md @@ -67,7 +67,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` ဒီမှာ `ggplot2` package ကို install လုပ်ပြီး `library("ggplot2")` command ကိုအသုံးပြုကာ workspace ထဲသို့ import လုပ်ပါသည်။ ggplot တွင် plot တစ်ခုကိုဖော်ပြရန် `ggplot()` function ကိုအသုံးပြုပြီး dataset, x နှင့် y variable များကို attribute အဖြစ်သတ်မှတ်ပါသည်။ ဒီအခါမှာ line plot ကို plot လုပ်ရန် `geom_line()` function ကိုအသုံးပြုပါသည်။ -![MaxWingspan-lineplot](../../../../../translated_images/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.my.png) +![MaxWingspan-lineplot](../../../../../translated_images/my/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.png) သင်ဘာတွေသတိထားမိပါသလဲ? အနည်းဆုံး outlier တစ်ခုရှိသလိုပဲ - တောင်ပံအကျယ်တစ်ခုကတော်တော်လေးကြီးတယ်! 2000+ စင်တီမီတာတောင်ပံအကျယ်က 20 မီတာကျော်ရှိတယ် - Minnesota မှာ Pterodactyls တွေရှိနေသလား? စစ်ဆေးကြည့်ရအောင်။ @@ -85,7 +85,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` `theme` မှာ angle ကိုသတ်မှတ်ပြီး `xlab()` နှင့် `ylab()` မှာ x နှင့် y axis label များကိုသတ်မှတ်ပါသည်။ `ggtitle()` သည် graph/plot ကိုနာမည်ပေးသည်။ -![MaxWingspan-lineplot-improved](../../../../../translated_images/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.my.png) +![MaxWingspan-lineplot-improved](../../../../../translated_images/my/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.png) label များကို 45 ဒီဂရီလှည့်ထားသော်လည်း၊ ဖတ်ရန်အလွန်များနေသည်။ အခြား strategy တစ်ခုကိုစမ်းကြည့်ရအောင်- outlier များကိုသာ label လုပ်ပြီး label များကို chart ထဲမှာထားပါ။ scatter chart ကိုအသုံးပြုကာ label များအတွက်နေရာပိုမိုရရှိစေပါ: @@ -101,7 +101,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + သင်ဘာတွေရှာဖွေတွေ့ရှိပါသလဲ? -![MaxWingspan-scatterplot](../../../../../translated_images/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.my.png) +![MaxWingspan-scatterplot](../../../../../translated_images/my/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.png) ## သင့်ဒေတာကို filter လုပ်ပါ @@ -120,7 +120,7 @@ ggplot(data=birds_filtered, aes(x=Name, y=MaxWingspan,group=1)) + ``` `birds_filtered` ဆိုသော dataframe အသစ်တစ်ခုကိုဖန်တီးပြီး scatter plot တစ်ခုကို plot လုပ်ပါသည်။ outlier များကို filter လုပ်ခြင်းဖြင့် သင့်ဒေတာသည် ပိုမိုညီညွတ်ပြီးနားလည်ရလွယ်ကူလာသည်။ -![MaxWingspan-scatterplot-improved](../../../../../translated_images/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.my.png) +![MaxWingspan-scatterplot-improved](../../../../../translated_images/my/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.png) အနည်းဆုံးတောင်ပံအကျယ်အရ dataset ကိုရှင်းလင်းပြီးနောက်၊ ငှက်များအကြောင်းပိုမိုရှာဖွေကြည့်ရအောင်။ @@ -161,7 +161,7 @@ birds_filtered %>% group_by(Category) %>% ``` ဒီ snippet တွင် [dplyr](https://www.rdocumentation.org/packages/dplyr/versions/0.7.8) နှင့် [lubridate](https://www.rdocumentation.org/packages/lubridate/versions/1.8.0) package များကို install လုပ်ကာ ဒေတာကို manipulate လုပ်ရန်နှင့် group လုပ်ရန်အသုံးပြုသည်။ ငှက်၏ `Category` အပေါ်မူတည်၍ ဒေတာကို group လုပ်ပြီး `MinLength`, `MaxLength`, `MinBodyMass`, `MaxBodyMass`, `MinWingspan`, `MaxWingspan` column များကို summarise လုပ်သည်။ ထို့နောက် `ggplot2` package ကိုအသုံးပြုကာ bar chart ကို plot လုပ်ပြီး category များအတွက်အရောင်များနှင့် label များကိုသတ်မှတ်သည်။ -![Stacked bar chart](../../../../../translated_images/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.my.png) +![Stacked bar chart](../../../../../translated_images/my/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.png) ဒီ bar chart သည် များလွန်းသော non-grouped data ကြောင့် မဖတ်နိုင်ပါ။ plot လုပ်လိုသောဒေတာကိုသာရွေးချယ်ရန်လိုအပ်သည်၊ ဒါကြောင့် ငှက်၏ category အပေါ်မူတည်၍ length ကိုကြည့်ရှုပါ။ @@ -176,7 +176,7 @@ ggplot(birds_count,aes(Category,n))+geom_bar(stat="identity")+coord_flip() ``` `Category` column တွင်ရှိသော unique value များကို count လုပ်ပြီး `birds_count` ဆိုသော dataframe အသစ်တစ်ခုထဲသို့ sort လုပ်သည်။ ဒီ sorted data ကို level တူညီအတိုင်း factor လုပ်ကာ sorted ပုံစံဖြင့် plot လုပ်သည်။ `ggplot2` ကိုအသုံးပြုကာ bar chart ကို plot လုပ်သည်။ `coord_flip()` သည် horizontal bar များကို plot လုပ်သည်။ -![category-length](../../../../../translated_images/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.my.png) +![category-length](../../../../../translated_images/my/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.png) ဒီ bar chart သည် category တစ်ခုစီတွင်ရှိသောငှက်အရေအတွက်ကိုကောင်းစွာမြင်နိုင်စေသည်။ Minnesota တွင် Ducks/Geese/Waterfowl category တွင်ငှက်အများဆုံးရှိသည်ကို တစ်ချက်ကြည့်လိုက်တာနဲ့မြင်နိုင်သည်။ Minnesota သည် '10,000 ရေကန်များ၏မြေ' ဖြစ်သောကြောင့်၊ ဒီအရာသည်အံ့ဩစရာမဟုတ်ပါ။ @@ -199,7 +199,7 @@ ggplot(birds_grouped,aes(Category,MaxLength))+geom_bar(stat="identity")+coord_fl ``` `birds_filtered` ဒေတာကို `Category` အပေါ်မူတည်၍ group လုပ်ပြီး bar graph ကို plot လုပ်သည်။ -![comparing data](../../../../../translated_images/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.my.png) +![comparing data](../../../../../translated_images/my/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.png) ဒီမှာအံ့ဩစရာမရှိပါ- hummingbirds တွင် Pelicans သို့မဟုတ် Geese နှင့်နှိုင်းယှဉ်ပါက MaxLength အနည်းဆုံးရှိသည်။ ဒေတာသည် logical make sense ဖြစ်သည်မှာကောင်းပါသည်! @@ -211,7 +211,7 @@ ggplot(data=birds_grouped, aes(x=Category)) + geom_bar(aes(y=MinLength), stat="identity", position="identity", fill='orange')+ coord_flip() ``` -![super-imposed values](../../../../../translated_images/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.my.png) +![super-imposed values](../../../../../translated_images/my/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.png) ## 🚀 စိန်ခေါ်မှု diff --git a/translations/my/3-Data-Visualization/R/10-visualization-distributions/README.md b/translations/my/3-Data-Visualization/R/10-visualization-distributions/README.md index fb522fe2..87580dda 100644 --- a/translations/my/3-Data-Visualization/R/10-visualization-distributions/README.md +++ b/translations/my/3-Data-Visualization/R/10-visualization-distributions/README.md @@ -45,7 +45,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + geom_point() + ggtitle("Max Length per order") + coord_flip() ``` -![max length per order](../../../../../translated_images/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.my.png) +![max length per order](../../../../../translated_images/my/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.png) ဤအရာသည် ငှက်အမျိုးအစား (Order) အလိုက် ကိုယ်အရှည်၏ ယေဘူယျ အချိုးအစားကို ပြသပေးပါသည်။ သို့သော် ဒေတာ၏ အမှန်တကယ် အချိုးအစားကို ဖော်ပြရန် အကောင်းဆုံးနည်းလမ်းမဟုတ်ပါ။ ဤအလုပ်ကို Histogram တစ်ခု ဖန်တီးခြင်းဖြင့် 通常 ပြုလုပ်ပါသည်။ @@ -57,7 +57,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=10)+ylab('Frequency') ``` -![distribution over entire dataset](../../../../../translated_images/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.my.png) +![distribution over entire dataset](../../../../../translated_images/my/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.png) သင်မြင်နိုင်သည့်အတိုင်း, ဤ dataset တွင်ပါဝင်သော ငှက် 400+ များ၏ အများစုသည် Max Body Mass 2000 အောက်တွင် ရှိသည်။ `bins` parameter ကို 30 အထိ မြှင့်တင်ခြင်းဖြင့် ဒေတာအကြောင်းပိုမို နက်နက်ရှိုင်းရှိုင်း သိရှိနိုင်သည်။ @@ -65,7 +65,7 @@ ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![distribution-30bins](../../../../../translated_images/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.my.png) +![distribution-30bins](../../../../../translated_images/my/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.png) ဤ chart သည် အချိုးအစားကို ပိုမိုအသေးစိတ်ပြသသည်။ ပိုမိုလက်ဝဲဘက်သို့ မဆွဲထားသော chart တစ်ခုကို ဖန်တီးရန် သတ်မှတ်ထားသော အကွာအဝေးအတွင်းရှိ ဒေတာကိုသာ ရွေးချယ်ပါ။ @@ -77,7 +77,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![filtered histogram](../../../../../translated_images/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.my.png) +![filtered histogram](../../../../../translated_images/my/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.png) ✅ အခြား filter များနှင့် ဒေတာအချက်အလက်များကို စမ်းကြည့်ပါ။ ဒေတာ၏ အပြည့်အစုံသော အချိုးအစားကို မြင်ရန် `['MaxBodyMass']` filter ကို ဖယ်ရှားပြီး label ထည့်ထားသော အချိုးအစားများကို ပြပါ။ @@ -91,7 +91,7 @@ ggplot(data=birds_filtered_1, aes(x=MaxBodyMass, y=MaxLength) ) + ``` ဤအချိုးအစားနှစ်ခုအကြား မျှော်မှန်းထားသော အချိုးအစားတစ်ခုအတိုင်း ဆက်စပ်မှုရှိသည်ဟု မြင်ရပြီး တစ်နေရာတွင် အထူးအားကောင်းသော ဆက်စပ်မှုရှိသည်။ -![2d plot](../../../../../translated_images/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.my.png) +![2d plot](../../../../../translated_images/my/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.png) Histogram များသည် ယေဘူယျအားဖြင့် ကိန်းဂဏန်းဒေတာအတွက် အလွန်ကောင်းမွန်သည်။ သို့သော် စာသားဒေတာအလိုက် အချိုးအစားကို ကြည့်ရန်လိုပါက ဘာလုပ်ရမည်နည်း? @@ -123,7 +123,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + scale_fill_manual(name="Conservation Status",values=c("red","green","blue","pink"),labels=c("Endangered","Near Threathened","Vulnerable","Least Concern")) ``` -![wingspan and conservation collation](../../../../../translated_images/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.my.png) +![wingspan and conservation collation](../../../../../translated_images/my/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.png) အနည်းဆုံး အတောင်အရှည်နှင့် ထိန်းသိမ်းရေးအခြေအနေအကြား ဆက်စပ်မှုကောင်းမရှိဟု မြင်ရသည်။ ဤနည်းလမ်းကို အသုံးပြု၍ dataset ၏ အခြား element များကို စမ်းကြည့်ပါ။ အခြား filter များကိုလည်း စမ်းကြည့်ပါ။ ဆက်စပ်မှုတစ်ခုကို ရှာဖွေတွေ့ရှိနိုင်ပါသလား? @@ -137,7 +137,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + geom_density() ``` -![density plot](../../../../../translated_images/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.my.png) +![density plot](../../../../../translated_images/my/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.png) ဤ plot သည် အနည်းဆုံး အတောင်အရှည် (Minimum Wingspan) ဒေတာအတွက် ယခင် histogram ကို ပြန်လည်တူညီစေသည်။ သို့သော် ပိုမိုဖြောင့်မပြတ်ဖြစ်သည်။ ဒုတိယ chart တွင် မြင်ရသော jagged MaxBodyMass လိုင်းကို ပြန်လည်ဖန်တီး၍ ဤနည်းလမ်းဖြင့် အလွန်ကောင်းစွာ ဖြောင့်မပြတ်စေနိုင်သည်။ @@ -145,7 +145,7 @@ ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density() ``` -![bodymass density](../../../../../translated_images/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.my.png) +![bodymass density](../../../../../translated_images/my/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.png) ပိုမိုဖြောင့်မပြတ်သော လိုင်းတစ်ခုလိုအပ်သော်လည်း အလွန်ဖြောင့်မပြတ်စေလိုမည်ဆိုပါက `adjust` parameter ကို ပြင်ဆင်ပါ: @@ -153,7 +153,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density(adjust = 1/5) ``` -![less smooth bodymass](../../../../../translated_images/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.my.png) +![less smooth bodymass](../../../../../translated_images/my/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.png) ✅ ဤအမျိုးအစား plot အတွက် ရနိုင်သော parameter များအကြောင်း ဖတ်ရှုပြီး စမ်းကြည့်ပါ! @@ -163,7 +163,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + ggplot(data=birds_filtered_1,aes(x = MaxBodyMass, fill = Order)) + geom_density(alpha=0.5) ``` -![bodymass per order](../../../../../translated_images/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.my.png) +![bodymass per order](../../../../../translated_images/my/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.png) ## 🚀 စိန်ခေါ်မှု diff --git a/translations/my/3-Data-Visualization/R/11-visualization-proportions/README.md b/translations/my/3-Data-Visualization/R/11-visualization-proportions/README.md index 9dd71f80..e5b7c5ac 100644 --- a/translations/my/3-Data-Visualization/R/11-visualization-proportions/README.md +++ b/translations/my/3-Data-Visualization/R/11-visualization-proportions/README.md @@ -94,7 +94,7 @@ pie(grouped$count,grouped$class, main="Edible?") ``` Voila, ဒီဒေတာကို အမျိုးအစားနှစ်မျိုးအလိုက် ဖော်ပြထားသည့် ပိုင်းကတ်တစ်ခုဖြစ်သည်။ အမှတ်အသားများ၏ အစီအစဉ်ကို မှန်ကန်စေရန် အထူးသတိထားပါ။ -![pie chart](../../../../../translated_images/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.my.png) +![pie chart](../../../../../translated_images/my/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.png) ## ဒိုနတ်ကတ်များ! @@ -128,7 +128,7 @@ library(webr) PieDonut(habitat, aes(habitat, count=count)) ``` -![donut chart](../../../../../translated_images/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.my.png) +![donut chart](../../../../../translated_images/my/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.png) ဒီကုဒ်သည် ggplot2 နှင့် webr ဆိုသော လိုက်ဘရရီနှစ်ခုကို အသုံးပြုထားသည်။ webr လိုက်ဘရရီ၏ PieDonut function ကို အသုံးပြု၍ ဒိုနတ်ကတ်ကို လွယ်ကူစွာ ဖန်တီးနိုင်သည်။ @@ -164,7 +164,7 @@ waffle((cap_color$count/10), rows = 7, title = "Waffle Chart")+scale_fill_manual ဝဖယ်ကတ်ကို အသုံးပြု၍ မှို cap color များ၏ အချိုးအစားကို ရှင်းလင်းစွာ မြင်နိုင်သည်။ စိတ်ဝင်စားစရာကောင်းသည်မှာ အစိမ်းရောင် cap များစွာရှိနေခြင်းဖြစ်သည်။ -![waffle chart](../../../../../translated_images/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.my.png) +![waffle chart](../../../../../translated_images/my/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.png) ဒီသင်ခန်းစာတွင် သင်သည် အချိုးအစားများကို ဖော်ပြရန် နည်းလမ်းသုံးမျိုးကို သင်ယူခဲ့သည်- ပိုင်းကတ်၊ ဒိုနတ်ကတ်၊ နှင့် ဝဖယ်ကတ်။ ဒေတာကို အုပ်စုဖွဲ့ပြီး အကောင်းဆုံး ဖော်ပြနည်းကို ရွေးချယ်ပါ။ diff --git a/translations/my/3-Data-Visualization/R/12-visualization-relationships/README.md b/translations/my/3-Data-Visualization/R/12-visualization-relationships/README.md index b11ee659..21bbc8d0 100644 --- a/translations/my/3-Data-Visualization/R/12-visualization-relationships/README.md +++ b/translations/my/3-Data-Visualization/R/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ library(ggplot2) ggplot(honey, aes(x = priceperlb, y = state)) + geom_point(colour = "blue") ``` -![scatterplot 1](../../../../../translated_images/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.my.png) +![scatterplot 1](../../../../../translated_images/my/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.png) ယခုအခါ၊ ပျားရည်အရောင်စနစ်ကို အသုံးပြု၍ ဤဒေတာကို ပြသပါ၊ ဈေးနှုန်းသည် နှစ်အလိုက် ဘယ်လိုပြောင်းလဲလာသည်ကို ပြပါ။ ဤအရာကို 'scale_color_gradientn' parameter ကို ထည့်သွင်းခြင်းဖြင့် ပြုလုပ်နိုင်သည်၊ နှစ်အလိုက်ပြောင်းလဲမှုကို ပြသရန်: @@ -61,7 +61,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ggplot(honey, aes(x = priceperlb, y = state, color=year)) + geom_point()+scale_color_gradientn(colours = colorspace::heat_hcl(7)) ``` -![scatterplot 2](../../../../../translated_images/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.my.png) +![scatterplot 2](../../../../../translated_images/my/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.png) ဤအရောင်စနစ်ပြောင်းလဲမှုဖြင့် ပျားရည်တစ်ပေါင်လျှင်ဈေးနှုန်းသည် နှစ်အလိုက် ပြောင်းလဲမှုရှိနေသည်ကို အလွယ်တကူမြင်နိုင်သည်။ အမှန်တကယ်၊ ဒေတာထဲမှ နမူနာတစ်ခုကို စစ်ဆေးပါက (ဥပမာအားဖြင့် Arizona ပြည်နယ်) နှစ်အလိုက် ဈေးနှုန်းတိုးလာမှုပုံစံကို အချို့သောအထွေထွေကိစ္စများမှလွဲ၍ မြင်နိုင်ပါသည်: @@ -92,7 +92,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ``` dot အရွယ်အစားသည် တဖြည်းဖြည်းတိုးလာနေသည်ကို မြင်နိုင်ပါသည်။ -![scatterplot 3](../../../../../translated_images/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.my.png) +![scatterplot 3](../../../../../translated_images/my/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.png) ဤသည်သည် တိုးတက်မှုနှင့် တိုက်ရိုက်ဆက်စပ်မှုရှိသော supply နှင့် demand ဖြစ်နိုင်ပါသလား။ ရာသီဥတုပြောင်းလဲမှုနှင့် ပျားအုပ်ပျက်စီးမှုကြောင့် နှစ်အလိုက် ဝယ်ယူနိုင်သော ပျားရည်ပမာဏလျော့နည်းလာပြီး ဈေးနှုန်းတိုးလာနေပါသလား။ @@ -107,7 +107,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab ``` အဖြေ- ဟုတ်ကဲ့၊ ၂၀၀၃ ခုနှစ်အနီးတွင် အချို့သောထူးခြားမှုများရှိသည်: -![line chart 1](../../../../../translated_images/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.my.png) +![line chart 1](../../../../../translated_images/my/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.png) မေးခွန်း- ၂၀၀၃ ခုနှစ်တွင် ပျားရည်ပမာဏတိုးတက်မှုကိုလည်း မြင်နိုင်ပါသလား။ စုစုပေါင်းထုတ်လုပ်မှုကို နှစ်အလိုက်ကြည့်ပါက ဘာတွေ့နိုင်မလဲ။ @@ -115,7 +115,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab qplot(honey$year,honey$totalprod, geom='smooth', span =0.5, xlab = "year",ylab = "totalprod") ``` -![line chart 2](../../../../../translated_images/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.my.png) +![line chart 2](../../../../../translated_images/my/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.png) အဖြေ- အမှန်တကယ်တော့ မဟုတ်ပါ။ စုစုပေါင်းထုတ်လုပ်မှုကိုကြည့်ပါက၊ ထိုနှစ်တွင် တိုးတက်မှုရှိသော်လည်း၊ ယေဘူယျအားဖြင့် ထုတ်လုပ်မှုသည် လျော့နည်းလာနေသည်ကို မြင်နိုင်ပါသည်။ @@ -135,7 +135,7 @@ ggplot(honey, aes(x=yieldpercol, y = numcol,group = 1)) + ``` ဤမြင်ကွင်းတွင် yield per colony နှင့် number of colonies ကို နှစ်အလိုက် နှိုင်းယှဉ်ကြည့်နိုင်သည်၊ column ကို ၃ အထိ wrap ပြုလုပ်ထားသည်: -![facet grid](../../../../../translated_images/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.my.png) +![facet grid](../../../../../translated_images/my/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.png) ဤဒေတာစဉ်အတွက်၊ နှစ်အလိုက်နှင့် ပြည်နယ်အလိုက် number of colonies နှင့် yield အကြား ထူးခြားမှုမရှိပါ။ ဤ variable နှစ်ခုအကြား ဆက်စပ်မှုကို ရှာဖွေရန် အခြားနည်းလမ်းတစ်ခုရှိပါသလား။ @@ -152,7 +152,7 @@ plot(honey$year, honey$yieldpercol, pch = 17, col = 3, axis(side = 4, at = pretty(range(y2))) mtext("colony yield", side = 4, line = 3) ``` -![superimposed plots](../../../../../translated_images/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.my.png) +![superimposed plots](../../../../../translated_images/my/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.png) ၂၀၀၃ ခုနှစ်အနီးတွင် ထူးခြားမှုမရှိသော်လည်း၊ ပျားအုပ်အရေအတွက်သည် လျော့နည်းနေသော်လည်း တည်ငြိမ်နေပြီး၊ တစ်အုပ်လျှင်ထွက်ရှိမှုသည် လျော့နည်းနေသည်ကို မြင်နိုင်ပါသည်။ diff --git a/translations/my/3-Data-Visualization/R/13-meaningful-vizualizations/README.md b/translations/my/3-Data-Visualization/R/13-meaningful-vizualizations/README.md index b5e465b5..215dd129 100644 --- a/translations/my/3-Data-Visualization/R/13-meaningful-vizualizations/README.md +++ b/translations/my/3-Data-Visualization/R/13-meaningful-vizualizations/README.md @@ -47,25 +47,25 @@ CO_OP_TRANSLATOR_METADATA: ဒေတာသိပ္ပံပညာရှင်တစ်ဦးသည် မှန်ကန်သော ချတ်ကို မှန်ကန်သော ဒေတာအတွက် ရွေးချယ်သော်လည်း၊ ဒေတာကို အမှန်မဟုတ်သော အချက်အလက်များကို သက်သေပြရန် ဖော်ပြနိုင်သော နည်းလမ်းများစွာရှိသည်။ လှည့်စားသော ချတ်များနှင့် အင်ဖိုဂရပ်များ၏ ဥပမာများစွာရှိသည်။ -[![How Charts Lie by Alberto Cairo](../../../../../translated_images/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.my.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "How charts lie") +[![How Charts Lie by Alberto Cairo](../../../../../translated_images/my/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "How charts lie") > 🎥 အထက်ပါပုံကို နှိပ်၍ လှည့်စားသော ချတ်များအကြောင်း ဆွေးနွေးမှုကို ကြည့်ပါ ဒီချတ်သည် X axis ကို ပြောင်းလဲထားပြီး အမှန်တရား၏ ဆန့်ကျင်ဘက်ကို ဖော်ပြသည် - -![bad chart 1](../../../../../translated_images/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.my.png) +![bad chart 1](../../../../../translated_images/my/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.png) [ဒီချတ်](https://media.firstcoastnews.com/assets/WTLV/images/170ae16f-4643-438f-b689-50d66ca6a8d8/170ae16f-4643-438f-b689-50d66ca6a8d8_1140x641.jpg) သည် အလွန်လှည့်စားမှုများဖြစ်ပြီး၊ COVID အမှုများသည် အချိန်အလိုက် လျော့နည်းလာသည်ဟု သက်သေပြရန် ရည်ရွယ်ထားသည်။ သို့သော်၊ ရက်စွဲများကို အနည်းငယ်ကြည့်ပါက လျော့နည်းမှုကို ဖန်တီးရန် ပြောင်းလဲထားသည်ကို တွေ့ရမည်။ -![bad chart 2](../../../../../translated_images/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.my.jpg) +![bad chart 2](../../../../../translated_images/my/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.jpg) ဒီနာမည်ဆိုးရှိသော ဥပမာသည် အရောင်နှင့် ပြောင်းလဲထားသော Y axis ကို အသုံးပြု၍ လှည့်စားထားသည်။ ဥပမာအားဖြင့် သက်တမ်းတိုးသော ဥပဒေကြောင့် သေနတ်သေဆုံးမှုများ တက်လာသည်ဟု မဟုတ်ဘဲ ဆန့်ကျင်ဘက်ကို ဖော်ပြရန် ရည်ရွယ်ထားသည်။ -![bad chart 3](../../../../../translated_images/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.my.jpg) +![bad chart 3](../../../../../translated_images/my/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.jpg) ဒီထူးဆန်းသော ချတ်သည် အချိုးအစားကို ပြောင်းလဲထားပြီး ဟာသဆန်သော အကျိုးသက်ရောက်မှုကို ဖန်တီးထားသည်။ -![bad chart 4](../../../../../translated_images/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.my.jpg) +![bad chart 4](../../../../../translated_images/my/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.jpg) မတူညီသော အရာများကို နှိုင်းယှဉ်ခြင်းသည် လှည့်စားမှုတစ်ခုဖြစ်သည်။ [spurious correlations](https://tylervigen.com/spurious-correlations) ဆိုသော ဝဘ်ဆိုဒ်သည် Maine တွင် အိမ်ထောင်ဖျက်နှင့် Margarine စားသုံးမှုတို့ကို ဆက်စပ်ထားသော 'အချက်အလက်' များကို ဖော်ပြထားသည်။ Reddit မှာလည်း [ugly uses](https://www.reddit.com/r/dataisugly/top/?t=all) ဆိုသော ဒေတာအသုံးပြုမှုများကို စုဆောင်းထားသည်။ @@ -100,13 +100,13 @@ Axis များကို အမှတ်အသားပြုပါ၊ လိ X axis တွင် စာသားများသည် အလွန်ရှည်လျားပါက၊ ဖတ်ရှုရလွယ်ကူစေရန် စာသားကို ထောင့်ချပါ။ [plot3D](https://cran.r-project.org/web/packages/plot3D/index.html) သည် 3D plotting ကို ပေးသည်။ -![3d plots](../../../../../translated_images/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.my.png) +![3d plots](../../../../../translated_images/my/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.png) ## အနုစိတ်လှုပ်ရှားမှုနှင့် 3D ချတ်ဖန်တီးခြင်း ယနေ့အချိန်တွင် အကောင်းဆုံး ဒေတာအမြင်များသည် အနုစိတ်လှုပ်ရှားမှုများပါဝင်သည်။ Shirley Wu ၏ '[film flowers](http://bl.ocks.org/sxywu/raw/d612c6c653fb8b4d7ff3d422be164a5d/)' ကဲ့သို့သော D3 ဖြင့် ဖန်တီးထားသော အံ့ဩဖွယ်အမြင်များရှိသည်။ Guardian ၏ 'bussed out' သည် NYC ၏ အိမ်မဲ့သူများကို မြို့မှ ထုတ်ပေးသော နည်းလမ်းကို ဖော်ပြရန် visualizations နှင့် scrollytelling ကို ပေါင်းစပ်ထားသည်။ -![busing](../../../../../translated_images/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.my.png) +![busing](../../../../../translated_images/my/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.png) > "Bussed Out: How America Moves its Homeless" from [the Guardian](https://www.theguardian.com/us-news/ng-interactive/2017/dec/20/bussed-out-america-moves-homeless-people-country-study). Visualizations by Nadieh Bremer & Shirley Wu @@ -116,7 +116,7 @@ X axis တွင် စာသားများသည် အလွန်ရှ သင်သည် Vue.js နှင့် D3 ကို အသုံးပြု၍ animated social network ကို ဖန်တီးမည့် web app ကို ပြီးစီးမည်ဖြစ်သည်။ -![liaisons](../../../../../translated_images/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.my.png) +![liaisons](../../../../../translated_images/my/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.png) ## Project: D3.js ကို အသုံးပြု၍ network ကို ဖော်ပြသော ချတ်တစ်ခု ဖန်တီးပါ diff --git a/translations/my/3-Data-Visualization/README.md b/translations/my/3-Data-Visualization/README.md index cfa6d235..ac35fba6 100644 --- a/translations/my/3-Data-Visualization/README.md +++ b/translations/my/3-Data-Visualization/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # ရုပ်ပုံဖော်ပြမှုများ -![ပန်းပေါ်မှာပျားတစ်ကောင်](../../../translated_images/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.my.jpg) +![ပန်းပေါ်မှာပျားတစ်ကောင်](../../../translated_images/my/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.jpg) > ဓာတ်ပုံကို Jenna Lee မှ Unsplash တွင် ရိုက်ထားသည်။ ဒေတာကို ရုပ်ပုံဖော်ပြခြင်းသည် ဒေတာသိပ္ပံပညာရှင်တစ်ဦးအတွက် အရေးကြီးဆုံးအလုပ်များထဲမှ တစ်ခုဖြစ်သည်။ ပုံတစ်ပုံသည် စကားလုံး ၁၀၀၀ တန်ဖိုးရှိသလို၊ ရုပ်ပုံဖော်ပြမှုတစ်ခုက သင့်ဒေတာအတွင်းရှိ စိတ်ဝင်စားဖွယ် အချက်များကို (ဥပမာ - ထိပ်တန်းတက်မှုများ၊ ထူးခြားမှုများ၊ အုပ်စုဖွဲ့မှုများ၊ လမ်းကြောင်းများ စသည်) ရှာဖွေဖော်ထုတ်ပေးနိုင်ပြီး၊ သင့်ဒေတာက ပြောပြချင်တဲ့ ဇာတ်လမ်းကို နားလည်ရန် ကူညီပေးနိုင်ပါသည်။ diff --git a/translations/my/4-Data-Science-Lifecycle/14-Introduction/README.md b/translations/my/4-Data-Science-Lifecycle/14-Introduction/README.md index 075d60d1..45cd0c5b 100644 --- a/translations/my/4-Data-Science-Lifecycle/14-Introduction/README.md +++ b/translations/my/4-Data-Science-Lifecycle/14-Introduction/README.md @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: ဒီသင်ခန်းစာမှာ အသက်တာစဉ်ရဲ့ အရေးကြီးတဲ့ အပိုင်း ၃ ခုကို အဓိကထားပြီး လေ့လာပါမယ်- ဒေတာရယူခြင်း၊ အလုပ်လုပ်ခြင်း၊ စီမံခန့်ခွဲခြင်း။ -![Diagram of the data science lifecycle](../../../../translated_images/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.my.jpg) +![Diagram of the data science lifecycle](../../../../translated_images/my/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.jpg) > [Berkeley School of Information](https://ischoolonline.berkeley.edu/data-science/what-is-data-science/) မှဓာတ်ပုံ ## ဒေတာရယူခြင်း @@ -97,7 +97,7 @@ Data Science Lifecycle ဟာ အမျိုးမျိုးသော ဗာ |Team Data Science Process (TDSP)|Cross-industry standard process for data mining (CRISP-DM)| |--|--| -|![Team Data Science Lifecycle](../../../../translated_images/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.my.png) | ![Data Science Process Alliance Image](../../../../translated_images/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.my.png) | +|![Team Data Science Lifecycle](../../../../translated_images/my/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.png) | ![Data Science Process Alliance Image](../../../../translated_images/my/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.png) | | [Microsoft](https://docs.microsoft.comazure/architecture/data-science-process/lifecycle) မှပုံ | [Data Science Process Alliance](https://www.datascience-pm.com/crisp-dm-2/) မှပုံ | ## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/quiz/27) diff --git a/translations/my/4-Data-Science-Lifecycle/README.md b/translations/my/4-Data-Science-Lifecycle/README.md index 3670bcf5..16a9d794 100644 --- a/translations/my/4-Data-Science-Lifecycle/README.md +++ b/translations/my/4-Data-Science-Lifecycle/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # ဒေတာသိပ္ပံ၏ အသက်ရှည်လက်ဆောင် -![communication](../../../translated_images/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.my.jpg) +![communication](../../../translated_images/my/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.jpg) > ဓာတ်ပုံ - Headway မှ Unsplash တွင် ရရှိသည် ဒီသင်ခန်းစာများတွင် ဒေတာသိပ္ပံ၏ အသက်ရှည်လက်ဆောင်အပိုင်းအစများကို လေ့လာနိုင်ပါမည်။ ဒေတာဆန်းစစ်ခြင်းနှင့် ဆက်သွယ်ပြောဆိုမှုတို့အပါအဝင် အချက်အလက်များကို လေ့လာဆန်းစစ်ခြင်းနှင့် ပတ်သက်သော အကြောင်းအရာများကို လေ့လာမည်ဖြစ်သည်။ diff --git a/translations/my/5-Data-Science-In-Cloud/README.md b/translations/my/5-Data-Science-In-Cloud/README.md index fe699c44..177d1e96 100644 --- a/translations/my/5-Data-Science-In-Cloud/README.md +++ b/translations/my/5-Data-Science-In-Cloud/README.md @@ -9,13 +9,13 @@ CO_OP_TRANSLATOR_METADATA: --> # Cloud တွင် ဒေတာသိပ္ပံ -![cloud-picture](../../../translated_images/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.my.jpg) +![cloud-picture](../../../translated_images/my/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.jpg) > ဓာတ်ပုံကို [Jelleke Vanooteghem](https://unsplash.com/@ilumire) မှ [Unsplash](https://unsplash.com/s/photos/cloud?orientation=landscape) တွင် ရယူထားပါသည်။ အကြီးမားသော ဒေတာများနှင့်အတူ ဒေတာသိပ္ပံလုပ်ဆောင်ရာတွင် Cloud သည် အရေးပါသော အပြောင်းအလဲတစ်ခုဖြစ်နိုင်ပါသည်။ လာမည့် သုံးခုသော သင်ခန်းစာများတွင် Cloud ဆိုတာဘာလဲ၊ ဘာကြောင့် အထောက်အကူဖြစ်နိုင်သလဲဆိုတာကို ကြည့်ရှုမည်ဖြစ်သည်။ ထို့အပြင် နှလုံးရောဂါ dataset တစ်ခုကိုလည်း လေ့လာပြီး၊ တစ်ဦးတစ်ယောက်တွင် နှလုံးရောဂါဖြစ်နိုင်ခြေကို ခန့်မှန်းနိုင်ရန် မော်ဒယ်တစ်ခုကို တည်ဆောက်မည်ဖြစ်သည်။ Cloud ၏ အားသာချက်ကို အသုံးပြု၍ မော်ဒယ်ကို လေ့ကျင့်ခြင်း၊ တင်သွင်းခြင်းနှင့် အသုံးပြုခြင်းကို နည်းလမ်းနှစ်မျိုးဖြင့် ပြုလုပ်မည်ဖြစ်သည်။ နည်းလမ်းတစ်ခုမှာ Low code/No code ပုံစံဖြင့် အသုံးပြုသူအင်တာဖေ့စ်ကိုသာ အသုံးပြုခြင်းဖြစ်ပြီး၊ နောက်တစ်ခုမှာ Azure Machine Learning Software Developer Kit (Azure ML SDK) ကို အသုံးပြုခြင်းဖြစ်သည်။ -![project-schema](../../../translated_images/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.my.png) +![project-schema](../../../translated_images/my/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.png) ### ခေါင်းစဉ်များ diff --git a/translations/my/6-Data-Science-In-Wild/20-Real-World-Examples/README.md b/translations/my/6-Data-Science-In-Wild/20-Real-World-Examples/README.md index b3f379f3..067802fb 100644 --- a/translations/my/6-Data-Science-In-Wild/20-Real-World-Examples/README.md +++ b/translations/my/6-Data-Science-In-Wild/20-Real-World-Examples/README.md @@ -41,7 +41,7 @@ AI ကို လူတိုင်းအသုံးပြုနိုင်အ * [Data Science in Healthcare](https://data-flair.training/blogs/data-science-in-healthcare/) - medical imaging (ဥပမာ MRI, X-Ray, CT-Scan), genomics (DNA sequencing), drug development (risk assessment, success prediction), predictive analytics (patient care & supply logistics), disease tracking & prevention စသည်တို့ကဲ့သို့သော applications များကို ဖော်ပြထားပါတယ်။ -![Data Science Applications in The Real World](../../../../translated_images/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.my.png) Image Credit: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) +![Data Science Applications in The Real World](../../../../translated_images/my/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.png) Image Credit: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) ဤပုံသည် ဒေတာသိပ္ပံနည်းလမ်းများကို အသုံးချနိုင်သော အခြား domains နှင့် နမူနာများကို ဖော်ပြထားသည်။ အခြား applications များကို လေ့လာလိုပါသလား? [Review & Self Study](../../../../6-Data-Science-In-Wild/20-Real-World-Examples) အပိုင်းကို ကြည့်ပါ။ diff --git a/translations/my/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md b/translations/my/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md index 7a51379b..a4a0f6a4 100644 --- a/translations/my/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md +++ b/translations/my/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md @@ -22,7 +22,7 @@ Explorer အင်တာဖေ့စ် (အောက်ပါ screenshot မှ 2. [Catalog](https://planetarycomputer.microsoft.com/catalog) ဒေတာအစုအဝေးကို စူးစမ်းပါ - ဒါ့ရည်ရွယ်ချက်ကို လေ့လာပါ။ 3. Explorer ကို အသုံးပြုပါ - စိတ်ဝင်စားစရာ ဒေတာအစုအဝေးတစ်ခုကို ရွေးချယ်ပြီး သက်ဆိုင်ရာ query နဲ့ rendering ရွေးချယ်မှုကို ရွေးပါ။ -![The Planetary Computer Explorer](../../../../translated_images/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.my.png) +![The Planetary Computer Explorer](../../../../translated_images/my/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.png) `သင့်တာဝန်:` Browser မှာ ပြသထားတဲ့ အမြင်ပြမှုကို လေ့လာပြီး အောက်ပါအတိုင်း ဖြေကြားပါ: diff --git a/translations/my/CONTRIBUTING.md b/translations/my/CONTRIBUTING.md index 2e276b9d..0b9dc306 100644 --- a/translations/my/CONTRIBUTING.md +++ b/translations/my/CONTRIBUTING.md @@ -312,7 +312,7 @@ Quiz application ကို ပြောင်းလဲတဲ့အခါ: import pandas as pd ``` ```` -- ပုံများအတွက် alt text ထည့်ပါ: `![Alt text](../../translated_images/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.my.png)` +- ပုံများအတွက် alt text ထည့်ပါ: `![Alt text](../../translated_images/my/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.png)` - Line အရှည်ကို သင့်တော်စွာ ထိန်းသိမ်းပါ (80-100 characters အနီး) ### Python diff --git a/translations/my/README.md b/translations/my/README.md index b60c3a6c..bbde0871 100644 --- a/translations/my/README.md +++ b/translations/my/README.md @@ -28,7 +28,7 @@ Microsoft ရှိ Azure Cloud Advocates အဖွဲ့သည် Data Science **🙏 အထူးကျေးဇူးတင်လှည်း 🙏 Microsoft Student Ambassador ဖြစ်ကြသည့် စာရေးသူများ၊ ပြန်လည်ဆန်းစစ်သူများနှင့် အကြောင်းအရာ အကျိုးပေးသူများအား,** အထူးသဖြင့် Aaryan Arora, [Aditya Garg](https://github.com/AdityaGarg00), [Alondra Sanchez](https://www.linkedin.com/in/alondra-sanchez-molina/), [Ankita Singh](https://www.linkedin.com/in/ankitasingh007), [Anupam Mishra](https://www.linkedin.com/in/anupam--mishra/), [Arpita Das](https://www.linkedin.com/in/arpitadas01/), ChhailBihari Dubey, [Dibri Nsofor](https://www.linkedin.com/in/dibrinsofor), [Dishita Bhasin](https://www.linkedin.com/in/dishita-bhasin-7065281bb), [Majd Safi](https://www.linkedin.com/in/majd-s/), [Max Blum](https://www.linkedin.com/in/max-blum-6036a1186/), [Miguel Correa](https://www.linkedin.com/in/miguelmque/), [Mohamma Iftekher (Iftu) Ebne Jalal](https://twitter.com/iftu119), [Nawrin Tabassum](https://www.linkedin.com/in/nawrin-tabassum), [Raymond Wangsa Putra](https://www.linkedin.com/in/raymond-wp/), [Rohit Yadav](https://www.linkedin.com/in/rty2423), Samridhi Sharma, [Sanya Sinha](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200), [Sheena Narula](https://www.linkedin.com/in/sheena-narua-n/), [Tauqeer Ahmad](https://www.linkedin.com/in/tauqeerahmad5201/), Yogendrasingh Pawar , [Vidushi Gupta](https://www.linkedin.com/in/vidushi-gupta07/), [Jasleen Sondhi](https://www.linkedin.com/in/jasleen-sondhi/) -|![Sketchnote များ - @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.my.png)| +|![Sketchnote များ - @sketchthedocs https://sketchthedocs.dev](../../translated_images/my/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.png)| |:---:| | Data Science For Beginners - _Sketchnote (ဖန်တီးသူ [@nitya](https://twitter.com/nitya))_ | @@ -47,7 +47,7 @@ Microsoft ရှိ Azure Cloud Advocates အဖွဲ့သည် Data Science ကျွန်တော်တို့မှာ Discord ပေါ်တွင် "Learn with AI" စီးရီး လေ့လာမှု ဆက်လက်ပြုလုပ်နေပါသည်။ အပိုင်းများအကြောင်း ပိုမိုသိရှိရန်နှင့် ပူးပေါင်းရန် [Learn with AI Series](https://aka.ms/learnwithai/discord) တွင် 18 - 30 စက်တင်ဘာ, 2025 အချိန်ကာလအတွင်း ပူးပေါင်းပါ။ သင်သည် GitHub Copilot ကို Data Science အတွက် အသုံးပြုရာတွင် ထိရောက်သော အကြံပေးနည်းများကို ရယူနိုင်မည်။ -![Learn with AI စီးရီး](../../translated_images/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.my.jpg) +![Learn with AI စီးရီး](../../translated_images/my/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.jpg) # သင်က ကျောင်းသား/သူလား? @@ -127,7 +127,7 @@ Microsoft ရှိ Azure Cloud Advocates အဖွဲ့သည် Data Science ## သင်ခန်းစာများ -|![ စကက်ချ်မှတ်စု - @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.my.png)| +|![ စကက်ချ်မှတ်စု - @sketchthedocs https://sketchthedocs.dev](../../translated_images/my/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png)| |:---:| | Data Science For Beginners: Roadmap - _Sketchnote by [@nitya](https://twitter.com/nitya)_ | diff --git a/translations/my/sketchnotes/README.md b/translations/my/sketchnotes/README.md index 2df9ce90..89955f4e 100644 --- a/translations/my/sketchnotes/README.md +++ b/translations/my/sketchnotes/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: Nitya Narasimhan, အနုပညာရှင် -![roadmap sketchnote](../../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.my.png) +![roadmap sketchnote](../../../translated_images/my/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png) --- diff --git a/translations/ne/1-Introduction/01-defining-data-science/README.md b/translations/ne/1-Introduction/01-defining-data-science/README.md index 4d329b8b..158ae0a2 100644 --- a/translations/ne/1-Introduction/01-defining-data-science/README.md +++ b/translations/ne/1-Introduction/01-defining-data-science/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: --- -[![डाटा साइन्सको परिभाषा भिडियो](../../../../translated_images/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.ne.png)](https://youtu.be/beZ7Mb_oz9I) +[![डाटा साइन्सको परिभाषा भिडियो](../../../../translated_images/ne/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.png)](https://youtu.be/beZ7Mb_oz9I) ## [पूर्व-व्याख्यान क्विज](https://ff-quizzes.netlify.app/en/ds/quiz/0) @@ -153,7 +153,7 @@ CO_OP_TRANSLATOR_METADATA: यस चुनौतीमा, हामी डेटा साइन्सको क्षेत्रमा सान्दर्भिक अवधारणाहरू पत्ता लगाउने प्रयास गर्नेछौं। हामी डेटा साइन्सको विषयमा विकिपिडिया लेख लिनेछौं, पाठ डाउनलोड र प्रक्रिया गर्नेछौं, र त्यसपछि यस्तो वर्ड क्लाउड बनाउनेछौं: -![डेटा साइन्सको लागि वर्ड क्लाउड](../../../../translated_images/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.ne.png) +![डेटा साइन्सको लागि वर्ड क्लाउड](../../../../translated_images/ne/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.png) कोड पढ्नका लागि [`notebook.ipynb`](../../../../1-Introduction/01-defining-data-science/notebook.ipynb ':ignore') मा जानुहोस्। तपाईं कोड चलाउन सक्नुहुन्छ, र वास्तविक समयमा सबै डेटा रूपान्तरणहरू कसरी प्रदर्शन हुन्छन् हेर्न सक्नुहुन्छ। diff --git a/translations/ne/1-Introduction/04-stats-and-probability/README.md b/translations/ne/1-Introduction/04-stats-and-probability/README.md index 735415f5..90bad026 100644 --- a/translations/ne/1-Introduction/04-stats-and-probability/README.md +++ b/translations/ne/1-Introduction/04-stats-and-probability/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: तथ्यांक र सम्भाव्यता सिद्धान्त गणितका दुई अत्यन्तै सम्बन्धित क्षेत्रहरू हुन्, जसको डाटा विज्ञानमा ठूलो महत्त्व छ। गहिरो गणितीय ज्ञान बिना पनि डाटासँग काम गर्न सम्भव छ, तर कम्तीमा केही आधारभूत अवधारणाहरू थाहा हुनु राम्रो हुन्छ। यहाँ हामी तपाईंलाई सुरु गर्न मद्दत गर्ने छोटो परिचय प्रस्तुत गर्नेछौं। -[![Intro Video](../../../../translated_images/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.ne.png)](https://youtu.be/Z5Zy85g4Yjw) +[![Intro Video](../../../../translated_images/ne/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.png)](https://youtu.be/Z5Zy85g4Yjw) ## [पाठ अघि क्विज](https://ff-quizzes.netlify.app/en/ds/quiz/6) @@ -39,7 +39,7 @@ CO_OP_TRANSLATOR_METADATA: हामी केवल कुनै निश्चित मानहरूको दायरामा भेरिएबल पर्ने सम्भाव्यताको कुरा गर्न सक्छौं, जस्तै P(t1≤X2)। यस अवस्थामा, सम्भाव्यता वितरणलाई **सम्भाव्यता घनत्व कार्य (probability density function)** p(x) द्वारा वर्णन गरिन्छ, जसले गर्दा -![P(t_1\le X सहसंबंध र सहविचलनका थप उदाहरणहरू [संगत नोटबुक](notebook.ipynb) मा भेट्न सकिन्छ। diff --git a/translations/ne/1-Introduction/README.md b/translations/ne/1-Introduction/README.md index 76b1adf8..06131743 100644 --- a/translations/ne/1-Introduction/README.md +++ b/translations/ne/1-Introduction/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # डाटा साइन्सको परिचय -![डाटा क्रियाशीलतामा](../../../translated_images/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.ne.jpg) +![डाटा क्रियाशीलतामा](../../../translated_images/ne/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.jpg) > फोटो Stephen Dawson द्वारा Unsplash **नोट** कि हामीले साधारण `total_items+additional_items` सिन्ट्याक्स प्रयोग गरेका छैनौं। यदि हामीले त्यसो गरेका भए, हामीले परिणामस्वरूप धेरै `NaN` (*Not a Number*) मानहरू पाउने थियौं। यो किनभने `additional_items` सिरिजमा केही इन्डेक्स बिन्दुहरूको लागि मानहरू हराइरहेका छन्, र `NaN` लाई कुनै पनि चीजमा जोड्दा `NaN` परिणाम दिन्छ। त्यसैले हामीले थप गर्दा `fill_value` प्यारामिटर निर्दिष्ट गर्न आवश्यक छ। @@ -94,7 +94,7 @@ total_items.plot() monthly = total_items.resample("1M").mean() ax = monthly.plot(kind='bar') ``` -![मासिक समय सिरिज औसत](../../../../translated_images/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.ne.png) +![मासिक समय सिरिज औसत](../../../../translated_images/ne/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.png) ### डाटाफ्रेम @@ -220,7 +220,7 @@ df = pd.read_csv('file.csv') हामी डाटासँग कसरी व्यवहार गर्ने देखाउन चाहन्छौं, त्यसैले कृपया [`notebook-covidspread.ipynb`](notebook-covidspread.ipynb) खोल्नुहोस् र माथिदेखि तलसम्म पढ्नुहोस्। तपाईंले सेलहरू चलाउन सक्नुहुन्छ, र अन्त्यमा हामीले तपाईंका लागि छोडेका केही चुनौतीहरू गर्न सक्नुहुन्छ। -![COVID फैलावट](../../../../translated_images/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.ne.png) +![COVID फैलावट](../../../../translated_images/ne/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.png) > यदि तपाईंलाई Jupyter Notebook मा कोड कसरी चलाउने थाहा छैन भने, [यस लेख](https://soshnikov.com/education/how-to-execute-notebooks-from-github/) हेर्नुहोस्। @@ -242,7 +242,7 @@ df = pd.read_csv('file.csv') [`notebook-papers.ipynb`](notebook-papers.ipynb) खोल्नुहोस् र माथिदेखि तलसम्म पढ्नुहोस्। तपाईंले सेलहरू चलाउन सक्नुहुन्छ, र अन्त्यमा हामीले तपाईंका लागि छोडेका केही चुनौतीहरू गर्न सक्नुहुन्छ। -![Covid मेडिकल उपचार](../../../../translated_images/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.ne.png) +![Covid मेडिकल उपचार](../../../../translated_images/ne/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.png) ## छवि डाटा प्रशोधन diff --git a/translations/ne/2-Working-With-Data/README.md b/translations/ne/2-Working-With-Data/README.md index d123fe58..fe186aa7 100644 --- a/translations/ne/2-Working-With-Data/README.md +++ b/translations/ne/2-Working-With-Data/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # डाटासँग काम गर्ने -![data love](../../../translated_images/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.ne.jpg) +![data love](../../../translated_images/ne/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.jpg) > फोटो: एलेक्जेन्डर सिन द्वारा अनस्प्ल्यास मा उपलब्ध यी पाठहरूमा, तपाईंले डाटालाई व्यवस्थापन, हेरफेर, र अनुप्रयोगहरूमा प्रयोग गर्न सकिने केही तरिकाहरू सिक्नुहुनेछ। तपाईंले सम्बन्धात्मक (relational) र गैर-सम्बन्धात्मक (non-relational) डाटाबेसहरूको बारेमा सिक्नुहुनेछ र तिनमा डाटा कसरी भण्डारण गर्न सकिन्छ भन्ने कुरा बुझ्नुहुनेछ। तपाईंले डाटालाई व्यवस्थापन गर्न Python प्रयोग गर्ने आधारभूत कुराहरू सिक्नुहुनेछ, साथै Python प्रयोग गरेर डाटालाई व्यवस्थापन र खनन गर्ने विभिन्न तरिकाहरू पत्ता लगाउनुहुनेछ। diff --git a/translations/ne/3-Data-Visualization/12-visualization-relationships/README.md b/translations/ne/3-Data-Visualization/12-visualization-relationships/README.md index 635aac6b..55fa39fb 100644 --- a/translations/ne/3-Data-Visualization/12-visualization-relationships/README.md +++ b/translations/ne/3-Data-Visualization/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ honey.head() ```python sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ``` -![scatterplot 1](../../../../translated_images/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.ne.png) +![scatterplot 1](../../../../translated_images/ne/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.png) अब, महको रंग योजनाको प्रयोग गरेर वर्ष दर वर्ष मूल्य कसरी परिवर्तन भएको छ भन्ने देखाउनुहोस्। तपाईंले 'hue' प्यारामिटर थपेर परिवर्तन देखाउन सक्नुहुन्छ: @@ -60,7 +60,7 @@ sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ```python sns.relplot(x="priceperlb", y="state", hue="year", palette="YlOrBr", data=honey, height=15, aspect=.5); ``` -![scatterplot 2](../../../../translated_images/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.ne.png) +![scatterplot 2](../../../../translated_images/ne/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.png) यो रंग योजनाको परिवर्तनसँगै, तपाईंले स्पष्ट रूपमा देख्न सक्नुहुन्छ कि महको प्रति पाउन्ड मूल्यमा वर्ष दर वर्ष बलियो प्रगति भएको छ। वास्तवमा, यदि तपाईं डेटाको नमूना सेट हेर्नुहुन्छ (उदाहरणका लागि, एरिजोना राज्य चयन गर्नुहोस्) तपाईंले वर्ष दर वर्ष मूल्य वृद्धि भएको ढाँचा देख्न सक्नुहुन्छ, केही अपवादहरू सहित: @@ -89,7 +89,7 @@ sns.relplot(x="priceperlb", y="state", size="year", data=honey, height=15, aspec ``` तपाईंले बिन्दुको आकार क्रमशः बढ्दै गएको देख्न सक्नुहुन्छ। -![scatterplot 3](../../../../translated_images/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.ne.png) +![scatterplot 3](../../../../translated_images/ne/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.png) के यो आपूर्ति र मागको साधारण मामला हो? जलवायु परिवर्तन र उपनिवेश पतन जस्ता कारणहरूले गर्दा, वर्ष दर वर्ष किन महको मूल्य बढ्दै गएको छ? @@ -104,7 +104,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); ``` उत्तर: हो, केही अपवादहरू सहित, विशेष गरी २००३ को वरिपरि: -![line chart 1](../../../../translated_images/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.ne.png) +![line chart 1](../../../../translated_images/ne/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.png) ✅ किनकि Seaborn ले एक लाइन वरिपरि डेटा समेट्दै छ, यसले "प्रत्येक x मानमा बहु मापनहरू औसत र औसत वरिपरि ९५% विश्वास अन्तराल प्लट गरेर देखाउँछ।" [स्रोत](https://seaborn.pydata.org/tutorial/relational.html)। यो समय लाग्ने व्यवहारलाई `ci=None` थपेर अक्षम गर्न सकिन्छ। @@ -114,7 +114,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); sns.relplot(x="year", y="totalprod", kind="line", data=honey); ``` -![line chart 2](../../../../translated_images/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.ne.png) +![line chart 2](../../../../translated_images/ne/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.png) उत्तर: वास्तवमा होइन। यदि तपाईंले कुल उत्पादन हेर्नुभयो भने, यो विशेष वर्षमा बढेको जस्तो देखिन्छ, यद्यपि सामान्य रूपमा मह उत्पादनको मात्रा यी वर्षहरूमा घट्दै गएको छ। @@ -139,7 +139,7 @@ sns.relplot( ``` यस दृश्यमा, तपाईंले प्रति उपनिवेश उत्पादन र उपनिवेशहरूको संख्या वर्ष दर वर्ष, राज्य दर राज्य, सँगसँगै तुलना गर्न सक्नुहुन्छ, स्तम्भहरूको लागि wrap लाई ३ मा सेट गरेर: -![facet grid](../../../../translated_images/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.ne.png) +![facet grid](../../../../translated_images/ne/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.png) यस डेटासेटका लागि, उपनिवेशहरूको संख्या र तिनीहरूको उत्पादनको सम्बन्धमा वर्ष दर वर्ष र राज्य दर राज्य केही विशेष कुरा बाहिर देखिँदैन। के यी दुई चरहरू बीचको सम्बन्ध पत्ता लगाउन हेर्ने फरक तरिका छ? @@ -162,7 +162,7 @@ sns.despine(right=False) plt.ylabel('colony yield') ax.figure.legend(); ``` -![superimposed plots](../../../../translated_images/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.ne.png) +![superimposed plots](../../../../translated_images/ne/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.png) २००३ को वरिपरि आँखा बाहिर केही नजम्पे पनि, यसले हामीलाई यो पाठलाई अलिकति खुशीको नोटमा अन्त्य गर्न अनुमति दिन्छ: उपनिवेशहरूको संख्या घट्दै गएको भए पनि, उपनिवेशहरूको संख्या स्थिर हुँदैछ, यद्यपि तिनीहरूको प्रति उपनिवेश उत्पादन घट्दैछ। diff --git a/translations/ne/3-Data-Visualization/R/09-visualization-quantities/README.md b/translations/ne/3-Data-Visualization/R/09-visualization-quantities/README.md index dd34929d..9699b7ca 100644 --- a/translations/ne/3-Data-Visualization/R/09-visualization-quantities/README.md +++ b/translations/ne/3-Data-Visualization/R/09-visualization-quantities/README.md @@ -66,7 +66,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` यहाँ, तपाईंले `ggplot2` प्याकेज इन्स्टल गर्नुभयो र त्यसलाई `library("ggplot2")` आदेश प्रयोग गरेर कार्यक्षेत्रमा आयात गर्नुभयो। ggplot मा कुनै पनि प्लट बनाउन `ggplot()` फङ्क्सन प्रयोग गरिन्छ र तपाईं डेटासेट, x र y चरहरूलाई विशेषता रूपमा निर्दिष्ट गर्नुहुन्छ। यस अवस्थामा, हामीले लाइन प्लट बनाउन `geom_line()` फङ्क्सन प्रयोग गर्यौं। -![MaxWingspan-lineplot](../../../../../translated_images/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.ne.png) +![MaxWingspan-lineplot](../../../../../translated_images/ne/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.png) तपाईंले के तुरुन्तै देख्नुभयो? कम्तीमा एउटा बाहिरको मान (outlier) देखिन्छ - यो त धेरै ठूलो पखेटा फैलावट हो! २०००+ सेन्टिमिटरको पखेटा फैलावट भनेको २० मिटरभन्दा बढी हो - के मिनेसोटामा प्टेरोड्याक्टाइलहरू छन्? अनुसन्धान गरौं। @@ -84,7 +84,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` हामीले `theme` मा कोण निर्दिष्ट गर्यौं र `xlab()` र `ylab()` मा x र y अक्षका लेबलहरू निर्दिष्ट गर्यौं। `ggtitle()` ले ग्राफ/प्लटलाई नाम दिन्छ। -![MaxWingspan-lineplot-improved](../../../../../translated_images/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.ne.png) +![MaxWingspan-lineplot-improved](../../../../../translated_images/ne/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.png) लेबलहरूलाई ४५ डिग्रीमा घुमाउँदा पनि धेरै पढ्न गाह्रो छ। अर्को रणनीति प्रयास गरौं: बाहिरका मानहरूलाई मात्र लेबल गरौं र चार्टभित्रै लेबल राखौं। स्क्याटर चार्ट प्रयोग गरेर लेबलिङका लागि बढी ठाउँ बनाऔं: @@ -100,7 +100,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + तपाईंले के पत्ता लगाउनुभयो? -![MaxWingspan-scatterplot](../../../../../translated_images/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.ne.png) +![MaxWingspan-scatterplot](../../../../../translated_images/ne/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.png) ## तपाईंको डेटा फिल्टर गर्नुहोस् @@ -119,7 +119,7 @@ ggplot(data=birds_filtered, aes(x=Name, y=MaxWingspan,group=1)) + ``` हामीले नयाँ डेटाफ्रेम `birds_filtered` बनायौं र त्यसपछि स्क्याटर प्लट बनायौं। बाहिरका मानहरू हटाएर, तपाईंको डेटा अब बढी सुसंगत र बुझ्न योग्य छ। -![MaxWingspan-scatterplot-improved](../../../../../translated_images/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.ne.png) +![MaxWingspan-scatterplot-improved](../../../../../translated_images/ne/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.png) अब हामीसँग पखेटा फैलावटको हिसाबले सफा डेटासेट छ, यी चराहरूका बारेमा थप पत्ता लगाऔं। @@ -161,7 +161,7 @@ birds_filtered %>% group_by(Category) %>% ``` तलको स्निपेटमा, हामीले डेटा हेरफेर र समूह गर्नका लागि [dplyr](https://www.rdocumentation.org/packages/dplyr/versions/0.7.8) र [lubridate](https://www.rdocumentation.org/packages/lubridate/versions/1.8.0) प्याकेजहरू इन्स्टल गर्यौं। त्यसपछि, `ggplot2` प्रयोग गरेर बार चार्ट प्लट गर्यौं। -![Stacked bar chart](../../../../../translated_images/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.ne.png) +![Stacked bar chart](../../../../../translated_images/ne/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.png) तर यो बार चार्ट पढ्न गाह्रो छ किनभने धेरै गैर-समूहित डेटा छ। तपाईंले प्लट गर्न चाहेको डेटा मात्र चयन गर्न आवश्यक छ। चराहरूको श्रेणीको आधारमा लम्बाइ हेर्नुहोस्। @@ -176,7 +176,7 @@ ggplot(birds_count,aes(Category,n))+geom_bar(stat="identity")+coord_flip() ``` तपाईंले `Category` स्तम्भमा अद्वितीय मानहरूको गणना गर्नुभयो र त्यसलाई नयाँ डेटाफ्रेम `birds_count` मा क्रमबद्ध गर्नुभयो। यो क्रमबद्ध डेटालाई `ggplot2` प्रयोग गरेर बार चार्टमा प्लट गर्यौं। `coord_flip()` ले तेर्सो बारहरू प्लट गर्छ। -![category-length](../../../../../translated_images/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.ne.png) +![category-length](../../../../../translated_images/ne/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.png) यो बार चार्टले प्रत्येक श्रेणीमा चराहरूको सङ्ख्याको राम्रो दृश्य दिन्छ। एक झलकमा, तपाईंले देख्न सक्नुहुन्छ कि यस क्षेत्रमा सबैभन्दा धेरै सङ्ख्यामा चरा हाँस/बतासे/पानीपक्षी श्रेणीमा छन्। मिनेसोटा '१०,००० तालहरूको भूमि' भएकाले यो आश्चर्यजनक छैन! @@ -199,7 +199,7 @@ ggplot(birds_grouped,aes(Category,MaxLength))+geom_bar(stat="identity")+coord_fl ``` हामीले `birds_filtered` डेटालाई `Category` द्वारा समूहबद्ध गर्यौं र बार चार्ट प्लट गर्यौं। -![comparing data](../../../../../translated_images/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.ne.png) +![comparing data](../../../../../translated_images/ne/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.png) यहाँ केही आश्चर्यजनक छैन: हमिङबर्डहरूको अधिकतम लम्बाइ पेलिकन वा हाँसको तुलनामा सबैभन्दा कम छ। डेटा तार्किक रूपमा सही हुँदा राम्रो लाग्छ! @@ -211,7 +211,7 @@ ggplot(data=birds_grouped, aes(x=Category)) + geom_bar(aes(y=MinLength), stat="identity", position="identity", fill='orange')+ coord_flip() ``` -![super-imposed values](../../../../../translated_images/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.ne.png) +![super-imposed values](../../../../../translated_images/ne/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.png) ## 🚀 चुनौती diff --git a/translations/ne/3-Data-Visualization/R/10-visualization-distributions/README.md b/translations/ne/3-Data-Visualization/R/10-visualization-distributions/README.md index cfec28d3..de75f260 100644 --- a/translations/ne/3-Data-Visualization/R/10-visualization-distributions/README.md +++ b/translations/ne/3-Data-Visualization/R/10-visualization-distributions/README.md @@ -45,7 +45,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + geom_point() + ggtitle("Max Length per order") + coord_flip() ``` -![प्रत्येक क्रमको अधिकतम लम्बाइ](../../../../../translated_images/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.ne.png) +![प्रत्येक क्रमको अधिकतम लम्बाइ](../../../../../translated_images/ne/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.png) यसले प्रत्येक चराको क्रम अनुसार शरीरको लम्बाइको सामान्य वितरणको झलक दिन्छ, तर यो वास्तविक वितरण देखाउनको लागि उत्तम तरिका होइन। यो कार्य सामान्यतया हिस्टोग्राम बनाएर गरिन्छ। @@ -57,7 +57,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=10)+ylab('Frequency') ``` -![सम्पूर्ण डेटासेटमा वितरण](../../../../../translated_images/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.ne.png) +![सम्पूर्ण डेटासेटमा वितरण](../../../../../translated_images/ne/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.png) जस्तो देखिन्छ, यस डेटासेटका 400+ चराहरूको अधिकांश Max Body Mass 2000 भन्दा कमको दायरामा पर्दछन्। `bins` प्यारामिटरलाई उच्च संख्यामा, जस्तै 30 मा परिवर्तन गरेर डाटाको बारेमा थप जानकारी प्राप्त गर्नुहोस्: @@ -65,7 +65,7 @@ ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![30 बिन्सको वितरण](../../../../../translated_images/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.ne.png) +![30 बिन्सको वितरण](../../../../../translated_images/ne/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.png) यो चार्टले वितरणलाई अलि बढी विस्तृत रूपमा देखाउँछ। कम बाँया तिर झुकिएको चार्ट बनाउन, तपाईंले निश्चित दायराभित्रको डाटा मात्र चयन गरेर सुनिश्चित गर्न सक्नुहुन्छ: @@ -77,7 +77,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![फिल्टर गरिएको हिस्टोग्राम](../../../../../translated_images/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.ne.png) +![फिल्टर गरिएको हिस्टोग्राम](../../../../../translated_images/ne/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.png) ✅ अन्य फिल्टरहरू र डाटाका बिन्दुहरू प्रयास गर्नुहोस्। डाटाको पूर्ण वितरण हेर्नको लागि, `['MaxBodyMass']` फिल्टर हटाएर लेबल गरिएको वितरणहरू देखाउनुहोस्। @@ -91,7 +91,7 @@ ggplot(data=birds_filtered_1, aes(x=MaxBodyMass, y=MaxLength) ) + ``` जस्तो देखिन्छ, यी दुई तत्वहरू बीच अपेक्षित अक्षमा सम्बन्ध छ, एक विशेष रूपमा बलियो एकीकृत बिन्दु सहित: -![2D प्लट](../../../../../translated_images/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.ne.png) +![2D प्लट](../../../../../translated_images/ne/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.png) हिस्टोग्रामहरू संख्यात्मक डाटाको लागि डिफल्ट रूपमा राम्रोसँग काम गर्छन्। यदि तपाईंलाई पाठ डाटाको अनुसार वितरणहरू हेर्न आवश्यक छ भने के गर्ने? ## पाठ डाटाको प्रयोग गरेर वितरणहरू अन्वेषण गर्नुहोस् @@ -122,7 +122,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + scale_fill_manual(name="Conservation Status",values=c("red","green","blue","pink"),labels=c("Endangered","Near Threathened","Vulnerable","Least Concern")) ``` -![पखेटा फैलावट र संरक्षण स्थिति](../../../../../translated_images/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.ne.png) +![पखेटा फैलावट र संरक्षण स्थिति](../../../../../translated_images/ne/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.png) न्यूनतम पखेटा फैलावट र संरक्षण स्थितिको बीचमा राम्रो सम्बन्ध देखिँदैन। यस विधि प्रयोग गरेर डेटासेटका अन्य तत्वहरू परीक्षण गर्नुहोस्। तपाईं विभिन्न फिल्टरहरू पनि प्रयास गर्न सक्नुहुन्छ। के तपाईं कुनै सम्बन्ध पाउनुहुन्छ? @@ -136,7 +136,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + geom_density() ``` -![घनत्व प्लट](../../../../../translated_images/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.ne.png) +![घनत्व प्लट](../../../../../translated_images/ne/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.png) तपाईंले देख्न सक्नुहुन्छ कि यो प्लटले न्यूनतम पखेटा फैलावट डाटाको लागि अघिल्लो चार्टलाई प्रतिध्वनित गर्दछ; यो केवल अलि चिल्लो छ। यदि तपाईंले दोस्रो चार्टमा रहेको जंगली MaxBodyMass लाइनलाई चिल्लो बनाउन चाहनुहुन्छ भने, यस विधि प्रयोग गरेर यसलाई धेरै राम्रोसँग पुनः निर्माण गर्न सक्नुहुन्छ: @@ -144,7 +144,7 @@ ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density() ``` -![शरीर तौल घनत्व](../../../../../translated_images/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.ne.png) +![शरीर तौल घनत्व](../../../../../translated_images/ne/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.png) यदि तपाईं चिल्लो तर धेरै चिल्लो नभएको लाइन चाहनुहुन्छ भने, `adjust` प्यारामिटर सम्पादन गर्नुहोस्: @@ -152,7 +152,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density(adjust = 1/5) ``` -![कम चिल्लो शरीर तौल](../../../../../translated_images/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.ne.png) +![कम चिल्लो शरीर तौल](../../../../../translated_images/ne/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.png) ✅ यस प्रकारको प्लटको लागि उपलब्ध प्यारामिटरहरूको बारेमा पढ्नुहोस् र प्रयोग गर्नुहोस्! @@ -162,7 +162,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + ggplot(data=birds_filtered_1,aes(x = MaxBodyMass, fill = Order)) + geom_density(alpha=0.5) ``` -![प्रत्येक क्रमको शरीर तौल](../../../../../translated_images/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.ne.png) +![प्रत्येक क्रमको शरीर तौल](../../../../../translated_images/ne/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.png) ## 🚀 चुनौती diff --git a/translations/ne/3-Data-Visualization/R/11-visualization-proportions/README.md b/translations/ne/3-Data-Visualization/R/11-visualization-proportions/README.md index c5a14e89..dfaf2c7a 100644 --- a/translations/ne/3-Data-Visualization/R/11-visualization-proportions/README.md +++ b/translations/ne/3-Data-Visualization/R/11-visualization-proportions/README.md @@ -92,7 +92,7 @@ pie(grouped$count,grouped$class, main="Edible?") ``` ल, पाई चार्ट तयार भयो, जसले यी दुई वर्गका च्याउहरूको अनुपातलाई देखाउँछ। लेबलहरूको क्रम सही राख्नु यहाँ धेरै महत्त्वपूर्ण छ, त्यसैले लेबल एरे बनाउँदा क्रम जाँच गर्न निश्चित गर्नुहोस्! -![पाई चार्ट](../../../../../translated_images/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.ne.png) +![पाई चार्ट](../../../../../translated_images/ne/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.png) ## डोनट! @@ -126,7 +126,7 @@ library(webr) PieDonut(habitat, aes(habitat, count=count)) ``` -![डोनट चार्ट](../../../../../translated_images/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.ne.png) +![डोनट चार्ट](../../../../../translated_images/ne/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.png) यो कोडले ggplot2 र webr नामक दुई पुस्तकालयहरू प्रयोग गर्दछ। webr पुस्तकालयको PieDonut फङ्क्शन प्रयोग गरेर, हामी सजिलै डोनट चार्ट बनाउन सक्छौं! @@ -164,7 +164,7 @@ waffle((cap_color$count/10), rows = 7, title = "Waffle Chart")+scale_fill_manual वाफल चार्ट प्रयोग गरेर, तपाईंले च्याउको टोपीका रङहरूको अनुपात स्पष्ट रूपमा देख्न सक्नुहुन्छ। रोचक कुरा, धेरै हरियो टोपी भएका च्याउहरू छन्! -![वाफल चार्ट](../../../../../translated_images/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.ne.png) +![वाफल चार्ट](../../../../../translated_images/ne/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.png) यस पाठमा, तपाईंले अनुपातहरू दृश्यात्मक बनाउन तीन तरिकाहरू सिक्नुभयो। पहिलो, तपाईंले आफ्नो डेटा श्रेणीमा समूह गर्नुपर्छ र त्यसपछि डेटा देखाउन सबैभन्दा उपयुक्त तरिका निर्णय गर्नुपर्छ - पाई, डोनट, वा वाफल। यी सबै स्वादिष्ट छन् र प्रयोगकर्तालाई डाटासेटको झलक तुरुन्तै दिन्छन्। diff --git a/translations/ne/3-Data-Visualization/R/12-visualization-relationships/README.md b/translations/ne/3-Data-Visualization/R/12-visualization-relationships/README.md index 7af93bf1..04acd01c 100644 --- a/translations/ne/3-Data-Visualization/R/12-visualization-relationships/README.md +++ b/translations/ne/3-Data-Visualization/R/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ library(ggplot2) ggplot(honey, aes(x = priceperlb, y = state)) + geom_point(colour = "blue") ``` -![scatterplot 1](../../../../../translated_images/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.ne.png) +![scatterplot 1](../../../../../translated_images/ne/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.png) अब, महको मूल्य वर्ष-प्रति-वर्ष कसरी परिवर्तन भएको छ भनेर देखाउन महको रंग योजना प्रयोग गरेर उही डेटा देखाउनुहोस्। तपाईंले 'scale_color_gradientn' प्यारामिटर थपेर यो गर्न सक्नुहुन्छ: @@ -61,7 +61,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ggplot(honey, aes(x = priceperlb, y = state, color=year)) + geom_point()+scale_color_gradientn(colours = colorspace::heat_hcl(7)) ``` -![scatterplot 2](../../../../../translated_images/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.ne.png) +![scatterplot 2](../../../../../translated_images/ne/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.png) यो रंग योजनाको परिवर्तनसँगै, तपाईंले स्पष्ट रूपमा देख्न सक्नुहुन्छ कि महको प्रति पाउन्ड मूल्य वर्ष-प्रति-वर्ष बलियो रूपमा बढिरहेको छ। उदाहरणका लागि, एरिजोना राज्यको डेटा हेर्दा, मूल्यमा वर्ष-प्रति-वर्ष वृद्धि भएको देखिन्छ, केही अपवादहरू बाहेक: @@ -92,7 +92,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ``` तपाईंले डटहरूको आकार क्रमशः बढिरहेको देख्न सक्नुहुन्छ। -![scatterplot 3](../../../../../translated_images/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.ne.png) +![scatterplot 3](../../../../../translated_images/ne/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.png) के यो आपूर्ति र मागको साधारण मामला हो? जलवायु परिवर्तन र कोलोनी कोलाप्स जस्ता कारकहरूको कारण, के वर्ष-प्रति-वर्ष किन्नको लागि कम मह उपलब्ध छ, जसका कारण मूल्य बढिरहेको छ? @@ -107,7 +107,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab ``` उत्तर: हो, २००३ को आसपास केही अपवादहरूका साथ: -![line chart 1](../../../../../translated_images/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.ne.png) +![line chart 1](../../../../../translated_images/ne/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.png) प्रश्न: २००३ मा के महको आपूर्तिमा पनि वृद्धि देखिन्छ? कुल उत्पादन वर्ष-प्रति-वर्ष हेर्दा के देखिन्छ? @@ -115,7 +115,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab qplot(honey$year,honey$totalprod, geom='smooth', span =0.5, xlab = "year",ylab = "totalprod") ``` -![line chart 2](../../../../../translated_images/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.ne.png) +![line chart 2](../../../../../translated_images/ne/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.png) उत्तर: खासै होइन। कुल उत्पादन हेर्दा, त्यो वर्षमा वास्तवमा वृद्धि भएको देखिन्छ, यद्यपि सामान्य रूपमा मह उत्पादन ती वर्षहरूमा घट्दो क्रममा छ। @@ -135,7 +135,7 @@ ggplot(honey, aes(x=yieldpercol, y = numcol,group = 1)) + ``` यस दृश्यमा, तपाईंले प्रति उपनिवेश उत्पादन र उपनिवेशहरूको संख्या वर्ष-प्रति-वर्ष, राज्य-प्रति-राज्य तुलना गर्न सक्नुहुन्छ, ३ स्तम्भमा सेट गरिएको र्यापसँग: -![facet grid](../../../../../translated_images/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.ne.png) +![facet grid](../../../../../translated_images/ne/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.png) यस डेटासेटका लागि, उपनिवेशहरूको संख्या र तिनको उत्पादनमा वर्ष-प्रति-वर्ष र राज्य-प्रति-राज्य केही विशेष कुरा देखिँदैन। के यी दुई चरहरू बीचको सम्बन्ध पत्ता लगाउन हेर्ने अर्को तरिका छ? @@ -152,7 +152,7 @@ plot(honey$year, honey$yieldpercol, pch = 17, col = 3, axis(side = 4, at = pretty(range(y2))) mtext("colony yield", side = 4, line = 3) ``` -![superimposed plots](../../../../../translated_images/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.ne.png) +![superimposed plots](../../../../../translated_images/ne/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.png) २००३ को आसपास आँखा तान्ने केही देखिँदैन, तर यसले हामीलाई यो पाठलाई अलिकति खुसीको नोटमा अन्त्य गर्न अनुमति दिन्छ: उपनिवेशहरूको संख्या घट्दो भए पनि, उपनिवेशहरूको संख्या स्थिर हुँदैछ, यद्यपि तिनको प्रति उपनिवेश उत्पादन घट्दो छ। diff --git a/translations/ne/3-Data-Visualization/R/13-meaningful-vizualizations/README.md b/translations/ne/3-Data-Visualization/R/13-meaningful-vizualizations/README.md index 4e6fb951..6ab39576 100644 --- a/translations/ne/3-Data-Visualization/R/13-meaningful-vizualizations/README.md +++ b/translations/ne/3-Data-Visualization/R/13-meaningful-vizualizations/README.md @@ -47,25 +47,25 @@ CO_OP_TRANSLATOR_METADATA: डाटा वैज्ञानिकले सही डाटाको लागि सही चार्ट चयन गर्न सावधान भए पनि, डाटालाई कुनै बिन्दु प्रमाणित गर्न, प्रायः डाटाको विश्वसनीयता कमजोर पार्ने तरिकामा प्रस्तुत गर्न सकिन्छ। भ्रामक चार्ट र इन्फोग्राफिक्सका धेरै उदाहरणहरू छन्! -[![How Charts Lie by Alberto Cairo](../../../../../translated_images/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.ne.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "How charts lie") +[![How Charts Lie by Alberto Cairo](../../../../../translated_images/ne/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "How charts lie") > 🎥 माथिको चित्रमा क्लिक गरेर भ्रामक चार्टहरूको बारेमा सम्मेलनको कुरा हेर्नुहोस् यो चार्टले X अक्षलाई उल्टाएर सत्यको विपरीत देखाउँछ, मिति अनुसार: -![खराब चार्ट 1](../../../../../translated_images/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.ne.png) +![खराब चार्ट 1](../../../../../translated_images/ne/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.png) [यो चार्ट](https://media.firstcoastnews.com/assets/WTLV/images/170ae16f-4643-438f-b689-50d66ca6a8d8/170ae16f-4643-438f-b689-50d66ca6a8d8_1140x641.jpg) अझ भ्रामक छ, किनकि आँखा दायाँतिर तानिन्छ र निष्कर्ष निकाल्छ कि समयको साथ, विभिन्न काउन्टीहरूमा COVID केसहरू घटेका छन्। वास्तवमा, यदि तपाईं मितिहरूलाई ध्यानपूर्वक हेर्नुहुन्छ भने, तपाईंले पत्ता लगाउनुहुनेछ कि ती मितिहरूलाई भ्रामक घट्दो प्रवृत्ति देखाउन पुनः व्यवस्था गरिएको छ। -![खराब चार्ट 2](../../../../../translated_images/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.ne.jpg) +![खराब चार्ट 2](../../../../../translated_images/ne/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.jpg) यो कुख्यात उदाहरणले रंग र उल्टिएको Y अक्ष प्रयोग गरेर भ्रम सिर्जना गर्दछ: बन्दुक-मैत्री कानून पारित भएपछि बन्दुक मृत्युहरू बढेको निष्कर्ष निकाल्नुको सट्टा, वास्तवमा आँखा उल्टो सोच्न बाध्य पारिन्छ: -![खराब चार्ट 3](../../../../../translated_images/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.ne.jpg) +![खराब चार्ट 3](../../../../../translated_images/ne/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.jpg) यो अनौठो चार्टले अनुपातलाई हाँसउठ्दो तरिकामा हेरफेर गर्न देखाउँछ: -![खराब चार्ट 4](../../../../../translated_images/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.ne.jpg) +![खराब चार्ट 4](../../../../../translated_images/ne/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.jpg) अतुलनीयको तुलना गर्नु अर्को छायादार चाल हो। 'स्प्युरियस कोरिलेसन' नामक [एक अद्भुत वेबसाइट](https://tylervigen.com/spurious-correlations) छ जसले 'तथ्यहरू' प्रदर्शन गर्दछ, जस्तै माइनको डिभोर्स दर र मार्जरीनको खपत। एक Reddit समूहले डाटाको [खराब प्रयोगहरू](https://www.reddit.com/r/dataisugly/top/?t=all) पनि सङ्कलन गर्दछ। @@ -100,13 +100,13 @@ CO_OP_TRANSLATOR_METADATA: यदि तपाईंको डाटा X अक्षमा पाठ्य र विस्तृत छ भने, राम्रो पढ्नका लागि पाठलाई कोण दिन सक्नुहुन्छ। [plot3D](https://cran.r-project.org/web/packages/plot3D/index.html) ले 3D प्लटिङ प्रदान गर्दछ, यदि तपाईंको डाटाले समर्थन गर्दछ भने। यसले परिष्कृत डाटा दृश्यहरू उत्पादन गर्न सक्छ। -![3D प्लटहरू](../../../../../translated_images/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.ne.png) +![3D प्लटहरू](../../../../../translated_images/ne/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.png) ## एनिमेसन र 3D चार्ट प्रदर्शन आजका केही उत्कृष्ट डाटा दृश्यहरू एनिमेटेड छन्। Shirley Wu ले D3 प्रयोग गरेर अद्भुत दृश्यहरू बनाएकी छिन्, जस्तै '[फिल्म फ्लावरहरू](http://bl.ocks.org/sxywu/raw/d612c6c653fb8b4d7ff3d422be164a5d/)', जहाँ प्रत्येक फूल एउटा चलचित्रको दृश्य हो। Guardian का लागि अर्को उदाहरण 'बुस्ड आउट' हो, जसले NYC ले आफ्नो बेघर समस्या समाधान गर्न मानिसहरूलाई शहरबाट बाहिर बसमा पठाउने तरिका देखाउन दृश्यहरूलाई Greensock र D3 सँग मिलाएर स्क्रोलिटेलिङ लेखको ढाँचामा प्रस्तुत गर्दछ। -![बसिङ](../../../../../translated_images/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.ne.png) +![बसिङ](../../../../../translated_images/ne/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.png) > "बुस्ड आउट: कसरी अमेरिका आफ्नो बेघर मानिसहरूलाई सार्छ" [Guardian](https://www.theguardian.com/us-news/ng-interactive/2017/dec/20/bussed-out-america-moves-homeless-people-country-study) बाट। दृश्यहरू Nadieh Bremer & Shirley Wu द्वारा। @@ -116,7 +116,7 @@ CO_OP_TRANSLATOR_METADATA: तपाईंले एक वेब एप पूरा गर्नुहुनेछ जसले यस सामाजिक नेटवर्कको एनिमेटेड दृश्य प्रदर्शन गर्नेछ। यसले Vue.js र D3 प्रयोग गरेर [नेटवर्कको दृश्य](https://github.com/emiliorizzo/vue-d3-network) बनाउनको लागि बनाइएको पुस्तकालय प्रयोग गर्दछ। जब एप चलिरहेको छ, तपाईं स्क्रिनमा नोडहरू तान्न सक्नुहुन्छ ताकि डाटालाई वरिपरि मिलाउन सकियोस्। -![liaisons](../../../../../translated_images/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.ne.png) +![liaisons](../../../../../translated_images/ne/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.png) ## परियोजना: D3.js प्रयोग गरेर नेटवर्क देखाउने चार्ट बनाउनुहोस् diff --git a/translations/ne/3-Data-Visualization/README.md b/translations/ne/3-Data-Visualization/README.md index a8027145..ad7f89e9 100644 --- a/translations/ne/3-Data-Visualization/README.md +++ b/translations/ne/3-Data-Visualization/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # दृश्यात्मकता -![ल्याभेन्डर फूलमा भमरा](../../../translated_images/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.ne.jpg) +![ल्याभेन्डर फूलमा भमरा](../../../translated_images/ne/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.jpg) > फोटो जेन्ना ली द्वारा अनस्प्ल्यास मा डेटा दृश्यात्मक बनाउनु डेटा वैज्ञानिकको सबैभन्दा महत्त्वपूर्ण कामहरूमध्ये एक हो। एउटा तस्बिरले १००० शब्द बराबरको मूल्य राख्छ, र दृश्यात्मकताले तपाईंलाई तपाईंको डेटाको रोचक पक्षहरू जस्तै उचालो, असामान्य डाटा, समूहहरू, प्रवृत्तिहरू, र अन्य धेरै कुरा पत्ता लगाउन मद्दत गर्न सक्छ, जसले तपाईंको डेटा भन्न खोजिरहेको कथा बुझ्न सहयोग पुर्‍याउँछ। diff --git a/translations/ne/4-Data-Science-Lifecycle/14-Introduction/README.md b/translations/ne/4-Data-Science-Lifecycle/14-Introduction/README.md index 124ba002..7482ee20 100644 --- a/translations/ne/4-Data-Science-Lifecycle/14-Introduction/README.md +++ b/translations/ne/4-Data-Science-Lifecycle/14-Introduction/README.md @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: यो पाठ जीवनचक्रका तीन भागहरूमा केन्द्रित छ: डाटा संकलन, प्रशोधन, र मर्मत। -![डाटा साइन्स जीवनचक्रको चित्र](../../../../translated_images/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.ne.jpg) +![डाटा साइन्स जीवनचक्रको चित्र](../../../../translated_images/ne/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.jpg) > [Berkeley School of Information](https://ischoolonline.berkeley.edu/data-science/what-is-data-science/) द्वारा फोटो ## डाटा संकलन @@ -97,7 +97,7 @@ CO_OP_TRANSLATOR_METADATA: |Team Data Science Process (TDSP)|Cross-industry standard process for data mining (CRISP-DM)| |--|--| -|![Team Data Science Lifecycle](../../../../translated_images/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.ne.png) | ![Data Science Process Alliance Image](../../../../translated_images/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.ne.png) | +|![Team Data Science Lifecycle](../../../../translated_images/ne/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.png) | ![Data Science Process Alliance Image](../../../../translated_images/ne/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.png) | | [Microsoft](https://docs.microsoft.comazure/architecture/data-science-process/lifecycle) द्वारा छवि | [Data Science Process Alliance](https://www.datascience-pm.com/crisp-dm-2/) द्वारा छवि | ## [पाठ पछि क्विज](https://ff-quizzes.netlify.app/en/ds/quiz/27) diff --git a/translations/ne/4-Data-Science-Lifecycle/README.md b/translations/ne/4-Data-Science-Lifecycle/README.md index df16cce6..ddcbc887 100644 --- a/translations/ne/4-Data-Science-Lifecycle/README.md +++ b/translations/ne/4-Data-Science-Lifecycle/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # डाटा साइन्स जीवनचक्र -![communication](../../../translated_images/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.ne.jpg) +![communication](../../../translated_images/ne/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.jpg) > फोटो Headway द्वारा Unsplash मा यी पाठहरूमा, तपाईंले डाटा साइन्स जीवनचक्रका केही पक्षहरू, जस्तै डाटाको विश्लेषण र संवादको बारेमा अध्ययन गर्नुहुनेछ। diff --git a/translations/ne/5-Data-Science-In-Cloud/README.md b/translations/ne/5-Data-Science-In-Cloud/README.md index b42c231a..32cab482 100644 --- a/translations/ne/5-Data-Science-In-Cloud/README.md +++ b/translations/ne/5-Data-Science-In-Cloud/README.md @@ -9,13 +9,13 @@ CO_OP_TRANSLATOR_METADATA: --> # क्लाउडमा डेटा विज्ञान -![cloud-picture](../../../translated_images/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.ne.jpg) +![cloud-picture](../../../translated_images/ne/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.jpg) > फोटो [Jelleke Vanooteghem](https://unsplash.com/@ilumire) द्वारा [Unsplash](https://unsplash.com/s/photos/cloud?orientation=landscape) बाट ठूलो डेटा प्रयोग गरेर डेटा विज्ञान गर्न आउँदा, क्लाउडले खेल बदल्न सक्छ। आगामी तीन पाठहरूमा, हामी क्लाउड के हो र यो किन उपयोगी हुन सक्छ भन्ने कुरा हेर्नेछौं। हामी हृदय विफलता सम्बन्धी डेटा सेटको अन्वेषण गर्नेछौं र कसैलाई हृदय विफलता हुने सम्भावना मूल्यांकन गर्न मद्दत गर्ने मोडेल निर्माण गर्नेछौं। हामी क्लाउडको शक्ति प्रयोग गरेर मोडेललाई दुई फरक तरिकामा प्रशिक्षण, तैनात र उपभोग गर्नेछौं। एउटा तरिका केवल प्रयोगकर्ता इन्टरफेस प्रयोग गरेर "Low code/No code" शैलीमा हुनेछ भने अर्को तरिका Azure Machine Learning Software Developer Kit (Azure ML SDK) प्रयोग गरेर हुनेछ। -![project-schema](../../../translated_images/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.ne.png) +![project-schema](../../../translated_images/ne/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.png) ### विषयवस्तुहरू diff --git a/translations/ne/6-Data-Science-In-Wild/20-Real-World-Examples/README.md b/translations/ne/6-Data-Science-In-Wild/20-Real-World-Examples/README.md index e874d366..a6c0d185 100644 --- a/translations/ne/6-Data-Science-In-Wild/20-Real-World-Examples/README.md +++ b/translations/ne/6-Data-Science-In-Wild/20-Real-World-Examples/README.md @@ -41,7 +41,7 @@ CO_OP_TRANSLATOR_METADATA: * [स्वास्थ्य सेवामा डाटा साइन्स](https://data-flair.training/blogs/data-science-in-healthcare/) - मेडिकल इमेजिङ (जस्तै, MRI, X-Ray, CT-Scan), जीनोमिक्स (DNA अनुक्रमण), औषधि विकास (जोखिम मूल्याङ्कन, सफलता भविष्यवाणी), पूर्वानुमानात्मक विश्लेषण (रोगी हेरचाह र आपूर्ति रसद), रोग ट्र्याकिङ र रोकथाम जस्ता अनुप्रयोगहरूलाई हाइलाइट गर्दछ। -![वास्तविक संसारमा डाटा साइन्स अनुप्रयोगहरू](../../../../translated_images/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.ne.png) छवि श्रेय: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) +![वास्तविक संसारमा डाटा साइन्स अनुप्रयोगहरू](../../../../translated_images/ne/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.png) छवि श्रेय: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) चित्रले डाटा साइन्स प्रविधिहरू लागू गर्नका लागि अन्य डोमेनहरू र उदाहरणहरू देखाउँछ। अन्य अनुप्रयोगहरू अन्वेषण गर्न चाहनुहुन्छ? तलको [समीक्षा र आत्म अध्ययन](../../../../6-Data-Science-In-Wild/20-Real-World-Examples) खण्ड जाँच गर्नुहोस्। diff --git a/translations/ne/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md b/translations/ne/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md index e297120b..46cdc85e 100644 --- a/translations/ne/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md +++ b/translations/ne/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md @@ -22,7 +22,7 @@ Explorer इन्टरफेस (तलको स्क्रिनसटम 2. डेटासेट [Catalog](https://planetarycomputer.microsoft.com/catalog) अन्वेषण गर्नुहोस् - प्रत्येकको उद्देश्य सिक्नुहोस्। 3. Explorer प्रयोग गर्नुहोस् - रुचिको डेटासेट चयन गर्नुहोस्, सम्बन्धित क्वेरी र रेंडरिङ विकल्प चयन गर्नुहोस्। -![ग्रह कम्प्युटर एक्सप्लोरर](../../../../translated_images/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.ne.png) +![ग्रह कम्प्युटर एक्सप्लोरर](../../../../translated_images/ne/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.png) `तपाईंको कार्य:` अब ब्राउजरमा रेंडर गरिएको भिजुअलाइजेसन अध्ययन गर्नुहोस् र निम्न प्रश्नहरूको उत्तर दिनुहोस्: diff --git a/translations/ne/CONTRIBUTING.md b/translations/ne/CONTRIBUTING.md index 17bcf6ed..0c1a4007 100644 --- a/translations/ne/CONTRIBUTING.md +++ b/translations/ne/CONTRIBUTING.md @@ -312,7 +312,7 @@ def calculate_mean(data): import pandas as pd ``` ```` -- छविहरूमा alt टेक्स्ट थप्नुहोस्: `![Alt text](../../translated_images/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.ne.png)` +- छविहरूमा alt टेक्स्ट थप्नुहोस्: `![Alt text](../../translated_images/ne/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.png)` - लाइन लम्बाइलाई उचित राख्नुहोस् (लगभग 80-100 अक्षर) ### Python diff --git a/translations/ne/README.md b/translations/ne/README.md index d83c1ec2..d354d991 100644 --- a/translations/ne/README.md +++ b/translations/ne/README.md @@ -33,7 +33,7 @@ Microsoft मा Azure Cloud Advocates खुशीसाथ 10 हप्ता **🙏 विशेष धन्यवाद 🙏 हाम्रो [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/) लेखक, समिक्षक र सामग्री योगदानकर्ताहरूलाई,** विशेष गरी Aaryan Arora, [Aditya Garg](https://github.com/AdityaGarg00), [Alondra Sanchez](https://www.linkedin.com/in/alondra-sanchez-molina/), [Ankita Singh](https://www.linkedin.com/in/ankitasingh007), [Anupam Mishra](https://www.linkedin.com/in/anupam--mishra/), [Arpita Das](https://www.linkedin.com/in/arpitadas01/), ChhailBihari Dubey, [Dibri Nsofor](https://www.linkedin.com/in/dibrinsofor), [Dishita Bhasin](https://www.linkedin.com/in/dishita-bhasin-7065281bb), [Majd Safi](https://www.linkedin.com/in/majd-s/), [Max Blum](https://www.linkedin.com/in/max-blum-6036a1186/), [Miguel Correa](https://www.linkedin.com/in/miguelmque/), [Mohamma Iftekher (Iftu) Ebne Jalal](https://twitter.com/iftu119), [Nawrin Tabassum](https://www.linkedin.com/in/nawrin-tabassum), [Raymond Wangsa Putra](https://www.linkedin.com/in/raymond-wp/), [Rohit Yadav](https://www.linkedin.com/in/rty2423), Samridhi Sharma, [Sanya Sinha](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200), [Sheena Narula](https://www.linkedin.com/in/sheena-narua-n/), [Tauqeer Ahmad](https://www.linkedin.com/in/tauqeerahmad5201/), Yogendrasingh Pawar , [Vidushi Gupta](https://www.linkedin.com/in/vidushi-gupta07/), [Jasleen Sondhi](https://www.linkedin.com/in/jasleen-sondhi/) -|![@sketchthedocs द्वारा स्केचनोट https://sketchthedocs.dev](../../translated_images/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.ne.png)| +|![@sketchthedocs द्वारा स्केचनोट https://sketchthedocs.dev](../../translated_images/ne/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.png)| |:---:| | बिगिनर्सका लागि डेटा साइन्स - _स्केचनोट द्वारा [@nitya](https://twitter.com/nitya)_ | @@ -52,7 +52,7 @@ Microsoft मा Azure Cloud Advocates खुशीसाथ 10 हप्ता हाम्रो Discord मा Learn with AI श्रृंखला चलिरहेको छ, थप जान्न र हामीसँग सामेल हुन [Learn with AI Series](https://aka.ms/learnwithai/discord) मा 18 - 30 सेप्टेम्बर, 2025 मा आउनुहोस्। तपाईंले GitHub Copilot प्रयोग गरेर डेटा साइन्सका टिप्स र ट्रिक्स पाउनुहुनेछ। -![AI सँग सिक्ने श्रृंखला](../../translated_images/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.ne.jpg) +![AI सँग सिक्ने श्रृंखला](../../translated_images/ne/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.jpg) # के तपाईं विद्यार्थी हुनुहुन्छ? @@ -132,7 +132,7 @@ Microsoft मा Azure Cloud Advocates खुशीसाथ 10 हप्ता ## पाठहरू -|![ स्केचनोट द्वारा @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.ne.png)| +|![ स्केचनोट द्वारा @sketchthedocs https://sketchthedocs.dev](../../translated_images/ne/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png)| |:---:| | डेटा साइन्सका लागि शुरुवातकर्ताहरू: रोडम्याप - _Sketchnote by [@nitya](https://twitter.com/nitya)_ | diff --git a/translations/ne/sketchnotes/README.md b/translations/ne/sketchnotes/README.md index 6bd00cf5..5c1d6fcc 100644 --- a/translations/ne/sketchnotes/README.md +++ b/translations/ne/sketchnotes/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: नित्या नरसिम्हन, कलाकार -![रोडम्याप स्केच नोट](../../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.ne.png) +![रोडम्याप स्केच नोट](../../../translated_images/ne/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png) --- diff --git a/translations/nl/1-Introduction/01-defining-data-science/README.md b/translations/nl/1-Introduction/01-defining-data-science/README.md index 2fae0bb6..f17646d0 100644 --- a/translations/nl/1-Introduction/01-defining-data-science/README.md +++ b/translations/nl/1-Introduction/01-defining-data-science/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: --- -[![Video over de definitie van Data Science](../../../../translated_images/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.nl.png)](https://youtu.be/beZ7Mb_oz9I) +[![Video over de definitie van Data Science](../../../../translated_images/nl/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.png)](https://youtu.be/beZ7Mb_oz9I) ## [Quiz voorafgaand aan de les](https://ff-quizzes.netlify.app/en/ds/quiz/0) @@ -153,7 +153,7 @@ Als we het nog ingewikkelder willen maken, kunnen we de tijd die nodig is voor e In deze uitdaging gaan we proberen concepten te vinden die relevant zijn voor het vakgebied Data Science door naar teksten te kijken. We nemen een Wikipedia-artikel over Data Science, downloaden en verwerken de tekst, en bouwen vervolgens een woordwolk zoals deze: -![Woordwolk voor Data Science](../../../../translated_images/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.nl.png) +![Woordwolk voor Data Science](../../../../translated_images/nl/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.png) Bezoek [`notebook.ipynb`](../../../../1-Introduction/01-defining-data-science/notebook.ipynb ':ignore') om de code door te nemen. Je kunt de code ook uitvoeren en zien hoe het alle datatransformaties in real-time uitvoert. diff --git a/translations/nl/1-Introduction/04-stats-and-probability/README.md b/translations/nl/1-Introduction/04-stats-and-probability/README.md index 6d61a282..67b51531 100644 --- a/translations/nl/1-Introduction/04-stats-and-probability/README.md +++ b/translations/nl/1-Introduction/04-stats-and-probability/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: Statistiek en Kansberekening zijn twee sterk verwante gebieden binnen de wiskunde die zeer relevant zijn voor Data Science. Het is mogelijk om met data te werken zonder diepgaande kennis van wiskunde, maar het is toch beter om ten minste enkele basisconcepten te begrijpen. Hier bieden we een korte introductie die je op weg helpt. -[![Introductievideo](../../../../translated_images/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.nl.png)](https://youtu.be/Z5Zy85g4Yjw) +[![Introductievideo](../../../../translated_images/nl/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.png)](https://youtu.be/Z5Zy85g4Yjw) ## [Quiz voorafgaand aan de les](https://ff-quizzes.netlify.app/en/ds/quiz/6) @@ -39,7 +39,7 @@ Het is moeilijker om de kansverdeling van een continue variabele te beschrijven, We kunnen alleen praten over de kans dat een variabele binnen een bepaald interval van waarden valt, bijvoorbeeld P(t1≤X2). In dit geval wordt de kansverdeling beschreven door een **kansdichtheidsfunctie** p(x), zodanig dat -![P(t_1\le X Meer voorbeelden van correlatie en covariantie zijn te vinden in [bijbehorende notebook](notebook.ipynb). diff --git a/translations/nl/1-Introduction/README.md b/translations/nl/1-Introduction/README.md index cd2b57c0..2f58f1ba 100644 --- a/translations/nl/1-Introduction/README.md +++ b/translations/nl/1-Introduction/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Introductie tot Data Science -![data in actie](../../../translated_images/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.nl.jpg) +![data in actie](../../../translated_images/nl/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.jpg) > Foto door Stephen Dawson op Unsplash In deze lessen ontdek je hoe Data Science wordt gedefinieerd en leer je over ethische overwegingen waar een datawetenschapper rekening mee moet houden. Je leert ook hoe data wordt gedefinieerd en krijgt een introductie tot statistiek en kansberekening, de kerngebieden van Data Science. diff --git a/translations/nl/2-Working-With-Data/07-python/README.md b/translations/nl/2-Working-With-Data/07-python/README.md index 57634324..ed26f397 100644 --- a/translations/nl/2-Working-With-Data/07-python/README.md +++ b/translations/nl/2-Working-With-Data/07-python/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: | :-------------------------------------------------------------------------------------------------------: | | Werken met Python - _Sketchnote door [@nitya](https://twitter.com/nitya)_ | -[![Intro Video](../../../../translated_images/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.nl.png)](https://youtu.be/dZjWOGbsN4Y) +[![Intro Video](../../../../translated_images/nl/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.png)](https://youtu.be/dZjWOGbsN4Y) Hoewel databases zeer efficiënte manieren bieden om gegevens op te slaan en te raadplegen met behulp van querytalen, is de meest flexibele manier van gegevensverwerking het schrijven van je eigen programma om gegevens te manipuleren. In veel gevallen is een databasequery een effectievere aanpak. Maar in sommige gevallen, wanneer complexere gegevensverwerking nodig is, kan dit niet eenvoudig met SQL worden gedaan. Gegevensverwerking kan worden geprogrammeerd in elke programmeertaal, maar er zijn bepaalde talen die beter geschikt zijn voor het werken met data. Datawetenschappers geven meestal de voorkeur aan een van de volgende talen: @@ -73,7 +73,7 @@ print(f"Length of index is {len(idx)}") items_sold = pd.Series(np.random.randint(25,50,size=len(idx)),index=idx) items_sold.plot() ``` -![Time Series Plot](../../../../translated_images/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.nl.png) +![Time Series Plot](../../../../translated_images/nl/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.png) Stel nu dat we elke week een feestje organiseren voor vrienden en we nemen 10 extra pakken ijs mee voor het feest. We kunnen een andere series maken, geïndexeerd per week, om dat te laten zien: ```python @@ -84,7 +84,7 @@ Wanneer we twee series bij elkaar optellen, krijgen we het totaal: total_items = items_sold.add(additional_items,fill_value=0) total_items.plot() ``` -![Time Series Plot](../../../../translated_images/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.nl.png) +![Time Series Plot](../../../../translated_images/nl/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.png) > **Opmerking** dat we niet de eenvoudige syntaxis `total_items+additional_items` gebruiken. Als we dat deden, zouden we veel `NaN` (*Not a Number*) waarden krijgen in de resulterende series. Dit komt omdat er ontbrekende waarden zijn voor sommige indexpunten in de `additional_items` series, en het optellen van `NaN` met iets resulteert in `NaN`. Daarom moeten we de parameter `fill_value` specificeren tijdens het optellen. @@ -93,7 +93,7 @@ Met tijdreeksen kunnen we ook **herbemonsteren** met verschillende tijdsinterval monthly = total_items.resample("1M").mean() ax = monthly.plot(kind='bar') ``` -![Monthly Time Series Averages](../../../../translated_images/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.nl.png) +![Monthly Time Series Averages](../../../../translated_images/nl/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.png) ### DataFrame @@ -219,7 +219,7 @@ Het eerste probleem waarop we ons zullen richten is het modelleren van de epidem Omdat we willen demonstreren hoe je met data omgaat, nodigen we je uit om [`notebook-covidspread.ipynb`](notebook-covidspread.ipynb) te openen en deze van boven naar beneden te lezen. Je kunt ook cellen uitvoeren en enkele uitdagingen aangaan die we aan het einde voor je hebben achtergelaten. -![COVID Spread](../../../../translated_images/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.nl.png) +![COVID Spread](../../../../translated_images/nl/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.png) > Als je niet weet hoe je code uitvoert in Jupyter Notebook, bekijk dan [dit artikel](https://soshnikov.com/education/how-to-execute-notebooks-from-github/). @@ -241,7 +241,7 @@ Een volledig voorbeeld van het analyseren van deze dataset met behulp van de [Te Open [`notebook-papers.ipynb`](notebook-papers.ipynb) en lees deze van boven naar beneden. Je kunt ook cellen uitvoeren en enkele uitdagingen aangaan die we aan het einde voor je hebben achtergelaten. -![Covid Medical Treatment](../../../../translated_images/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.nl.png) +![Covid Medical Treatment](../../../../translated_images/nl/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.png) ## Verwerken van afbeeldingsdata diff --git a/translations/nl/2-Working-With-Data/README.md b/translations/nl/2-Working-With-Data/README.md index 4ed98929..646e3829 100644 --- a/translations/nl/2-Working-With-Data/README.md +++ b/translations/nl/2-Working-With-Data/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Werken met Data -![data liefde](../../../translated_images/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.nl.jpg) +![data liefde](../../../translated_images/nl/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.jpg) > Foto door Alexander Sinn op Unsplash In deze lessen leer je enkele manieren waarop data kan worden beheerd, gemanipuleerd en gebruikt in applicaties. Je leert over relationele en niet-relationele databases en hoe data daarin kan worden opgeslagen. Je leert de basisprincipes van werken met Python om data te beheren, en je ontdekt enkele van de vele manieren waarop je met Python data kunt beheren en analyseren. diff --git a/translations/nl/3-Data-Visualization/12-visualization-relationships/README.md b/translations/nl/3-Data-Visualization/12-visualization-relationships/README.md index 0a73af2f..347f263a 100644 --- a/translations/nl/3-Data-Visualization/12-visualization-relationships/README.md +++ b/translations/nl/3-Data-Visualization/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ Maak een eenvoudige scatterplot om de relatie tussen de prijs per pond honing en ```python sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ``` -![scatterplot 1](../../../../translated_images/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.nl.png) +![scatterplot 1](../../../../translated_images/nl/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.png) Laat nu dezelfde data zien met een honingkleurenschema om te laten zien hoe de prijs zich door de jaren heen ontwikkelt. Dit kun je doen door een 'hue'-parameter toe te voegen om de verandering per jaar te tonen: @@ -60,7 +60,7 @@ Laat nu dezelfde data zien met een honingkleurenschema om te laten zien hoe de p ```python sns.relplot(x="priceperlb", y="state", hue="year", palette="YlOrBr", data=honey, height=15, aspect=.5); ``` -![scatterplot 2](../../../../translated_images/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.nl.png) +![scatterplot 2](../../../../translated_images/nl/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.png) Met deze kleurenschemawijziging kun je duidelijk zien dat er door de jaren heen een sterke stijging is in de honingprijs per pond. Als je een steekproef uit de data neemt om dit te controleren (bijvoorbeeld Arizona), zie je een patroon van prijsstijgingen per jaar, met enkele uitzonderingen: @@ -89,7 +89,7 @@ sns.relplot(x="priceperlb", y="state", size="year", data=honey, height=15, aspec ``` Je ziet dat de grootte van de stippen geleidelijk toeneemt. -![scatterplot 3](../../../../translated_images/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.nl.png) +![scatterplot 3](../../../../translated_images/nl/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.png) Is dit een eenvoudig geval van vraag en aanbod? Door factoren zoals klimaatverandering en kolonie-instorting is er misschien minder honing beschikbaar om te kopen, waardoor de prijs stijgt? @@ -104,7 +104,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); ``` Antwoord: Ja, met enkele uitzonderingen rond het jaar 2003: -![line chart 1](../../../../translated_images/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.nl.png) +![line chart 1](../../../../translated_images/nl/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.png) ✅ Omdat Seaborn data rond één lijn aggregeert, toont het "de meerdere metingen bij elke x-waarde door het gemiddelde en het 95% betrouwbaarheidsinterval rond het gemiddelde te plotten". [Bron](https://seaborn.pydata.org/tutorial/relational.html). Dit tijdrovende gedrag kan worden uitgeschakeld door `ci=None` toe te voegen. @@ -114,7 +114,7 @@ Vraag: Kunnen we in 2003 ook een piek in de honingvoorraad zien? Wat als je kijk sns.relplot(x="year", y="totalprod", kind="line", data=honey); ``` -![line chart 2](../../../../translated_images/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.nl.png) +![line chart 2](../../../../translated_images/nl/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.png) Antwoord: Niet echt. Als je kijkt naar de totale productie, lijkt deze in dat specifieke jaar zelfs te zijn toegenomen, hoewel de hoeveelheid geproduceerde honing over het algemeen in deze jaren afneemt. @@ -139,7 +139,7 @@ sns.relplot( ``` In deze visualisatie kun je de opbrengst per kolonie en het aantal kolonies door de jaren heen vergelijken, naast elkaar met een wrap ingesteld op 3 voor de kolommen: -![facet grid](../../../../translated_images/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.nl.png) +![facet grid](../../../../translated_images/nl/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.png) Voor deze dataset valt er niets bijzonders op met betrekking tot het aantal kolonies en hun opbrengst, jaar na jaar en staat na staat. Is er een andere manier om een correlatie tussen deze twee variabelen te vinden? @@ -162,7 +162,7 @@ sns.despine(right=False) plt.ylabel('colony yield') ax.figure.legend(); ``` -![superimposed plots](../../../../translated_images/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.nl.png) +![superimposed plots](../../../../translated_images/nl/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.png) Hoewel er rond het jaar 2003 niets opvallends te zien is, eindigen we deze les toch met een iets positiever noot: hoewel het aantal kolonies over het algemeen afneemt, stabiliseert het aantal kolonies zich, zelfs als hun opbrengst per kolonie afneemt. diff --git a/translations/nl/3-Data-Visualization/R/09-visualization-quantities/README.md b/translations/nl/3-Data-Visualization/R/09-visualization-quantities/README.md index 90824624..50d6ebda 100644 --- a/translations/nl/3-Data-Visualization/R/09-visualization-quantities/README.md +++ b/translations/nl/3-Data-Visualization/R/09-visualization-quantities/README.md @@ -66,7 +66,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` Hier installeer je het `ggplot2` pakket en importeer je het vervolgens in de werkruimte met het commando `library("ggplot2")`. Om een plot te maken in ggplot, gebruik je de functie `ggplot()` en specificeer je de dataset, x- en y-variabelen als attributen. In dit geval gebruiken we de functie `geom_line()` omdat we een lijnplot willen maken. -![MaxWingspan-lineplot](../../../../../translated_images/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.nl.png) +![MaxWingspan-lineplot](../../../../../translated_images/nl/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.png) Wat valt je meteen op? Er lijkt minstens één uitschieter te zijn - dat is een behoorlijke vleugelspanwijdte! Een vleugelspanwijdte van meer dan 2000 centimeter komt overeen met meer dan 20 meter - zijn er Pterodactylen in Minnesota? Laten we dit onderzoeken. @@ -84,7 +84,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` We specificeren de hoek in het `theme` en geven de x- en y-as labels met `xlab()` en `ylab()` respectievelijk. De `ggtitle()` geeft een naam aan de grafiek/plot. -![MaxWingspan-lineplot-improved](../../../../../translated_images/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.nl.png) +![MaxWingspan-lineplot-improved](../../../../../translated_images/nl/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.png) Zelfs met de rotatie van de labels ingesteld op 45 graden, zijn er te veel om te lezen. Laten we een andere strategie proberen: label alleen die uitschieters en plaats de labels binnen de grafiek. Je kunt een spreidingsdiagram gebruiken om meer ruimte te maken voor de labeling: @@ -100,7 +100,7 @@ Wat gebeurt hier? Je hebt de functie `geom_point()` gebruikt om spreidingspunten Wat ontdek je? -![MaxWingspan-scatterplot](../../../../../translated_images/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.nl.png) +![MaxWingspan-scatterplot](../../../../../translated_images/nl/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.png) ## Filter je gegevens @@ -119,7 +119,7 @@ ggplot(data=birds_filtered, aes(x=Name, y=MaxWingspan,group=1)) + ``` We hebben een nieuwe dataframe `birds_filtered` gemaakt en vervolgens een spreidingsdiagram geplot. Door uitschieters te filteren, zijn je gegevens nu meer samenhangend en begrijpelijk. -![MaxWingspan-scatterplot-improved](../../../../../translated_images/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.nl.png) +![MaxWingspan-scatterplot-improved](../../../../../translated_images/nl/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.png) Nu we een schonere dataset hebben, althans wat betreft vleugelspanwijdte, laten we meer ontdekken over deze vogels. @@ -160,7 +160,7 @@ birds_filtered %>% group_by(Category) %>% ``` In de volgende snippet installeren we de [dplyr](https://www.rdocumentation.org/packages/dplyr/versions/0.7.8) en [lubridate](https://www.rdocumentation.org/packages/lubridate/versions/1.8.0) pakketten om gegevens te manipuleren en te groeperen om een gestapeld staafdiagram te plotten. Eerst groepeer je de gegevens op de `Category` van de vogel en vat je de kolommen `MinLength`, `MaxLength`, `MinBodyMass`, `MaxBodyMass`, `MinWingspan`, `MaxWingspan` samen. Vervolgens plot je het staafdiagram met het `ggplot2` pakket en specificeer je de kleuren voor de verschillende categorieën en de labels. -![Stacked bar chart](../../../../../translated_images/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.nl.png) +![Stacked bar chart](../../../../../translated_images/nl/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.png) Dit staafdiagram is echter onleesbaar omdat er te veel niet-gegroepeerde gegevens zijn. Je moet alleen de gegevens selecteren die je wilt plotten, dus laten we kijken naar de lengte van vogels op basis van hun categorie. @@ -175,7 +175,7 @@ ggplot(birds_count,aes(Category,n))+geom_bar(stat="identity")+coord_flip() ``` Je telt eerst unieke waarden in de `Category` kolom en sorteert ze vervolgens in een nieuwe dataframe `birds_count`. Deze gesorteerde gegevens worden vervolgens op hetzelfde niveau gefactoreerd zodat ze op een gesorteerde manier worden geplot. Met `ggplot2` plot je vervolgens de gegevens in een staafdiagram. De `coord_flip()` plot horizontale balken. -![category-length](../../../../../translated_images/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.nl.png) +![category-length](../../../../../translated_images/nl/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.png) Dit staafdiagram geeft een goed overzicht van het aantal vogels in elke categorie. In één oogopslag zie je dat het grootste aantal vogels in deze regio behoort tot de categorie Eenden/Ganzen/Watervogels. Minnesota is het 'land van 10.000 meren', dus dit is niet verrassend! @@ -198,7 +198,7 @@ ggplot(birds_grouped,aes(Category,MaxLength))+geom_bar(stat="identity")+coord_fl ``` We groeperen de `birds_filtered` gegevens op `Category` en plotten vervolgens een staafdiagram. -![comparing data](../../../../../translated_images/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.nl.png) +![comparing data](../../../../../translated_images/nl/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.png) Hier is niets verrassends: kolibries hebben de minste MaxLength in vergelijking met pelikanen of ganzen. Het is goed wanneer gegevens logisch zijn! @@ -210,7 +210,7 @@ ggplot(data=birds_grouped, aes(x=Category)) + geom_bar(aes(y=MinLength), stat="identity", position="identity", fill='orange')+ coord_flip() ``` -![super-imposed values](../../../../../translated_images/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.nl.png) +![super-imposed values](../../../../../translated_images/nl/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.png) ## 🚀 Uitdaging diff --git a/translations/nl/3-Data-Visualization/R/10-visualization-distributions/README.md b/translations/nl/3-Data-Visualization/R/10-visualization-distributions/README.md index 5fd00699..de318419 100644 --- a/translations/nl/3-Data-Visualization/R/10-visualization-distributions/README.md +++ b/translations/nl/3-Data-Visualization/R/10-visualization-distributions/README.md @@ -45,7 +45,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + geom_point() + ggtitle("Max Length per order") + coord_flip() ``` -![max lengte per orde](../../../../../translated_images/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.nl.png) +![max lengte per orde](../../../../../translated_images/nl/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.png) Dit geeft een overzicht van de algemene verdeling van lichaamslengte per vogelorde, maar het is niet de optimale manier om echte distributies weer te geven. Die taak wordt meestal uitgevoerd door een histogram te maken. ## Werken met histogrammen @@ -56,7 +56,7 @@ Dit geeft een overzicht van de algemene verdeling van lichaamslengte per vogelor ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=10)+ylab('Frequency') ``` -![distributie over de hele dataset](../../../../../translated_images/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.nl.png) +![distributie over de hele dataset](../../../../../translated_images/nl/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.png) Zoals je kunt zien, valt het merendeel van de 400+ vogels in deze dataset in het bereik van minder dan 2000 voor hun maximale lichaamsmassa. Krijg meer inzicht in de gegevens door de parameter `bins` te wijzigen naar een hoger aantal, bijvoorbeeld 30: @@ -64,7 +64,7 @@ Zoals je kunt zien, valt het merendeel van de 400+ vogels in deze dataset in het ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![distributie-30bins](../../../../../translated_images/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.nl.png) +![distributie-30bins](../../../../../translated_images/nl/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.png) Dit diagram toont de distributie op een iets meer gedetailleerde manier. Een diagram dat minder naar links is scheefgetrokken, kan worden gemaakt door ervoor te zorgen dat je alleen gegevens selecteert binnen een bepaald bereik: @@ -76,7 +76,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![gefilterd histogram](../../../../../translated_images/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.nl.png) +![gefilterd histogram](../../../../../translated_images/nl/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.png) ✅ Probeer enkele andere filters en gegevenspunten. Om de volledige distributie van de gegevens te zien, verwijder je de `['MaxBodyMass']` filter om gelabelde distributies weer te geven. @@ -90,7 +90,7 @@ ggplot(data=birds_filtered_1, aes(x=MaxBodyMass, y=MaxLength) ) + ``` Er lijkt een verwachte correlatie te zijn tussen deze twee elementen langs een verwachte as, met één bijzonder sterk convergentiepunt: -![2d plot](../../../../../translated_images/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.nl.png) +![2d plot](../../../../../translated_images/nl/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.png) Histogrammen werken standaard goed voor numerieke gegevens. Wat als je distributies wilt zien op basis van tekstgegevens? ## Verken de dataset voor distributies met behulp van tekstgegevens @@ -121,7 +121,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + scale_fill_manual(name="Conservation Status",values=c("red","green","blue","pink"),labels=c("Endangered","Near Threathened","Vulnerable","Least Concern")) ``` -![vleugelspan en beschermingsstatus](../../../../../translated_images/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.nl.png) +![vleugelspan en beschermingsstatus](../../../../../translated_images/nl/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.png) Er lijkt geen goede correlatie te zijn tussen minimale vleugelspanwijdte en beschermingsstatus. Test andere elementen van de dataset met deze methode. Je kunt ook verschillende filters proberen. Vind je enige correlatie? @@ -135,7 +135,7 @@ Laten we nu werken met dichtheidsdiagrammen! ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + geom_density() ``` -![dichtheidsdiagram](../../../../../translated_images/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.nl.png) +![dichtheidsdiagram](../../../../../translated_images/nl/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.png) Je kunt zien hoe het diagram het vorige voor minimale vleugelspanwijdte gegevens weerspiegelt; het is gewoon iets vloeiender. Als je die hoekige MaxBodyMass-lijn in het tweede diagram dat je hebt gemaakt opnieuw wilt bekijken, kun je deze heel goed gladstrijken door deze opnieuw te maken met deze methode: @@ -143,7 +143,7 @@ Je kunt zien hoe het diagram het vorige voor minimale vleugelspanwijdte gegevens ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density() ``` -![lichaamsmassa dichtheid](../../../../../translated_images/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.nl.png) +![lichaamsmassa dichtheid](../../../../../translated_images/nl/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.png) Als je een gladde, maar niet te gladde lijn wilt, bewerk dan de parameter `adjust`: @@ -151,7 +151,7 @@ Als je een gladde, maar niet te gladde lijn wilt, bewerk dan de parameter `adjus ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density(adjust = 1/5) ``` -![minder gladde lichaamsmassa](../../../../../translated_images/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.nl.png) +![minder gladde lichaamsmassa](../../../../../translated_images/nl/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.png) ✅ Lees over de beschikbare parameters voor dit type diagram en experimenteer! @@ -161,7 +161,7 @@ Dit type diagram biedt prachtig verklarende visualisaties. Met een paar regels c ggplot(data=birds_filtered_1,aes(x = MaxBodyMass, fill = Order)) + geom_density(alpha=0.5) ``` -![lichaamsmassa per orde](../../../../../translated_images/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.nl.png) +![lichaamsmassa per orde](../../../../../translated_images/nl/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.png) ## 🚀 Uitdaging diff --git a/translations/nl/3-Data-Visualization/R/11-visualization-proportions/README.md b/translations/nl/3-Data-Visualization/R/11-visualization-proportions/README.md index 423269cc..f1ca8946 100644 --- a/translations/nl/3-Data-Visualization/R/11-visualization-proportions/README.md +++ b/translations/nl/3-Data-Visualization/R/11-visualization-proportions/README.md @@ -93,7 +93,7 @@ pie(grouped$count,grouped$class, main="Edible?") ``` Voila, een taartdiagram dat de verhoudingen van deze data toont volgens deze twee klassen van champignons. Het is erg belangrijk om de volgorde van de labels correct te krijgen, vooral hier, dus zorg ervoor dat je de volgorde controleert waarmee de labelarray is gebouwd! -![taartdiagram](../../../../../translated_images/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.nl.png) +![taartdiagram](../../../../../translated_images/nl/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.png) ## Donuts! @@ -127,7 +127,7 @@ library(webr) PieDonut(habitat, aes(habitat, count=count)) ``` -![donutdiagram](../../../../../translated_images/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.nl.png) +![donutdiagram](../../../../../translated_images/nl/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.png) Deze code gebruikt de twee bibliotheken - ggplot2 en webr. Met de PieDonut-functie van de webr-bibliotheek kun je eenvoudig een donutdiagram maken! @@ -165,7 +165,7 @@ waffle((cap_color$count/10), rows = 7, title = "Waffle Chart")+scale_fill_manual Met een wafeldiagram kun je duidelijk de verhoudingen van hoedkleuren in deze champignon-dataset zien. Interessant genoeg zijn er veel champignons met groene hoeden! -![wafeldiagram](../../../../../translated_images/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.nl.png) +![wafeldiagram](../../../../../translated_images/nl/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.png) In deze les heb je drie manieren geleerd om verhoudingen te visualiseren. Eerst moet je je data groeperen in categorieën en vervolgens beslissen wat de beste manier is om de data weer te geven - taart, donut of wafel. Allemaal zijn ze smakelijk en geven de gebruiker een direct overzicht van een dataset. diff --git a/translations/nl/3-Data-Visualization/R/12-visualization-relationships/README.md b/translations/nl/3-Data-Visualization/R/12-visualization-relationships/README.md index 42bb9a9c..566ee11d 100644 --- a/translations/nl/3-Data-Visualization/R/12-visualization-relationships/README.md +++ b/translations/nl/3-Data-Visualization/R/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ library(ggplot2) ggplot(honey, aes(x = priceperlb, y = state)) + geom_point(colour = "blue") ``` -![scatterplot 1](../../../../../translated_images/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.nl.png) +![scatterplot 1](../../../../../translated_images/nl/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.png) Laat nu dezelfde data zien met een honingkleurenschema om te laten zien hoe de prijs zich door de jaren heen ontwikkelt. Dit kun je doen door een 'scale_color_gradientn'-parameter toe te voegen om de verandering per jaar weer te geven: @@ -61,7 +61,7 @@ Laat nu dezelfde data zien met een honingkleurenschema om te laten zien hoe de p ggplot(honey, aes(x = priceperlb, y = state, color=year)) + geom_point()+scale_color_gradientn(colours = colorspace::heat_hcl(7)) ``` -![scatterplot 2](../../../../../translated_images/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.nl.png) +![scatterplot 2](../../../../../translated_images/nl/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.png) Met deze kleurenschemawijziging kun je duidelijk zien dat er door de jaren heen een sterke stijging is in de prijs per pond honing. Als je een steekproef uit de data neemt om dit te controleren (bijvoorbeeld Arizona), zie je een patroon van prijsstijgingen per jaar, met enkele uitzonderingen: @@ -92,7 +92,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ``` Je ziet dat de grootte van de stippen geleidelijk toeneemt. -![scatterplot 3](../../../../../translated_images/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.nl.png) +![scatterplot 3](../../../../../translated_images/nl/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.png) Is dit een eenvoudig geval van vraag en aanbod? Door factoren zoals klimaatverandering en het instorten van kolonies is er misschien minder honing beschikbaar, waardoor de prijs jaar na jaar stijgt? @@ -107,7 +107,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab ``` Antwoord: Ja, met enkele uitzonderingen rond het jaar 2003: -![line chart 1](../../../../../translated_images/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.nl.png) +![line chart 1](../../../../../translated_images/nl/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.png) Vraag: Kunnen we in 2003 ook een piek in de honingvoorraad zien? Wat als je kijkt naar de totale productie door de jaren heen? @@ -115,7 +115,7 @@ Vraag: Kunnen we in 2003 ook een piek in de honingvoorraad zien? Wat als je kijk qplot(honey$year,honey$totalprod, geom='smooth', span =0.5, xlab = "year",ylab = "totalprod") ``` -![line chart 2](../../../../../translated_images/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.nl.png) +![line chart 2](../../../../../translated_images/nl/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.png) Antwoord: Niet echt. Als je kijkt naar de totale productie, lijkt deze in dat specifieke jaar zelfs te zijn toegenomen, hoewel de hoeveelheid geproduceerde honing over het algemeen afneemt in deze jaren. @@ -135,7 +135,7 @@ ggplot(honey, aes(x=yieldpercol, y = numcol,group = 1)) + ``` In deze visualisatie kun je de opbrengst per kolonie en het aantal kolonies door de jaren heen vergelijken, naast elkaar met een wrap ingesteld op 3 kolommen: -![facet grid](../../../../../translated_images/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.nl.png) +![facet grid](../../../../../translated_images/nl/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.png) Voor deze dataset valt er niets bijzonders op met betrekking tot het aantal kolonies en hun opbrengst, jaar na jaar en staat na staat. Is er een andere manier om een correlatie tussen deze twee variabelen te vinden? @@ -152,7 +152,7 @@ plot(honey$year, honey$yieldpercol, pch = 17, col = 3, axis(side = 4, at = pretty(range(y2))) mtext("colony yield", side = 4, line = 3) ``` -![superimposed plots](../../../../../translated_images/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.nl.png) +![superimposed plots](../../../../../translated_images/nl/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.png) Hoewel er rond 2003 niets opvallends te zien is, eindigen we deze les met een iets positiever noot: hoewel het aantal kolonies over het algemeen afneemt, stabiliseert het aantal kolonies, zelfs als hun opbrengst per kolonie afneemt. diff --git a/translations/nl/3-Data-Visualization/R/13-meaningful-vizualizations/README.md b/translations/nl/3-Data-Visualization/R/13-meaningful-vizualizations/README.md index 5f948816..4762c6f9 100644 --- a/translations/nl/3-Data-Visualization/R/13-meaningful-vizualizations/README.md +++ b/translations/nl/3-Data-Visualization/R/13-meaningful-vizualizations/README.md @@ -47,25 +47,25 @@ In eerdere lessen heb je geëxperimenteerd met het maken van allerlei interessan Zelfs als een datawetenschapper zorgvuldig het juiste type grafiek kiest, zijn er genoeg manieren waarop data kan worden weergegeven om een punt te bewijzen, vaak ten koste van de integriteit van de data. Er zijn veel voorbeelden van misleidende grafieken en infographics! -[![How Charts Lie door Alberto Cairo](../../../../../translated_images/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.nl.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "How charts lie") +[![How Charts Lie door Alberto Cairo](../../../../../translated_images/nl/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "How charts lie") > 🎥 Klik op de afbeelding hierboven voor een conferentietoespraak over misleidende grafieken Deze grafiek keert de X-as om om het tegenovergestelde van de waarheid te tonen, gebaseerd op de datum: -![slechte grafiek 1](../../../../../translated_images/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.nl.png) +![slechte grafiek 1](../../../../../translated_images/nl/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.png) [Deze grafiek](https://media.firstcoastnews.com/assets/WTLV/images/170ae16f-4643-438f-b689-50d66ca6a8d8/170ae16f-4643-438f-b689-50d66ca6a8d8_1140x641.jpg) is nog misleidender, omdat het oog naar rechts wordt getrokken om te concluderen dat COVID-gevallen in de loop van de tijd zijn afgenomen in verschillende provincies. Als je echter goed naar de data kijkt, zie je dat de datums zijn herschikt om die misleidende dalende trend te creëren. -![slechte grafiek 2](../../../../../translated_images/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.nl.jpg) +![slechte grafiek 2](../../../../../translated_images/nl/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.jpg) Dit beruchte voorbeeld gebruikt kleur EN een omgekeerde Y-as om te misleiden: in plaats van te concluderen dat het aantal schietincidenten steeg na de invoering van gun-vriendelijke wetgeving, wordt het oog misleid om te denken dat het tegenovergestelde waar is: -![slechte grafiek 3](../../../../../translated_images/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.nl.jpg) +![slechte grafiek 3](../../../../../translated_images/nl/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.jpg) Deze vreemde grafiek laat zien hoe verhoudingen kunnen worden gemanipuleerd, met hilarisch effect: -![slechte grafiek 4](../../../../../translated_images/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.nl.jpg) +![slechte grafiek 4](../../../../../translated_images/nl/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.jpg) Het vergelijken van onvergelijkbare zaken is nog een schimmige truc. Er is een [geweldige website](https://tylervigen.com/spurious-correlations) over 'spurious correlations' die 'feiten' toont zoals de correlatie tussen het echtscheidingspercentage in Maine en de consumptie van margarine. Een Reddit-groep verzamelt ook de [lelijke toepassingen](https://www.reddit.com/r/dataisugly/top/?t=all) van data. @@ -100,13 +100,13 @@ Label je assen, geef een legenda indien nodig, en bied tooltips aan voor een bet Als je data tekstueel en uitgebreid is op de X-as, kun je de tekst schuin zetten voor betere leesbaarheid. [plot3D](https://cran.r-project.org/web/packages/plot3D/index.html) biedt 3D-plotmogelijkheden als je data dit ondersteunt. Geavanceerde datavisualisaties kunnen hiermee worden gemaakt. -![3d grafieken](../../../../../translated_images/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.nl.png) +![3d grafieken](../../../../../translated_images/nl/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.png) ## Animatie en 3D-grafiekweergave Sommige van de beste datavisualisaties van vandaag zijn geanimeerd. Shirley Wu heeft geweldige voorbeelden gemaakt met D3, zoals '[film flowers](http://bl.ocks.org/sxywu/raw/d612c6c653fb8b4d7ff3d422be164a5d/)', waarbij elke bloem een visualisatie van een film is. Een ander voorbeeld voor de Guardian is 'bussed out', een interactieve ervaring die visualisaties combineert met Greensock en D3, plus een scrollytelling-artikelformaat om te laten zien hoe NYC omgaat met zijn daklozenprobleem door mensen de stad uit te sturen. -![busing](../../../../../translated_images/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.nl.png) +![busing](../../../../../translated_images/nl/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.png) > "Bussed Out: How America Moves its Homeless" van [the Guardian](https://www.theguardian.com/us-news/ng-interactive/2017/dec/20/bussed-out-america-moves-homeless-people-country-study). Visualisaties door Nadieh Bremer & Shirley Wu @@ -116,7 +116,7 @@ Hoewel deze les niet diepgaand genoeg is om deze krachtige visualisatiebibliothe Je voltooit een webapp die een geanimeerd overzicht van dit sociale netwerk weergeeft. Het gebruikt een bibliotheek die is gebouwd om een [visualisatie van een netwerk](https://github.com/emiliorizzo/vue-d3-network) te maken met Vue.js en D3. Wanneer de app draait, kun je de knooppunten op het scherm verplaatsen om de data te herschikken. -![liaisons](../../../../../translated_images/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.nl.png) +![liaisons](../../../../../translated_images/nl/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.png) ## Project: Bouw een grafiek om een netwerk te tonen met D3.js diff --git a/translations/nl/3-Data-Visualization/README.md b/translations/nl/3-Data-Visualization/README.md index 20eac188..b59441a2 100644 --- a/translations/nl/3-Data-Visualization/README.md +++ b/translations/nl/3-Data-Visualization/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Visualisaties -![een bij op een lavendelbloem](../../../translated_images/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.nl.jpg) +![een bij op een lavendelbloem](../../../translated_images/nl/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.jpg) > Foto door Jenna Lee op Unsplash Het visualiseren van data is een van de belangrijkste taken van een datawetenschapper. Beelden zeggen meer dan duizend woorden, en een visualisatie kan je helpen allerlei interessante aspecten van je data te identificeren, zoals pieken, uitschieters, groeperingen, tendensen en meer, die je kunnen helpen het verhaal van je data te begrijpen. diff --git a/translations/nl/4-Data-Science-Lifecycle/14-Introduction/README.md b/translations/nl/4-Data-Science-Lifecycle/14-Introduction/README.md index ee21deb6..55c3ce91 100644 --- a/translations/nl/4-Data-Science-Lifecycle/14-Introduction/README.md +++ b/translations/nl/4-Data-Science-Lifecycle/14-Introduction/README.md @@ -25,7 +25,7 @@ Op dit punt heb je waarschijnlijk al door dat data science een proces is. Dit pr Deze les richt zich op 3 delen van de lifecycle: vastleggen, verwerken en onderhouden. -![Diagram van de data science lifecycle](../../../../translated_images/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.nl.jpg) +![Diagram van de data science lifecycle](../../../../translated_images/nl/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.jpg) > Foto door [Berkeley School of Information](https://ischoolonline.berkeley.edu/data-science/what-is-data-science/) ## Vastleggen @@ -101,7 +101,7 @@ Verken de [Team Data Science Process lifecycle](https://docs.microsoft.com/en-us |Team Data Science Process (TDSP)|Cross-industry standard process for data mining (CRISP-DM)| |--|--| -|![Team Data Science Lifecycle](../../../../translated_images/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.nl.png) | ![Data Science Process Alliance Image](../../../../translated_images/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.nl.png) | +|![Team Data Science Lifecycle](../../../../translated_images/nl/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.png) | ![Data Science Process Alliance Image](../../../../translated_images/nl/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.png) | | Afbeelding door [Microsoft](https://docs.microsoft.comazure/architecture/data-science-process/lifecycle) | Afbeelding door [Data Science Process Alliance](https://www.datascience-pm.com/crisp-dm-2/) | ## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/quiz/27) diff --git a/translations/nl/4-Data-Science-Lifecycle/README.md b/translations/nl/4-Data-Science-Lifecycle/README.md index cf58320c..b79d6c77 100644 --- a/translations/nl/4-Data-Science-Lifecycle/README.md +++ b/translations/nl/4-Data-Science-Lifecycle/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # De levenscyclus van Data Science -![communicatie](../../../translated_images/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.nl.jpg) +![communicatie](../../../translated_images/nl/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.jpg) > Foto door Headway op Unsplash In deze lessen verken je enkele aspecten van de levenscyclus van Data Science, waaronder analyse en communicatie rondom data. diff --git a/translations/nl/5-Data-Science-In-Cloud/README.md b/translations/nl/5-Data-Science-In-Cloud/README.md index c301ab77..e4935988 100644 --- a/translations/nl/5-Data-Science-In-Cloud/README.md +++ b/translations/nl/5-Data-Science-In-Cloud/README.md @@ -9,13 +9,13 @@ CO_OP_TRANSLATOR_METADATA: --> # Data Science in de Cloud -![cloud-picture](../../../translated_images/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.nl.jpg) +![cloud-picture](../../../translated_images/nl/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.jpg) > Foto door [Jelleke Vanooteghem](https://unsplash.com/@ilumire) van [Unsplash](https://unsplash.com/s/photos/cloud?orientation=landscape) Als het gaat om data science met big data, kan de cloud een echte gamechanger zijn. In de komende drie lessen gaan we bekijken wat de cloud is en waarom deze zo nuttig kan zijn. We gaan ook een dataset over hartfalen verkennen en een model bouwen om de kans op hartfalen bij iemand te beoordelen. We zullen de kracht van de cloud gebruiken om een model op twee verschillende manieren te trainen, implementeren en gebruiken. Eén manier maakt gebruik van alleen de gebruikersinterface in een Low code/No code-stijl, de andere manier maakt gebruik van de Azure Machine Learning Software Developer Kit (Azure ML SDK). -![project-schema](../../../translated_images/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.nl.png) +![project-schema](../../../translated_images/nl/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.png) ### Onderwerpen diff --git a/translations/nl/6-Data-Science-In-Wild/20-Real-World-Examples/README.md b/translations/nl/6-Data-Science-In-Wild/20-Real-World-Examples/README.md index 27d5cf79..67bc75f4 100644 --- a/translations/nl/6-Data-Science-In-Wild/20-Real-World-Examples/README.md +++ b/translations/nl/6-Data-Science-In-Wild/20-Real-World-Examples/README.md @@ -41,7 +41,7 @@ Dankzij de democratisering van AI vinden ontwikkelaars het nu gemakkelijker om A * [Datawetenschap in de Gezondheidszorg](https://data-flair.training/blogs/data-science-in-healthcare/) - benadrukt toepassingen zoals medische beeldvorming (bijv. MRI, röntgenfoto, CT-scan), genomica (DNA-sequencing), medicijnontwikkeling (risicobeoordeling, succesvoorspelling), voorspellende analyse (patiëntenzorg en logistiek), ziekteopsporing en -preventie, etc. -![Toepassingen van Datawetenschap in de Praktijk](../../../../translated_images/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.nl.png) Afbeeldingscredit: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) +![Toepassingen van Datawetenschap in de Praktijk](../../../../translated_images/nl/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.png) Afbeeldingscredit: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) De afbeelding toont andere domeinen en voorbeelden van het toepassen van technieken uit de datawetenschap. Wil je meer toepassingen verkennen? Bekijk de sectie [Review & Zelfstudie](../../../../6-Data-Science-In-Wild/20-Real-World-Examples) hieronder. diff --git a/translations/nl/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md b/translations/nl/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md index b38f6846..ad266d85 100644 --- a/translations/nl/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md +++ b/translations/nl/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md @@ -22,7 +22,7 @@ De Explorer-interface (afgebeeld in de screenshot hieronder) laat je een dataset 2. Verken de dataset [Catalogus](https://planetarycomputer.microsoft.com/catalog) - leer het doel van elke dataset. 3. Gebruik de Explorer - kies een dataset die je interesseert, selecteer een relevante query en weergaveoptie. -![De Planetary Computer Explorer](../../../../translated_images/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.nl.png) +![De Planetary Computer Explorer](../../../../translated_images/nl/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.png) `Jouw taak:` Bestudeer nu de visualisatie die in de browser wordt weergegeven en beantwoord de volgende vragen: diff --git a/translations/nl/CONTRIBUTING.md b/translations/nl/CONTRIBUTING.md index b66d14f1..0d143457 100644 --- a/translations/nl/CONTRIBUTING.md +++ b/translations/nl/CONTRIBUTING.md @@ -315,7 +315,7 @@ Neem in je PR-beschrijving op: ``` ```` -- Voeg alt-tekst toe aan afbeeldingen: `![Alt-tekst](../../translated_images/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.nl.png)` +- Voeg alt-tekst toe aan afbeeldingen: `![Alt-tekst](../../translated_images/nl/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.png)` - Houd de regellengte redelijk (ongeveer 80-100 tekens) ### Python diff --git a/translations/nl/README.md b/translations/nl/README.md index d2f62e69..687568bf 100644 --- a/translations/nl/README.md +++ b/translations/nl/README.md @@ -33,7 +33,7 @@ Azure Cloud Advocates bij Microsoft bieden met plezier een 10-weekse, 20-lescurr **🙏 Speciale dank 🙏 aan onze [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/) auteurs, reviewers en inhoudsbijdragers,** met name Aaryan Arora, [Aditya Garg](https://github.com/AdityaGarg00), [Alondra Sanchez](https://www.linkedin.com/in/alondra-sanchez-molina/), [Ankita Singh](https://www.linkedin.com/in/ankitasingh007), [Anupam Mishra](https://www.linkedin.com/in/anupam--mishra/), [Arpita Das](https://www.linkedin.com/in/arpitadas01/), ChhailBihari Dubey, [Dibri Nsofor](https://www.linkedin.com/in/dibrinsofor), [Dishita Bhasin](https://www.linkedin.com/in/dishita-bhasin-7065281bb), [Majd Safi](https://www.linkedin.com/in/majd-s/), [Max Blum](https://www.linkedin.com/in/max-blum-6036a1186/), [Miguel Correa](https://www.linkedin.com/in/miguelmque/), [Mohamma Iftekher (Iftu) Ebne Jalal](https://twitter.com/iftu119), [Nawrin Tabassum](https://www.linkedin.com/in/nawrin-tabassum), [Raymond Wangsa Putra](https://www.linkedin.com/in/raymond-wp/), [Rohit Yadav](https://www.linkedin.com/in/rty2423), Samridhi Sharma, [Sanya Sinha](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200), [Sheena Narula](https://www.linkedin.com/in/sheena-narua-n/), [Tauqeer Ahmad](https://www.linkedin.com/in/tauqeerahmad5201/), Yogendrasingh Pawar , [Vidushi Gupta](https://www.linkedin.com/in/vidushi-gupta07/), [Jasleen Sondhi](https://www.linkedin.com/in/jasleen-sondhi/) -|![Sketchnote door @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.nl.png)| +|![Sketchnote door @sketchthedocs https://sketchthedocs.dev](../../translated_images/nl/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.png)| |:---:| | Data Science voor Beginners - _Sketchnote door [@nitya](https://twitter.com/nitya)_ | @@ -52,7 +52,7 @@ Azure Cloud Advocates bij Microsoft bieden met plezier een 10-weekse, 20-lescurr We hebben een doorlopende Discord-serie "Learn with AI", lees meer en sluit je bij ons aan op [Learn with AI-serie](https://aka.ms/learnwithai/discord) van 18 - 30 september 2025. Je krijgt tips en trucs voor het gebruik van GitHub Copilot voor Data Science. -![Leer met AI-serie](../../translated_images/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.nl.jpg) +![Leer met AI-serie](../../translated_images/nl/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.jpg) # Ben je een student? @@ -132,7 +132,7 @@ Elk voorbeeld bevat gedetailleerde commentaren die elke stap uitleggen, waardoor ## Lessen -|![ Sketchnote door @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.nl.png)| +|![ Sketchnote door @sketchthedocs https://sketchthedocs.dev](../../translated_images/nl/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png)| |:---:| | Data Science voor Beginners: Routekaart - _Sketchnote door [@nitya](https://twitter.com/nitya)_ | diff --git a/translations/nl/sketchnotes/README.md b/translations/nl/sketchnotes/README.md index ff61b47d..37a1fc3d 100644 --- a/translations/nl/sketchnotes/README.md +++ b/translations/nl/sketchnotes/README.md @@ -13,7 +13,7 @@ Vind hier alle sketchnotes! Nitya Narasimhan, kunstenaar -![roadmap sketchnote](../../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.nl.png) +![roadmap sketchnote](../../../translated_images/nl/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png) --- diff --git a/translations/no/1-Introduction/01-defining-data-science/README.md b/translations/no/1-Introduction/01-defining-data-science/README.md index a9e9bc7c..975b174a 100644 --- a/translations/no/1-Introduction/01-defining-data-science/README.md +++ b/translations/no/1-Introduction/01-defining-data-science/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: --- -[![Definere Data Science Video](../../../../translated_images/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.no.png)](https://youtu.be/beZ7Mb_oz9I) +[![Definere Data Science Video](../../../../translated_images/no/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.png)](https://youtu.be/beZ7Mb_oz9I) ## [Quiz før forelesning](https://ff-quizzes.netlify.app/en/ds/quiz/0) @@ -153,7 +153,7 @@ Hvis vi vil gjøre det enda mer komplisert, kan vi plotte tiden brukt på hver m I denne utfordringen skal vi prøve å finne konsepter som er relevante for feltet Data Science ved å se på tekster. Vi skal ta en Wikipedia-artikkel om Data Science, laste ned og behandle teksten, og deretter lage en ordsky som denne: -![Ordsky for Data Science](../../../../translated_images/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.no.png) +![Ordsky for Data Science](../../../../translated_images/no/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.png) Besøk [`notebook.ipynb`](../../../../1-Introduction/01-defining-data-science/notebook.ipynb ':ignore') for å lese gjennom koden. Du kan også kjøre koden og se hvordan den utfører alle datatransformasjonene i sanntid. diff --git a/translations/no/1-Introduction/04-stats-and-probability/README.md b/translations/no/1-Introduction/04-stats-and-probability/README.md index 42cecb73..c07fc8ef 100644 --- a/translations/no/1-Introduction/04-stats-and-probability/README.md +++ b/translations/no/1-Introduction/04-stats-and-probability/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: Statistikk og sannsynlighetsteori er to nært relaterte områder innen matematikk som er svært relevante for datavitenskap. Det er mulig å jobbe med data uten dyp kunnskap om matematikk, men det er likevel bedre å kjenne til noen grunnleggende konsepter. Her vil vi gi en kort introduksjon som kan hjelpe deg i gang. -[![Introduksjonsvideo](../../../../translated_images/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.no.png)](https://youtu.be/Z5Zy85g4Yjw) +[![Introduksjonsvideo](../../../../translated_images/no/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.png)](https://youtu.be/Z5Zy85g4Yjw) ## [Quiz før forelesning](https://ff-quizzes.netlify.app/en/ds/quiz/6) @@ -39,7 +39,7 @@ Det er mer utfordrende å beskrive sannsynlighetsfordelingen for en kontinuerlig Vi kan bare snakke om sannsynligheten for at en variabel faller innenfor et gitt intervall av verdier, f.eks. P(t1≤X2). I dette tilfellet beskrives sannsynlighetsfordelingen av en **sannsynlighetstetthetsfunksjon** p(x), slik at -![P(t_1\le X Flere eksempler på korrelasjon og kovarians kan finnes i [tilhørende notatbok](notebook.ipynb). diff --git a/translations/no/1-Introduction/README.md b/translations/no/1-Introduction/README.md index 1cd9e059..677034ae 100644 --- a/translations/no/1-Introduction/README.md +++ b/translations/no/1-Introduction/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Introduksjon til Data Science -![data i aksjon](../../../translated_images/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.no.jpg) +![data i aksjon](../../../translated_images/no/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.jpg) > Foto av Stephen Dawson på Unsplash I disse leksjonene vil du oppdage hvordan Data Science defineres og lære om etiske hensyn som en dataforsker må ta i betraktning. Du vil også lære hvordan data defineres og få en introduksjon til statistikk og sannsynlighet, de sentrale akademiske områdene innen Data Science. diff --git a/translations/no/2-Working-With-Data/07-python/README.md b/translations/no/2-Working-With-Data/07-python/README.md index 077e06b7..16a53e0d 100644 --- a/translations/no/2-Working-With-Data/07-python/README.md +++ b/translations/no/2-Working-With-Data/07-python/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: | :-------------------------------------------------------------------------------------------------------: | | Arbeide med Python - _Sketchnote av [@nitya](https://twitter.com/nitya)_ | -[![Introduksjonsvideo](../../../../translated_images/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.no.png)](https://youtu.be/dZjWOGbsN4Y) +[![Introduksjonsvideo](../../../../translated_images/no/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.png)](https://youtu.be/dZjWOGbsN4Y) Mens databaser tilbyr svært effektive måter å lagre data og hente dem ved hjelp av spørringsspråk, er den mest fleksible måten å behandle data på å skrive ditt eget program for å manipulere data. I mange tilfeller vil en databasespørring være en mer effektiv løsning. Men i noen tilfeller, når mer kompleks databehandling er nødvendig, kan det ikke enkelt gjøres med SQL. Databehandling kan programmeres i hvilket som helst programmeringsspråk, men det finnes visse språk som er mer tilpasset arbeid med data. Dataforskere foretrekker vanligvis ett av følgende språk: @@ -75,7 +75,7 @@ print(f"Length of index is {len(idx)}") items_sold = pd.Series(np.random.randint(25,50,size=len(idx)),index=idx) items_sold.plot() ``` -![Tidsserieplott](../../../../translated_images/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.no.png) +![Tidsserieplott](../../../../translated_images/no/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.png) Nå antar vi at vi hver uke arrangerer en fest for venner, og vi tar med oss 10 ekstra pakker med iskrem til festen. Vi kan lage en annen series, indeksert etter uke, for å demonstrere dette: ```python @@ -86,7 +86,7 @@ Når vi legger sammen to series, får vi totalt antall: total_items = items_sold.add(additional_items,fill_value=0) total_items.plot() ``` -![Tidsserieplott](../../../../translated_images/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.no.png) +![Tidsserieplott](../../../../translated_images/no/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.png) > **Merk** at vi ikke bruker enkel syntaks `total_items+additional_items`. Hvis vi gjorde det, ville vi fått mange `NaN` (*Not a Number*) verdier i den resulterende serien. Dette skyldes at det mangler verdier for noen av indeksene i `additional_items`-serien, og å legge til `NaN` til noe resulterer i `NaN`. Derfor må vi spesifisere `fill_value`-parameteren under addisjonen. @@ -95,7 +95,7 @@ Med tidsserier kan vi også **resample** serien med forskjellige tidsintervaller monthly = total_items.resample("1M").mean() ax = monthly.plot(kind='bar') ``` -![Månedlige tidsserie-gjennomsnitt](../../../../translated_images/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.no.png) +![Månedlige tidsserie-gjennomsnitt](../../../../translated_images/no/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.png) ### DataFrame @@ -221,7 +221,7 @@ Det første problemet vi skal fokusere på er modellering av epidemisk spredning Siden vi ønsker å demonstrere hvordan man håndterer data, inviterer vi deg til å åpne [`notebook-covidspread.ipynb`](notebook-covidspread.ipynb) og lese det fra topp til bunn. Du kan også kjøre cellene og gjøre noen utfordringer som vi har lagt igjen til deg på slutten. -![COVID-spredning](../../../../translated_images/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.no.png) +![COVID-spredning](../../../../translated_images/no/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.png) > Hvis du ikke vet hvordan du kjører kode i Jupyter Notebook, ta en titt på [denne artikkelen](https://soshnikov.com/education/how-to-execute-notebooks-from-github/). @@ -243,7 +243,7 @@ Et fullstendig eksempel på analyse av dette datasettet ved bruk av [Text Analyt Åpne [`notebook-papers.ipynb`](notebook-papers.ipynb) og les det fra topp til bunn. Du kan også kjøre cellene og gjøre noen utfordringer som vi har lagt igjen til deg på slutten. -![Covid Medisinsk Behandling](../../../../translated_images/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.no.png) +![Covid Medisinsk Behandling](../../../../translated_images/no/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.png) ## Behandling av Bildedata diff --git a/translations/no/2-Working-With-Data/README.md b/translations/no/2-Working-With-Data/README.md index c2fa941b..d46be6a8 100644 --- a/translations/no/2-Working-With-Data/README.md +++ b/translations/no/2-Working-With-Data/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Arbeide med data -![data love](../../../translated_images/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.no.jpg) +![data love](../../../translated_images/no/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.jpg) > Foto av Alexander Sinn på Unsplash I disse leksjonene vil du lære noen av måtene data kan administreres, manipuleres og brukes i applikasjoner. Du vil lære om relasjonelle og ikke-relasjonelle databaser og hvordan data kan lagres i dem. Du vil lære det grunnleggende om å jobbe med Python for å håndtere data, og du vil oppdage noen av de mange måtene du kan bruke Python til å administrere og analysere data. diff --git a/translations/no/3-Data-Visualization/12-visualization-relationships/README.md b/translations/no/3-Data-Visualization/12-visualization-relationships/README.md index d5702768..b4300a3c 100644 --- a/translations/no/3-Data-Visualization/12-visualization-relationships/README.md +++ b/translations/no/3-Data-Visualization/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ Lag et grunnleggende spredningsdiagram for å vise relasjonen mellom prisen per ```python sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ``` -![spredningsdiagram 1](../../../../translated_images/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.no.png) +![spredningsdiagram 1](../../../../translated_images/no/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.png) Vis nå de samme dataene med et honningfarget fargetema for å vise hvordan prisen utvikler seg over årene. Du kan gjøre dette ved å legge til en 'hue'-parameter for å vise endringen, år for år: @@ -60,7 +60,7 @@ Vis nå de samme dataene med et honningfarget fargetema for å vise hvordan pris ```python sns.relplot(x="priceperlb", y="state", hue="year", palette="YlOrBr", data=honey, height=15, aspect=.5); ``` -![spredningsdiagram 2](../../../../translated_images/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.no.png) +![spredningsdiagram 2](../../../../translated_images/no/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.png) Med denne fargeendringen kan du tydelig se en sterk progresjon over årene når det gjelder honningpris per pund. Faktisk, hvis du ser på et utvalg i dataene for å verifisere (velg en gitt stat, for eksempel Arizona), kan du se et mønster av prisøkninger år for år, med få unntak: @@ -89,7 +89,7 @@ sns.relplot(x="priceperlb", y="state", size="year", data=honey, height=15, aspec ``` Du kan se at størrelsen på prikkene gradvis øker. -![spredningsdiagram 3](../../../../translated_images/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.no.png) +![spredningsdiagram 3](../../../../translated_images/no/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.png) Er dette et enkelt tilfelle av tilbud og etterspørsel? På grunn av faktorer som klimaendringer og kollaps av kolonier, er det mindre honning tilgjengelig for kjøp år for år, og dermed øker prisen? @@ -104,7 +104,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); ``` Svar: Ja, med noen unntak rundt året 2003: -![linjediagram 1](../../../../translated_images/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.no.png) +![linjediagram 1](../../../../translated_images/no/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.png) ✅ Fordi Seaborn aggregerer data rundt én linje, viser det "de flere målingene ved hver x-verdi ved å plotte gjennomsnittet og 95% konfidensintervallet rundt gjennomsnittet". [Kilde](https://seaborn.pydata.org/tutorial/relational.html). Denne tidkrevende oppførselen kan deaktiveres ved å legge til `ci=None`. @@ -114,7 +114,7 @@ Spørsmål: Vel, i 2003 kan vi også se en topp i honningtilførselen? Hva om du sns.relplot(x="year", y="totalprod", kind="line", data=honey); ``` -![linjediagram 2](../../../../translated_images/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.no.png) +![linjediagram 2](../../../../translated_images/no/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.png) Svar: Ikke egentlig. Hvis du ser på total produksjon, ser det faktisk ut til å ha økt det året, selv om mengden honning som produseres generelt sett er i nedgang i disse årene. @@ -139,7 +139,7 @@ sns.relplot( ``` I denne visualiseringen kan du sammenligne avkastning per koloni og antall kolonier år for år, side om side med wrap satt til 3 for kolonnene: -![facet grid](../../../../translated_images/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.no.png) +![facet grid](../../../../translated_images/no/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.png) For dette datasettet skiller ingenting seg spesielt ut med hensyn til antall kolonier og deres avkastning, år for år og stat for stat. Er det en annen måte å se på korrelasjonen mellom disse to variablene? @@ -162,7 +162,7 @@ sns.despine(right=False) plt.ylabel('colony yield') ax.figure.legend(); ``` -![superimposed plots](../../../../translated_images/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.no.png) +![superimposed plots](../../../../translated_images/no/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.png) Selv om ingenting skiller seg ut rundt året 2003, lar det oss avslutte denne leksjonen på en litt lykkeligere tone: selv om det generelt er et synkende antall kolonier, stabiliserer antallet kolonier seg, selv om deres avkastning per koloni er i nedgang. diff --git a/translations/no/3-Data-Visualization/R/09-visualization-quantities/README.md b/translations/no/3-Data-Visualization/R/09-visualization-quantities/README.md index 69dbc886..ff427684 100644 --- a/translations/no/3-Data-Visualization/R/09-visualization-quantities/README.md +++ b/translations/no/3-Data-Visualization/R/09-visualization-quantities/README.md @@ -66,7 +66,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` Her installerer du `ggplot2`-pakken og importerer den deretter til arbeidsområdet ved hjelp av kommandoen `library("ggplot2")`. For å plotte et diagram i ggplot brukes funksjonen `ggplot()`, og du spesifiserer datasettet, x- og y-variablene som attributter. I dette tilfellet bruker vi funksjonen `geom_line()` siden vi ønsker å plotte et linjediagram. -![MaxWingspan-lineplot](../../../../../translated_images/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.no.png) +![MaxWingspan-lineplot](../../../../../translated_images/no/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.png) Hva legger du merke til med en gang? Det ser ut til å være minst én uteligger - det er et ganske vingespenn! Et vingespenn på over 2000 centimeter tilsvarer mer enn 20 meter - er det Pterodaktyler som flyr rundt i Minnesota? La oss undersøke. @@ -84,7 +84,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` Vi spesifiserer vinkelen i `theme` og angir x- og y-akseetiketter i `xlab()` og `ylab()` henholdsvis. `ggtitle()` gir diagrammet et navn. -![MaxWingspan-lineplot-improved](../../../../../translated_images/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.no.png) +![MaxWingspan-lineplot-improved](../../../../../translated_images/no/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.png) Selv med rotasjonen av etikettene satt til 45 grader, er det for mange til å lese. La oss prøve en annen strategi: merk bare uteliggerne og sett etikettene innenfor diagrammet. Du kan bruke et spredningsdiagram for å få mer plass til merkingen: @@ -100,7 +100,7 @@ Hva skjer her? Du brukte funksjonen `geom_point()` for å plotte spredningspunkt Hva oppdager du? -![MaxWingspan-scatterplot](../../../../../translated_images/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.no.png) +![MaxWingspan-scatterplot](../../../../../translated_images/no/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.png) ## Filtrer dataene dine @@ -119,7 +119,7 @@ ggplot(data=birds_filtered, aes(x=Name, y=MaxWingspan,group=1)) + ``` Vi laget en ny dataframe `birds_filtered` og plottet deretter et spredningsdiagram. Ved å filtrere ut uteliggerne er dataene dine nå mer sammenhengende og forståelige. -![MaxWingspan-scatterplot-improved](../../../../../translated_images/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.no.png) +![MaxWingspan-scatterplot-improved](../../../../../translated_images/no/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.png) Nå som vi har et renere datasett, i det minste når det gjelder vingespenn, la oss oppdage mer om disse fuglene. @@ -161,7 +161,7 @@ birds_filtered %>% group_by(Category) %>% ``` I følgende kodebit installerer vi [dplyr](https://www.rdocumentation.org/packages/dplyr/versions/0.7.8)- og [lubridate](https://www.rdocumentation.org/packages/lubridate/versions/1.8.0)-pakkene for å hjelpe med å manipulere og gruppere data for å plotte et stablet stolpediagram. Først grupperer du dataene etter fuglenes `Category` og oppsummerer deretter kolonnene `MinLength`, `MaxLength`, `MinBodyMass`, `MaxBodyMass`, `MinWingspan`, `MaxWingspan`. Deretter plotter du stolpediagrammet ved hjelp av `ggplot2`-pakken og spesifiserer farger for de ulike kategoriene og etikettene. -![Stacked bar chart](../../../../../translated_images/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.no.png) +![Stacked bar chart](../../../../../translated_images/no/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.png) Dette stolpediagrammet er imidlertid uleselig fordi det er for mye ugruppert data. Du må velge bare dataene du vil plotte, så la oss se på lengden av fugler basert på deres kategori. @@ -176,7 +176,7 @@ ggplot(birds_count,aes(Category,n))+geom_bar(stat="identity")+coord_flip() ``` Du teller først unike verdier i `Category`-kolonnen og sorterer dem deretter inn i en ny dataframe `birds_count`. Disse sorterte dataene faktoreres deretter på samme nivå slik at de plottes i sortert rekkefølge. Ved hjelp av `ggplot2` plotter du deretter dataene i et stolpediagram. `coord_flip()` plasserer horisontale stolper. -![category-length](../../../../../translated_images/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.no.png) +![category-length](../../../../../translated_images/no/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.png) Dette stolpediagrammet gir et godt innblikk i antallet fugler i hver kategori. Med et blikk ser du at det største antallet fugler i denne regionen er i kategorien Ender/Gjess/Vannfugl. Minnesota er "landet med 10 000 innsjøer", så dette er ikke overraskende! @@ -199,7 +199,7 @@ ggplot(birds_grouped,aes(Category,MaxLength))+geom_bar(stat="identity")+coord_fl ``` Vi grupperer `birds_filtered`-dataene etter `Category` og plottet deretter et stolpediagram. -![comparing data](../../../../../translated_images/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.no.png) +![comparing data](../../../../../translated_images/no/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.png) Ingenting er overraskende her: kolibrier har den minste MaksLengden sammenlignet med pelikaner eller gjess. Det er bra når data gir logisk mening! @@ -211,7 +211,7 @@ ggplot(data=birds_grouped, aes(x=Category)) + geom_bar(aes(y=MinLength), stat="identity", position="identity", fill='orange')+ coord_flip() ``` -![super-imposed values](../../../../../translated_images/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.no.png) +![super-imposed values](../../../../../translated_images/no/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.png) ## 🚀 Utfordring diff --git a/translations/no/3-Data-Visualization/R/10-visualization-distributions/README.md b/translations/no/3-Data-Visualization/R/10-visualization-distributions/README.md index cb6b3008..409f4cea 100644 --- a/translations/no/3-Data-Visualization/R/10-visualization-distributions/README.md +++ b/translations/no/3-Data-Visualization/R/10-visualization-distributions/README.md @@ -45,7 +45,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + geom_point() + ggtitle("Max Length per order") + coord_flip() ``` -![maks lengde per orden](../../../../../translated_images/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.no.png) +![maks lengde per orden](../../../../../translated_images/no/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.png) Dette gir en oversikt over den generelle fordelingen av kroppslengde per fugleorden, men det er ikke den optimale måten å vise sanne fordelinger på. Den oppgaven håndteres vanligvis ved å lage et histogram. ## Arbeide med histogrammer @@ -56,7 +56,7 @@ Dette gir en oversikt over den generelle fordelingen av kroppslengde per fugleor ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=10)+ylab('Frequency') ``` -![fordeling over hele datasettet](../../../../../translated_images/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.no.png) +![fordeling over hele datasettet](../../../../../translated_images/no/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.png) Som du kan se, faller de fleste av de 400+ fuglene i dette datasettet innenfor området under 2000 for deres Maks Kroppsmasse. Få mer innsikt i dataene ved å endre `bins`-parameteren til et høyere tall, for eksempel 30: @@ -64,7 +64,7 @@ Som du kan se, faller de fleste av de 400+ fuglene i dette datasettet innenfor o ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![fordeling-30bins](../../../../../translated_images/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.no.png) +![fordeling-30bins](../../../../../translated_images/no/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.png) Dette diagrammet viser fordelingen på en litt mer detaljert måte. Et diagram som er mindre skjevt mot venstre kan opprettes ved å sørge for at du bare velger data innenfor et gitt område: @@ -76,7 +76,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![filtrert histogram](../../../../../translated_images/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.no.png) +![filtrert histogram](../../../../../translated_images/no/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.png) ✅ Prøv noen andre filtre og datapunkter. For å se hele fordelingen av dataene, fjern `['MaxBodyMass']`-filteret for å vise merkede fordelinger. @@ -90,7 +90,7 @@ ggplot(data=birds_filtered_1, aes(x=MaxBodyMass, y=MaxLength) ) + ``` Det ser ut til å være en forventet korrelasjon mellom disse to elementene langs en forventet akse, med ett spesielt sterkt punkt av konvergens: -![2d plot](../../../../../translated_images/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.no.png) +![2d plot](../../../../../translated_images/no/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.png) Histogrammer fungerer godt som standard for numeriske data. Hva om du trenger å se fordelinger basert på tekstdata? ## Utforsk datasettet for fordelinger ved hjelp av tekstdata @@ -121,7 +121,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + scale_fill_manual(name="Conservation Status",values=c("red","green","blue","pink"),labels=c("Endangered","Near Threathened","Vulnerable","Least Concern")) ``` -![vingespenn og bevaringsstatus](../../../../../translated_images/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.no.png) +![vingespenn og bevaringsstatus](../../../../../translated_images/no/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.png) Det ser ikke ut til å være en god korrelasjon mellom minimum vingespenn og bevaringsstatus. Test andre elementer i datasettet ved hjelp av denne metoden. Du kan også prøve forskjellige filtre. Finner du noen korrelasjon? @@ -135,7 +135,7 @@ La oss jobbe med tetthetsdiagrammer nå! ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + geom_density() ``` -![tetthetsdiagram](../../../../../translated_images/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.no.png) +![tetthetsdiagram](../../../../../translated_images/no/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.png) Du kan se hvordan diagrammet gjenspeiler det forrige for Minimum Vingespenn-data; det er bare litt jevnere. Hvis du ønsket å gå tilbake til den hakkete MaksKroppsmasse-linjen i det andre diagrammet du laget, kunne du jevne den ut veldig godt ved å gjenskape den ved hjelp av denne metoden: @@ -143,7 +143,7 @@ Du kan se hvordan diagrammet gjenspeiler det forrige for Minimum Vingespenn-data ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density() ``` -![kroppsmasse tetthet](../../../../../translated_images/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.no.png) +![kroppsmasse tetthet](../../../../../translated_images/no/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.png) Hvis du ønsket en jevn, men ikke for jevn linje, rediger `adjust`-parameteren: @@ -151,7 +151,7 @@ Hvis du ønsket en jevn, men ikke for jevn linje, rediger `adjust`-parameteren: ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density(adjust = 1/5) ``` -![mindre jevn kroppsmasse](../../../../../translated_images/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.no.png) +![mindre jevn kroppsmasse](../../../../../translated_images/no/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.png) ✅ Les om parameterne som er tilgjengelige for denne typen diagram og eksperimenter! @@ -161,7 +161,7 @@ Denne typen diagram tilbyr vakkert forklarende visualiseringer. Med noen få lin ggplot(data=birds_filtered_1,aes(x = MaxBodyMass, fill = Order)) + geom_density(alpha=0.5) ``` -![kroppsmasse per orden](../../../../../translated_images/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.no.png) +![kroppsmasse per orden](../../../../../translated_images/no/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.png) ## 🚀 Utfordring diff --git a/translations/no/3-Data-Visualization/R/11-visualization-proportions/README.md b/translations/no/3-Data-Visualization/R/11-visualization-proportions/README.md index 352025ce..b4c137aa 100644 --- a/translations/no/3-Data-Visualization/R/11-visualization-proportions/README.md +++ b/translations/no/3-Data-Visualization/R/11-visualization-proportions/README.md @@ -97,7 +97,7 @@ pie(grouped$count,grouped$class, main="Edible?") ``` Voila, et kakediagram som viser proporsjonene av disse dataene basert på de to klassene av sopp. Det er ganske viktig å få rekkefølgen på etikettene riktig, spesielt her, så sørg for å verifisere rekkefølgen etikettarrayet er bygget med! -![kakediagram](../../../../../translated_images/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.no.png) +![kakediagram](../../../../../translated_images/no/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.png) ## Smultringer! @@ -132,7 +132,7 @@ library(webr) PieDonut(habitat, aes(habitat, count=count)) ``` -![smultringdiagram](../../../../../translated_images/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.no.png) +![smultringdiagram](../../../../../translated_images/no/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.png) Denne koden bruker de to bibliotekene - ggplot2 og webr. Ved å bruke PieDonut-funksjonen i webr-biblioteket kan vi enkelt lage et smultringdiagram! @@ -169,7 +169,7 @@ waffle((cap_color$count/10), rows = 7, title = "Waffle Chart")+scale_fill_manual Ved å bruke et vaffeldiagram kan du tydelig se proporsjonene av hattfarger i dette soppdatasettet. Interessant nok er det mange sopper med grønne hatter! -![vaffeldiagram](../../../../../translated_images/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.no.png) +![vaffeldiagram](../../../../../translated_images/no/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.png) I denne leksjonen lærte du tre måter å visualisere proporsjoner på. Først må du gruppere dataene dine i kategorier og deretter bestemme hvilken som er den beste måten å vise dataene på - kake, smultring eller vaffel. Alle er smakfulle og gir brukeren et øyeblikkelig innblikk i et datasett. diff --git a/translations/no/3-Data-Visualization/R/12-visualization-relationships/README.md b/translations/no/3-Data-Visualization/R/12-visualization-relationships/README.md index 43cf86d8..abc42b38 100644 --- a/translations/no/3-Data-Visualization/R/12-visualization-relationships/README.md +++ b/translations/no/3-Data-Visualization/R/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ library(ggplot2) ggplot(honey, aes(x = priceperlb, y = state)) + geom_point(colour = "blue") ``` -![spredningsdiagram 1](../../../../../translated_images/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.no.png) +![spredningsdiagram 1](../../../../../translated_images/no/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.png) Vis nå de samme dataene med et honningfarget fargeskjema for å vise hvordan prisen utvikler seg over årene. Du kan gjøre dette ved å legge til en 'scale_color_gradientn'-parameter for å vise endringen år for år: @@ -61,7 +61,7 @@ Vis nå de samme dataene med et honningfarget fargeskjema for å vise hvordan pr ggplot(honey, aes(x = priceperlb, y = state, color=year)) + geom_point()+scale_color_gradientn(colours = colorspace::heat_hcl(7)) ``` -![spredningsdiagram 2](../../../../../translated_images/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.no.png) +![spredningsdiagram 2](../../../../../translated_images/no/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.png) Med denne fargeendringen kan du tydelig se en sterk progresjon over årene når det gjelder honningpris per pund. Faktisk, hvis du ser på et utvalg i dataene for å verifisere (velg en gitt stat, for eksempel Arizona), kan du se et mønster med prisøkninger år for år, med få unntak: @@ -92,7 +92,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ``` Du kan se at størrelsen på prikkene gradvis øker. -![spredningsdiagram 3](../../../../../translated_images/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.no.png) +![spredningsdiagram 3](../../../../../translated_images/no/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.png) Er dette et enkelt tilfelle av tilbud og etterspørsel? På grunn av faktorer som klimaendringer og kollaps av kolonier, er det mindre honning tilgjengelig for kjøp år for år, og dermed øker prisen? @@ -107,7 +107,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab ``` Svar: Ja, med noen unntak rundt året 2003: -![linjediagram 1](../../../../../translated_images/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.no.png) +![linjediagram 1](../../../../../translated_images/no/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.png) Spørsmål: Vel, i 2003 kan vi også se en topp i honningforsyningen? Hva om du ser på total produksjon år for år? @@ -115,7 +115,7 @@ Spørsmål: Vel, i 2003 kan vi også se en topp i honningforsyningen? Hva om du qplot(honey$year,honey$totalprod, geom='smooth', span =0.5, xlab = "year",ylab = "totalprod") ``` -![linjediagram 2](../../../../../translated_images/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.no.png) +![linjediagram 2](../../../../../translated_images/no/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.png) Svar: Ikke egentlig. Hvis du ser på total produksjon, ser det faktisk ut til å ha økt det året, selv om mengden honning som produseres generelt sett er i nedgang i disse årene. @@ -135,7 +135,7 @@ ggplot(honey, aes(x=yieldpercol, y = numcol,group = 1)) + ``` I denne visualiseringen kan du sammenligne avkastning per koloni og antall kolonier år for år, side om side med en wrap satt til 3 for kolonnene: -![facet grid](../../../../../translated_images/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.no.png) +![facet grid](../../../../../translated_images/no/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.png) For dette datasettet skiller ingenting seg spesielt ut når det gjelder antall kolonier og deres avkastning, år for år og stat for stat. Er det en annen måte å finne en korrelasjon mellom disse to variablene? @@ -152,7 +152,7 @@ plot(honey$year, honey$yieldpercol, pch = 17, col = 3, axis(side = 4, at = pretty(range(y2))) mtext("colony yield", side = 4, line = 3) ``` -![superimponerte diagrammer](../../../../../translated_images/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.no.png) +![superimponerte diagrammer](../../../../../translated_images/no/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.png) Selv om ingenting skiller seg ut rundt året 2003, lar det oss avslutte denne leksjonen på en litt lykkeligere tone: selv om det generelt er et synkende antall kolonier, stabiliserer antallet kolonier seg, selv om deres avkastning per koloni avtar. diff --git a/translations/no/3-Data-Visualization/R/13-meaningful-vizualizations/README.md b/translations/no/3-Data-Visualization/R/13-meaningful-vizualizations/README.md index 24bb1bff..5a811b03 100644 --- a/translations/no/3-Data-Visualization/R/13-meaningful-vizualizations/README.md +++ b/translations/no/3-Data-Visualization/R/13-meaningful-vizualizations/README.md @@ -47,25 +47,25 @@ I tidligere leksjoner eksperimenterte du med å lage alle slags interessante dat Selv om en dataforsker er nøye med å velge riktig diagram for riktige data, finnes det mange måter data kan vises på for å bevise et poeng, ofte på bekostning av å undergrave dataene selv. Det finnes mange eksempler på villedende diagrammer og infografikk! -[![How Charts Lie av Alberto Cairo](../../../../../translated_images/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.no.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "How charts lie") +[![How Charts Lie av Alberto Cairo](../../../../../translated_images/no/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "How charts lie") > 🎥 Klikk på bildet over for en konferansesamtale om villedende diagrammer Dette diagrammet reverserer X-aksen for å vise det motsatte av sannheten, basert på dato: -![dårlig diagram 1](../../../../../translated_images/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.no.png) +![dårlig diagram 1](../../../../../translated_images/no/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.png) [Dette diagrammet](https://media.firstcoastnews.com/assets/WTLV/images/170ae16f-4643-438f-b689-50d66ca6a8d8/170ae16f-4643-438f-b689-50d66ca6a8d8_1140x641.jpg) er enda mer villedende, da øyet trekkes mot høyre for å konkludere med at COVID-tilfeller har gått ned over tid i de ulike fylkene. Faktisk, hvis du ser nøye på datoene, finner du at de har blitt omorganisert for å gi den villedende nedadgående trenden. -![dårlig diagram 2](../../../../../translated_images/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.no.jpg) +![dårlig diagram 2](../../../../../translated_images/no/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.jpg) Dette beryktede eksempelet bruker farge OG en snudd Y-akse for å villede: i stedet for å konkludere med at våpendødsfall økte etter vedtaket av våpenvennlig lovgivning, blir øyet lurt til å tro at det motsatte er sant: -![dårlig diagram 3](../../../../../translated_images/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.no.jpg) +![dårlig diagram 3](../../../../../translated_images/no/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.jpg) Dette merkelige diagrammet viser hvordan proporsjoner kan manipuleres, til komisk effekt: -![dårlig diagram 4](../../../../../translated_images/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.no.jpg) +![dårlig diagram 4](../../../../../translated_images/no/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.jpg) Å sammenligne det som ikke kan sammenlignes er nok et skyggefullt triks. Det finnes et [fantastisk nettsted](https://tylervigen.com/spurious-correlations) som handler om 'spuriøse korrelasjoner' og viser 'fakta' som korrelerer ting som skilsmisseraten i Maine og forbruket av margarin. En Reddit-gruppe samler også [stygge eksempler](https://www.reddit.com/r/dataisugly/top/?t=all) på databruk. @@ -100,13 +100,13 @@ Merk aksene dine, gi en legende hvis nødvendig, og tilby verktøytips for bedre Hvis dataene dine er tekstbaserte og detaljerte på X-aksen, kan du vinkle teksten for bedre lesbarhet. [plot3D](https://cran.r-project.org/web/packages/plot3D/index.html) tilbyr 3D-plotting, hvis dataene dine støtter det. Sofistikerte datavisualiseringer kan produseres ved hjelp av det. -![3D-diagrammer](../../../../../translated_images/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.no.png) +![3D-diagrammer](../../../../../translated_images/no/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.png) ## Animasjon og 3D-diagramvisning Noen av de beste datavisualiseringene i dag er animerte. Shirley Wu har fantastiske eksempler laget med D3, som '[film flowers](http://bl.ocks.org/sxywu/raw/d612c6c653fb8b4d7ff3d422be164a5d/)', hvor hver blomst er en visualisering av en film. Et annet eksempel for Guardian er 'bussed out', en interaktiv opplevelse som kombinerer visualiseringer med Greensock og D3 pluss en scrollytelling-artikkelformat for å vise hvordan NYC håndterer sitt hjemløse problem ved å sende folk ut av byen. -![bussing](../../../../../translated_images/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.no.png) +![bussing](../../../../../translated_images/no/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.png) > "Bussed Out: How America Moves its Homeless" fra [the Guardian](https://www.theguardian.com/us-news/ng-interactive/2017/dec/20/bussed-out-america-moves-homeless-people-country-study). Visualiseringer av Nadieh Bremer & Shirley Wu @@ -116,7 +116,7 @@ Selv om denne leksjonen ikke går i dybden for å lære disse kraftige visualise Du vil fullføre en webapp som viser en animert visning av dette sosiale nettverket. Den bruker et bibliotek som ble bygget for å lage en [visualisering av et nettverk](https://github.com/emiliorizzo/vue-d3-network) ved hjelp av Vue.js og D3. Når appen kjører, kan du dra nodene rundt på skjermen for å omorganisere dataene. -![liaisons](../../../../../translated_images/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.no.png) +![liaisons](../../../../../translated_images/no/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.png) ## Prosjekt: Lag et diagram for å vise et nettverk ved hjelp av D3.js diff --git a/translations/no/3-Data-Visualization/README.md b/translations/no/3-Data-Visualization/README.md index 25f95cac..b1d9262e 100644 --- a/translations/no/3-Data-Visualization/README.md +++ b/translations/no/3-Data-Visualization/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Visualiseringer -![en bie på en lavendelblomst](../../../translated_images/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.no.jpg) +![en bie på en lavendelblomst](../../../translated_images/no/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.jpg) > Foto av Jenna Lee på Unsplash Å visualisere data er en av de viktigste oppgavene for en dataforsker. Bilder sier mer enn tusen ord, og en visualisering kan hjelpe deg med å identifisere alle slags interessante aspekter ved dataene dine, som topper, avvik, grupperinger, tendenser og mer, som kan hjelpe deg med å forstå historien dataene dine prøver å fortelle. diff --git a/translations/no/4-Data-Science-Lifecycle/14-Introduction/README.md b/translations/no/4-Data-Science-Lifecycle/14-Introduction/README.md index eac58e5c..0380e6e7 100644 --- a/translations/no/4-Data-Science-Lifecycle/14-Introduction/README.md +++ b/translations/no/4-Data-Science-Lifecycle/14-Introduction/README.md @@ -25,7 +25,7 @@ Du har sannsynligvis innsett at datavitenskap er en prosess. Denne prosessen kan Denne leksjonen fokuserer på tre deler av livssyklusen: innsamling, bearbeiding og vedlikehold. -![Diagram over livssyklusen for datavitenskap](../../../../translated_images/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.no.jpg) +![Diagram over livssyklusen for datavitenskap](../../../../translated_images/no/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.jpg) > Foto av [Berkeley School of Information](https://ischoolonline.berkeley.edu/data-science/what-is-data-science/) ## Innsamling @@ -101,7 +101,7 @@ Utforsk [Team Data Science Process-livssyklusen](https://docs.microsoft.com/en-u |Team Data Science Process (TDSP)|Cross-industry standard process for data mining (CRISP-DM)| |--|--| -|![Team Data Science Lifecycle](../../../../translated_images/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.no.png) | ![Data Science Process Alliance Image](../../../../translated_images/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.no.png) | +|![Team Data Science Lifecycle](../../../../translated_images/no/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.png) | ![Data Science Process Alliance Image](../../../../translated_images/no/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.png) | | Bilde av [Microsoft](https://docs.microsoft.comazure/architecture/data-science-process/lifecycle) | Bilde av [Data Science Process Alliance](https://www.datascience-pm.com/crisp-dm-2/) | ## [Quiz etter forelesning](https://ff-quizzes.netlify.app/en/ds/quiz/27) diff --git a/translations/no/4-Data-Science-Lifecycle/README.md b/translations/no/4-Data-Science-Lifecycle/README.md index eab3f039..0f8a0645 100644 --- a/translations/no/4-Data-Science-Lifecycle/README.md +++ b/translations/no/4-Data-Science-Lifecycle/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Livssyklusen for Data Science -![kommunikasjon](../../../translated_images/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.no.jpg) +![kommunikasjon](../../../translated_images/no/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.jpg) > Foto av Headway på Unsplash I disse leksjonene vil du utforske noen av aspektene ved livssyklusen for Data Science, inkludert analyse og kommunikasjon rundt data. diff --git a/translations/no/5-Data-Science-In-Cloud/README.md b/translations/no/5-Data-Science-In-Cloud/README.md index ad0d3178..7f166f87 100644 --- a/translations/no/5-Data-Science-In-Cloud/README.md +++ b/translations/no/5-Data-Science-In-Cloud/README.md @@ -9,13 +9,13 @@ CO_OP_TRANSLATOR_METADATA: --> # Data Science i skyen -![cloud-picture](../../../translated_images/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.no.jpg) +![cloud-picture](../../../translated_images/no/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.jpg) > Foto av [Jelleke Vanooteghem](https://unsplash.com/@ilumire) fra [Unsplash](https://unsplash.com/s/photos/cloud?orientation=landscape) Når det gjelder å utføre dataanalyse med store datamengder, kan skyen være en game changer. I de neste tre leksjonene skal vi se hva skyen er og hvorfor den kan være svært nyttig. Vi skal også utforske et datasett om hjertesvikt og bygge en modell for å vurdere sannsynligheten for at noen opplever hjertesvikt. Vi vil bruke kraften i skyen til å trene, distribuere og bruke en modell på to forskjellige måter. Den ene måten bruker kun brukergrensesnittet i en Low code/No code-tilnærming, den andre måten bruker Azure Machine Learning Software Developer Kit (Azure ML SDK). -![project-schema](../../../translated_images/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.no.png) +![project-schema](../../../translated_images/no/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.png) ### Emner diff --git a/translations/no/6-Data-Science-In-Wild/20-Real-World-Examples/README.md b/translations/no/6-Data-Science-In-Wild/20-Real-World-Examples/README.md index ac374c1f..4dd2a1f8 100644 --- a/translations/no/6-Data-Science-In-Wild/20-Real-World-Examples/README.md +++ b/translations/no/6-Data-Science-In-Wild/20-Real-World-Examples/README.md @@ -41,7 +41,7 @@ Takket være demokratiseringen av AI, finner utviklere det nå enklere å design * [Data Science i helsevesenet](https://data-flair.training/blogs/data-science-in-healthcare/) - fremhever applikasjoner som medisinsk bildediagnostikk (f.eks. MR, røntgen, CT-skanning), genomikk (DNA-sekvensering), legemiddelutvikling (risikovurdering, suksessprediksjon), prediktiv analyse (pasientomsorg og logistikk), sykdomssporing og forebygging osv. -![Data Science-applikasjoner i den virkelige verden](../../../../translated_images/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.no.png) Bildekreditt: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) +![Data Science-applikasjoner i den virkelige verden](../../../../translated_images/no/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.png) Bildekreditt: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) Figuren viser andre domener og eksempler på anvendelse av data science-teknikker. Vil du utforske andre applikasjoner? Sjekk ut [Gjennomgang og selvstudium](../../../../6-Data-Science-In-Wild/20-Real-World-Examples)-seksjonen nedenfor. diff --git a/translations/no/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md b/translations/no/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md index e3c590f3..0dead861 100644 --- a/translations/no/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md +++ b/translations/no/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md @@ -22,7 +22,7 @@ Grensesnittet til Explorer (vist i skjermbildet nedenfor) lar deg velge et datas 2. Utforsk datasettkatalogen [Catalog](https://planetarycomputer.microsoft.com/catalog) - lær formålet med hvert datasett. 3. Bruk Explorer - velg et datasett som interesserer deg, velg en relevant spørring og visningsmetode. -![The Planetary Computer Explorer](../../../../translated_images/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.no.png) +![The Planetary Computer Explorer](../../../../translated_images/no/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.png) `Din oppgave:` Studer nå visualiseringen som vises i nettleseren og svar på følgende: diff --git a/translations/no/CONTRIBUTING.md b/translations/no/CONTRIBUTING.md index 54306275..7e98eb4c 100644 --- a/translations/no/CONTRIBUTING.md +++ b/translations/no/CONTRIBUTING.md @@ -318,7 +318,7 @@ Inkluder i PR-beskrivelsen: ``` ```` -- Legg til alt-tekst til bilder: `![Alt-tekst](../../translated_images/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.no.png)` +- Legg til alt-tekst til bilder: `![Alt-tekst](../../translated_images/no/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.png)` - Hold linjelengder rimelige (rundt 80-100 tegn) ### Python diff --git a/translations/no/README.md b/translations/no/README.md index 643397a3..63e667e1 100644 --- a/translations/no/README.md +++ b/translations/no/README.md @@ -33,7 +33,7 @@ Azure Cloud Advocates hos Microsoft er glade for å tilby en 10-ukers, 20-leksjo **🙏 Spesiell takk 🙏 til våre [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/) forfattere, gjennomgåere og innholdsbidragsytere,** særlig Aaryan Arora, [Aditya Garg](https://github.com/AdityaGarg00), [Alondra Sanchez](https://www.linkedin.com/in/alondra-sanchez-molina/), [Ankita Singh](https://www.linkedin.com/in/ankitasingh007), [Anupam Mishra](https://www.linkedin.com/in/anupam--mishra/), [Arpita Das](https://www.linkedin.com/in/arpitadas01/), ChhailBihari Dubey, [Dibri Nsofor](https://www.linkedin.com/in/dibrinsofor), [Dishita Bhasin](https://www.linkedin.com/in/dishita-bhasin-7065281bb), [Majd Safi](https://www.linkedin.com/in/majd-s/), [Max Blum](https://www.linkedin.com/in/max-blum-6036a1186/), [Miguel Correa](https://www.linkedin.com/in/miguelmque/), [Mohamma Iftekher (Iftu) Ebne Jalal](https://twitter.com/iftu119), [Nawrin Tabassum](https://www.linkedin.com/in/nawrin-tabassum), [Raymond Wangsa Putra](https://www.linkedin.com/in/raymond-wp/), [Rohit Yadav](https://www.linkedin.com/in/rty2423), Samridhi Sharma, [Sanya Sinha](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200), [Sheena Narula](https://www.linkedin.com/in/sheena-narua-n/), [Tauqeer Ahmad](https://www.linkedin.com/in/tauqeerahmad5201/), Yogendrasingh Pawar , [Vidushi Gupta](https://www.linkedin.com/in/vidushi-gupta07/), [Jasleen Sondhi](https://www.linkedin.com/in/jasleen-sondhi/) -|![Sketchnote av @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.no.png)| +|![Sketchnote av @sketchthedocs https://sketchthedocs.dev](../../translated_images/no/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.png)| |:---:| | Data Science For Beginners - _Sketchnote av [@nitya](https://twitter.com/nitya)_ | @@ -52,7 +52,7 @@ Azure Cloud Advocates hos Microsoft er glade for å tilby en 10-ukers, 20-leksjo Vi har en pågående Discord "Lær med AI"-serie, les mer og bli med oss på [Learn with AI Series](https://aka.ms/learnwithai/discord) fra 18. - 30. september 2025. Du vil få tips og triks for å bruke GitHub Copilot for Data Science. -![Lær med AI-serien](../../translated_images/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.no.jpg) +![Lær med AI-serien](../../translated_images/no/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.jpg) # Er du student? @@ -132,7 +132,7 @@ Hvert eksempel inneholder detaljerte kommentarer som forklarer hvert trinn, noe ## Leksjoner -|![ Sketchnote av @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.no.png)| +|![ Sketchnote av @sketchthedocs https://sketchthedocs.dev](../../translated_images/no/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png)| |:---:| | Data Science for nybegynnere: Veikart - _Sketchnote av [@nitya](https://twitter.com/nitya)_ | diff --git a/translations/no/sketchnotes/README.md b/translations/no/sketchnotes/README.md index 053f0592..f1dbecdd 100644 --- a/translations/no/sketchnotes/README.md +++ b/translations/no/sketchnotes/README.md @@ -13,7 +13,7 @@ Finn alle sketchnoter her! Nitya Narasimhan, kunstner -![roadmap sketchnote](../../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.no.png) +![roadmap sketchnote](../../../translated_images/no/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png) --- diff --git a/translations/pa/1-Introduction/01-defining-data-science/README.md b/translations/pa/1-Introduction/01-defining-data-science/README.md index 7000e11d..2d535a9a 100644 --- a/translations/pa/1-Introduction/01-defining-data-science/README.md +++ b/translations/pa/1-Introduction/01-defining-data-science/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: --- -[![ਡਾਟਾ ਸਾਇੰਸ ਦੀ ਪਰਿਭਾਸ਼ਾ ਵੀਡੀਓ](../../../../translated_images/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.pa.png)](https://youtu.be/beZ7Mb_oz9I) +[![ਡਾਟਾ ਸਾਇੰਸ ਦੀ ਪਰਿਭਾਸ਼ਾ ਵੀਡੀਓ](../../../../translated_images/pa/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.png)](https://youtu.be/beZ7Mb_oz9I) ## [ਪ੍ਰੀ-ਲੈਕਚਰ ਕਵਿਜ਼](https://ff-quizzes.netlify.app/en/ds/quiz/0) @@ -111,7 +111,7 @@ CO_OP_TRANSLATOR_METADATA: ਇਸ ਚੁਣੌਤੀ ਵਿੱਚ, ਅਸੀਂ ਟੈਕਸਟਾਂ ਨੂੰ ਦੇਖ ਕੇ ਡੇਟਾ ਸਾਇੰਸ ਦੇ ਖੇਤਰ ਨਾਲ ਸਬੰਧਤ ਸੰਕਲਪਾਂ ਨੂੰ ਪਤਾ ਕਰਨ ਦੀ ਕੋਸ਼ਿਸ਼ ਕਰਾਂਗੇ। ਅਸੀਂ ਡੇਟਾ ਸਾਇੰਸ 'ਤੇ ਇੱਕ ਵਿਕੀਪੀਡੀਆ ਲੇਖ ਲਵਾਂਗੇ, ਟੈਕਸਟ ਨੂੰ ਡਾਊਨਲੋਡ ਅਤੇ ਪ੍ਰੋਸੈਸ ਕਰਾਂਗੇ, ਅਤੇ ਫਿਰ ਇੱਕ ਵਰਡ ਕਲਾਉਡ ਬਣਾਵਾਂਗੇ ਜਿਵੇਂ ਕਿ ਇਹ ਹੈ: -![ਡੇਟਾ ਸਾਇੰਸ ਲਈ ਵਰਡ ਕਲਾਉਡ](../../../../translated_images/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.pa.png) +![ਡੇਟਾ ਸਾਇੰਸ ਲਈ ਵਰਡ ਕਲਾਉਡ](../../../../translated_images/pa/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.png) ਕੋਡ ਨੂੰ ਪੜ੍ਹਨ ਲਈ [`notebook.ipynb`](../../../../1-Introduction/01-defining-data-science/notebook.ipynb ':ignore') 'ਤੇ ਜਾਓ। ਤੁਸੀਂ ਕੋਡ ਨੂੰ ਚਲਾ ਵੀ ਸਕਦੇ ਹੋ, ਅਤੇ ਦੇਖ ਸਕਦੇ ਹੋ ਕਿ ਇਹ ਸਾਰੇ ਡੇਟਾ ਰੂਪਾਂਤਰਨਾਂ ਨੂੰ ਅਸਲ ਸਮੇਂ ਵਿੱਚ ਕਿਵੇਂ ਕਰਦਾ ਹੈ। diff --git a/translations/pa/1-Introduction/04-stats-and-probability/README.md b/translations/pa/1-Introduction/04-stats-and-probability/README.md index 8da01fce..a9fad73b 100644 --- a/translations/pa/1-Introduction/04-stats-and-probability/README.md +++ b/translations/pa/1-Introduction/04-stats-and-probability/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: ਸਟੈਟਿਸਟਿਕਸ ਅਤੇ ਪ੍ਰੋਬੈਬਿਲਿਟੀ ਥਿਊਰੀ ਗਣਿਤ ਦੇ ਦੋ ਬਹੁਤ ਹੀ ਜੁੜੇ ਹੋਏ ਖੇਤਰ ਹਨ ਜੋ ਡਾਟਾ ਸਾਇੰਸ ਲਈ ਬਹੁਤ ਮਹੱਤਵਪੂਰਨ ਹਨ। ਡਾਟਾ ਨਾਲ ਬਿਨਾ ਗਣਿਤ ਦੀ ਗਹਿਰਾਈ ਵਾਲੀ ਜਾਣਕਾਰੀ ਦੇ ਕੰਮ ਕਰਨਾ ਸੰਭਵ ਹੈ, ਪਰ ਕੁਝ ਮੂਲ ਧਾਰਨਾਵਾਂ ਜਾਣਨਾ ਫਿਰ ਵੀ ਵਧੀਆ ਹੈ। ਇੱਥੇ ਅਸੀਂ ਇੱਕ ਛੋਟਾ ਪਰੀਚਯ ਪੇਸ਼ ਕਰਾਂਗੇ ਜੋ ਤੁਹਾਨੂੰ ਸ਼ੁਰੂਆਤ ਕਰਨ ਵਿੱਚ ਮਦਦ ਕਰੇਗਾ। -[![ਇੰਟਰੋ ਵੀਡੀਓ](../../../../translated_images/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.pa.png)](https://youtu.be/Z5Zy85g4Yjw) +[![ਇੰਟਰੋ ਵੀਡੀਓ](../../../../translated_images/pa/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.png)](https://youtu.be/Z5Zy85g4Yjw) ## [ਪ੍ਰੀ-ਲੈਕਚਰ ਕਵਿਜ਼](https://ff-quizzes.netlify.app/en/ds/quiz/6) @@ -39,7 +39,7 @@ CO_OP_TRANSLATOR_METADATA: ਅਸੀਂ ਸਿਰਫ਼ ਕਿਸੇ ਵੈਰੀਏਬਲ ਦੇ ਮੁੱਲਾਂ ਦੇ ਦਿੱਤੇ ਇੰਟਰਵਾਲ ਵਿੱਚ ਪੈਣ ਦੀ ਸੰਭਾਵਨਾ ਬਾਰੇ ਗੱਲ ਕਰ ਸਕਦੇ ਹਾਂ, ਜਿਵੇਂ P(t1≤X2)। ਇਸ ਮਾਮਲੇ ਵਿੱਚ, ਪ੍ਰੋਬੈਬਿਲਿਟੀ ਡਿਸਟ੍ਰੀਬਿਊਸ਼ਨ ਨੂੰ **ਪ੍ਰੋਬੈਬਿਲਿਟੀ ਡੈਂਸਿਟੀ ਫੰਕਸ਼ਨ** p(x) ਦੁਆਰਾ ਵਰਣਨ ਕੀਤਾ ਜਾਂਦਾ ਹੈ, ਇਸ ਤਰ੍ਹਾਂ ਕਿ -![P(t_1\le X # ਡਾਟਾ ਸਾਇੰਸ ਦਾ ਪਰਿਚਯ -![ਡਾਟਾ ਦੀ ਕਾਰਵਾਈ](../../../translated_images/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.pa.jpg) +![ਡਾਟਾ ਦੀ ਕਾਰਵਾਈ](../../../translated_images/pa/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.jpg) > ਫੋਟੋ ਸਟੀਫਨ ਡਾਸਨ ਦੁਆਰਾ ਅਨਸਪਲੈਸ਼ 'ਤੇ ਇਨ੍ਹਾਂ ਪਾਠਾਂ ਵਿੱਚ, ਤੁਸੀਂ ਜਾਣੋਗੇ ਕਿ ਡਾਟਾ ਸਾਇੰਸ ਨੂੰ ਕਿਵੇਂ ਪਰਿਭਾਸ਼ਿਤ ਕੀਤਾ ਜਾਂਦਾ ਹੈ ਅਤੇ ਉਹ ਨੈਤਿਕ ਵਿਚਾਰ ਜੋ ਇੱਕ ਡਾਟਾ ਸਾਇੰਟਿਸਟ ਨੂੰ ਧਿਆਨ ਵਿੱਚ ਰੱਖਣੇ ਚਾਹੀਦੇ ਹਨ। ਤੁਸੀਂ ਇਹ ਵੀ ਸਿੱਖੋਗੇ ਕਿ ਡਾਟਾ ਨੂੰ ਕਿਵੇਂ ਪਰਿਭਾਸ਼ਿਤ ਕੀਤਾ ਜਾਂਦਾ ਹੈ ਅਤੇ ਅੰਕੜੇ ਅਤੇ ਸੰਭਾਵਨਾ ਬਾਰੇ ਕੁਝ ਜਾਣਕਾਰੀ ਪ੍ਰਾਪਤ ਕਰੋਗੇ, ਜੋ ਕਿ ਡਾਟਾ ਸਾਇੰਸ ਦੇ ਮੁੱਖ ਅਕਾਦਮਿਕ ਖੇਤਰ ਹਨ। diff --git a/translations/pa/2-Working-With-Data/07-python/README.md b/translations/pa/2-Working-With-Data/07-python/README.md index df44cbb8..31d9ea2b 100644 --- a/translations/pa/2-Working-With-Data/07-python/README.md +++ b/translations/pa/2-Working-With-Data/07-python/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: | :---------------------------------------------------------------------------------------------------------------: | | ਪਾਇਥਨ ਨਾਲ ਕੰਮ ਕਰਨਾ - _[@nitya](https://twitter.com/nitya) ਦੁਆਰਾ ਬਣਾਈ ਗਈ ਸਕੈਚਨੋਟ_ | -[![ਜਾਣ-ਪਛਾਣ ਵੀਡੀਓ](../../../../translated_images/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.pa.png)](https://youtu.be/dZjWOGbsN4Y) +[![ਜਾਣ-ਪਛਾਣ ਵੀਡੀਓ](../../../../translated_images/pa/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.png)](https://youtu.be/dZjWOGbsN4Y) ਜਦੋਂ ਕਿ ਡਾਟਾਬੇਸ ਡਾਟਾ ਨੂੰ ਸਟੋਰ ਕਰਨ ਅਤੇ ਕਵੈਰੀ ਲੈਂਗਵੇਜਾਂ ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਕਵੈਰੀ ਕਰਨ ਦੇ ਲਈ ਬਹੁਤ ਕੁਸ਼ਲ ਤਰੀਕੇ ਪੇਸ਼ ਕਰਦੇ ਹਨ, ਡਾਟਾ ਪ੍ਰੋਸੈਸਿੰਗ ਦਾ ਸਭ ਤੋਂ ਲਚਕਦਾਰ ਤਰੀਕਾ ਆਪਣਾ ਪ੍ਰੋਗਰਾਮ ਲਿਖਣਾ ਹੈ ਜੋ ਡਾਟਾ ਨੂੰ ਮੋੜ ਸਕੇ। ਕਈ ਮਾਮਲਿਆਂ ਵਿੱਚ, ਡਾਟਾਬੇਸ ਕਵੈਰੀ ਕਰਨਾ ਜ਼ਿਆਦਾ ਪ੍ਰਭਾਵਸ਼ਾਲੀ ਹੋਵੇਗਾ। ਹਾਲਾਂਕਿ ਕੁਝ ਮਾਮਲਿਆਂ ਵਿੱਚ ਜਦੋਂ ਜ਼ਿਆਦਾ ਜਟਿਲ ਡਾਟਾ ਪ੍ਰੋਸੈਸਿੰਗ ਦੀ ਲੋੜ ਹੁੰਦੀ ਹੈ, ਇਹ SQL ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਆਸਾਨੀ ਨਾਲ ਨਹੀਂ ਕੀਤਾ ਜਾ ਸਕਦਾ। ਡਾਟਾ ਪ੍ਰੋਸੈਸਿੰਗ ਕਿਸੇ ਵੀ ਪ੍ਰੋਗਰਾਮਿੰਗ ਭਾਸ਼ਾ ਵਿੱਚ ਕੀਤੀ ਜਾ ਸਕਦੀ ਹੈ, ਪਰ ਕੁਝ ਭਾਸ਼ਾਵਾਂ ਹਨ ਜੋ ਡਾਟਾ ਨਾਲ ਕੰਮ ਕਰਨ ਦੇ ਹਿਸਾਬ ਨਾਲ ਉੱਚ ਪੱਧਰ ਦੀਆਂ ਹਨ। ਡਾਟਾ ਸਾਇੰਟਿਸਟ ਆਮ ਤੌਰ 'ਤੇ ਹੇਠਾਂ ਦਿੱਤੀਆਂ ਭਾਸ਼ਾਵਾਂ ਵਿੱਚੋਂ ਇੱਕ ਨੂੰ ਤਰਜੀਹ ਦਿੰਦੇ ਹਨ: @@ -73,7 +73,7 @@ print(f"Length of index is {len(idx)}") items_sold = pd.Series(np.random.randint(25,50,size=len(idx)),index=idx) items_sold.plot() ``` -![ਟਾਈਮ ਸਿਰੀਜ਼ ਪਲਾਟ](../../../../translated_images/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.pa.png) +![ਟਾਈਮ ਸਿਰੀਜ਼ ਪਲਾਟ](../../../../translated_images/pa/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.png) ਹੁਣ ਮੰਨ ਲਓ ਕਿ ਹਰ ਹਫ਼ਤੇ ਅਸੀਂ ਦੋਸਤਾਂ ਲਈ ਇੱਕ ਪਾਰਟੀ ਦਾ ਆਯੋਜਨ ਕਰਦੇ ਹਾਂ, ਅਤੇ ਪਾਰਟੀ ਲਈ 10 ਪੈਕ ਆਈਸ-ਕ੍ਰੀਮ ਵਾਧੂ ਲੈਂਦੇ ਹਾਂ। ਅਸੀਂ ਹਫ਼ਤੇ ਦੇ ਸੂਚਕ ਦੁਆਰਾ ਇੱਕ ਹੋਰ ਸਿਰੀਜ਼ ਬਣਾਉਣ ਲਈ ਇਹ ਦਿਖਾ ਸਕਦੇ ਹਾਂ: ```python @@ -84,7 +84,7 @@ additional_items = pd.Series(10,index=pd.date_range(start_date,end_date,freq="W" total_items = items_sold.add(additional_items,fill_value=0) total_items.plot() ``` -![ਟਾਈਮ ਸਿਰੀਜ਼ ਪਲਾਟ](../../../../translated_images/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.pa.png) +![ਟਾਈਮ ਸਿਰੀਜ਼ ਪਲਾਟ](../../../../translated_images/pa/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.png) > **ਨੋਟ** ਕਿ ਅਸੀਂ ਸਧਾਰਨ syntax `total_items+additional_items` ਦੀ ਵਰਤੋਂ ਨਹੀਂ ਕਰ ਰਹੇ। ਜੇ ਅਸੀਂ ਕਰਦੇ, ਤਾਂ ਸਾਨੂੰ resulting ਸਿਰੀਜ਼ ਵਿੱਚ ਬਹੁਤ ਸਾਰੇ `NaN` (*Not a Number*) ਮੁੱਲ ਮਿਲਦੇ। ਇਹ ਇਸ ਲਈ ਹੈ ਕਿਉਂਕਿ `additional_items` ਸਿਰੀਜ਼ ਵਿੱਚ ਕੁਝ ਸੂਚਕ ਬਿੰਦੂਆਂ ਲਈ ਮੁੱਲ ਗੁੰਮ ਹਨ, ਅਤੇ `Nan` ਨੂੰ ਕਿਸੇ ਵੀ ਚੀਜ਼ ਵਿੱਚ ਜੋੜਨਾ `NaN` ਦੇ ਨਤੀਜੇ ਵਿੱਚ ਹੁੰਦਾ ਹੈ। ਇਸ ਲਈ ਅਸੀਂ ਜੋੜਦੇ ਸਮੇਂ `fill_value` ਪੈਰਾਮੀਟਰ ਨੂੰ ਨਿਰਧਾਰਤ ਕਰਨ ਦੀ ਲੋੜ ਹੁੰਦੀ ਹੈ। @@ -93,7 +93,7 @@ total_items.plot() monthly = total_items.resample("1M").mean() ax = monthly.plot(kind='bar') ``` -![ਮਹੀਨਾਵਾਰ ਟਾਈਮ ਸਿਰੀਜ਼ ਐਵਰੇਜ](../../../../translated_images/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.pa.png) +![ਮਹੀਨਾਵਾਰ ਟਾਈਮ ਸਿਰੀਜ਼ ਐਵਰੇਜ](../../../../translated_images/pa/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.png) ### ਡਾਟਾਫ੍ਰੇਮ @@ -158,7 +158,7 @@ df = pd.read_csv('file.csv') ਜਦੋਂ ਕਿ ਅਸੀਂ ਡਾਟਾ ਨਾਲ ਨਿਪਟਣ ਦਾ ਪ੍ਰਦਰਸ਼ਨ ਕਰਨਾ ਚਾਹੁੰਦੇ ਹਾਂ, ਅਸੀਂ ਤੁਹਾਨੂੰ ਸਲਾਹ ਦਿੰਦੇ ਹਾਂ ਕਿ [`notebook-covidspread.ipynb`](notebook-covidspread.ipynb) ਖੋਲ੍ਹੋ ਅਤੇ ਇਸਨੂੰ ਉੱਪਰ ਤੋਂ ਹੇਠਾਂ ਪੜ੍ਹੋ। ਤੁਸੀਂ ਸੈੱਲ ਚਲਾ ਸਕਦੇ ਹੋ ਅਤੇ ਕੁਝ ਚੁਣੌਤੀਆਂ ਕਰ ਸਕਦੇ ਹੋ ਜੋ ਅਸੀਂ ਤੁਹਾਡੇ ਲਈ ਅੰਤ ਵਿੱਚ ਛੱਡੀਆਂ ਹਨ। -![COVID ਫੈਲਾਅ](../../../../translated_images/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.pa.png) +![COVID ਫੈਲਾਅ](../../../../translated_images/pa/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.png) > ਜੇ ਤੁਸੀਂ Jupyter Notebook ਵਿੱਚ ਕੋਡ ਚਲਾਉਣ ਦਾ ਤਰੀਕਾ ਨਹੀਂ ਜਾਣਦੇ, ਤਾਂ [ਇਸ ਲੇਖ](https://soshnikov.com/education/how-to-execute-notebooks-from-github/) ਨੂੰ ਵੇਖੋ। @@ -180,7 +180,7 @@ df = pd.read_csv('file.csv') [`notebook-papers.ipynb`](notebook-papers.ipynb) ਖੋਲ੍ਹੋ ਅਤੇ ਇਸਨੂੰ ਉੱਪਰ ਤੋਂ ਹੇਠਾਂ ਪੜ੍ਹੋ। ਤੁਸੀਂ ਸੈੱਲ ਚਲਾ ਸਕਦੇ ਹੋ ਅਤੇ ਕੁਝ ਚੁਣੌਤੀਆਂ ਕਰ ਸਕਦੇ ਹੋ ਜੋ ਅਸੀਂ ਤੁਹਾਡੇ ਲਈ ਅੰਤ ਵਿੱਚ ਛੱਡੀਆਂ ਹਨ। -![Covid ਮੈਡੀਕਲ ਇਲਾਜ](../../../../translated_images/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.pa.png) +![Covid ਮੈਡੀਕਲ ਇਲਾਜ](../../../../translated_images/pa/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.png) ## ਚਿੱਤਰ ਡਾਟਾ ਦੀ ਪ੍ਰੋਸੈਸਿੰਗ diff --git a/translations/pa/2-Working-With-Data/README.md b/translations/pa/2-Working-With-Data/README.md index b0b90973..ccbaadec 100644 --- a/translations/pa/2-Working-With-Data/README.md +++ b/translations/pa/2-Working-With-Data/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # ਡਾਟਾ ਨਾਲ ਕੰਮ ਕਰਨਾ -![ਡਾਟਾ ਪਿਆਰ](../../../translated_images/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.pa.jpg) +![ਡਾਟਾ ਪਿਆਰ](../../../translated_images/pa/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.jpg) > ਫੋਟੋ Alexander Sinn ਦੁਆਰਾ Unsplash 'ਤੇ ਇਨ੍ਹਾਂ ਪਾਠਾਂ ਵਿੱਚ, ਤੁਸੀਂ ਸਿੱਖੋਗੇ ਕਿ ਡਾਟਾ ਨੂੰ ਕਿਵੇਂ ਪ੍ਰਬੰਧਿਤ, ਹੇਰਫੇਰ ਅਤੇ ਐਪਲੀਕੇਸ਼ਨਾਂ ਵਿੱਚ ਵਰਤਿਆ ਜਾ ਸਕਦਾ ਹੈ। ਤੁਸੀਂ ਰਿਲੇਸ਼ਨਲ ਅਤੇ ਨਾਨ-ਰਿਲੇਸ਼ਨਲ ਡਾਟਾਬੇਸਾਂ ਬਾਰੇ ਸਿੱਖੋਗੇ ਅਤੇ ਇਹ ਵੀ ਕਿ ਡਾਟਾ ਨੂੰ ਇਨ੍ਹਾਂ ਵਿੱਚ ਕਿਵੇਂ ਸਟੋਰ ਕੀਤਾ ਜਾ ਸਕਦਾ ਹੈ। ਤੁਸੀਂ ਡਾਟਾ ਨੂੰ ਪ੍ਰਬੰਧਿਤ ਕਰਨ ਲਈ Python ਨਾਲ ਕੰਮ ਕਰਨ ਦੇ ਮੁੱਢਲੇ ਸਿਧਾਂਤ ਸਿੱਖੋਗੇ ਅਤੇ ਇਹ ਵੀ ਪਤਾ ਲਗਾਓਗੇ ਕਿ Python ਨਾਲ ਡਾਟਾ ਨੂੰ ਪ੍ਰਬੰਧਿਤ ਅਤੇ ਖੋਜਣ ਦੇ ਕਈ ਤਰੀਕੇ ਕੀ ਹਨ। diff --git a/translations/pa/3-Data-Visualization/12-visualization-relationships/README.md b/translations/pa/3-Data-Visualization/12-visualization-relationships/README.md index 93e082d8..7a37601c 100644 --- a/translations/pa/3-Data-Visualization/12-visualization-relationships/README.md +++ b/translations/pa/3-Data-Visualization/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ honey.head() ```python sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ``` -![scatterplot 1](../../../../translated_images/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.pa.png) +![scatterplot 1](../../../../translated_images/pa/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.png) ਹੁਣ, ਸਾਲ ਦਰ ਸਾਲ ਸ਼ਹਿਦ ਦੀ ਕੀਮਤ ਦੇ ਵਿਕਾਸ ਨੂੰ ਦਰਸਾਉਣ ਲਈ ਸ਼ਹਿਦ ਦੇ ਰੰਗ ਦੀ ਯੋਜਨਾ ਨਾਲ ਉਹੀ ਡਾਟਾ ਦਿਖਾਓ। ਤੁਸੀਂ 'hue' ਪੈਰਾਮੀਟਰ ਸ਼ਾਮਲ ਕਰਕੇ ਇਹ ਕਰ ਸਕਦੇ ਹੋ: @@ -60,7 +60,7 @@ sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ```python sns.relplot(x="priceperlb", y="state", hue="year", palette="YlOrBr", data=honey, height=15, aspect=.5); ``` -![scatterplot 2](../../../../translated_images/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.pa.png) +![scatterplot 2](../../../../translated_images/pa/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.png) ਇਸ ਰੰਗ ਯੋਜਨਾ ਦੇ ਬਦਲਾਅ ਨਾਲ, ਤੁਸੀਂ ਸਪਸ਼ਟ ਤੌਰ 'ਤੇ ਸਾਲ ਦਰ ਸਾਲ ਸ਼ਹਿਦ ਦੀ ਕੀਮਤ ਵਿੱਚ ਇੱਕ ਮਜ਼ਬੂਤ ਪ੍ਰਗਤੀ ਦੇਖ ਸਕਦੇ ਹੋ। ਜੇ ਤੁਸੀਂ ਡਾਟਾ ਦੇ ਨਮੂਨਾ ਸੈੱਟ ਨੂੰ ਜਾਂਚਣ ਲਈ (ਉਦਾਹਰਣ ਲਈ, ਅਰੀਜ਼ੋਨਾ) ਦੇਖੋ, ਤਾਂ ਤੁਸੀਂ ਸਾਲ ਦਰ ਸਾਲ ਕੀਮਤ ਵਿੱਚ ਵਾਧੇ ਦਾ ਪੈਟਰਨ ਦੇਖ ਸਕਦੇ ਹੋ, ਕੁਝ ਛੋਟੇ-ਮੋਟੇ ਅਪਵਾਦਾਂ ਦੇ ਨਾਲ: @@ -89,7 +89,7 @@ sns.relplot(x="priceperlb", y="state", size="year", data=honey, height=15, aspec ``` ਤੁਸੀਂ ਡਾਟ ਦੇ ਆਕਾਰ ਨੂੰ ਹੌਲੀ-ਹੌਲੀ ਵਧਦੇ ਹੋਏ ਦੇਖ ਸਕਦੇ ਹੋ। -![scatterplot 3](../../../../translated_images/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.pa.png) +![scatterplot 3](../../../../translated_images/pa/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.png) ਕੀ ਇਹ ਸਪਲਾਈ ਅਤੇ ਡਿਮਾਂਡ ਦਾ ਸਧਾਰਨ ਮਾਮਲਾ ਹੈ? ਜਿਵੇਂ ਕਿ ਮੌਸਮ ਬਦਲਾਅ ਅਤੇ ਕਾਲੋਨੀ ਕਾਲਾਪਸ ਦੇ ਕਾਰਨ, ਕੀ ਸਾਲ ਦਰ ਸਾਲ ਖਰੀਦਣ ਲਈ ਘੱਟ ਸ਼ਹਿਦ ਉਪਲਬਧ ਹੈ, ਅਤੇ ਇਸ ਲਈ ਕੀਮਤ ਵਧਦੀ ਹੈ? @@ -104,7 +104,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); ``` ਜਵਾਬ: ਹਾਂ, ਕੁਝ ਅਪਵਾਦਾਂ ਦੇ ਨਾਲ, ਖਾਸ ਤੌਰ 'ਤੇ 2003 ਦੇ ਆਸ-ਪਾਸ: -![line chart 1](../../../../translated_images/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.pa.png) +![line chart 1](../../../../translated_images/pa/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.png) ✅ ਕਿਉਂਕਿ Seaborn ਇੱਕ ਲਾਈਨ 'ਤੇ ਡਾਟਾ ਨੂੰ ਸਮੂਹਬੱਧ ਕਰ ਰਿਹਾ ਹੈ, ਇਹ "x ਮੁੱਲ 'ਤੇ ਕਈ ਮਾਪਾਂ ਨੂੰ ਦਰਸਾਉਣ ਲਈ ਮੀਨ ਅਤੇ ਮੀਨ ਦੇ ਆਸ-ਪਾਸ 95% ਭਰੋਸੇਯੋਗ ਅੰਤਰ" ਦਿਖਾਉਂਦਾ ਹੈ। [ਸਰੋਤ](https://seaborn.pydata.org/tutorial/relational.html)। ਇਸ ਸਮਾਂ-ਖਪਤ ਵਿਵਹਾਰ ਨੂੰ `ci=None` ਸ਼ਾਮਲ ਕਰਕੇ ਅਯੋਗ ਕੀਤਾ ਜਾ ਸਕਦਾ ਹੈ। @@ -114,7 +114,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); sns.relplot(x="year", y="totalprod", kind="line", data=honey); ``` -![line chart 2](../../../../translated_images/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.pa.png) +![line chart 2](../../../../translated_images/pa/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.png) ਜਵਾਬ: ਸੱਚਮੁੱਚ ਨਹੀਂ। ਜੇ ਤੁਸੀਂ ਕੁੱਲ ਉਤਪਾਦਨ ਨੂੰ ਦੇਖੋ, ਤਾਂ ਇਹ ਵਿਸ਼ੇਸ਼ ਸਾਲ ਵਿੱਚ ਵਾਧੇ ਵਿੱਚ ਦਿਖਾਈ ਦਿੰਦਾ ਹੈ, ਹਾਲਾਂਕਿ ਆਮ ਤੌਰ 'ਤੇ ਸ਼ਹਿਦ ਦੇ ਉਤਪਾਦਨ ਦੀ ਮਾਤਰਾ ਇਨ੍ਹਾਂ ਸਾਲਾਂ ਦੌਰਾਨ ਘਟ ਰਹੀ ਹੈ। @@ -139,7 +139,7 @@ sns.relplot( ``` ਇਸ ਦ੍ਰਿਸ਼ਟੀਕਰਣ ਵਿੱਚ, ਤੁਸੀਂ ਸਾਲ ਦਰ ਸਾਲ ਅਤੇ ਰਾਜ ਦਰ ਰਾਜ ਪ੍ਰਤੀ ਕਾਲੋਨੀ ਉਤਪਾਦਨ ਅਤੇ ਕਾਲੋਨੀਆਂ ਦੀ ਗਿਣਤੀ ਦੀ ਤੁਲਨਾ ਕਰ ਸਕਦੇ ਹੋ, ਕਾਲਮਾਂ ਲਈ 3 'ਤੇ ਰੈਪ ਸੈਟ ਕਰਕੇ: -![facet grid](../../../../translated_images/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.pa.png) +![facet grid](../../../../translated_images/pa/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.png) ਇਸ ਡਾਟਾਸੈੱਟ ਲਈ, ਸਾਲ ਦਰ ਸਾਲ ਅਤੇ ਰਾਜ ਦਰ ਰਾਜ ਕਾਲੋਨੀਆਂ ਦੀ ਗਿਣਤੀ ਅਤੇ ਉਨ੍ਹਾਂ ਦੇ ਉਤਪਾਦਨ ਵਿੱਚ ਕੁਝ ਵਿਸ਼ੇਸ਼ ਦਿਖਾਈ ਨਹੀਂ ਦਿੰਦਾ। ਕੀ ਇਹ ਦੋ ਚਰਾਂ ਦੇ ਰਿਸ਼ਤੇ ਨੂੰ ਖੋਜਣ ਲਈ ਦੇਖਣ ਦਾ ਇੱਕ ਵੱਖਰਾ ਤਰੀਕਾ ਹੈ? @@ -162,7 +162,7 @@ sns.despine(right=False) plt.ylabel('colony yield') ax.figure.legend(); ``` -![superimposed plots](../../../../translated_images/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.pa.png) +![superimposed plots](../../../../translated_images/pa/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.png) ਹਾਲਾਂਕਿ 2003 ਦੇ ਆਸ-ਪਾਸ ਕੁਝ ਵੀ ਸਪਸ਼ਟ ਤੌਰ 'ਤੇ ਦਿਖਾਈ ਨਹੀਂ ਦਿੰਦਾ, ਇਹ ਸਾਨੂੰ ਇਸ ਪਾਠ ਨੂੰ ਇੱਕ ਖੁਸ਼ੀਦਾਇਕ ਨੋਟ 'ਤੇ ਖਤਮ ਕਰਨ ਦੀ ਆਗਿਆ ਦਿੰਦਾ ਹੈ: ਹਾਲਾਂਕਿ ਕਾਲੋਨੀਆਂ ਦੀ ਗਿਣਤੀ ਵਿੱਚ ਕੁੱਲ ਘਟਾਅ ਹੈ, ਕਾਲੋਨੀਆਂ ਦੀ ਗਿਣਤੀ ਸਥਿਰ ਹੋ ਰਹੀ ਹੈ, ਭਾਵੇਂ ਉਨ੍ਹਾਂ ਦਾ ਪ੍ਰਤੀ ਕਾਲੋਨੀ ਉਤਪਾਦਨ ਘਟ ਰਿਹਾ ਹੈ। diff --git a/translations/pa/3-Data-Visualization/R/09-visualization-quantities/README.md b/translations/pa/3-Data-Visualization/R/09-visualization-quantities/README.md index 355e4a7b..394d3725 100644 --- a/translations/pa/3-Data-Visualization/R/09-visualization-quantities/README.md +++ b/translations/pa/3-Data-Visualization/R/09-visualization-quantities/README.md @@ -66,7 +66,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` ਇੱਥੇ, ਤੁਸੀਂ `ggplot2` ਪੈਕੇਜ ਇੰਸਟਾਲ ਕਰਦੇ ਹੋ ਅਤੇ ਫਿਰ ਇਸਨੂੰ `library("ggplot2")` ਕਮਾਂਡ ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਵਰਕਸਪੇਸ ਵਿੱਚ ਇੰਪੋਰਟ ਕਰਦੇ ਹੋ। ggplot ਵਿੱਚ ਕੋਈ ਵੀ ਪਲਾਟ ਬਣਾਉਣ ਲਈ, `ggplot()` ਫੰਕਸ਼ਨ ਦੀ ਵਰਤੋਂ ਕੀਤੀ ਜਾਂਦੀ ਹੈ ਅਤੇ ਤੁਸੀਂ ਡਾਟਾਸੈੱਟ, x ਅਤੇ y ਵੈਰੀਏਬਲਾਂ ਨੂੰ ਗੁਣਾਂ ਵਜੋਂ ਨਿਰਧਾਰਤ ਕਰਦੇ ਹੋ। ਇਸ ਮਾਮਲੇ ਵਿੱਚ, ਅਸੀਂ ਲਾਈਨ ਪਲਾਟ ਪਲਾਟ ਕਰਨ ਲਈ `geom_line()` ਫੰਕਸ਼ਨ ਦੀ ਵਰਤੋਂ ਕਰਦੇ ਹਾਂ। -![MaxWingspan-lineplot](../../../../../translated_images/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.pa.png) +![MaxWingspan-lineplot](../../../../../translated_images/pa/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.png) ਤੁਰੰਤ ਕੀ ਨਜ਼ਰ ਆਉਂਦਾ ਹੈ? ਘੱਟੋ-ਘੱਟ ਇੱਕ ਆਊਟਲਾਇਰ ਹੈ - ਇਹ ਕਾਫ਼ੀ ਵੱਡੀ ਪੰਖਾਂ ਦੀ ਲੰਬਾਈ ਹੈ! 2000+ ਸੈਂਟੀਮੀਟਰ ਪੰਖਾਂ ਦੀ ਲੰਬਾਈ 20 ਮੀਟਰ ਤੋਂ ਵੱਧ ਹੈ - ਕੀ ਮਿਨੇਸੋਟਾ ਵਿੱਚ ਪਟੇਰੋਡੈਕਟਿਲ ਉੱਡ ਰਹੇ ਹਨ? ਆਓ ਜਾਂਚ ਕਰੀਏ। @@ -84,7 +84,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` ਅਸੀਂ `theme` ਵਿੱਚ ਕੋਣ ਨਿਰਧਾਰਤ ਕਰਦੇ ਹਾਂ ਅਤੇ `xlab()` ਅਤੇ `ylab()` ਵਿੱਚ x ਅਤੇ y ਐਕਸਿਸ ਲੇਬਲ ਨਿਰਧਾਰਤ ਕਰਦੇ ਹਾਂ। `ggtitle()` ਗ੍ਰਾਫ/ਪਲਾਟ ਨੂੰ ਇੱਕ ਨਾਮ ਦਿੰਦਾ ਹੈ। -![MaxWingspan-lineplot-improved](../../../../../translated_images/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.pa.png) +![MaxWingspan-lineplot-improved](../../../../../translated_images/pa/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.png) 45 ਡਿਗਰੀ 'ਤੇ ਲੇਬਲਾਂ ਦੇ ਰੋਟੇਸ਼ਨ ਦੇ ਨਾਲ ਵੀ, ਇਹ ਪੜ੍ਹਨ ਲਈ ਬਹੁਤ ਜ਼ਿਆਦਾ ਹਨ। ਆਓ ਇੱਕ ਵੱਖਰੀ ਰਣਨੀਤੀ ਅਪਣਾਈਏ: ਸਿਰਫ ਉਹ ਆਊਟਲਾਇਰ ਲੇਬਲ ਕਰੋ ਅਤੇ ਪਲਾਟ ਦੇ ਅੰਦਰ ਲੇਬਲ ਸੈੱਟ ਕਰੋ। ਤੁਸੀਂ ਲੇਬਲਿੰਗ ਲਈ ਹੋਰ ਜਗ੍ਹਾ ਬਣਾਉਣ ਲਈ ਇੱਕ ਸਕੈਟਰ ਚਾਰਟ ਦੀ ਵਰਤੋਂ ਕਰ ਸਕਦੇ ਹੋ: @@ -100,7 +100,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ਤੁਸੀਂ ਕੀ ਪਤਾ ਲਗਾਇਆ? -![MaxWingspan-scatterplot](../../../../../translated_images/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.pa.png) +![MaxWingspan-scatterplot](../../../../../translated_images/pa/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.png) ## ਆਪਣੇ ਡਾਟੇ ਨੂੰ ਫਿਲਟਰ ਕਰੋ @@ -119,7 +119,7 @@ ggplot(data=birds_filtered, aes(x=Name, y=MaxWingspan,group=1)) + ``` ਅਸੀਂ ਇੱਕ ਨਵਾਂ ਡਾਟਾਫਰੇਮ `birds_filtered` ਬਣਾਇਆ ਅਤੇ ਫਿਰ ਇੱਕ ਸਕੈਟਰ ਪਲਾਟ ਪਲਾਟ ਕੀਤਾ। ਆਊਟਲਾਇਰਾਂ ਨੂੰ ਫਿਲਟਰ ਕਰਕੇ, ਹੁਣ ਤੁਹਾਡਾ ਡਾਟਾ ਹੋਰ ਸੰਗਠਿਤ ਅਤੇ ਸਮਝਣਯੋਗ ਹੈ। -![MaxWingspan-scatterplot-improved](../../../../../translated_images/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.pa.png) +![MaxWingspan-scatterplot-improved](../../../../../translated_images/pa/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.png) ਹੁਣ ਜਦੋਂ ਸਾਡੇ ਕੋਲ ਪੰਖਾਂ ਦੀ ਲੰਬਾਈ ਦੇ ਹਿਸਾਬ ਨਾਲ ਘੱਟੋ-ਘੱਟ ਇੱਕ ਸਾਫ ਡਾਟਾਸੈੱਟ ਹੈ, ਆਓ ਇਨ੍ਹਾਂ ਪੰਛੀਆਂ ਬਾਰੇ ਹੋਰ ਪਤਾ ਲਗਾਈਏ। @@ -161,7 +161,7 @@ birds_filtered %>% group_by(Category) %>% ``` ਹੇਠਾਂ ਦਿੱਤੇ ਕੋਡ ਵਿੱਚ, ਅਸੀਂ [dplyr](https://www.rdocumentation.org/packages/dplyr/versions/0.7.8) ਅਤੇ [lubridate](https://www.rdocumentation.org/packages/lubridate/versions/1.8.0) ਪੈਕੇਜਾਂ ਨੂੰ ਇੰਸਟਾਲ ਕਰਦੇ ਹਾਂ ਜੋ ਡਾਟੇ ਨੂੰ ਮੈਨਿਪੂਲੇਟ ਅਤੇ ਗਰੁੱਪ ਕਰਨ ਵਿੱਚ ਮਦਦ ਕਰਦੇ ਹਨ ਤਾਂ ਜੋ ਇੱਕ ਸਟੈਕਡ ਬਾਰ ਚਾਰਟ ਪਲਾਟ ਕੀਤਾ ਜਾ ਸਕੇ। ਪਹਿਲਾਂ, ਤੁਸੀਂ ਡਾਟੇ ਨੂੰ ਪੰਛੀ ਦੀ `Category` ਦੇ ਅਨੁਸਾਰ ਗਰੁੱਪ ਕਰਦੇ ਹੋ ਅਤੇ ਫਿਰ `MinLength`, `MaxLength`, `MinBodyMass`, `MaxBodyMass`, `MinWingspan`, `MaxWingspan` ਕਾਲਮਾਂ ਨੂੰ ਸੰਖੇਪ ਕਰਦੇ ਹੋ। ਫਿਰ, `ggplot2` ਪੈਕੇਜ ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਬਾਰ ਚਾਰਟ ਪਲਾਟ ਕਰੋ ਅਤੇ ਵੱਖ-ਵੱਖ ਸ਼੍ਰੇਣੀਆਂ ਲਈ ਰੰਗ ਅਤੇ ਲੇਬਲ ਨਿਰਧਾਰਤ ਕਰੋ। -![Stacked bar chart](../../../../../translated_images/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.pa.png) +![Stacked bar chart](../../../../../translated_images/pa/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.png) ਹਾਲਾਂਕਿ, ਇਹ ਬਾਰ ਚਾਰਟ ਪੜ੍ਹਨ ਯੋਗ ਨਹੀਂ ਹੈ ਕਿਉਂਕਿ ਬਹੁਤ ਸਾਰਾ ਗੈਰ-ਗਰੁੱਪ ਕੀਤਾ ਡਾਟਾ ਹੈ। ਤੁਹਾਨੂੰ ਸਿਰਫ ਉਹ ਡਾਟਾ ਚੁਣਨ ਦੀ ਲੋੜ ਹੈ ਜੋ ਤੁਸੀਂ ਪਲਾਟ ਕਰਨਾ ਚਾਹੁੰਦੇ ਹੋ, ਇਸ ਲਈ ਆਓ ਪੰਛੀ ਦੀ ਸ਼੍ਰੇਣੀ ਦੇ ਆਧਾਰ 'ਤੇ ਪੰਛੀਆਂ ਦੀ ਲੰਬਾਈ ਦੇਖੀਏ। @@ -176,7 +176,7 @@ ggplot(birds_count,aes(Category,n))+geom_bar(stat="identity")+coord_flip() ``` ਤੁਸੀਂ `Category` ਕਾਲਮ ਵਿੱਚ ਵਿਲੱਖਣ ਮੁੱਲਾਂ ਦੀ ਗਿਣਤੀ ਕਰਦੇ ਹੋ ਅਤੇ ਫਿਰ ਉਨ੍ਹਾਂ ਨੂੰ ਇੱਕ ਨਵੇਂ ਡਾਟਾਫਰੇਮ `birds_count` ਵਿੱਚ ਸੌਰਟ ਕਰਦੇ ਹੋ। ਇਹ ਸੌਰਟ ਕੀਤਾ ਡਾਟਾ ਫਿਰ ਇੱਕੋ ਪੱਧਰ 'ਤੇ ਫੈਕਟਰ ਕੀਤਾ ਜਾਂਦਾ ਹੈ ਤਾਂ ਜੋ ਇਹ ਸੌਰਟ ਕੀਤੇ ਤਰੀਕੇ ਨਾਲ ਪਲਾਟ ਕੀਤਾ ਜਾ ਸਕੇ। `ggplot2` ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਤੁਸੀਂ ਫਿਰ ਡਾਟੇ ਨੂੰ ਇੱਕ ਬਾਰ ਚਾਰਟ ਵਿੱਚ ਪਲਾਟ ਕਰਦੇ ਹੋ। `coord_flip()` ਖੜ੍ਹੇ ਬਾਰ ਪਲਾਟ ਕਰਦਾ ਹੈ। -![category-length](../../../../../translated_images/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.pa.png) +![category-length](../../../../../translated_images/pa/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.png) ਇਹ ਬਾਰ ਚਾਰਟ diff --git a/translations/pa/3-Data-Visualization/R/10-visualization-distributions/README.md b/translations/pa/3-Data-Visualization/R/10-visualization-distributions/README.md index 7a562277..9e60b452 100644 --- a/translations/pa/3-Data-Visualization/R/10-visualization-distributions/README.md +++ b/translations/pa/3-Data-Visualization/R/10-visualization-distributions/README.md @@ -45,7 +45,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + geom_point() + ggtitle("Max Length per order") + coord_flip() ``` -![ਕ੍ਰਮ ਪ੍ਰਤੀ ਵੱਧ ਤੋਂ ਵੱਧ ਲੰਬਾਈ](../../../../../translated_images/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.pa.png) +![ਕ੍ਰਮ ਪ੍ਰਤੀ ਵੱਧ ਤੋਂ ਵੱਧ ਲੰਬਾਈ](../../../../../translated_images/pa/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.png) ਇਹ ਪੰਛੀ ਦੇ ਕ੍ਰਮ ਪ੍ਰਤੀ ਸਰੀਰਕ ਲੰਬਾਈ ਦੀ ਆਮ ਵੰਡ ਦਾ ਝਲਕ ਦਿੰਦਾ ਹੈ, ਪਰ ਇਹ ਸੱਚੀ ਵੰਡਾਂ ਨੂੰ ਦਿਖਾਉਣ ਦਾ ਸਭ ਤੋਂ ਵਧੀਆ ਤਰੀਕਾ ਨਹੀਂ ਹੈ। ਇਹ ਕੰਮ ਆਮ ਤੌਰ 'ਤੇ ਹਿਸਟੋਗ੍ਰਾਮ ਬਣਾਉਣ ਦੁਆਰਾ ਕੀਤਾ ਜਾਂਦਾ ਹੈ। @@ -57,7 +57,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=10)+ylab('Frequency') ``` -![ਪੂਰੇ ਡਾਟਾਸੈਟ 'ਤੇ ਵੰਡ](../../../../../translated_images/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.pa.png) +![ਪੂਰੇ ਡਾਟਾਸੈਟ 'ਤੇ ਵੰਡ](../../../../../translated_images/pa/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.png) ਜਿਵੇਂ ਤੁਸੀਂ ਦੇਖ ਸਕਦੇ ਹੋ, ਇਸ ਡਾਟਾਸੈਟ ਵਿੱਚ ਮੌਜੂਦ 400+ ਪੰਛੀਆਂ ਵਿੱਚੋਂ ਜ਼ਿਆਦਾਤਰ ਦਾ ਵੱਧ ਤੋਂ ਵੱਧ ਸਰੀਰਕ ਭਾਰ 2000 ਤੋਂ ਘੱਟ ਹੈ। `bins` ਪੈਰਾਮੀਟਰ ਨੂੰ ਵਧੇਰੇ ਸੰਖਿਆ, ਜਿਵੇਂ ਕਿ 30, ਵਿੱਚ ਬਦਲ ਕੇ ਡਾਟਾ ਬਾਰੇ ਹੋਰ ਜਾਣਕਾਰੀ ਪ੍ਰਾਪਤ ਕਰੋ: @@ -65,7 +65,7 @@ ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![30 ਬਿਨਾਂ ਨਾਲ ਵੰਡ](../../../../../translated_images/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.pa.png) +![30 ਬਿਨਾਂ ਨਾਲ ਵੰਡ](../../../../../translated_images/pa/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.png) ਇਹ ਚਾਰਟ ਵੰਡ ਨੂੰ ਹੋਰ ਵਿਸਤ੍ਰਿਤ ਢੰਗ ਨਾਲ ਦਿਖਾਉਂਦਾ ਹੈ। ਇੱਕ ਚਾਰਟ ਜੋ ਖੱਬੇ ਵੱਲ ਘੱਟ ਝੁਕਿਆ ਹੋਵੇ, ਉਹ ਇਸ ਤਰੀਕੇ ਨਾਲ ਬਣਾਇਆ ਜਾ ਸਕਦਾ ਹੈ ਕਿ ਤੁਸੀਂ ਸਿਰਫ਼ ਇੱਕ ਦਿੱਤੇ ਗਏ ਰੇਂਜ ਦੇ ਅੰਦਰ ਡਾਟਾ ਚੁਣੋ: @@ -77,7 +77,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![ਫਿਲਟਰ ਕੀਤਾ ਹਿਸਟੋਗ੍ਰਾਮ](../../../../../translated_images/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.pa.png) +![ਫਿਲਟਰ ਕੀਤਾ ਹਿਸਟੋਗ੍ਰਾਮ](../../../../../translated_images/pa/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.png) ✅ ਕੁਝ ਹੋਰ ਫਿਲਟਰ ਅਤੇ ਡਾਟਾ ਪੌਇੰਟਸ ਦੀ ਕੋਸ਼ਿਸ਼ ਕਰੋ। ਡਾਟਾ ਦੀ ਪੂਰੀ ਵੰਡ ਦੇਖਣ ਲਈ, `['MaxBodyMass']` ਫਿਲਟਰ ਨੂੰ ਹਟਾਓ ਅਤੇ ਲੇਬਲ ਕੀਤੀਆਂ ਵੰਡਾਂ ਦਿਖਾਓ। @@ -91,7 +91,7 @@ ggplot(data=birds_filtered_1, aes(x=MaxBodyMass, y=MaxLength) ) + ``` ਇਹ ਦਿਖਾਈ ਦਿੰਦਾ ਹੈ ਕਿ ਉਮੀਦ ਕੀਤੀ ਗਈ ਧੁਰੇ ਦੇ ਨਾਲ ਇਹ ਦੋ ਤੱਤ ਇੱਕ ਦੂਜੇ ਨਾਲ ਸੰਬੰਧਿਤ ਹਨ, ਇੱਕ ਖਾਸ ਤੌਰ 'ਤੇ ਮਜ਼ਬੂਤ ਮਿਲਾਪ ਦੇ ਬਿੰਦੂ ਨਾਲ: -![2D ਪਲਾਟ](../../../../../translated_images/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.pa.png) +![2D ਪਲਾਟ](../../../../../translated_images/pa/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.png) ਹਿਸਟੋਗ੍ਰਾਮ ਆਮ ਤੌਰ 'ਤੇ ਸੰਖਿਆਤਮਕ ਡਾਟਾ ਲਈ ਚੰਗੇ ਕੰਮ ਕਰਦੇ ਹਨ। ਪਰ ਜੇ ਤੁਸੀਂ ਟੈਕਸਟ ਡਾਟਾ ਦੇ ਅਨੁਸਾਰ ਵੰਡਾਂ ਨੂੰ ਦੇਖਣਾ ਚਾਹੁੰਦੇ ਹੋ ਤਾਂ ਕੀ ਕਰਨਾ ਚਾਹੀਦਾ ਹੈ? ## ਟੈਕਸਟ ਡਾਟਾ ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਡਾਟਾਸੈਟ ਦੀ ਖੋਜ ਕਰੋ @@ -122,7 +122,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + scale_fill_manual(name="Conservation Status",values=c("red","green","blue","pink"),labels=c("Endangered","Near Threathened","Vulnerable","Least Concern")) ``` -![ਪੰਖਾਂ ਦਾ ਫੈਲਾਅ ਅਤੇ ਸੰਰਕਸ਼ਣ ਸਥਿਤੀ](../../../../../translated_images/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.pa.png) +![ਪੰਖਾਂ ਦਾ ਫੈਲਾਅ ਅਤੇ ਸੰਰਕਸ਼ਣ ਸਥਿਤੀ](../../../../../translated_images/pa/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.png) ਘੱਟੋ-ਘੱਟ ਪੰਖਾਂ ਦੇ ਫੈਲਾਅ ਅਤੇ ਸੰਰਕਸ਼ਣ ਸਥਿਤੀ ਦੇ ਵਿਚਕਾਰ ਕੋਈ ਵਧੀਆ ਸੰਬੰਧ ਨਹੀਂ ਦਿਖਾਈ ਦਿੰਦਾ। ਇਸ ਤਰੀਕੇ ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਡਾਟਾਸੈਟ ਦੇ ਹੋਰ ਤੱਤਾਂ ਦੀ ਜਾਂਚ ਕਰੋ। ਤੁਸੀਂ ਵੱਖ-ਵੱਖ ਫਿਲਟਰਾਂ ਦੀ ਕੋਸ਼ਿਸ਼ ਕਰ ਸਕਦੇ ਹੋ। ਕੀ ਤੁਹਾਨੂੰ ਕੋਈ ਸੰਬੰਧ ਮਿਲਦਾ ਹੈ? @@ -136,7 +136,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + geom_density() ``` -![ਡੈਂਸਿਟੀ ਪਲਾਟ](../../../../../translated_images/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.pa.png) +![ਡੈਂਸਿਟੀ ਪਲਾਟ](../../../../../translated_images/pa/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.png) ਤੁਸੀਂ ਦੇਖ ਸਕਦੇ ਹੋ ਕਿ ਇਹ ਪਲਾਟ ਘੱਟੋ-ਘੱਟ ਪੰਖਾਂ ਦੇ ਫੈਲਾਅ ਲਈ ਪਿਛਲੇ ਚਾਰਟ ਨੂੰ ਦੁਹਰਾਉਂਦਾ ਹੈ; ਇਹ ਸਿਰਫ਼ ਕੁਝ ਸਮੂਥ ਹੈ। ਜੇ ਤੁਸੀਂ ਉਸ ਜੱਗਡ ਵੱਧ ਤੋਂ ਵੱਧ ਸਰੀਰਕ ਭਾਰ ਦੀ ਲਾਈਨ ਨੂੰ ਦੁਬਾਰਾ ਦੇਖਣਾ ਚਾਹੁੰਦੇ ਹੋ ਜੋ ਤੁਸੀਂ ਦੂਜੇ ਚਾਰਟ ਵਿੱਚ ਬਣਾਈ ਸੀ, ਤਾਂ ਤੁਸੀਂ ਇਸ ਤਰੀਕੇ ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਇਸਨੂੰ ਬਹੁਤ ਚੰਗੀ ਤਰ੍ਹਾਂ ਸਮੂਥ ਕਰ ਸਕਦੇ ਹੋ: @@ -144,7 +144,7 @@ ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density() ``` -![ਸਰੀਰਕ ਭਾਰ ਡੈਂਸਿਟੀ](../../../../../translated_images/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.pa.png) +![ਸਰੀਰਕ ਭਾਰ ਡੈਂਸਿਟੀ](../../../../../translated_images/pa/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.png) ਜੇ ਤੁਸੀਂ ਇੱਕ ਸਮੂਥ, ਪਰ ਬਹੁਤ ਜ਼ਿਆਦਾ ਸਮੂਥ ਲਾਈਨ ਨਹੀਂ ਚਾਹੁੰਦੇ, ਤਾਂ `adjust` ਪੈਰਾਮੀਟਰ ਨੂੰ ਸੋਧੋ: @@ -152,7 +152,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density(adjust = 1/5) ``` -![ਘੱਟ ਸਮੂਥ ਸਰੀਰਕ ਭਾਰ](../../../../../translated_images/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.pa.png) +![ਘੱਟ ਸਮੂਥ ਸਰੀਰਕ ਭਾਰ](../../../../../translated_images/pa/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.png) ✅ ਇਸ ਪ੍ਰਕਾਰ ਦੇ ਪਲਾਟ ਲਈ ਉਪਲਬਧ ਪੈਰਾਮੀਟਰਾਂ ਬਾਰੇ ਪੜ੍ਹੋ ਅਤੇ ਪ੍ਰਯੋਗ ਕਰੋ! @@ -162,7 +162,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + ggplot(data=birds_filtered_1,aes(x = MaxBodyMass, fill = Order)) + geom_density(alpha=0.5) ``` -![ਕ੍ਰਮ ਪ੍ਰਤੀ ਸਰੀਰਕ ਭਾਰ](../../../../../translated_images/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.pa.png) +![ਕ੍ਰਮ ਪ੍ਰਤੀ ਸਰੀਰਕ ਭਾਰ](../../../../../translated_images/pa/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.png) ## 🚀 ਚੁਣੌਤੀ diff --git a/translations/pa/3-Data-Visualization/R/11-visualization-proportions/README.md b/translations/pa/3-Data-Visualization/R/11-visualization-proportions/README.md index b156d15a..bc8f2756 100644 --- a/translations/pa/3-Data-Visualization/R/11-visualization-proportions/README.md +++ b/translations/pa/3-Data-Visualization/R/11-visualization-proportions/README.md @@ -92,7 +92,7 @@ pie(grouped$count,grouped$class, main="Edible?") ``` ਲੋ ਜੀ, ਇੱਕ ਪਾਈ ਚਾਰਟ ਜੋ ਮਸ਼ਰੂਮਾਂ ਦੀਆਂ ਦੋ ਸ਼੍ਰੇਣੀਆਂ ਦੇ ਅਨੁਸਾਰ ਡਾਟੇ ਦੇ ਅਨੁਪਾਤਾਂ ਨੂੰ ਦਿਖਾਉਂਦਾ ਹੈ। ਲੇਬਲਾਂ ਦੇ ਕ੍ਰਮ ਨੂੰ ਸਹੀ ਰੱਖਣਾ ਬਹੁਤ ਮਹੱਤਵਪੂਰਨ ਹੈ, ਖਾਸ ਕਰਕੇ ਇੱਥੇ, ਇਸ ਲਈ ਯਕੀਨੀ ਬਣਾਓ ਕਿ ਲੇਬਲ ਐਰੇ ਬਣਾਉਣ ਦੇ ਕ੍ਰਮ ਦੀ ਪੁਸ਼ਟੀ ਕਰੋ! -![pie chart](../../../../../translated_images/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.pa.png) +![pie chart](../../../../../translated_images/pa/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.png) ## ਡੋਨਟ! @@ -126,7 +126,7 @@ library(webr) PieDonut(habitat, aes(habitat, count=count)) ``` -![donut chart](../../../../../translated_images/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.pa.png) +![donut chart](../../../../../translated_images/pa/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.png) ਇਹ ਕੋਡ ਦੋ ਲਾਇਬ੍ਰੇਰੀਆਂ - ggplot2 ਅਤੇ webr ਦੀ ਵਰਤੋਂ ਕਰਦਾ ਹੈ। webr ਲਾਇਬ੍ਰੇਰੀ ਦੇ PieDonut ਫੰਕਸ਼ਨ ਦੀ ਵਰਤੋਂ ਕਰਕੇ, ਅਸੀਂ ਆਸਾਨੀ ਨਾਲ ਡੋਨਟ ਚਾਰਟ ਬਣਾ ਸਕਦੇ ਹਾਂ! @@ -164,7 +164,7 @@ waffle((cap_color$count/10), rows = 7, title = "Waffle Chart")+scale_fill_manual ਵਾਫਲ ਚਾਰਟ ਦੀ ਵਰਤੋਂ ਕਰਕੇ, ਤੁਸੀਂ ਮਸ਼ਰੂਮਾਂ ਦੇ ਕੈਪ ਰੰਗਾਂ ਦੇ ਅਨੁਪਾਤਾਂ ਨੂੰ ਸਪਸ਼ਟ ਤੌਰ 'ਤੇ ਦੇਖ ਸਕਦੇ ਹੋ। ਦਿਲਚਸਪ ਗੱਲ ਇਹ ਹੈ ਕਿ ਬਹੁਤ ਸਾਰੇ ਹਰੇ ਕੈਪ ਵਾਲੇ ਮਸ਼ਰੂਮ ਹਨ! -![waffle chart](../../../../../translated_images/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.pa.png) +![waffle chart](../../../../../translated_images/pa/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.png) ਇਸ ਪਾਠ ਵਿੱਚ, ਤੁਸੀਂ ਅਨੁਪਾਤਾਂ ਨੂੰ ਦਿਖਾਉਣ ਦੇ ਤਿੰਨ ਤਰੀਕੇ ਸਿੱਖੇ। ਪਹਿਲਾਂ, ਤੁਹਾਨੂੰ ਆਪਣੇ ਡਾਟੇ ਨੂੰ ਸ਼੍ਰੇਣੀਆਂ ਵਿੱਚ ਗਰੁੱਪ ਕਰਨਾ ਪੈਂਦਾ ਹੈ ਅਤੇ ਫਿਰ ਇਹ ਫੈਸਲਾ ਕਰਨਾ ਪੈਂਦਾ ਹੈ ਕਿ ਡਾਟੇ ਨੂੰ ਦਿਖਾਉਣ ਦਾ ਸਭ ਤੋਂ ਵਧੀਆ ਤਰੀਕਾ ਕਿਹੜਾ ਹੈ - ਪਾਈ, ਡੋਨਟ, ਜਾਂ ਵਾਫਲ। ਸਾਰੇ ਸੁਆਦਿਸ਼ਟ ਹਨ ਅਤੇ ਯੂਜ਼ਰ ਨੂੰ ਡਾਟਾਸੈਟ ਦੀ ਤੁਰੰਤ ਝਲਕ ਦਿੰਦੇ ਹਨ। diff --git a/translations/pa/3-Data-Visualization/R/12-visualization-relationships/README.md b/translations/pa/3-Data-Visualization/R/12-visualization-relationships/README.md index 4231b684..db6ac596 100644 --- a/translations/pa/3-Data-Visualization/R/12-visualization-relationships/README.md +++ b/translations/pa/3-Data-Visualization/R/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ library(ggplot2) ggplot(honey, aes(x = priceperlb, y = state)) + geom_point(colour = "blue") ``` -![scatterplot 1](../../../../../translated_images/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.pa.png) +![scatterplot 1](../../../../../translated_images/pa/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.png) ਹੁਣ, ਸਾਲਾਂ ਦੇ ਦੌਰਾਨ ਸ਼ਹਿਦ ਦੀ ਕੀਮਤ ਕਿਵੇਂ ਵਿਕਸਿਤ ਹੁੰਦੀ ਹੈ, ਇਹ ਦਰਸਾਉਣ ਲਈ ਸ਼ਹਿਦ ਦੇ ਰੰਗ ਦੀ ਸਕੀਮ ਨਾਲ ਉਹੀ ਡਾਟਾ ਦਿਖਾਓ। ਤੁਸੀਂ ਇਹ 'scale_color_gradientn' ਪੈਰਾਮੀਟਰ ਸ਼ਾਮਲ ਕਰਕੇ ਕਰ ਸਕਦੇ ਹੋ ਜੋ ਸਾਲ ਦਰ ਸਾਲ ਬਦਲਾਅ ਦਿਖਾਉਂਦਾ ਹੈ: @@ -61,7 +61,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ggplot(honey, aes(x = priceperlb, y = state, color=year)) + geom_point()+scale_color_gradientn(colours = colorspace::heat_hcl(7)) ``` -![scatterplot 2](../../../../../translated_images/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.pa.png) +![scatterplot 2](../../../../../translated_images/pa/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.png) ਇਸ ਰੰਗ ਸਕੀਮ ਬਦਲਾਅ ਨਾਲ, ਤੁਸੀਂ ਦੇਖ ਸਕਦੇ ਹੋ ਕਿ ਸਾਲਾਂ ਦੇ ਦੌਰਾਨ ਸ਼ਹਿਦ ਦੀ ਕੀਮਤ ਪ੍ਰਤੀ ਪਾਉਂਡ ਵਿੱਚ ਸਪਸ਼ਟ ਤੌਰ 'ਤੇ ਇੱਕ ਮਜ਼ਬੂਤ ​​ਤਰੱਕੀ ਹੈ। ਦਰਅਸਲ, ਜੇ ਤੁਸੀਂ ਡਾਟਾ ਵਿੱਚ ਇੱਕ ਨਮੂਨਾ ਸੈੱਟ ਦੀ ਜਾਂਚ ਕਰੋ (ਉਦਾਹਰਣ ਲਈ, ਅਰੀਜ਼ੋਨਾ ਨੂੰ ਚੁਣੋ) ਤਾਂ ਤੁਸੀਂ ਸਾਲ ਦਰ ਸਾਲ ਕੀਮਤ ਵਿੱਚ ਵਾਧੇ ਦਾ ਪੈਟਰਨ ਦੇਖ ਸਕਦੇ ਹੋ, ਕੁਝ ਛੋਟ ਦੇ ਨਾਲ: @@ -92,7 +92,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ``` ਤੁਸੀਂ ਡਾਟ ਦੇ ਆਕਾਰ ਨੂੰ ਹੌਲੀ-ਹੌਲੀ ਵਧਦੇ ਹੋਏ ਦੇਖ ਸਕਦੇ ਹੋ। -![scatterplot 3](../../../../../translated_images/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.pa.png) +![scatterplot 3](../../../../../translated_images/pa/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.png) ਕੀ ਇਹ ਸਪਲਾਈ ਅਤੇ ਡਿਮਾਂਡ ਦਾ ਸਧਾਰਨ ਮਾਮਲਾ ਹੈ? ਜਿਵੇਂ ਕਿ ਮੌਸਮ ਵਿੱਚ ਬਦਲਾਅ ਅਤੇ ਕਾਲੋਨੀ ਕਾਲਾਪਸ ਦੇ ਕਾਰਨ, ਕੀ ਸਾਲ ਦਰ ਸਾਲ ਖਰੀਦਣ ਲਈ ਘੱਟ ਸ਼ਹਿਦ ਉਪਲਬਧ ਹੈ, ਅਤੇ ਇਸ ਲਈ ਕੀਮਤ ਵਧਦੀ ਹੈ? @@ -107,7 +107,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab ``` ਜਵਾਬ: ਹਾਂ, ਕੁਝ ਛੋਟਾਂ ਦੇ ਨਾਲ, 2003 ਦੇ ਆਸ-ਪਾਸ: -![line chart 1](../../../../../translated_images/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.pa.png) +![line chart 1](../../../../../translated_images/pa/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.png) ਸਵਾਲ: ਖੈਰ, 2003 ਵਿੱਚ ਕੀ ਤੁਸੀਂ ਸ਼ਹਿਦ ਦੀ ਸਪਲਾਈ ਵਿੱਚ ਵੀ ਵਾਧਾ ਦੇਖ ਸਕਦੇ ਹੋ? ਜੇ ਤੁਸੀਂ ਸਾਲ ਦਰ ਸਾਲ ਕੁੱਲ ਉਤਪਾਦਨ ਨੂੰ ਦੇਖੋ ਤਾਂ ਕੀ ਹੋਵੇਗਾ? @@ -115,7 +115,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab qplot(honey$year,honey$totalprod, geom='smooth', span =0.5, xlab = "year",ylab = "totalprod") ``` -![line chart 2](../../../../../translated_images/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.pa.png) +![line chart 2](../../../../../translated_images/pa/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.png) ਜਵਾਬ: ਸੱਚਮੁੱਚ ਨਹੀਂ। ਜੇ ਤੁਸੀਂ ਕੁੱਲ ਉਤਪਾਦਨ ਨੂੰ ਦੇਖੋ, ਤਾਂ ਇਹ ਵਿਸ਼ੇਸ਼ ਸਾਲ ਵਿੱਚ ਵਾਧਾ ਹੋਇਆ ਜਾਪਦਾ ਹੈ, ਹਾਲਾਂਕਿ ਆਮ ਤੌਰ 'ਤੇ ਸ਼ਹਿਦ ਦੇ ਉਤਪਾਦਨ ਦੀ ਮਾਤਰਾ ਇਨ੍ਹਾਂ ਸਾਲਾਂ ਦੌਰਾਨ ਘਟ ਰਹੀ ਹੈ। @@ -135,7 +135,7 @@ ggplot(honey, aes(x=yieldpercol, y = numcol,group = 1)) + ``` ਇਸ ਦ੍ਰਿਸ਼ਟੀਕਰਨ ਵਿੱਚ, ਤੁਸੀਂ ਸਾਲ ਦਰ ਸਾਲ ਅਤੇ ਰਾਜ ਦਰ ਰਾਜ ਕਾਲੋਨੀ ਦੀ ਉਪਜ ਅਤੇ ਕਾਲੋਨੀਆਂ ਦੀ ਗਿਣਤੀ ਦੀ ਤੁਲਨਾ ਕਰ ਸਕਦੇ ਹੋ, ਕਾਲਮਾਂ ਲਈ 3 'ਤੇ ਰੈਪ ਸੈਟ ਨਾਲ: -![facet grid](../../../../../translated_images/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.pa.png) +![facet grid](../../../../../translated_images/pa/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.png) ਇਸ ਡਾਟਾਸੈੱਟ ਲਈ, ਸਾਲ ਦਰ ਸਾਲ ਅਤੇ ਰਾਜ ਦਰ ਰਾਜ ਕਾਲੋਨੀਆਂ ਦੀ ਗਿਣਤੀ ਅਤੇ ਉਨ੍ਹਾਂ ਦੀ ਉਪਜ ਦੇ ਸਬੰਧ ਵਿੱਚ ਕੁਝ ਖਾਸ ਦਿਖਾਈ ਨਹੀਂ ਦਿੰਦਾ। ਕੀ ਇਹ ਦੋ ਵੈਰੀਏਬਲਾਂ ਦੇ ਰਿਸ਼ਤੇ ਨੂੰ ਲੱਭਣ ਦਾ ਇੱਕ ਵੱਖਰਾ ਤਰੀਕਾ ਹੈ? @@ -152,7 +152,7 @@ plot(honey$year, honey$yieldpercol, pch = 17, col = 3, axis(side = 4, at = pretty(range(y2))) mtext("colony yield", side = 4, line = 3) ``` -![superimposed plots](../../../../../translated_images/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.pa.png) +![superimposed plots](../../../../../translated_images/pa/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.png) ਹਾਲਾਂਕਿ 2003 ਦੇ ਆਸ-ਪਾਸ ਕੁਝ ਵੀ ਸਪਸ਼ਟ ਤੌਰ 'ਤੇ ਦਿਖਾਈ ਨਹੀਂ ਦਿੰਦਾ, ਇਹ ਸਾਨੂੰ ਇਸ ਪਾਠ ਨੂੰ ਇੱਕ ਖੁਸ਼ੀਦਾਇਕ ਨੋਟ 'ਤੇ ਖਤਮ ਕਰਨ ਦੀ ਆਗਿਆ ਦਿੰਦਾ ਹੈ: ਹਾਲਾਂਕਿ ਕੁੱਲ ਕਾਲੋਨੀਆਂ ਦੀ ਗਿਣਤੀ ਘਟ ਰਹੀ ਹੈ, ਕਾਲੋਨੀਆਂ ਦੀ ਗਿਣਤੀ ਸਥਿਰ ਹੋ ਰਹੀ ਹੈ ਭਾਵੇਂ ਉਨ੍ਹਾਂ ਦੀ ਪ੍ਰਤੀ ਕਾਲੋਨੀ ਉਪਜ ਘਟ ਰਹੀ ਹੈ। diff --git a/translations/pa/3-Data-Visualization/R/13-meaningful-vizualizations/README.md b/translations/pa/3-Data-Visualization/R/13-meaningful-vizualizations/README.md index d6acdd9f..d769ec39 100644 --- a/translations/pa/3-Data-Visualization/R/13-meaningful-vizualizations/README.md +++ b/translations/pa/3-Data-Visualization/R/13-meaningful-vizualizations/README.md @@ -47,25 +47,25 @@ CO_OP_TRANSLATOR_METADATA: ਭਾਵੇਂ ਕਿ ਡਾਟਾ ਸਾਇੰਸਟ ਸਹੀ ਡਾਟਾ ਲਈ ਸਹੀ ਚਾਰਟ ਚੁਣਨ ਵਿੱਚ ਸਾਵਧਾਨ ਹੋਵੇ, ਫਿਰ ਵੀ ਕਈ ਤਰੀਕੇ ਹਨ ਜਿਨ੍ਹਾਂ ਨਾਲ ਡਾਟਾ ਨੂੰ ਇਸ ਤਰੀਕੇ ਨਾਲ ਪੇਸ਼ ਕੀਤਾ ਜਾ ਸਕਦਾ ਹੈ ਜੋ ਕਿਸੇ ਨਤੀਜੇ ਨੂੰ ਸਾਬਤ ਕਰੇ, ਪਰ ਕਈ ਵਾਰ ਇਹ ਡਾਟਾ ਨੂੰ ਗਲਤ ਢੰਗ ਨਾਲ ਪੇਸ਼ ਕਰ ਸਕਦਾ ਹੈ। ਧੋਖੇਬਾਜ਼ ਚਾਰਟਾਂ ਅਤੇ ਇਨਫੋਗ੍ਰਾਫਿਕਸ ਦੇ ਕਈ ਉਦਾਹਰਨ ਹਨ! -[![ਅਲਬਰਟੋ ਕਾਇਰੋ ਦੁਆਰਾ "ਹਾਊ ਚਾਰਟਸ ਲਾਈ"](../../../../../translated_images/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.pa.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "ਹਾਊ ਚਾਰਟਸ ਲਾਈ") +[![ਅਲਬਰਟੋ ਕਾਇਰੋ ਦੁਆਰਾ "ਹਾਊ ਚਾਰਟਸ ਲਾਈ"](../../../../../translated_images/pa/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "ਹਾਊ ਚਾਰਟਸ ਲਾਈ") > 🎥 ਉੱਪਰ ਦਿੱਤੀ ਤਸਵੀਰ 'ਤੇ ਕਲਿਕ ਕਰੋ ਧੋਖੇਬਾਜ਼ ਚਾਰਟਾਂ ਬਾਰੇ ਕਾਨਫਰੰਸ ਟਾਕ ਦੇਖਣ ਲਈ ਇਹ ਚਾਰਟ X ਅਕਸ ਨੂੰ ਉਲਟਾ ਦਿਖਾਉਂਦਾ ਹੈ, ਜਿਸ ਨਾਲ ਸੱਚ ਦੇ ਉਲਟ ਨਤੀਜਾ ਦਿਖਾਇਆ ਜਾਂਦਾ ਹੈ: -![ਖਰਾਬ ਚਾਰਟ 1](../../../../../translated_images/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.pa.png) +![ਖਰਾਬ ਚਾਰਟ 1](../../../../../translated_images/pa/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.png) [ਇਹ ਚਾਰਟ](https://media.firstcoastnews.com/assets/WTLV/images/170ae16f-4643-438f-b689-50d66ca6a8d8/170ae16f-4643-438f-b689-50d66ca6a8d8_1140x641.jpg) ਹੋਰ ਵੀ ਧੋਖੇਬਾਜ਼ ਹੈ, ਕਿਉਂਕਿ ਇਹ ਅੱਖ ਨੂੰ ਇਹ ਨਤੀਜਾ ਕੱਢਣ ਲਈ ਖਿੱਚਦਾ ਹੈ ਕਿ ਸਮੇਂ ਦੇ ਨਾਲ COVID ਕੇਸ ਘਟੇ ਹਨ। ਪਰ ਜੇ ਤੁਸੀਂ ਤਾਰੀਖਾਂ ਨੂੰ ਧਿਆਨ ਨਾਲ ਦੇਖੋ, ਤਾਂ ਪਤਾ ਲੱਗਦਾ ਹੈ ਕਿ ਉਹਨਾਂ ਨੂੰ ਧੋਖੇਬਾਜ਼ੀ ਨਾਲ ਦੁਬਾਰਾ ਕ੍ਰਮਬੱਧ ਕੀਤਾ ਗਿਆ ਹੈ। -![ਖਰਾਬ ਚਾਰਟ 2](../../../../../translated_images/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.pa.jpg) +![ਖਰਾਬ ਚਾਰਟ 2](../../../../../translated_images/pa/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.jpg) ਇਹ ਮਸ਼ਹੂਰ ਉਦਾਹਰਨ ਰੰਗ ਅਤੇ ਉਲਟੇ Y ਅਕਸ ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਧੋਖਾ ਦਿੰਦੀ ਹੈ: ਬਜਾਏ ਇਸਦੇ ਕਿ ਗਨ ਮੌਤਾਂ ਵਿੱਚ ਵਾਧਾ ਹੋਇਆ, ਅੱਖ ਨੂੰ ਇਹ ਗਲਤਫਹਮੀ ਹੋ ਸਕਦੀ ਹੈ ਕਿ ਉਲਟਾ ਸੱਚ ਹੈ: -![ਖਰਾਬ ਚਾਰਟ 3](../../../../../translated_images/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.pa.jpg) +![ਖਰਾਬ ਚਾਰਟ 3](../../../../../translated_images/pa/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.jpg) ਇਹ ਅਜੀਬ ਚਾਰਟ ਦਿਖਾਉਂਦਾ ਹੈ ਕਿ ਅਨੁਪਾਤ ਨੂੰ ਕਿਵੇਂ ਮਜ਼ਾਕੀਆ ਤਰੀਕੇ ਨਾਲ ਮੋੜਿਆ ਜਾ ਸਕਦਾ ਹੈ: -![ਖਰਾਬ ਚਾਰਟ 4](../../../../../translated_images/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.pa.jpg) +![ਖਰਾਬ ਚਾਰਟ 4](../../../../../translated_images/pa/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.jpg) ਅਤੁਲਨਾਤਮਕ ਚੀਜ਼ਾਂ ਦੀ ਤੁਲਨਾ ਕਰਨਾ ਇੱਕ ਹੋਰ ਧੋਖੇਬਾਜ਼ ਤਰੀਕਾ ਹੈ। ਇੱਕ [ਸ਼ਾਨਦਾਰ ਵੈੱਬਸਾਈਟ](https://tylervigen.com/spurious-correlations) 'ਸਪਿਊਰੀਅਸ ਕੋਰਲੇਸ਼ਨਜ਼' ਬਾਰੇ ਹੈ ਜੋ 'ਤੱਥਾਂ' ਨੂੰ ਦਿਖਾਉਂਦੀ ਹੈ ਜਿਵੇਂ ਕਿ ਮੈਨ ਵਿੱਚ ਤਲਾਕ ਦੀ ਦਰ ਅਤੇ ਮਾਰਜਰੀਨ ਦੀ ਖਪਤ। ਇੱਕ Reddit ਗਰੁੱਪ ਵੀ [ਡਾਟਾ ਦੇ ਬੁਰੇ ਉਪਯੋਗ](https://www.reddit.com/r/dataisugly/top/?t=all) ਨੂੰ ਇਕੱਠਾ ਕਰਦਾ ਹੈ। @@ -100,13 +100,13 @@ CO_OP_TRANSLATOR_METADATA: ਜੇ ਤੁਹਾਡਾ ਡਾਟਾ X ਅਕਸ 'ਤੇ ਟੈਕਸਟ ਅਤੇ ਲੰਬੇ ਵਾਕਾਂਸ਼ਾਂ ਵਾਲਾ ਹੈ, ਤਾਂ ਪੜ੍ਹਨਯੋਗਤਾ ਲਈ ਟੈਕਸਟ ਨੂੰ ਝੁਕਾ ਸਕਦੇ ਹੋ। [plot3D](https://cran.r-project.org/web/packages/plot3D/index.html) 3D ਪਲੌਟਿੰਗ ਦੀ ਪੇਸ਼ਕਸ਼ ਕਰਦਾ ਹੈ, ਜੇ ਤੁਹਾਡਾ ਡਾਟਾ ਇਸਨੂੰ ਸਹਾਰਦਾ ਹੈ। ਇਸ ਨਾਲ ਸੁਧਾਰਸ਼ੀਲ ਡਾਟਾ ਵਿਜੁਅਲਾਈਜ਼ੇਸ਼ਨ ਬਣਾਈ ਜਾ ਸਕਦੀ ਹੈ। -![3D ਪਲੌਟ](../../../../../translated_images/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.pa.png) +![3D ਪਲੌਟ](../../../../../translated_images/pa/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.png) ## ਐਨੀਮੇਸ਼ਨ ਅਤੇ 3D ਚਾਰਟ ਡਿਸਪਲੇਅ ਅੱਜ ਦੇ ਕੁਝ ਸਭ ਤੋਂ ਵਧੀਆ ਡਾਟਾ ਵਿਜੁਅਲਾਈਜ਼ੇਸ਼ਨ ਐਨੀਮੇਟਡ ਹਨ। Shirley Wu ਨੇ D3 ਨਾਲ ਬਹੁਤ ਹੀ ਸ਼ਾਨਦਾਰ ਵਿਜੁਅਲਾਈਜ਼ੇਸ਼ਨ ਬਣਾਈਆਂ ਹਨ, ਜਿਵੇਂ ਕਿ '[ਫਿਲਮ ਫਲਾਵਰਜ਼](http://bl.ocks.org/sxywu/raw/d612c6c653fb8b4d7ff3d422be164a5d/)', ਜਿੱਥੇ ਹਰ ਫੁੱਲ ਇੱਕ ਫਿਲਮ ਦੀ ਵਿਜੁਅਲਾਈਜ਼ੇਸ਼ਨ ਹੈ। ਇੱਕ ਹੋਰ ਉਦਾਹਰਨ 'ਗਾਰਡੀਅਨ' ਲਈ 'ਬੱਸਡ ਆਉਟ' ਹੈ, ਜੋ ਇੱਕ ਇੰਟਰੈਕਟਿਵ ਅਨੁਭਵ ਹੈ ਜੋ ਵਿਜੁਅਲਾਈਜ਼ੇਸ਼ਨ ਨੂੰ ਗ੍ਰੀਨਸਾਕ ਅਤੇ D3 ਨਾਲ ਜੋੜਦਾ ਹੈ ਅਤੇ ਇੱਕ ਸਕ੍ਰੋਲਿੰਗ ਲੇਖ ਫਾਰਮੈਟ ਵਿੱਚ ਦਿਖਾਉਂਦਾ ਹੈ ਕਿ ਨਿਊਯਾਰਕ ਸਿਟੀ ਆਪਣੇ ਬੇਘਰ ਲੋਕਾਂ ਨੂੰ ਸ਼ਹਿਰ ਤੋਂ ਬਾਹਰ ਕਿਵੇਂ ਭੇਜਦਾ ਹੈ। -![ਬੱਸਿੰਗ](../../../../../translated_images/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.pa.png) +![ਬੱਸਿੰਗ](../../../../../translated_images/pa/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.png) > "ਬੱਸਡ ਆਉਟ: ਅਮਰੀਕਾ ਆਪਣੇ ਬੇਘਰ ਲੋਕਾਂ ਨੂੰ ਕਿਵੇਂ ਹਿਲਾਉਂਦਾ ਹੈ" [ਗਾਰਡੀਅਨ](https://www.theguardian.com/us-news/ng-interactive/2017/dec/20/bussed-out-america-moves-homeless-people-country-study) ਤੋਂ। ਵਿਜੁਅਲਾਈਜ਼ੇਸ਼ਨ Nadieh Bremer ਅਤੇ Shirley Wu ਦੁਆਰਾ @@ -116,7 +116,7 @@ CO_OP_TRANSLATOR_METADATA: ਤੁਸੀਂ ਇੱਕ ਵੈੱਬ ਐਪ ਪੂਰਾ ਕਰੋਗੇ ਜੋ ਇਸ ਸਮਾਜਿਕ ਨੈੱਟਵਰਕ ਦਾ ਐਨੀਮੇਟਡ ਦ੍ਰਿਸ਼ ਦਿਖਾਵੇਗਾ। -![liaisons](../../../../../translated_images/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.pa.png) +![liaisons](../../../../../translated_images/pa/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.png) ## ਪ੍ਰੋਜੈਕਟ: D3.js ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਨੈੱਟਵਰਕ ਦਿਖਾਉਣ ਲਈ ਚਾਰਟ ਬਣਾਓ diff --git a/translations/pa/3-Data-Visualization/README.md b/translations/pa/3-Data-Visualization/README.md index 405aa7e4..507bcaba 100644 --- a/translations/pa/3-Data-Visualization/README.md +++ b/translations/pa/3-Data-Visualization/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # ਵਿਜੁਅਲਾਈਜ਼ੇਸ਼ਨ -![ਲੈਵੈਂਡਰ ਫੁੱਲ 'ਤੇ ਮੱਖੀ](../../../translated_images/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.pa.jpg) +![ਲੈਵੈਂਡਰ ਫੁੱਲ 'ਤੇ ਮੱਖੀ](../../../translated_images/pa/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.jpg) > ਫੋਟੋ ਜੈਨਾ ਲੀ ਦੁਆਰਾ ਅਨਸਪਲੈਸ਼ 'ਤੇ ਡਾਟਾ ਨੂੰ ਵਿਜੁਅਲਾਈਜ਼ ਕਰਨਾ ਇੱਕ ਡਾਟਾ ਸਾਇੰਟਿਸਟ ਦੇ ਸਭ ਤੋਂ ਮਹੱਤਵਪੂਰਨ ਕੰਮਾਂ ਵਿੱਚੋਂ ਇੱਕ ਹੈ। ਚਿੱਤਰ 1000 ਸ਼ਬਦਾਂ ਦੇ ਬਰਾਬਰ ਹੁੰਦੇ ਹਨ, ਅਤੇ ਵਿਜੁਅਲਾਈਜ਼ੇਸ਼ਨ ਤੁਹਾਨੂੰ ਆਪਣੇ ਡਾਟਾ ਦੇ ਵੱਖ-ਵੱਖ ਦਿਲਚਸਪ ਪੱਖਾਂ ਜਿਵੇਂ ਕਿ ਸਪਾਈਕਸ, ਆਉਟਲਾਇਰਜ਼, ਗਰੁੱਪਿੰਗ, ਰੁਝਾਨ ਅਤੇ ਹੋਰ ਬਹੁਤ ਕੁਝ ਦੀ ਪਛਾਣ ਕਰਨ ਵਿੱਚ ਮਦਦ ਕਰ ਸਕਦੀ ਹੈ, ਜੋ ਤੁਹਾਨੂੰ ਤੁਹਾਡੇ ਡਾਟਾ ਦੀ ਕਹਾਣੀ ਨੂੰ ਸਮਝਣ ਵਿੱਚ ਮਦਦ ਕਰਦੀ ਹੈ। diff --git a/translations/pa/4-Data-Science-Lifecycle/14-Introduction/README.md b/translations/pa/4-Data-Science-Lifecycle/14-Introduction/README.md index 3beb2ac0..00a66976 100644 --- a/translations/pa/4-Data-Science-Lifecycle/14-Introduction/README.md +++ b/translations/pa/4-Data-Science-Lifecycle/14-Introduction/README.md @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: ਇਹ ਪਾਠ ਲਾਈਫਸਾਈਕਲ ਦੇ 3 ਹਿੱਸਿਆਂ 'ਤੇ ਧਿਆਨ ਕੇਂਦਰਿਤ ਕਰਦਾ ਹੈ: ਡਾਟਾ ਇਕੱਠਾ ਕਰਨਾ, ਪ੍ਰੋਸੈਸਿੰਗ ਅਤੇ ਰੱਖ-ਰਖਾਵ। -![ਡਾਟਾ ਸਾਇੰਸ ਲਾਈਫਸਾਈਕਲ ਦਾ ਚਿੱਤਰ](../../../../translated_images/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.pa.jpg) +![ਡਾਟਾ ਸਾਇੰਸ ਲਾਈਫਸਾਈਕਲ ਦਾ ਚਿੱਤਰ](../../../../translated_images/pa/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.jpg) > [ਬਰਕਲੇ ਸਕੂਲ ਆਫ ਇਨਫਾਰਮੇਸ਼ਨ](https://ischoolonline.berkeley.edu/data-science/what-is-data-science/) ਦੁਆਰਾ ਫੋਟੋ ## ਡਾਟਾ ਇਕੱਠਾ ਕਰਨਾ @@ -97,7 +97,7 @@ CO_OP_TRANSLATOR_METADATA: |Team Data Science Process (TDSP)|Cross-industry standard process for data mining (CRISP-DM)| |--|--| -|![Team Data Science Lifecycle](../../../../translated_images/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.pa.png) | ![Data Science Process Alliance Image](../../../../translated_images/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.pa.png) | +|![Team Data Science Lifecycle](../../../../translated_images/pa/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.png) | ![Data Science Process Alliance Image](../../../../translated_images/pa/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.png) | | [Microsoft](https://docs.microsoft.comazure/architecture/data-science-process/lifecycle) ਦੁਆਰਾ ਚਿੱਤਰ | [Data Science Process Alliance](https://www.datascience-pm.com/crisp-dm-2/) ਦੁਆਰਾ ਚਿੱਤਰ | ## [ਪੋਸਟ-ਲੈਕਚਰ ਕਵਿਜ਼](https://ff-quizzes.netlify.app/en/ds/quiz/27) diff --git a/translations/pa/4-Data-Science-Lifecycle/README.md b/translations/pa/4-Data-Science-Lifecycle/README.md index 8b196156..ca2ee9fe 100644 --- a/translations/pa/4-Data-Science-Lifecycle/README.md +++ b/translations/pa/4-Data-Science-Lifecycle/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # ਡਾਟਾ ਸਾਇੰਸ ਲਾਈਫਸਾਈਕਲ -![ਸੰਚਾਰ](../../../translated_images/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.pa.jpg) +![ਸੰਚਾਰ](../../../translated_images/pa/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.jpg) > ਫੋਟੋ Headway ਦੁਆਰਾ Unsplash 'ਤੇ ਇਨ੍ਹਾਂ ਪਾਠਾਂ ਵਿੱਚ, ਤੁਸੀਂ ਡਾਟਾ ਸਾਇੰਸ ਲਾਈਫਸਾਈਕਲ ਦੇ ਕੁਝ ਪਹਲੂਆਂ ਦੀ ਜਾਂਚ ਕਰੋਗੇ, ਜਿਸ ਵਿੱਚ ਡਾਟਾ ਦੇ ਵਿਸ਼ਲੇਸ਼ਣ ਅਤੇ ਸੰਚਾਰ ਸ਼ਾਮਲ ਹਨ। diff --git a/translations/pa/5-Data-Science-In-Cloud/README.md b/translations/pa/5-Data-Science-In-Cloud/README.md index 9cecdab0..0613bd9f 100644 --- a/translations/pa/5-Data-Science-In-Cloud/README.md +++ b/translations/pa/5-Data-Science-In-Cloud/README.md @@ -9,13 +9,13 @@ CO_OP_TRANSLATOR_METADATA: --> # ਕਲਾਉਡ ਵਿੱਚ ਡਾਟਾ ਸਾਇੰਸ -![cloud-picture](../../../translated_images/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.pa.jpg) +![cloud-picture](../../../translated_images/pa/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.jpg) > ਫੋਟੋ [Jelleke Vanooteghem](https://unsplash.com/@ilumire) ਵੱਲੋਂ [Unsplash](https://unsplash.com/s/photos/cloud?orientation=landscape) ਤੋਂ ਜਦੋਂ ਵੱਡੇ ਡਾਟਾ ਨਾਲ ਡਾਟਾ ਸਾਇੰਸ ਕਰਨ ਦੀ ਗੱਲ ਆਉਂਦੀ ਹੈ, ਤਾਂ ਕਲਾਉਡ ਇੱਕ ਬਹੁਤ ਵੱਡਾ ਬਦਲਾਅ ਲਿਆ ਸਕਦਾ ਹੈ। ਅਗਲੇ ਤਿੰਨ ਪਾਠਾਂ ਵਿੱਚ, ਅਸੀਂ ਦੇਖਾਂਗੇ ਕਿ ਕਲਾਉਡ ਕੀ ਹੈ ਅਤੇ ਇਹ ਕਿਵੇਂ ਬਹੁਤ ਮਦਦਗਾਰ ਹੋ ਸਕਦਾ ਹੈ। ਅਸੀਂ ਇੱਕ ਹਾਰਟ ਫੇਲਿਅਰ ਡਾਟਾਸੈਟ ਦੀ ਵੀ ਜਾਂਚ ਕਰਾਂਗੇ ਅਤੇ ਇੱਕ ਮਾਡਲ ਬਣਾਉਣ ਦੀ ਕੋਸ਼ਿਸ਼ ਕਰਾਂਗੇ ਜੋ ਕਿਸੇ ਵਿਅਕਤੀ ਦੇ ਹਾਰਟ ਫੇਲਿਅਰ ਹੋਣ ਦੀ ਸੰਭਾਵਨਾ ਦਾ ਅੰਕਲਨ ਕਰਨ ਵਿੱਚ ਮਦਦ ਕਰੇ। ਅਸੀਂ ਕਲਾਉਡ ਦੀ ਤਾਕਤ ਦਾ ਇਸਤੇਮਾਲ ਕਰਕੇ ਮਾਡਲ ਨੂੰ ਟ੍ਰੇਨ, ਡਿਪਲੌਇ ਅਤੇ ਦੋ ਵੱਖ-ਵੱਖ ਤਰੀਕਿਆਂ ਨਾਲ ਕਨਜ਼ਿਊਮ ਕਰਾਂਗੇ। ਇੱਕ ਤਰੀਕਾ ਸਿਰਫ ਯੂਜ਼ਰ ਇੰਟਰਫੇਸ ਦੀ ਵਰਤੋਂ ਕਰਕੇ "ਲੋ ਕੋਡ/ਨੋ ਕੋਡ" ਢੰਗ ਵਿੱਚ, ਅਤੇ ਦੂਜਾ ਤਰੀਕਾ Azure Machine Learning Software Developer Kit (Azure ML SDK) ਦੀ ਵਰਤੋਂ ਕਰਕੇ। -![project-schema](../../../translated_images/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.pa.png) +![project-schema](../../../translated_images/pa/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.png) ### ਵਿਸ਼ੇ diff --git a/translations/pa/6-Data-Science-In-Wild/20-Real-World-Examples/README.md b/translations/pa/6-Data-Science-In-Wild/20-Real-World-Examples/README.md index 4c8e7f8c..167260d8 100644 --- a/translations/pa/6-Data-Science-In-Wild/20-Real-World-Examples/README.md +++ b/translations/pa/6-Data-Science-In-Wild/20-Real-World-Examples/README.md @@ -41,7 +41,7 @@ AI ਦੇ ਲੋਕਤੰਤਰਿਕਰਨ ਦੇ ਕਾਰਨ, ਵਿਕਾਸ * [ਸਿਹਤ ਸੇਵਾਵਾਂ ਵਿੱਚ ਡਾਟਾ ਸਾਇੰਸ](https://data-flair.training/blogs/data-science-in-healthcare/) - ਐਪਲੀਕੇਸ਼ਨਾਂ ਨੂੰ ਹਾਈਲਾਈਟ ਕਰਦਾ ਹੈ ਜਿਵੇਂ ਕਿ ਮੈਡੀਕਲ ਇਮੇਜਿੰਗ (ਜਿਵੇਂ ਕਿ MRI, X-Ray, CT-Scan), ਜਿਨੋਮਿਕਸ (DNA ਸਿਕਵੈਂਸਿੰਗ), ਦਵਾਈ ਵਿਕਾਸ (ਖਤਰੇ ਦਾ ਮੁਲਾਂਕਣ, ਸਫਲਤਾ ਦੀ ਪੇਸ਼ਗੂਈ), ਪੇਸ਼ਗੂਈ ਵਿਸ਼ਲੇਸ਼ਣ (ਮਰੀਜ਼ ਦੀ ਦੇਖਭਾਲ ਅਤੇ ਸਪਲਾਈ ਲੌਜਿਸਟਿਕਸ), ਬਿਮਾਰੀ ਟ੍ਰੈਕਿੰਗ ਅਤੇ ਰੋਕਥਾਮ ਆਦਿ। -![ਹਕੀਕਤੀ ਦੁਨੀਆ ਵਿੱਚ ਡਾਟਾ ਸਾਇੰਸ ਦੇ ਐਪਲੀਕੇਸ਼ਨ](../../../../translated_images/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.pa.png) ਚਿੱਤਰ ਸ੍ਰੋਤ: [ਡਾਟਾ ਫਲੇਅਰ: ਡਾਟਾ ਸਾਇੰਸ ਦੇ 6 ਸ਼ਾਨਦਾਰ ਐਪਲੀਕੇਸ਼ਨ](https://data-flair.training/blogs/data-science-applications/) +![ਹਕੀਕਤੀ ਦੁਨੀਆ ਵਿੱਚ ਡਾਟਾ ਸਾਇੰਸ ਦੇ ਐਪਲੀਕੇਸ਼ਨ](../../../../translated_images/pa/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.png) ਚਿੱਤਰ ਸ੍ਰੋਤ: [ਡਾਟਾ ਫਲੇਅਰ: ਡਾਟਾ ਸਾਇੰਸ ਦੇ 6 ਸ਼ਾਨਦਾਰ ਐਪਲੀਕੇਸ਼ਨ](https://data-flair.training/blogs/data-science-applications/) ਇਸ ਚਿੱਤਰ ਵਿੱਚ ਹੋਰ ਖੇਤਰ ਅਤੇ ਡਾਟਾ ਸਾਇੰਸ ਤਕਨੀਕਾਂ ਨੂੰ ਲਾਗੂ ਕਰਨ ਦੇ ਉਦਾਹਰਣ ਦਿਖਾਏ ਗਏ ਹਨ। ਹੋਰ ਐਪਲੀਕੇਸ਼ਨਾਂ ਦੀ ਪੜਚੋਲ ਕਰਨਾ ਚਾਹੁੰਦੇ ਹੋ? ਹੇਠਾਂ ਦਿੱਤੇ [ਸਮੀਖਿਆ ਅਤੇ ਸਵੈ ਅਧਿਐਨ](../../../../6-Data-Science-In-Wild/20-Real-World-Examples) ਭਾਗ ਨੂੰ ਵੇਖੋ। diff --git a/translations/pa/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md b/translations/pa/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md index 74dd5724..3de63134 100644 --- a/translations/pa/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md +++ b/translations/pa/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md @@ -22,7 +22,7 @@ CO_OP_TRANSLATOR_METADATA: 2. ਡਾਟਾਸੈੱਟ [ਕੈਟਾਲੌਗ](https://planetarycomputer.microsoft.com/catalog) ਦੀ ਪੜਚੋਲ ਕਰੋ - ਹਰ ਇੱਕ ਦਾ ਉਦੇਸ਼ ਸਿੱਖੋ। 3. ਐਕਸਪਲੋਰਰ ਵਰਤੋ - ਇੱਕ ਰੁਚੀਕਰ ਡਾਟਾਸੈੱਟ ਚੁਣੋ, ਇੱਕ ਸੰਬੰਧਿਤ ਕਵੈਰੀ ਅਤੇ ਰੈਂਡਰਿੰਗ ਵਿਕਲਪ ਚੁਣੋ। -![ਗ੍ਰਹਿ ਕੰਪਿਊਟਰ ਐਕਸਪਲੋਰਰ](../../../../translated_images/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.pa.png) +![ਗ੍ਰਹਿ ਕੰਪਿਊਟਰ ਐਕਸਪਲੋਰਰ](../../../../translated_images/pa/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.png) `ਤੁਹਾਡਾ ਕੰਮ:` ਹੁਣ ਬ੍ਰਾਊਜ਼ਰ ਵਿੱਚ ਰੈਂਡਰ ਕੀਤੇ ਗਏ ਵਿਜੁਅਲਾਈਜ਼ੇਸ਼ਨ ਦਾ ਅਧਿਐਨ ਕਰੋ ਅਤੇ ਹੇਠਾਂ ਦਿੱਤੇ ਸਵਾਲਾਂ ਦੇ ਜਵਾਬ ਦਿਓ: diff --git a/translations/pa/CONTRIBUTING.md b/translations/pa/CONTRIBUTING.md index c4ea46e8..fdc8d27c 100644 --- a/translations/pa/CONTRIBUTING.md +++ b/translations/pa/CONTRIBUTING.md @@ -317,7 +317,7 @@ def calculate_mean(data): ``` ```` -- ਚਿੱਤਰਾਂ ਲਈ alt ਟੈਕਸਟ ਸ਼ਾਮਲ ਕਰੋ: `![Alt text](../../translated_images/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.pa.png)` +- ਚਿੱਤਰਾਂ ਲਈ alt ਟੈਕਸਟ ਸ਼ਾਮਲ ਕਰੋ: `![Alt text](../../translated_images/pa/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.png)` - ਲਾਈਨ ਦੀ ਲੰਬਾਈ ਵਾਜਬ ਰੱਖੋ (80-100 ਅੱਖਰਾਂ ਦੇ ਆਸਪਾਸ) ### Python diff --git a/translations/pa/README.md b/translations/pa/README.md index 1437d467..c7b89096 100644 --- a/translations/pa/README.md +++ b/translations/pa/README.md @@ -33,7 +33,7 @@ Microsoft ਵਿੱਚ Azure Cloud Advocates ਖ਼ੁਸ਼ ਹਨ ਕਿ ਉਹ **🙏 ਵਿਸ਼ੇਸ਼ ਧੰਨਵਾਦ 🙏 ਸਾਡੇ [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/) ਲੇਖਕਾਂ, ਸਮੀਖਿਆਕਾਂ ਅਤੇ ਸਮੱਗਰੀਯੋਗਦਾਨਕਾਰਾਂ ਨੂੰ,** ਖਾਸ ਕਰਕੇ Aaryan Arora, [Aditya Garg](https://github.com/AdityaGarg00), [Alondra Sanchez](https://www.linkedin.com/in/alondra-sanchez-molina/), [Ankita Singh](https://www.linkedin.com/in/ankitasingh007), [Anupam Mishra](https://www.linkedin.com/in/anupam--mishra/), [Arpita Das](https://www.linkedin.com/in/arpitadas01/), ChhailBihari Dubey, [Dibri Nsofor](https://www.linkedin.com/in/dibrinsofor), [Dishita Bhasin](https://www.linkedin.com/in/dishita-bhasin-7065281bb), [Majd Safi](https://www.linkedin.com/in/majd-s/), [Max Blum](https://www.linkedin.com/in/max-blum-6036a1186/), [Miguel Correa](https://www.linkedin.com/in/miguelmque/), [Mohamma Iftekher (Iftu) Ebne Jalal](https://twitter.com/iftu119), [Nawrin Tabassum](https://www.linkedin.com/in/nawrin-tabassum), [Raymond Wangsa Putra](https://www.linkedin.com/in/raymond-wp/), [Rohit Yadav](https://www.linkedin.com/in/rty2423), Samridhi Sharma, [Sanya Sinha](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200), [Sheena Narula](https://www.linkedin.com/in/sheena-narua-n/), [Tauqeer Ahmad](https://www.linkedin.com/in/tauqeerahmad5201/), Yogendrasingh Pawar , [Vidushi Gupta](https://www.linkedin.com/in/vidushi-gupta07/), [Jasleen Sondhi](https://www.linkedin.com/in/jasleen-sondhi/) -|![ਸਕੇਚਨੋਟ ਦੁਆਰਾ @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.pa.png)| +|![ਸਕੇਚਨੋਟ ਦੁਆਰਾ @sketchthedocs https://sketchthedocs.dev](../../translated_images/pa/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.png)| |:---:| | ਡੇਟਾ ਸਾਇੰਸ ਫਾਰ ਬਿਗਿਨਰਜ਼ - _ਸਕੇਚਨੋਟ ਦੁਆਰਾ [@nitya](https://twitter.com/nitya)_ | @@ -52,7 +52,7 @@ Microsoft ਵਿੱਚ Azure Cloud Advocates ਖ਼ੁਸ਼ ਹਨ ਕਿ ਉਹ ਸਾਡੇ ਕੋਲ ਇੱਕ Discord 'Learn with AI' ਸੀਰੀਜ਼ ਚੱਲ ਰਹੀ ਹੈ, ਹੋਰ ਜਾਣਕਾਰੀ ਲਈ ਅਤੇ 18 - 30 ਸਤੰਬਰ, 2025 ਦੌਰਾਨ ਸਾਡੇ ਨਾਲ ਜੁੜੋ [Learn with AI Series](https://aka.ms/learnwithai/discord)। ਤੁਸੀਂ GitHub Copilot ਨੂੰ ਡੇਟਾ ਸਾਇੰਸ ਲਈ ਵਰਤਣ ਦੇ ਟਿੱਪਸ ਅਤੇ ਚਾਲਾਂ ਸਿੱਖੋਗੇ। -![AI ਨਾਲ ਸਿੱਖੋ ਸੀਰੀਜ਼](../../translated_images/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.pa.jpg) +![AI ਨਾਲ ਸਿੱਖੋ ਸੀਰੀਜ਼](../../translated_images/pa/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.jpg) # ਕੀ ਤੁਸੀਂ ਵਿਦਿਆਰਥੀ ਹੋ? @@ -132,7 +132,7 @@ Microsoft ਵਿੱਚ Azure Cloud Advocates ਖ਼ੁਸ਼ ਹਨ ਕਿ ਉਹ ## ਪਾਠ -|![ ਸਕੈਚਨੋਟ @sketchthedocs ਵਲੋਂ https://sketchthedocs.dev](../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.pa.png)| +|![ ਸਕੈਚਨੋਟ @sketchthedocs ਵਲੋਂ https://sketchthedocs.dev](../../translated_images/pa/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png)| |:---:| | ਡੇਟਾ ਸਾਇੰਸ ਫਾਰ ਬਿਗਿਨਰਜ਼: ਰੋਡਮੈਪ - _ਸਕੈਚਨੋਟ [@nitya](https://twitter.com/nitya) ਵਲੋਂ_ | diff --git a/translations/pa/sketchnotes/README.md b/translations/pa/sketchnotes/README.md index 2f82669c..782ec83d 100644 --- a/translations/pa/sketchnotes/README.md +++ b/translations/pa/sketchnotes/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: ਨਿਤਿਆ ਨਰਸਿੰਮਨ, ਕਲਾਕਾਰ -![ਰੋਡਮੈਪ ਸਕੈਚਨੋਟ](../../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.pa.png) +![ਰੋਡਮੈਪ ਸਕੈਚਨੋਟ](../../../translated_images/pa/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png) --- diff --git a/translations/pl/1-Introduction/01-defining-data-science/README.md b/translations/pl/1-Introduction/01-defining-data-science/README.md index 358c575c..431ce6c7 100644 --- a/translations/pl/1-Introduction/01-defining-data-science/README.md +++ b/translations/pl/1-Introduction/01-defining-data-science/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: --- -[![Film o definicji Data Science](../../../../translated_images/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.pl.png)](https://youtu.be/beZ7Mb_oz9I) +[![Film o definicji Data Science](../../../../translated_images/pl/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.png)](https://youtu.be/beZ7Mb_oz9I) ## [Quiz przed wykładem](https://ff-quizzes.netlify.app/en/ds/quiz/0) @@ -153,7 +153,7 @@ Jeśli chcemy podejść do tego jeszcze bardziej szczegółowo, możemy zestawi W tym wyzwaniu spróbujemy znaleźć koncepcje związane z dziedziną Data Science, analizując teksty. Weźmiemy artykuł z Wikipedii na temat Data Science, pobierzemy i przetworzymy tekst, a następnie stworzymy chmurę słów, taką jak ta: -![Chmura słów dla Data Science](../../../../translated_images/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.pl.png) +![Chmura słów dla Data Science](../../../../translated_images/pl/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.png) Odwiedź [`notebook.ipynb`](../../../../1-Introduction/01-defining-data-science/notebook.ipynb ':ignore'), aby zapoznać się z kodem. Możesz również uruchomić kod i zobaczyć, jak w czasie rzeczywistym wykonuje wszystkie transformacje danych. diff --git a/translations/pl/1-Introduction/04-stats-and-probability/README.md b/translations/pl/1-Introduction/04-stats-and-probability/README.md index 02e698a1..855ce28d 100644 --- a/translations/pl/1-Introduction/04-stats-and-probability/README.md +++ b/translations/pl/1-Introduction/04-stats-and-probability/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: Statystyka i teoria prawdopodobieństwa to dwie ściśle powiązane dziedziny matematyki, które mają ogromne znaczenie w nauce o danych. Można pracować z danymi bez głębokiej wiedzy matematycznej, ale warto znać przynajmniej podstawowe pojęcia. Poniżej przedstawiamy krótkie wprowadzenie, które pomoże Ci zacząć. -[![Wideo wprowadzające](../../../../translated_images/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.pl.png)](https://youtu.be/Z5Zy85g4Yjw) +[![Wideo wprowadzające](../../../../translated_images/pl/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.png)](https://youtu.be/Z5Zy85g4Yjw) ## [Quiz przed wykładem](https://ff-quizzes.netlify.app/en/ds/quiz/6) @@ -39,7 +39,7 @@ Trudniej jest opisać rozkład prawdopodobieństwa zmiennej ciągłej, której w Możemy mówić jedynie o prawdopodobieństwie, że zmienna przyjmie wartość z określonego przedziału, np. P(t1≤X2). W takim przypadku rozkład prawdopodobieństwa opisuje **funkcja gęstości prawdopodobieństwa** p(x), taka że -![P(t_1\le X Więcej przykładów korelacji i kowariancji można znaleźć w [dołączonym notatniku](notebook.ipynb). diff --git a/translations/pl/1-Introduction/README.md b/translations/pl/1-Introduction/README.md index 614a5dcf..3e526786 100644 --- a/translations/pl/1-Introduction/README.md +++ b/translations/pl/1-Introduction/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Wprowadzenie do Data Science -![dane w akcji](../../../translated_images/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.pl.jpg) +![dane w akcji](../../../translated_images/pl/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.jpg) > Zdjęcie autorstwa Stephena Dawsona na Unsplash W tych lekcjach odkryjesz, czym jest Data Science, oraz poznasz kwestie etyczne, które musi brać pod uwagę data scientist. Dowiesz się również, jak definiowane są dane, a także zapoznasz się z podstawami statystyki i prawdopodobieństwa, które są kluczowymi dziedzinami akademickimi w Data Science. diff --git a/translations/pl/2-Working-With-Data/07-python/README.md b/translations/pl/2-Working-With-Data/07-python/README.md index d6646a4b..db523f59 100644 --- a/translations/pl/2-Working-With-Data/07-python/README.md +++ b/translations/pl/2-Working-With-Data/07-python/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: | :-------------------------------------------------------------------------------------------------------------: | | Praca z Pythonem - _Sketchnote autorstwa [@nitya](https://twitter.com/nitya)_ | -[![Wideo wprowadzające](../../../../translated_images/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.pl.png)](https://youtu.be/dZjWOGbsN4Y) +[![Wideo wprowadzające](../../../../translated_images/pl/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.png)](https://youtu.be/dZjWOGbsN4Y) Bazy danych oferują bardzo efektywne sposoby przechowywania danych i ich przeszukiwania za pomocą języków zapytań, ale najbardziej elastycznym sposobem przetwarzania danych jest napisanie własnego programu do ich manipulacji. W wielu przypadkach zapytanie do bazy danych byłoby bardziej efektywne. Jednak w sytuacjach, gdy potrzebne jest bardziej złożone przetwarzanie danych, nie zawsze można to łatwo zrobić za pomocą SQL. Przetwarzanie danych można zaprogramować w dowolnym języku programowania, ale istnieją języki, które są bardziej zaawansowane w pracy z danymi. Naukowcy zajmujący się danymi zazwyczaj preferują jeden z następujących języków: @@ -73,7 +73,7 @@ print(f"Length of index is {len(idx)}") items_sold = pd.Series(np.random.randint(25,50,size=len(idx)),index=idx) items_sold.plot() ``` -![Wykres szeregów czasowych](../../../../translated_images/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.pl.png) +![Wykres szeregów czasowych](../../../../translated_images/pl/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.png) Załóżmy teraz, że co tydzień organizujemy imprezę dla znajomych i zabieramy dodatkowe 10 opakowań lodów na imprezę. Możemy stworzyć kolejną serię, indeksowaną tygodniami, aby to pokazać: ```python @@ -84,7 +84,7 @@ Gdy dodamy dwie serie, otrzymamy całkowitą liczbę: total_items = items_sold.add(additional_items,fill_value=0) total_items.plot() ``` -![Wykres szeregów czasowych](../../../../translated_images/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.pl.png) +![Wykres szeregów czasowych](../../../../translated_images/pl/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.png) > **Uwaga**: Nie używamy prostego zapisu `total_items+additional_items`. Gdybyśmy to zrobili, otrzymalibyśmy wiele wartości `NaN` (*Not a Number*) w wynikowej serii. Dzieje się tak, ponieważ brakuje wartości dla niektórych punktów indeksu w serii `additional_items`, a dodanie `NaN` do czegokolwiek skutkuje `NaN`. Dlatego musimy określić parametr `fill_value` podczas dodawania. @@ -93,7 +93,7 @@ W przypadku szeregów czasowych możemy również **próbkować** serię z róż monthly = total_items.resample("1M").mean() ax = monthly.plot(kind='bar') ``` -![Miesięczne średnie szeregów czasowych](../../../../translated_images/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.pl.png) +![Miesięczne średnie szeregów czasowych](../../../../translated_images/pl/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.png) ### DataFrame @@ -219,7 +219,7 @@ Pierwszym problemem, na którym się skupimy, jest modelowanie rozprzestrzeniani Ponieważ chcemy pokazać, jak radzić sobie z danymi, zapraszamy do otwarcia [`notebook-covidspread.ipynb`](notebook-covidspread.ipynb) i przeczytania go od góry do dołu. Możesz również uruchomić komórki i wykonać wyzwania, które zostawiliśmy na końcu. -![COVID Spread](../../../../translated_images/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.pl.png) +![COVID Spread](../../../../translated_images/pl/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.png) > Jeśli nie wiesz, jak uruchomić kod w Jupyter Notebook, zapoznaj się z [tym artykułem](https://soshnikov.com/education/how-to-execute-notebooks-from-github/). @@ -241,7 +241,7 @@ Pełny przykład analizy tego zbioru danych za pomocą usługi [Text Analytics f Otwórz [`notebook-papers.ipynb`](notebook-papers.ipynb) i przeczytaj go od góry do dołu. Możesz również uruchomić komórki i wykonać wyzwania, które zostawiliśmy na końcu. -![Covid Medical Treatment](../../../../translated_images/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.pl.png) +![Covid Medical Treatment](../../../../translated_images/pl/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.png) ## Przetwarzanie Danych Obrazowych diff --git a/translations/pl/2-Working-With-Data/README.md b/translations/pl/2-Working-With-Data/README.md index cb1bea8f..ba5e4136 100644 --- a/translations/pl/2-Working-With-Data/README.md +++ b/translations/pl/2-Working-With-Data/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Praca z danymi -![data love](../../../translated_images/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.pl.jpg) +![data love](../../../translated_images/pl/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.jpg) > Zdjęcie autorstwa Alexander Sinn na Unsplash W tych lekcjach nauczysz się, jak dane mogą być zarządzane, manipulowane i wykorzystywane w aplikacjach. Dowiesz się o bazach danych relacyjnych i nierelacyjnych oraz o tym, jak dane mogą być w nich przechowywane. Poznasz podstawy pracy z Pythonem w celu zarządzania danymi oraz odkryjesz różne sposoby pracy z Pythonem, aby zarządzać i analizować dane. diff --git a/translations/pl/3-Data-Visualization/12-visualization-relationships/README.md b/translations/pl/3-Data-Visualization/12-visualization-relationships/README.md index 2550810c..c4489682 100644 --- a/translations/pl/3-Data-Visualization/12-visualization-relationships/README.md +++ b/translations/pl/3-Data-Visualization/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ Stwórz podstawowy wykres punktowy, aby pokazać relację między ceną za funt ```python sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ``` -![scatterplot 1](../../../../translated_images/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.pl.png) +![scatterplot 1](../../../../translated_images/pl/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.png) Teraz pokaż te same dane z kolorystyką nawiązującą do miodu, aby zobrazować, jak cena zmienia się na przestrzeni lat. Możesz to zrobić, dodając parametr 'hue', który pokaże zmiany rok po roku: @@ -60,7 +60,7 @@ Teraz pokaż te same dane z kolorystyką nawiązującą do miodu, aby zobrazowa ```python sns.relplot(x="priceperlb", y="state", hue="year", palette="YlOrBr", data=honey, height=15, aspect=.5); ``` -![scatterplot 2](../../../../translated_images/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.pl.png) +![scatterplot 2](../../../../translated_images/pl/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.png) Dzięki tej zmianie kolorystyki możesz zauważyć wyraźny wzrost ceny na przestrzeni lat. Jeśli spojrzysz na próbkę danych (na przykład dla stanu Arizona), zobaczysz wzorzec wzrostu cen rok po roku, z kilkoma wyjątkami: @@ -89,7 +89,7 @@ sns.relplot(x="priceperlb", y="state", size="year", data=honey, height=15, aspec ``` Możesz zauważyć, że rozmiar punktów stopniowo się zwiększa. -![scatterplot 3](../../../../translated_images/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.pl.png) +![scatterplot 3](../../../../translated_images/pl/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.png) Czy to prosty przypadek podaży i popytu? Czy z powodu takich czynników jak zmiany klimatyczne i zapaść kolonii dostępność miodu maleje z roku na rok, a cena rośnie? @@ -104,7 +104,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); ``` Odpowiedź: Tak, z pewnymi wyjątkami w okolicach roku 2003: -![line chart 1](../../../../translated_images/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.pl.png) +![line chart 1](../../../../translated_images/pl/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.png) ✅ Ponieważ Seaborn agreguje dane w jednej linii, wyświetla "wiele pomiarów dla każdej wartości x, rysując średnią i 95% przedział ufności wokół średniej". [Źródło](https://seaborn.pydata.org/tutorial/relational.html). To czasochłonne zachowanie można wyłączyć, dodając `ci=None`. @@ -114,7 +114,7 @@ Pytanie: Czy w 2003 roku można również zauważyć wzrost podaży miodu? Co je sns.relplot(x="year", y="totalprod", kind="line", data=honey); ``` -![line chart 2](../../../../translated_images/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.pl.png) +![line chart 2](../../../../translated_images/pl/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.png) Odpowiedź: Niekoniecznie. Jeśli spojrzysz na całkowitą produkcję, wydaje się, że w tym konkretnym roku faktycznie wzrosła, mimo że ogólnie rzecz biorąc ilość produkowanego miodu maleje w tych latach. @@ -139,7 +139,7 @@ sns.relplot( ``` Na tej wizualizacji możesz porównać wydajność na kolonię i liczbę kolonii rok po roku, obok siebie, z ustawieniem wrap na 3 dla kolumn: -![facet grid](../../../../translated_images/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.pl.png) +![facet grid](../../../../translated_images/pl/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.png) Dla tego zbioru danych nic szczególnego nie wyróżnia się w odniesieniu do liczby kolonii i ich wydajności rok po roku oraz stan po stanie. Czy istnieje inny sposób na znalezienie korelacji między tymi dwiema zmiennymi? @@ -162,7 +162,7 @@ sns.despine(right=False) plt.ylabel('colony yield') ax.figure.legend(); ``` -![superimposed plots](../../../../translated_images/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.pl.png) +![superimposed plots](../../../../translated_images/pl/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.png) Chociaż nic szczególnego nie rzuca się w oczy w okolicach roku 2003, pozwala to zakończyć tę lekcję na nieco bardziej optymistycznej nucie: mimo ogólnego spadku liczby kolonii, ich liczba stabilizuje się, nawet jeśli wydajność na kolonię maleje. diff --git a/translations/pl/3-Data-Visualization/R/09-visualization-quantities/README.md b/translations/pl/3-Data-Visualization/R/09-visualization-quantities/README.md index ae0a4941..87d8d1ab 100644 --- a/translations/pl/3-Data-Visualization/R/09-visualization-quantities/README.md +++ b/translations/pl/3-Data-Visualization/R/09-visualization-quantities/README.md @@ -66,7 +66,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` Tutaj instalujesz pakiet `ggplot2`, a następnie importujesz go do przestrzeni roboczej za pomocą polecenia `library("ggplot2")`. Aby wykreślić dowolny wykres w ggplot, używana jest funkcja `ggplot()`, w której określasz zestaw danych, zmienne x i y jako atrybuty. W tym przypadku używamy funkcji `geom_line()`, ponieważ chcemy wykreślić wykres liniowy. -![MaxWingspan-lineplot](../../../../../translated_images/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.pl.png) +![MaxWingspan-lineplot](../../../../../translated_images/pl/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.png) Co zauważasz od razu? Wydaje się, że jest co najmniej jeden odstający wynik - to całkiem spora rozpiętość skrzydeł! Rozpiętość skrzydeł ponad 2000 centymetrów to ponad 20 metrów - czy w Minnesocie żyją pterodaktyle? Zbadajmy to. @@ -84,7 +84,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` Określamy kąt w `theme` i ustawiamy etykiety osi x i y w `xlab()` i `ylab()` odpowiednio. `ggtitle()` nadaje nazwę wykresowi. -![MaxWingspan-lineplot-improved](../../../../../translated_images/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.pl.png) +![MaxWingspan-lineplot-improved](../../../../../translated_images/pl/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.png) Nawet przy obrocie etykiet o 45 stopni jest ich zbyt wiele, aby je odczytać. Spróbujmy innej strategii: oznacz tylko te odstające wyniki i ustaw etykiety wewnątrz wykresu. Możesz użyć wykresu punktowego, aby zrobić więcej miejsca na etykiety: @@ -100,7 +100,7 @@ Co tu się dzieje? Użyłeś funkcji `geom_point()`, aby wykreślić punkty. Dzi Co odkrywasz? -![MaxWingspan-scatterplot](../../../../../translated_images/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.pl.png) +![MaxWingspan-scatterplot](../../../../../translated_images/pl/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.png) ## Filtrowanie danych @@ -119,7 +119,7 @@ ggplot(data=birds_filtered, aes(x=Name, y=MaxWingspan,group=1)) + ``` Stworzyliśmy nową ramkę danych `birds_filtered`, a następnie wykreśliliśmy wykres punktowy. Po odfiltrowaniu odstających wyników dane są teraz bardziej spójne i zrozumiałe. -![MaxWingspan-scatterplot-improved](../../../../../translated_images/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.pl.png) +![MaxWingspan-scatterplot-improved](../../../../../translated_images/pl/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.png) Teraz, gdy mamy czystszy zestaw danych przynajmniej pod względem rozpiętości skrzydeł, odkryjmy więcej o tych ptakach. @@ -161,7 +161,7 @@ birds_filtered %>% group_by(Category) %>% ``` W poniższym fragmencie instalujemy pakiety [dplyr](https://www.rdocumentation.org/packages/dplyr/versions/0.7.8) i [lubridate](https://www.rdocumentation.org/packages/lubridate/versions/1.8.0), aby pomóc w manipulacji i grupowaniu danych w celu wykreślenia wykresu słupkowego. Najpierw grupujesz dane według `Category` ptaków, a następnie podsumowujesz kolumny `MinLength`, `MaxLength`, `MinBodyMass`, `MaxBodyMass`, `MinWingspan`, `MaxWingspan`. Następnie wykreślasz wykres słupkowy za pomocą pakietu `ggplot2`, określając kolory dla różnych kategorii i etykiety. -![Stacked bar chart](../../../../../translated_images/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.pl.png) +![Stacked bar chart](../../../../../translated_images/pl/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.png) Ten wykres słupkowy jest jednak nieczytelny, ponieważ jest zbyt wiele niepogrupowanych danych. Musisz wybrać tylko dane, które chcesz wykreślić, więc spójrzmy na długość ptaków w zależności od ich kategorii. @@ -176,7 +176,7 @@ ggplot(birds_count,aes(Category,n))+geom_bar(stat="identity")+coord_flip() ``` Najpierw liczysz unikalne wartości w kolumnie `Category`, a następnie sortujesz je w nowej ramce danych `birds_count`. Te posortowane dane są następnie uwzględniane na tym samym poziomie, aby były wykreślone w uporządkowany sposób. Korzystając z `ggplot2`, wykreślasz dane na wykresie słupkowym. Funkcja `coord_flip()` wykreśla poziome słupki. -![category-length](../../../../../translated_images/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.pl.png) +![category-length](../../../../../translated_images/pl/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.png) Ten wykres słupkowy pokazuje dobry widok liczby ptaków w każdej kategorii. Na pierwszy rzut oka widać, że największa liczba ptaków w tym regionie należy do kategorii Kaczki/Gęsi/Wodnopławy. Minnesota to "kraina 10 000 jezior", więc to nie jest zaskakujące! @@ -199,7 +199,7 @@ ggplot(birds_grouped,aes(Category,MaxLength))+geom_bar(stat="identity")+coord_fl ``` Grupujemy dane `birds_filtered` według `Category`, a następnie wykreślamy wykres słupkowy. -![comparing data](../../../../../translated_images/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.pl.png) +![comparing data](../../../../../translated_images/pl/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.png) Nic zaskakującego tutaj: kolibry mają najmniejszą MaxLength w porównaniu do pelikanów czy gęsi. Dobrze, gdy dane mają sens logiczny! @@ -211,7 +211,7 @@ ggplot(data=birds_grouped, aes(x=Category)) + geom_bar(aes(y=MinLength), stat="identity", position="identity", fill='orange')+ coord_flip() ``` -![super-imposed values](../../../../../translated_images/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.pl.png) +![super-imposed values](../../../../../translated_images/pl/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.png) ## 🚀 Wyzwanie diff --git a/translations/pl/3-Data-Visualization/R/10-visualization-distributions/README.md b/translations/pl/3-Data-Visualization/R/10-visualization-distributions/README.md index c5ea7d53..499022c0 100644 --- a/translations/pl/3-Data-Visualization/R/10-visualization-distributions/README.md +++ b/translations/pl/3-Data-Visualization/R/10-visualization-distributions/README.md @@ -45,7 +45,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + geom_point() + ggtitle("Max Length per order") + coord_flip() ``` -![maksymalna długość na rząd](../../../../../translated_images/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.pl.png) +![maksymalna długość na rząd](../../../../../translated_images/pl/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.png) To daje przegląd ogólnego rozkładu długości ciała w zależności od rzędu ptaków, ale nie jest to optymalny sposób na przedstawienie prawdziwych rozkładów. Do tego celu zazwyczaj używa się histogramu. @@ -57,7 +57,7 @@ To daje przegląd ogólnego rozkładu długości ciała w zależności od rzędu ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=10)+ylab('Frequency') ``` -![rozkład dla całego zbioru danych](../../../../../translated_images/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.pl.png) +![rozkład dla całego zbioru danych](../../../../../translated_images/pl/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.png) Jak widać, większość z ponad 400 ptaków w tym zbiorze danych mieści się w zakresie poniżej 2000 dla ich maksymalnej masy ciała. Uzyskaj więcej informacji o danych, zmieniając parametr `bins` na wyższą wartość, na przykład 30: @@ -65,7 +65,7 @@ Jak widać, większość z ponad 400 ptaków w tym zbiorze danych mieści się w ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![rozkład-30przedziałów](../../../../../translated_images/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.pl.png) +![rozkład-30przedziałów](../../../../../translated_images/pl/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.png) Ten wykres pokazuje rozkład w nieco bardziej szczegółowy sposób. Wykres mniej przesunięty w lewo można stworzyć, wybierając dane tylko z określonego zakresu: @@ -77,7 +77,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![przefiltrowany histogram](../../../../../translated_images/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.pl.png) +![przefiltrowany histogram](../../../../../translated_images/pl/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.png) ✅ Wypróbuj inne filtry i punkty danych. Aby zobaczyć pełny rozkład danych, usuń filtr `['MaxBodyMass']`, aby pokazać oznaczone rozkłady. @@ -91,7 +91,7 @@ ggplot(data=birds_filtered_1, aes(x=MaxBodyMass, y=MaxLength) ) + ``` Wydaje się, że istnieje oczekiwana korelacja między tymi dwoma elementami wzdłuż przewidywanej osi, z jednym szczególnie silnym punktem zbieżności: -![wykres 2D](../../../../../translated_images/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.pl.png) +![wykres 2D](../../../../../translated_images/pl/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.png) Histogramy dobrze działają domyślnie dla danych numerycznych. Co jeśli chcesz zobaczyć rozkłady według danych tekstowych? ## Eksploracja zbioru danych pod kątem rozkładów według danych tekstowych @@ -122,7 +122,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + scale_fill_manual(name="Conservation Status",values=c("red","green","blue","pink"),labels=c("Endangered","Near Threathened","Vulnerable","Least Concern")) ``` -![rozpiętość skrzydeł i status ochrony](../../../../../translated_images/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.pl.png) +![rozpiętość skrzydeł i status ochrony](../../../../../translated_images/pl/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.png) Nie wydaje się, aby istniała dobra korelacja między minimalną rozpiętością skrzydeł a statusem ochrony. Przetestuj inne elementy zbioru danych, korzystając z tej metody. Możesz również wypróbować różne filtry. Czy znajdujesz jakąś korelację? @@ -136,7 +136,7 @@ Przejdźmy teraz do pracy z wykresami gęstości! ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + geom_density() ``` -![wykres gęstości](../../../../../translated_images/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.pl.png) +![wykres gęstości](../../../../../translated_images/pl/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.png) Widać, że wykres odzwierciedla poprzedni dla danych o minimalnej rozpiętości skrzydeł; jest po prostu nieco bardziej płynny. Jeśli chciałbyś powrócić do tego poszarpanego wykresu MaxBodyMass z drugiego wykresu, który stworzyłeś, możesz go bardzo dobrze wygładzić, odtwarzając go za pomocą tej metody: @@ -144,7 +144,7 @@ Widać, że wykres odzwierciedla poprzedni dla danych o minimalnej rozpiętości ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density() ``` -![gęstość masy ciała](../../../../../translated_images/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.pl.png) +![gęstość masy ciała](../../../../../translated_images/pl/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.png) Jeśli chciałbyś uzyskać linię płynną, ale nie zbyt płynną, edytuj parametr `adjust`: @@ -152,7 +152,7 @@ Jeśli chciałbyś uzyskać linię płynną, ale nie zbyt płynną, edytuj param ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density(adjust = 1/5) ``` -![mniej płynna masa ciała](../../../../../translated_images/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.pl.png) +![mniej płynna masa ciała](../../../../../translated_images/pl/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.png) ✅ Przeczytaj o dostępnych parametrach dla tego typu wykresu i eksperymentuj! @@ -162,7 +162,7 @@ Ten typ wykresu oferuje piękne wizualizacje wyjaśniające. Na przykład, za po ggplot(data=birds_filtered_1,aes(x = MaxBodyMass, fill = Order)) + geom_density(alpha=0.5) ``` -![masa ciała na rząd](../../../../../translated_images/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.pl.png) +![masa ciała na rząd](../../../../../translated_images/pl/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.png) ## 🚀 Wyzwanie diff --git a/translations/pl/3-Data-Visualization/R/11-visualization-proportions/README.md b/translations/pl/3-Data-Visualization/R/11-visualization-proportions/README.md index 22c2f59d..3c41b742 100644 --- a/translations/pl/3-Data-Visualization/R/11-visualization-proportions/README.md +++ b/translations/pl/3-Data-Visualization/R/11-visualization-proportions/README.md @@ -93,7 +93,7 @@ pie(grouped$count,grouped$class, main="Edible?") ``` Voila, wykres kołowy pokazujący proporcje tych danych według dwóch klas grzybów. Bardzo ważne jest, aby kolejność etykiet była poprawna, szczególnie tutaj, więc upewnij się, że weryfikujesz kolejność, w jakiej budowana jest tablica etykiet! -![wykres kołowy](../../../../../translated_images/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.pl.png) +![wykres kołowy](../../../../../translated_images/pl/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.png) ## Pierścienie! @@ -127,7 +127,7 @@ library(webr) PieDonut(habitat, aes(habitat, count=count)) ``` -![wykres pierścieniowy](../../../../../translated_images/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.pl.png) +![wykres pierścieniowy](../../../../../translated_images/pl/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.png) Ten kod używa dwóch bibliotek - ggplot2 i webr. Korzystając z funkcji PieDonut z biblioteki webr, możemy łatwo stworzyć wykres pierścieniowy! @@ -165,7 +165,7 @@ waffle((cap_color$count/10), rows = 7, title = "Waffle Chart")+scale_fill_manual Korzystając z wykresu gofrowego, możesz wyraźnie zobaczyć proporcje kolorów kapeluszy w tym zestawie danych o grzybach. Co ciekawe, jest wiele grzybów z zielonymi kapeluszami! -![wykres gofrowy](../../../../../translated_images/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.pl.png) +![wykres gofrowy](../../../../../translated_images/pl/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.png) W tej lekcji nauczyłeś się trzech sposobów wizualizacji proporcji. Najpierw musisz pogrupować swoje dane w kategorie, a następnie zdecydować, który sposób ich wyświetlania jest najlepszy - koło, pierścień czy gofr. Wszystkie są apetyczne i dają użytkownikowi natychmiastowy wgląd w zestaw danych. diff --git a/translations/pl/3-Data-Visualization/R/12-visualization-relationships/README.md b/translations/pl/3-Data-Visualization/R/12-visualization-relationships/README.md index f0dcd1c0..5f33e514 100644 --- a/translations/pl/3-Data-Visualization/R/12-visualization-relationships/README.md +++ b/translations/pl/3-Data-Visualization/R/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ library(ggplot2) ggplot(honey, aes(x = priceperlb, y = state)) + geom_point(colour = "blue") ``` -![scatterplot 1](../../../../../translated_images/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.pl.png) +![scatterplot 1](../../../../../translated_images/pl/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.png) Teraz pokaż te same dane z kolorystyką inspirowaną miodem, aby zobrazować, jak cena zmienia się na przestrzeni lat. Możesz to zrobić, dodając parametr 'scale_color_gradientn', który pokazuje zmiany rok po roku: @@ -61,7 +61,7 @@ Teraz pokaż te same dane z kolorystyką inspirowaną miodem, aby zobrazować, j ggplot(honey, aes(x = priceperlb, y = state, color=year)) + geom_point()+scale_color_gradientn(colours = colorspace::heat_hcl(7)) ``` -![scatterplot 2](../../../../../translated_images/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.pl.png) +![scatterplot 2](../../../../../translated_images/pl/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.png) Dzięki tej zmianie kolorystyki można zauważyć wyraźny postęp w cenie miodu za funt na przestrzeni lat. Jeśli spojrzysz na próbkę danych, aby to zweryfikować (na przykład wybierając stan Arizona), można dostrzec wzorzec wzrostu cen rok po roku, z kilkoma wyjątkami: @@ -92,7 +92,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ``` Widać, że rozmiar punktów stopniowo się zwiększa. -![scatterplot 3](../../../../../translated_images/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.pl.png) +![scatterplot 3](../../../../../translated_images/pl/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.png) Czy to prosty przypadek podaży i popytu? Z powodu takich czynników jak zmiany klimatyczne i zjawisko zaniku kolonii, czy dostępność miodu na sprzedaż zmniejsza się rok po roku, a co za tym idzie, cena wzrasta? @@ -107,7 +107,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab ``` Odpowiedź: Tak, z pewnymi wyjątkami w okolicach roku 2003: -![line chart 1](../../../../../translated_images/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.pl.png) +![line chart 1](../../../../../translated_images/pl/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.png) Pytanie: Czy w 2003 roku można również zauważyć wzrost podaży miodu? Co jeśli spojrzysz na całkowitą produkcję rok po roku? @@ -115,7 +115,7 @@ Pytanie: Czy w 2003 roku można również zauważyć wzrost podaży miodu? Co je qplot(honey$year,honey$totalprod, geom='smooth', span =0.5, xlab = "year",ylab = "totalprod") ``` -![line chart 2](../../../../../translated_images/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.pl.png) +![line chart 2](../../../../../translated_images/pl/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.png) Odpowiedź: Niekoniecznie. Jeśli spojrzysz na całkowitą produkcję, wydaje się, że faktycznie wzrosła w tym konkretnym roku, mimo że ogólnie rzecz biorąc ilość produkowanego miodu maleje w tych latach. @@ -135,7 +135,7 @@ ggplot(honey, aes(x=yieldpercol, y = numcol,group = 1)) + ``` Na tej wizualizacji możesz porównać wydajność na kolonię i liczbę kolonii rok po roku, obok siebie, z ustawieniem wrap na 3 dla kolumn: -![facet grid](../../../../../translated_images/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.pl.png) +![facet grid](../../../../../translated_images/pl/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.png) Dla tego zestawu danych nic szczególnego nie wyróżnia się w odniesieniu do liczby kolonii i ich wydajności rok po roku oraz stan po stanie. Czy istnieje inny sposób na znalezienie korelacji między tymi dwoma zmiennymi? @@ -152,7 +152,7 @@ plot(honey$year, honey$yieldpercol, pch = 17, col = 3, axis(side = 4, at = pretty(range(y2))) mtext("colony yield", side = 4, line = 3) ``` -![superimposed plots](../../../../../translated_images/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.pl.png) +![superimposed plots](../../../../../translated_images/pl/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.png) Chociaż nic szczególnego nie rzuca się w oczy w okolicach roku 2003, pozwala nam to zakończyć tę lekcję na nieco bardziej optymistycznej nucie: mimo ogólnego spadku liczby kolonii, liczba kolonii stabilizuje się, nawet jeśli ich wydajność na kolonię maleje. diff --git a/translations/pl/3-Data-Visualization/R/13-meaningful-vizualizations/README.md b/translations/pl/3-Data-Visualization/R/13-meaningful-vizualizations/README.md index 916243da..ec076f53 100644 --- a/translations/pl/3-Data-Visualization/R/13-meaningful-vizualizations/README.md +++ b/translations/pl/3-Data-Visualization/R/13-meaningful-vizualizations/README.md @@ -47,25 +47,25 @@ W poprzednich lekcjach eksperymentowałeś z tworzeniem różnych interesującyc Nawet jeśli data scientist starannie dobierze odpowiedni wykres do danych, istnieje wiele sposobów, w jakie dane mogą być przedstawione w sposób wprowadzający w błąd, często kosztem ich wiarygodności. Istnieje wiele przykładów mylących wykresów i infografik! -[![Jak kłamią wykresy autorstwa Alberto Cairo](../../../../../translated_images/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.pl.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "How charts lie") +[![Jak kłamią wykresy autorstwa Alberto Cairo](../../../../../translated_images/pl/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "How charts lie") > 🎥 Kliknij obrazek powyżej, aby obejrzeć prezentację na temat mylących wykresów Ten wykres odwraca oś X, aby pokazać coś odwrotnego do prawdy, bazując na dacie: -![zły wykres 1](../../../../../translated_images/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.pl.png) +![zły wykres 1](../../../../../translated_images/pl/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.png) [Ten wykres](https://media.firstcoastnews.com/assets/WTLV/images/170ae16f-4643-438f-b689-50d66ca6a8d8/170ae16f-4643-438f-b689-50d66ca6a8d8_1140x641.jpg) jest jeszcze bardziej mylący, ponieważ wzrok kieruje się w prawo, sugerując, że liczba przypadków COVID spadła w różnych hrabstwach. W rzeczywistości, jeśli przyjrzysz się dokładnie datom, zauważysz, że zostały one przestawione, aby stworzyć fałszywy trend spadkowy. -![zły wykres 2](../../../../../translated_images/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.pl.jpg) +![zły wykres 2](../../../../../translated_images/pl/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.jpg) Ten znany przykład używa koloru ORAZ odwróconej osi Y, aby wprowadzić w błąd: zamiast wniosku, że liczba zgonów z użyciem broni wzrosła po wprowadzeniu przyjaznego broni ustawodawstwa, wzrok zostaje oszukany, by myśleć, że jest odwrotnie: -![zły wykres 3](../../../../../translated_images/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.pl.jpg) +![zły wykres 3](../../../../../translated_images/pl/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.jpg) Ten dziwny wykres pokazuje, jak proporcje mogą być zmanipulowane, co prowadzi do komicznego efektu: -![zły wykres 4](../../../../../translated_images/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.pl.jpg) +![zły wykres 4](../../../../../translated_images/pl/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.jpg) Porównywanie rzeczy nieporównywalnych to kolejny nieuczciwy trik. Istnieje [świetna strona internetowa](https://tylervigen.com/spurious-correlations) poświęcona 'fałszywym korelacjom', pokazująca 'fakty', takie jak korelacja między wskaźnikiem rozwodów w Maine a spożyciem margaryny. Grupa na Reddicie również zbiera [brzydkie przykłady](https://www.reddit.com/r/dataisugly/top/?t=all) użycia danych. @@ -100,13 +100,13 @@ Oznacz osie, dodaj legendę, jeśli to konieczne, i oferuj podpowiedzi, aby uła Jeśli Twoje dane są tekstowe i obszerne na osi X, możesz ustawić tekst pod kątem, aby poprawić czytelność. [plot3D](https://cran.r-project.org/web/packages/plot3D/index.html) oferuje wykresy 3D, jeśli Twoje dane to umożliwiają. Zaawansowane wizualizacje danych można tworzyć za jego pomocą. -![wykresy 3D](../../../../../translated_images/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.pl.png) +![wykresy 3D](../../../../../translated_images/pl/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.png) ## Animacja i wyświetlanie wykresów 3D Niektóre z najlepszych wizualizacji danych są dziś animowane. Shirley Wu stworzyła niesamowite wizualizacje za pomocą D3, takie jak '[film flowers](http://bl.ocks.org/sxywu/raw/d612c6c653fb8b4d7ff3d422be164a5d/)', gdzie każdy kwiat jest wizualizacją filmu. Innym przykładem jest projekt dla Guardiana 'bussed out', interaktywne doświadczenie łączące wizualizacje z Greensock i D3 oraz artykuł w formacie 'scrollytelling', pokazujący, jak Nowy Jork radzi sobie z problemem bezdomności, wysyłając ludzi poza miasto. -![busing](../../../../../translated_images/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.pl.png) +![busing](../../../../../translated_images/pl/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.png) > "Bussed Out: How America Moves its Homeless" z [Guardiana](https://www.theguardian.com/us-news/ng-interactive/2017/dec/20/bussed-out-america-moves-homeless-people-country-study). Wizualizacje autorstwa Nadieh Bremer & Shirley Wu @@ -116,7 +116,7 @@ Chociaż ta lekcja nie wystarczy, aby szczegółowo nauczyć się tych potężny Ukończysz aplikację internetową, która wyświetli animowany widok tej sieci społecznej. Wykorzystuje ona bibliotekę stworzoną do [wizualizacji sieci](https://github.com/emiliorizzo/vue-d3-network) za pomocą Vue.js i D3. Gdy aplikacja działa, możesz przeciągać węzły na ekranie, aby przemieszczać dane. -![liaisons](../../../../../translated_images/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.pl.png) +![liaisons](../../../../../translated_images/pl/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.png) ## Projekt: Stwórz wykres przedstawiający sieć za pomocą D3.js diff --git a/translations/pl/3-Data-Visualization/README.md b/translations/pl/3-Data-Visualization/README.md index 71bd3b1e..d74027df 100644 --- a/translations/pl/3-Data-Visualization/README.md +++ b/translations/pl/3-Data-Visualization/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Wizualizacje -![pszczoła na kwiecie lawendy](../../../translated_images/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.pl.jpg) +![pszczoła na kwiecie lawendy](../../../translated_images/pl/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.jpg) > Zdjęcie autorstwa Jenna Lee na Unsplash Wizualizacja danych to jedno z najważniejszych zadań data scientistów. Obrazy są warte tysiąca słów, a wizualizacja może pomóc w identyfikacji różnych interesujących aspektów danych, takich jak skoki, wartości odstające, grupowania, tendencje i wiele innych, które pomagają zrozumieć historię, jaką opowiadają dane. diff --git a/translations/pl/4-Data-Science-Lifecycle/14-Introduction/README.md b/translations/pl/4-Data-Science-Lifecycle/14-Introduction/README.md index 93bd9e9d..504be014 100644 --- a/translations/pl/4-Data-Science-Lifecycle/14-Introduction/README.md +++ b/translations/pl/4-Data-Science-Lifecycle/14-Introduction/README.md @@ -25,7 +25,7 @@ Na tym etapie prawdopodobnie zdajesz sobie sprawę, że nauka o danych to proces Ta lekcja skupia się na 3 częściach cyklu życia: zbieraniu, przetwarzaniu i utrzymaniu. -![Diagram cyklu życia nauki o danych](../../../../translated_images/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.pl.jpg) +![Diagram cyklu życia nauki o danych](../../../../translated_images/pl/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.jpg) > Zdjęcie autorstwa [Berkeley School of Information](https://ischoolonline.berkeley.edu/data-science/what-is-data-science/) ## Zbieranie @@ -101,7 +101,7 @@ Zapoznaj się z [cyklem życia procesu nauki o danych zespołu](https://docs.mic |Proces nauki o danych zespołu (TDSP)|Standardowy proces dla eksploracji danych (CRISP-DM)| |--|--| -|![Cykl życia nauki o danych zespołu](../../../../translated_images/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.pl.png) | ![Obraz procesu CRISP-DM](../../../../translated_images/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.pl.png) | +|![Cykl życia nauki o danych zespołu](../../../../translated_images/pl/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.png) | ![Obraz procesu CRISP-DM](../../../../translated_images/pl/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.png) | | Obraz autorstwa [Microsoft](https://docs.microsoft.comazure/architecture/data-science-process/lifecycle) | Obraz autorstwa [Data Science Process Alliance](https://www.datascience-pm.com/crisp-dm-2/) | ## [Quiz po wykładzie](https://ff-quizzes.netlify.app/en/ds/quiz/27) diff --git a/translations/pl/4-Data-Science-Lifecycle/README.md b/translations/pl/4-Data-Science-Lifecycle/README.md index 422c4b44..2171940f 100644 --- a/translations/pl/4-Data-Science-Lifecycle/README.md +++ b/translations/pl/4-Data-Science-Lifecycle/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Cykl życia Data Science -![communication](../../../translated_images/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.pl.jpg) +![communication](../../../translated_images/pl/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.jpg) > Zdjęcie autorstwa Headway na Unsplash W tych lekcjach poznasz niektóre aspekty cyklu życia Data Science, w tym analizę i komunikację związaną z danymi. diff --git a/translations/pl/5-Data-Science-In-Cloud/README.md b/translations/pl/5-Data-Science-In-Cloud/README.md index 7985b27e..31b39240 100644 --- a/translations/pl/5-Data-Science-In-Cloud/README.md +++ b/translations/pl/5-Data-Science-In-Cloud/README.md @@ -9,13 +9,13 @@ CO_OP_TRANSLATOR_METADATA: --> # Data Science w Chmurze -![cloud-picture](../../../translated_images/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.pl.jpg) +![cloud-picture](../../../translated_images/pl/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.jpg) > Zdjęcie autorstwa [Jelleke Vanooteghem](https://unsplash.com/@ilumire) z [Unsplash](https://unsplash.com/s/photos/cloud?orientation=landscape) Jeśli chodzi o analizę danych z dużymi zbiorami danych, chmura może być prawdziwym przełomem. W ciągu najbliższych trzech lekcji dowiemy się, czym jest chmura i dlaczego może być bardzo pomocna. Zbadamy również zbiór danych dotyczących niewydolności serca i zbudujemy model, który pomoże ocenić prawdopodobieństwo wystąpienia niewydolności serca u danej osoby. Wykorzystamy moc chmury, aby wytrenować, wdrożyć i używać modelu na dwa różne sposoby. Jeden sposób polega na użyciu wyłącznie interfejsu użytkownika w podejściu Low code/No code, a drugi sposób na wykorzystaniu Azure Machine Learning Software Developer Kit (Azure ML SDK). -![project-schema](../../../translated_images/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.pl.png) +![project-schema](../../../translated_images/pl/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.png) ### Tematy diff --git a/translations/pl/6-Data-Science-In-Wild/20-Real-World-Examples/README.md b/translations/pl/6-Data-Science-In-Wild/20-Real-World-Examples/README.md index 0fac8d4d..4eb1a771 100644 --- a/translations/pl/6-Data-Science-In-Wild/20-Real-World-Examples/README.md +++ b/translations/pl/6-Data-Science-In-Wild/20-Real-World-Examples/README.md @@ -41,7 +41,7 @@ Dzięki demokratyzacji AI, programistom jest teraz łatwiej projektować i integ * [Data Science w Ochronie Zdrowia](https://data-flair.training/blogs/data-science-in-healthcare/) - podkreśla zastosowania takie jak obrazowanie medyczne (np. MRI, RTG, tomografia komputerowa), genomika (sekwencjonowanie DNA), rozwój leków (ocena ryzyka, przewidywanie sukcesu), analiza predykcyjna (opieka nad pacjentem i logistyka dostaw), śledzenie i zapobieganie chorobom itp. -![Zastosowania Data Science w Rzeczywistym Świecie](../../../../translated_images/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.pl.png) Źródło obrazu: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) +![Zastosowania Data Science w Rzeczywistym Świecie](../../../../translated_images/pl/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.png) Źródło obrazu: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) Rysunek pokazuje inne dziedziny i przykłady zastosowania technik data science. Chcesz odkryć inne zastosowania? Sprawdź sekcję [Przegląd i Samodzielna Nauka](../../../../6-Data-Science-In-Wild/20-Real-World-Examples) poniżej. diff --git a/translations/pl/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md b/translations/pl/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md index 1dd798de..8fe264b0 100644 --- a/translations/pl/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md +++ b/translations/pl/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md @@ -22,7 +22,7 @@ Interfejs Explorer (pokazany na zrzucie ekranu poniżej) pozwala wybrać zbiór 2. Przeglądaj [Katalog zbiorów danych](https://planetarycomputer.microsoft.com/catalog) – dowiedz się, do czego służy każdy zbiór. 3. Skorzystaj z Explorer – wybierz interesujący Cię zbiór danych, odpowiednie zapytanie i opcję renderowania. -![The Planetary Computer Explorer](../../../../translated_images/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.pl.png) +![The Planetary Computer Explorer](../../../../translated_images/pl/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.png) `Twoje zadanie:` Przeanalizuj wizualizację, która zostanie wygenerowana w przeglądarce, i odpowiedz na następujące pytania: diff --git a/translations/pl/CONTRIBUTING.md b/translations/pl/CONTRIBUTING.md index b8685353..a6405957 100644 --- a/translations/pl/CONTRIBUTING.md +++ b/translations/pl/CONTRIBUTING.md @@ -315,7 +315,7 @@ W opisie PR uwzględnij: ``` ```` -- Dodawaj tekst alternatywny do obrazów: `![Alt text](../../translated_images/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.pl.png)` +- Dodawaj tekst alternatywny do obrazów: `![Alt text](../../translated_images/pl/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.png)` - Zachowuj rozsądną długość linii (około 80-100 znaków) ### Python diff --git a/translations/pl/README.md b/translations/pl/README.md index 7da59872..9ca1c859 100644 --- a/translations/pl/README.md +++ b/translations/pl/README.md @@ -33,7 +33,7 @@ Azure Cloud Advocates w Microsoft z przyjemnością oferują 10-tygodniowy, 20-l **🙏 Szczególne podziękowania 🙏 naszym [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/) autorom, recenzentom i współtwórcom treści,** w szczególności Aaryan Arora, [Aditya Garg](https://github.com/AdityaGarg00), [Alondra Sanchez](https://www.linkedin.com/in/alondra-sanchez-molina/), [Ankita Singh](https://www.linkedin.com/in/ankitasingh007), [Anupam Mishra](https://www.linkedin.com/in/anupam--mishra/), [Arpita Das](https://www.linkedin.com/in/arpitadas01/), ChhailBihari Dubey, [Dibri Nsofor](https://www.linkedin.com/in/dibrinsofor), [Dishita Bhasin](https://www.linkedin.com/in/dishita-bhasin-7065281bb), [Majd Safi](https://www.linkedin.com/in/majd-s/), [Max Blum](https://www.linkedin.com/in/max-blum-6036a1186/), [Miguel Correa](https://www.linkedin.com/in/miguelmque/), [Mohamma Iftekher (Iftu) Ebne Jalal](https://twitter.com/iftu119), [Nawrin Tabassum](https://www.linkedin.com/in/nawrin-tabassum), [Raymond Wangsa Putra](https://www.linkedin.com/in/raymond-wp/), [Rohit Yadav](https://www.linkedin.com/in/rty2423), Samridhi Sharma, [Sanya Sinha](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200), [Sheena Narula](https://www.linkedin.com/in/sheena-narua-n/), [Tauqeer Ahmad](https://www.linkedin.com/in/tauqeerahmad5201/), Yogendrasingh Pawar , [Vidushi Gupta](https://www.linkedin.com/in/vidushi-gupta07/), [Jasleen Sondhi](https://www.linkedin.com/in/jasleen-sondhi/) -|![Sketchnote autorstwa @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.pl.png)| +|![Sketchnote autorstwa @sketchthedocs https://sketchthedocs.dev](../../translated_images/pl/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.png)| |:---:| | Data Science dla początkujących - _Sketchnote autorstwa [@nitya](https://twitter.com/nitya)_ | @@ -52,7 +52,7 @@ Azure Cloud Advocates w Microsoft z przyjemnością oferują 10-tygodniowy, 20-l Prowadzimy serię „Learn with AI” na Discordzie — dowiedz się więcej i dołącz do nas na [Seria Learn with AI](https://aka.ms/learnwithai/discord) w dniach 18 - 30 września 2025. Otrzymasz wskazówki i triki dotyczące używania GitHub Copilot dla Data Science. -![Seria Learn with AI](../../translated_images/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.pl.jpg) +![Seria Learn with AI](../../translated_images/pl/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.jpg) # Czy jesteś studentem? @@ -132,7 +132,7 @@ Każdy przykład zawiera szczegółowe komentarze wyjaśniające każdy krok, co ## Lekcje -|![ Notatka graficzna (sketchnote) autorstwa @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.pl.png)| +|![ Notatka graficzna (sketchnote) autorstwa @sketchthedocs https://sketchthedocs.dev](../../translated_images/pl/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png)| |:---:| | Data Science dla początkujących: Plan nauki - _Sketchnote autorstwa [@nitya](https://twitter.com/nitya)_ | diff --git a/translations/pl/sketchnotes/README.md b/translations/pl/sketchnotes/README.md index 92337f53..57af7fd8 100644 --- a/translations/pl/sketchnotes/README.md +++ b/translations/pl/sketchnotes/README.md @@ -13,7 +13,7 @@ Znajdź wszystkie notatki wizualne tutaj! Nitya Narasimhan, artystka -![notatka wizualna - mapa drogowa](../../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.pl.png) +![notatka wizualna - mapa drogowa](../../../translated_images/pl/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png) **Zastrzeżenie**: Ten dokument został przetłumaczony za pomocą usługi tłumaczenia AI [Co-op Translator](https://github.com/Azure/co-op-translator). Chociaż staramy się zapewnić dokładność, prosimy mieć na uwadze, że automatyczne tłumaczenia mogą zawierać błędy lub nieścisłości. Oryginalny dokument w jego rodzimym języku powinien być uznawany za wiarygodne źródło. W przypadku informacji krytycznych zaleca się skorzystanie z profesjonalnego tłumaczenia wykonanego przez człowieka. Nie ponosimy odpowiedzialności za jakiekolwiek nieporozumienia lub błędne interpretacje wynikające z użycia tego tłumaczenia. \ No newline at end of file diff --git a/translations/pt/1-Introduction/01-defining-data-science/README.md b/translations/pt/1-Introduction/01-defining-data-science/README.md index 167402a2..abec1d7e 100644 --- a/translations/pt/1-Introduction/01-defining-data-science/README.md +++ b/translations/pt/1-Introduction/01-defining-data-science/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: --- -[![Vídeo Definindo Ciência de Dados](../../../../translated_images/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.pt.png)](https://youtu.be/beZ7Mb_oz9I) +[![Vídeo Definindo Ciência de Dados](../../../../translated_images/pt/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.png)](https://youtu.be/beZ7Mb_oz9I) ## [Questionário pré-aula](https://ff-quizzes.netlify.app/en/ds/quiz/0) @@ -153,7 +153,7 @@ Se quisermos ser ainda mais detalhados, podemos traçar o tempo gasto em cada m Neste desafio, vamos tentar encontrar conceitos relevantes para o campo da Ciência de Dados analisando textos. Vamos pegar um artigo da Wikipédia sobre Ciência de Dados, descarregar e processar o texto e, em seguida, criar uma nuvem de palavras como esta: -![Nuvem de Palavras para Ciência de Dados](../../../../translated_images/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.pt.png) +![Nuvem de Palavras para Ciência de Dados](../../../../translated_images/pt/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.png) Visite [`notebook.ipynb`](../../../../1-Introduction/01-defining-data-science/notebook.ipynb ':ignore') para ler o código. Também pode executar o código e ver como ele realiza todas as transformações de dados em tempo real. diff --git a/translations/pt/1-Introduction/04-stats-and-probability/README.md b/translations/pt/1-Introduction/04-stats-and-probability/README.md index 6c0455c1..04c312ca 100644 --- a/translations/pt/1-Introduction/04-stats-and-probability/README.md +++ b/translations/pt/1-Introduction/04-stats-and-probability/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: A Teoria da Estatística e Probabilidade são duas áreas altamente relacionadas da Matemática que têm grande relevância para a Ciência de Dados. É possível trabalhar com dados sem um conhecimento profundo de matemática, mas é sempre melhor conhecer pelo menos alguns conceitos básicos. Aqui apresentaremos uma breve introdução que o ajudará a começar. -[![Vídeo de Introdução](../../../../translated_images/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.pt.png)](https://youtu.be/Z5Zy85g4Yjw) +[![Vídeo de Introdução](../../../../translated_images/pt/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.png)](https://youtu.be/Z5Zy85g4Yjw) ## [Questionário pré-aula](https://ff-quizzes.netlify.app/en/ds/quiz/6) @@ -39,7 +39,7 @@ A distribuição discreta mais conhecida é a **distribuição uniforme**, na qu Só podemos falar sobre a probabilidade de uma variável cair em um determinado intervalo de valores, por exemplo, P(t1≤X2). Nesse caso, a distribuição de probabilidade é descrita por uma **função densidade de probabilidade** p(x), tal que -![P(t_1\le X Mais exemplos de correlação e covariância podem ser encontrados no [notebook associado](notebook.ipynb). diff --git a/translations/pt/1-Introduction/README.md b/translations/pt/1-Introduction/README.md index a6771276..e909d25e 100644 --- a/translations/pt/1-Introduction/README.md +++ b/translations/pt/1-Introduction/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Introdução à Ciência de Dados -![dados em ação](../../../translated_images/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.pt.jpg) +![dados em ação](../../../translated_images/pt/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.jpg) > Foto de Stephen Dawson no Unsplash Nestes módulos, irá descobrir como a Ciência de Dados é definida e aprender sobre as considerações éticas que devem ser tidas em conta por um cientista de dados. Também irá aprender como os dados são definidos e explorar um pouco de estatística e probabilidade, os domínios académicos centrais da Ciência de Dados. diff --git a/translations/pt/2-Working-With-Data/07-python/README.md b/translations/pt/2-Working-With-Data/07-python/README.md index 79bf25a4..429c880a 100644 --- a/translations/pt/2-Working-With-Data/07-python/README.md +++ b/translations/pt/2-Working-With-Data/07-python/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: | :-------------------------------------------------------------------------------------------------------: | | Trabalhar com Python - _Sketchnote por [@nitya](https://twitter.com/nitya)_ | -[![Vídeo de Introdução](../../../../translated_images/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.pt.png)](https://youtu.be/dZjWOGbsN4Y) +[![Vídeo de Introdução](../../../../translated_images/pt/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.png)](https://youtu.be/dZjWOGbsN4Y) Embora bases de dados ofereçam formas muito eficientes de armazenar e consultar dados usando linguagens de consulta, a maneira mais flexível de processar dados é escrever o seu próprio programa para manipulá-los. Em muitos casos, realizar uma consulta em uma base de dados seria mais eficaz. No entanto, em alguns casos, quando é necessário um processamento de dados mais complexo, isso não pode ser feito facilmente usando SQL. O processamento de dados pode ser programado em qualquer linguagem de programação, mas há certas linguagens que são mais adequadas para trabalhar com dados. Cientistas de dados geralmente preferem uma das seguintes linguagens: @@ -73,7 +73,7 @@ print(f"Length of index is {len(idx)}") items_sold = pd.Series(np.random.randint(25,50,size=len(idx)),index=idx) items_sold.plot() ``` -![Gráfico de Série Temporal](../../../../translated_images/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.pt.png) +![Gráfico de Série Temporal](../../../../translated_images/pt/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.png) Agora suponha que, a cada semana, organizamos uma festa para amigos e levamos 10 pacotes adicionais de sorvete para a festa. Podemos criar outra série, indexada por semana, para demonstrar isso: ```python @@ -84,7 +84,7 @@ Quando somamos duas séries, obtemos o número total: total_items = items_sold.add(additional_items,fill_value=0) total_items.plot() ``` -![Gráfico de Série Temporal](../../../../translated_images/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.pt.png) +![Gráfico de Série Temporal](../../../../translated_images/pt/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.png) > **Nota** que não estamos usando a sintaxe simples `total_items+additional_items`. Se o fizéssemos, receberíamos muitos valores `NaN` (*Not a Number*) na série resultante. Isso ocorre porque há valores ausentes para alguns dos pontos de índice na série `additional_items`, e somar `NaN` a qualquer coisa resulta em `NaN`. Assim, precisamos especificar o parâmetro `fill_value` durante a soma. @@ -93,7 +93,7 @@ Com séries temporais, também podemos **reamostrar** a série com diferentes in monthly = total_items.resample("1M").mean() ax = monthly.plot(kind='bar') ``` -![Médias Mensais de Série Temporal](../../../../translated_images/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.pt.png) +![Médias Mensais de Série Temporal](../../../../translated_images/pt/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.png) ### DataFrame @@ -219,7 +219,7 @@ O primeiro problema em que nos vamos focar é o modelo de propagação epidémic Como queremos demonstrar como lidar com dados, convidamo-lo a abrir [`notebook-covidspread.ipynb`](notebook-covidspread.ipynb) e lê-lo de cima para baixo. Pode também executar as células e realizar alguns desafios que deixámos para si no final. -![Propagação da COVID](../../../../translated_images/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.pt.png) +![Propagação da COVID](../../../../translated_images/pt/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.png) > Se não sabe como executar código no Jupyter Notebook, veja [este artigo](https://soshnikov.com/education/how-to-execute-notebooks-from-github/). @@ -241,7 +241,7 @@ Um exemplo completo de análise deste conjunto de dados usando o serviço cognit Abra [`notebook-papers.ipynb`](notebook-papers.ipynb) e leia-o de cima para baixo. Pode também executar as células e realizar alguns desafios que deixámos para si no final. -![Tratamento Médico COVID](../../../../translated_images/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.pt.png) +![Tratamento Médico COVID](../../../../translated_images/pt/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.png) ## Processamento de Dados de Imagem diff --git a/translations/pt/2-Working-With-Data/README.md b/translations/pt/2-Working-With-Data/README.md index 1b110fbd..c7f7314a 100644 --- a/translations/pt/2-Working-With-Data/README.md +++ b/translations/pt/2-Working-With-Data/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Trabalhar com Dados -![amor pelos dados](../../../translated_images/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.pt.jpg) +![amor pelos dados](../../../translated_images/pt/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.jpg) > Foto por Alexander Sinn no Unsplash Nestes módulos, vais aprender algumas formas de gerir, manipular e utilizar dados em aplicações. Vais aprender sobre bases de dados relacionais e não relacionais e como os dados podem ser armazenados nelas. Vais aprender os fundamentos de trabalhar com Python para gerir dados e descobrir algumas das muitas maneiras de usar Python para gerir e explorar dados. diff --git a/translations/pt/3-Data-Visualization/12-visualization-relationships/README.md b/translations/pt/3-Data-Visualization/12-visualization-relationships/README.md index 4852252c..36efdee2 100644 --- a/translations/pt/3-Data-Visualization/12-visualization-relationships/README.md +++ b/translations/pt/3-Data-Visualization/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ Crie um gráfico de dispersão básico para mostrar a relação entre o preço p ```python sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ``` -![gráfico de dispersão 1](../../../../translated_images/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.pt.png) +![gráfico de dispersão 1](../../../../translated_images/pt/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.png) Agora, mostre os mesmos dados com um esquema de cores de mel para ilustrar como o preço evolui ao longo dos anos. Pode-se fazer isso adicionando um parâmetro 'hue' para mostrar a mudança, ano após ano: @@ -60,7 +60,7 @@ Agora, mostre os mesmos dados com um esquema de cores de mel para ilustrar como ```python sns.relplot(x="priceperlb", y="state", hue="year", palette="YlOrBr", data=honey, height=15, aspect=.5); ``` -![gráfico de dispersão 2](../../../../translated_images/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.pt.png) +![gráfico de dispersão 2](../../../../translated_images/pt/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.png) Com esta mudança no esquema de cores, é possível perceber claramente uma forte progressão ao longo dos anos no preço do mel por libra. De fato, ao verificar um conjunto de amostras nos dados (escolha um estado, como o Arizona, por exemplo), é possível observar um padrão de aumento de preços ano após ano, com poucas exceções: @@ -89,7 +89,7 @@ sns.relplot(x="priceperlb", y="state", size="year", data=honey, height=15, aspec ``` Pode-se observar que o tamanho dos pontos aumenta gradualmente. -![gráfico de dispersão 3](../../../../translated_images/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.pt.png) +![gráfico de dispersão 3](../../../../translated_images/pt/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.png) Será este um caso simples de oferta e procura? Devido a fatores como mudanças climáticas e o colapso das colónias, haverá menos mel disponível para compra ano após ano, e, assim, o preço aumenta? @@ -104,7 +104,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); ``` Resposta: Sim, com algumas exceções por volta do ano 2003: -![gráfico de linhas 1](../../../../translated_images/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.pt.png) +![gráfico de linhas 1](../../../../translated_images/pt/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.png) ✅ Como o Seaborn está a agregar dados numa única linha, ele exibe "as múltiplas medições em cada valor de x, traçando a média e o intervalo de confiança de 95% em torno da média". [Fonte](https://seaborn.pydata.org/tutorial/relational.html). Este comportamento, que consome tempo, pode ser desativado adicionando `ci=None`. @@ -114,7 +114,7 @@ Pergunta: Bem, em 2003 também podemos observar um pico na oferta de mel? E se a sns.relplot(x="year", y="totalprod", kind="line", data=honey); ``` -![gráfico de linhas 2](../../../../translated_images/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.pt.png) +![gráfico de linhas 2](../../../../translated_images/pt/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.png) Resposta: Não exatamente. Ao observar a produção total, parece que ela realmente aumentou naquele ano específico, embora, de forma geral, a quantidade de mel produzido esteja em declínio durante esses anos. @@ -139,7 +139,7 @@ sns.relplot( ``` Nesta visualização, pode-se comparar a produção por colmeia e o número de colmeias ano após ano, lado a lado, com um limite de 3 colunas: -![grelha de facetas](../../../../translated_images/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.pt.png) +![grelha de facetas](../../../../translated_images/pt/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.png) Para este conjunto de dados, nada particularmente se destaca em relação ao número de colmeias e sua produção, ano após ano e estado por estado. Existe uma forma diferente de encontrar uma correlação entre estas duas variáveis? @@ -162,7 +162,7 @@ sns.despine(right=False) plt.ylabel('colony yield') ax.figure.legend(); ``` -![gráficos sobrepostos](../../../../translated_images/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.pt.png) +![gráficos sobrepostos](../../../../translated_images/pt/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.png) Embora nada salte aos olhos em relação ao ano de 2003, isso permite encerrar esta lição com uma nota um pouco mais feliz: embora o número de colmeias esteja em declínio geral, ele está a estabilizar, mesmo que a produção por colmeia esteja a diminuir. diff --git a/translations/pt/3-Data-Visualization/R/09-visualization-quantities/README.md b/translations/pt/3-Data-Visualization/R/09-visualization-quantities/README.md index 4c10fd22..6a060e6b 100644 --- a/translations/pt/3-Data-Visualization/R/09-visualization-quantities/README.md +++ b/translations/pt/3-Data-Visualization/R/09-visualization-quantities/README.md @@ -66,7 +66,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` Aqui, instalas o pacote `ggplot2` e depois importas para o ambiente de trabalho usando o comando `library("ggplot2")`. Para criar qualquer gráfico no ggplot, usa-se a função `ggplot()` e especifica-se o conjunto de dados, as variáveis x e y como atributos. Neste caso, usamos a função `geom_line()` porque queremos criar um gráfico de linhas. -![MaxWingspan-lineplot](../../../../../translated_images/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.pt.png) +![MaxWingspan-lineplot](../../../../../translated_images/pt/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.png) O que notas imediatamente? Parece haver pelo menos um valor atípico - que envergadura impressionante! Uma envergadura de mais de 2000 centímetros equivale a mais de 20 metros - será que há Pterodáctilos a voar em Minnesota? Vamos investigar. @@ -84,7 +84,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` Especificamos o ângulo no `theme` e definimos os rótulos dos eixos x e y em `xlab()` e `ylab()` respetivamente. O `ggtitle()` dá um nome ao gráfico. -![MaxWingspan-lineplot-improved](../../../../../translated_images/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.pt.png) +![MaxWingspan-lineplot-improved](../../../../../translated_images/pt/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.png) Mesmo com a rotação dos rótulos definida para 45 graus, há demasiados para ler. Vamos tentar uma estratégia diferente: rotular apenas os valores atípicos e definir os rótulos dentro do gráfico. Podes usar um gráfico de dispersão para criar mais espaço para os rótulos: @@ -100,7 +100,7 @@ O que está a acontecer aqui? Usaste a função `geom_point()` para criar pontos O que descobres? -![MaxWingspan-scatterplot](../../../../../translated_images/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.pt.png) +![MaxWingspan-scatterplot](../../../../../translated_images/pt/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.png) ## Filtrar os teus dados @@ -119,7 +119,7 @@ ggplot(data=birds_filtered, aes(x=Name, y=MaxWingspan,group=1)) + ``` Criámos um novo dataframe `birds_filtered` e depois representámos um gráfico de dispersão. Ao filtrar os valores atípicos, os teus dados tornam-se mais coesos e compreensíveis. -![MaxWingspan-scatterplot-improved](../../../../../translated_images/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.pt.png) +![MaxWingspan-scatterplot-improved](../../../../../translated_images/pt/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.png) Agora que temos um conjunto de dados mais limpo, pelo menos em termos de envergadura, vamos descobrir mais sobre estas aves. @@ -161,7 +161,7 @@ birds_filtered %>% group_by(Category) %>% ``` No seguinte trecho, instalamos os pacotes [dplyr](https://www.rdocumentation.org/packages/dplyr/versions/0.7.8) e [lubridate](https://www.rdocumentation.org/packages/lubridate/versions/1.8.0) para ajudar a manipular e agrupar dados para criar um gráfico de barras empilhado. Primeiro, agrupas os dados pela `Categoria` das aves e depois resumes as colunas `MinLength`, `MaxLength`, `MinBodyMass`, `MaxBodyMass`, `MinWingspan`, `MaxWingspan`. Em seguida, crias o gráfico de barras usando o pacote `ggplot2` e especificas as cores para as diferentes categorias e os rótulos. -![Stacked bar chart](../../../../../translated_images/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.pt.png) +![Stacked bar chart](../../../../../translated_images/pt/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.png) Este gráfico de barras, no entanto, é ilegível porque há demasiados dados não agrupados. Precisamos de selecionar apenas os dados que queremos representar, então vamos observar o comprimento das aves com base na sua categoria. @@ -176,7 +176,7 @@ ggplot(birds_count,aes(Category,n))+geom_bar(stat="identity")+coord_flip() ``` Primeiro, contas os valores únicos na coluna `Categoria` e depois ordenas num novo dataframe `birds_count`. Estes dados ordenados são então considerados no mesmo nível para que sejam representados de forma ordenada. Usando o `ggplot2`, crias o gráfico de barras. O `coord_flip()` cria barras horizontais. -![category-length](../../../../../translated_images/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.pt.png) +![category-length](../../../../../translated_images/pt/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.png) Este gráfico de barras mostra uma boa visão do número de aves em cada categoria. Num piscar de olhos, vês que o maior número de aves nesta região pertence à categoria de Patos/Gansos/AvesAquáticas. Minnesota é a 'terra dos 10.000 lagos', então isto não é surpreendente! @@ -199,7 +199,7 @@ ggplot(birds_grouped,aes(Category,MaxLength))+geom_bar(stat="identity")+coord_fl ``` Agrupamos os dados `birds_filtered` pela `Categoria` e depois criamos um gráfico de barras. -![comparing data](../../../../../translated_images/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.pt.png) +![comparing data](../../../../../translated_images/pt/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.png) Nada é surpreendente aqui: os beija-flores têm o menor MaxLength em comparação com os Pelicanos ou Gansos. É bom quando os dados fazem sentido lógico! @@ -211,7 +211,7 @@ ggplot(data=birds_grouped, aes(x=Category)) + geom_bar(aes(y=MinLength), stat="identity", position="identity", fill='orange')+ coord_flip() ``` -![super-imposed values](../../../../../translated_images/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.pt.png) +![super-imposed values](../../../../../translated_images/pt/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.png) ## 🚀 Desafio diff --git a/translations/pt/3-Data-Visualization/R/10-visualization-distributions/README.md b/translations/pt/3-Data-Visualization/R/10-visualization-distributions/README.md index 5fef5a6e..324a33de 100644 --- a/translations/pt/3-Data-Visualization/R/10-visualization-distributions/README.md +++ b/translations/pt/3-Data-Visualization/R/10-visualization-distributions/README.md @@ -45,7 +45,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + geom_point() + ggtitle("Max Length per order") + coord_flip() ``` -![comprimento máximo por ordem](../../../../../translated_images/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.pt.png) +![comprimento máximo por ordem](../../../../../translated_images/pt/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.png) Isto dá uma visão geral da distribuição do comprimento corporal por Ordem de aves, mas não é a forma ideal de exibir distribuições reais. Essa tarefa é geralmente realizada criando um Histograma. @@ -57,7 +57,7 @@ O `ggplot2` oferece ótimas formas de visualizar a distribuição de dados usand ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=10)+ylab('Frequency') ``` -![distribuição em todo o conjunto de dados](../../../../../translated_images/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.pt.png) +![distribuição em todo o conjunto de dados](../../../../../translated_images/pt/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.png) Como podes ver, a maioria das mais de 400 aves neste conjunto de dados tem uma Massa Corporal Máxima inferior a 2000. Obtém mais informações sobre os dados alterando o parâmetro `bins` para um número maior, como 30: @@ -65,7 +65,7 @@ Como podes ver, a maioria das mais de 400 aves neste conjunto de dados tem uma M ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![distribuição-30bins](../../../../../translated_images/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.pt.png) +![distribuição-30bins](../../../../../translated_images/pt/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.png) Este gráfico mostra a distribuição de forma um pouco mais detalhada. Um gráfico menos enviesado para a esquerda pode ser criado garantindo que apenas selecionas dados dentro de um determinado intervalo: @@ -77,7 +77,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![histograma filtrado](../../../../../translated_images/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.pt.png) +![histograma filtrado](../../../../../translated_images/pt/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.png) ✅ Experimenta outros filtros e pontos de dados. Para ver a distribuição completa dos dados, remove o filtro `['MaxBodyMass']` para mostrar distribuições rotuladas. @@ -91,7 +91,7 @@ ggplot(data=birds_filtered_1, aes(x=MaxBodyMass, y=MaxLength) ) + ``` Parece haver uma correlação esperada entre estes dois elementos ao longo de um eixo esperado, com um ponto de convergência particularmente forte: -![gráfico 2d](../../../../../translated_images/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.pt.png) +![gráfico 2d](../../../../../translated_images/pt/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.png) Os histogramas funcionam bem por padrão para dados numéricos. E se precisares de ver distribuições de acordo com dados textuais? @@ -123,7 +123,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + scale_fill_manual(name="Conservation Status",values=c("red","green","blue","pink"),labels=c("Endangered","Near Threathened","Vulnerable","Least Concern")) ``` -![envergadura e conservação](../../../../../translated_images/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.pt.png) +![envergadura e conservação](../../../../../translated_images/pt/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.png) Não parece haver uma boa correlação entre a envergadura mínima e o estado de conservação. Testa outros elementos do conjunto de dados usando este método. Podes experimentar diferentes filtros também. Encontras alguma correlação? @@ -137,7 +137,7 @@ Vamos trabalhar agora com gráficos de densidade! ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + geom_density() ``` -![gráfico de densidade](../../../../../translated_images/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.pt.png) +![gráfico de densidade](../../../../../translated_images/pt/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.png) Podes ver como o gráfico reflete o anterior para os dados de Envergadura Mínima; é apenas um pouco mais suave. Se quisesses revisitar aquela linha irregular de MaxBodyMass no segundo gráfico que construíste, poderias suavizá-la muito bem recriando-a usando este método: @@ -145,7 +145,7 @@ Podes ver como o gráfico reflete o anterior para os dados de Envergadura Mínim ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density() ``` -![densidade de massa corporal](../../../../../translated_images/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.pt.png) +![densidade de massa corporal](../../../../../translated_images/pt/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.png) Se quiseres uma linha suave, mas não demasiado suave, edita o parâmetro `adjust`: @@ -153,7 +153,7 @@ Se quiseres uma linha suave, mas não demasiado suave, edita o parâmetro `adjus ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density(adjust = 1/5) ``` -![massa corporal menos suave](../../../../../translated_images/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.pt.png) +![massa corporal menos suave](../../../../../translated_images/pt/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.png) ✅ Lê sobre os parâmetros disponíveis para este tipo de gráfico e experimenta! @@ -163,7 +163,7 @@ Este tipo de gráfico oferece visualizações explicativas muito bonitas. Com al ggplot(data=birds_filtered_1,aes(x = MaxBodyMass, fill = Order)) + geom_density(alpha=0.5) ``` -![massa corporal por ordem](../../../../../translated_images/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.pt.png) +![massa corporal por ordem](../../../../../translated_images/pt/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.png) ## 🚀 Desafio diff --git a/translations/pt/3-Data-Visualization/R/11-visualization-proportions/README.md b/translations/pt/3-Data-Visualization/R/11-visualization-proportions/README.md index 80785514..bd0d65ec 100644 --- a/translations/pt/3-Data-Visualization/R/11-visualization-proportions/README.md +++ b/translations/pt/3-Data-Visualization/R/11-visualization-proportions/README.md @@ -92,7 +92,7 @@ pie(grouped$count,grouped$class, main="Edible?") ``` Voilà, um gráfico de pizza que mostra as proporções destes dados de acordo com estas duas classes de cogumelos. É muito importante garantir que a ordem das etiquetas esteja correta, especialmente aqui, por isso verifica sempre a ordem com que o array de etiquetas é construído! -![gráfico de pizza](../../../../../translated_images/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.pt.png) +![gráfico de pizza](../../../../../translated_images/pt/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.png) ## Roscas! @@ -126,7 +126,7 @@ library(webr) PieDonut(habitat, aes(habitat, count=count)) ``` -![gráfico de rosca](../../../../../translated_images/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.pt.png) +![gráfico de rosca](../../../../../translated_images/pt/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.png) Este código utiliza duas bibliotecas - ggplot2 e webr. Usando a função PieDonut da biblioteca webr, podemos criar um gráfico de rosca facilmente! @@ -164,7 +164,7 @@ waffle((cap_color$count/10), rows = 7, title = "Waffle Chart")+scale_fill_manual Usando um gráfico de waffle, podes ver claramente as proporções das cores dos chapéus neste conjunto de dados de cogumelos. Curiosamente, existem muitos cogumelos com chapéus verdes! -![gráfico de waffle](../../../../../translated_images/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.pt.png) +![gráfico de waffle](../../../../../translated_images/pt/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.png) Nesta lição, aprendeste três formas de visualizar proporções. Primeiro, precisas de agrupar os teus dados em categorias e depois decidir qual é a melhor forma de exibir os dados - pizza, rosca ou waffle. Todas são deliciosas e oferecem ao utilizador uma visão instantânea de um conjunto de dados. diff --git a/translations/pt/3-Data-Visualization/R/12-visualization-relationships/README.md b/translations/pt/3-Data-Visualization/R/12-visualization-relationships/README.md index 3bf444f5..5d1daad1 100644 --- a/translations/pt/3-Data-Visualization/R/12-visualization-relationships/README.md +++ b/translations/pt/3-Data-Visualization/R/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ library(ggplot2) ggplot(honey, aes(x = priceperlb, y = state)) + geom_point(colour = "blue") ``` -![scatterplot 1](../../../../../translated_images/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.pt.png) +![scatterplot 1](../../../../../translated_images/pt/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.png) Agora, mostre os mesmos dados com um esquema de cores de mel para ilustrar como o preço evolui ao longo dos anos. Pode fazer isso adicionando o parâmetro 'scale_color_gradientn' para mostrar a mudança, ano após ano: @@ -61,7 +61,7 @@ Agora, mostre os mesmos dados com um esquema de cores de mel para ilustrar como ggplot(honey, aes(x = priceperlb, y = state, color=year)) + geom_point()+scale_color_gradientn(colours = colorspace::heat_hcl(7)) ``` -![scatterplot 2](../../../../../translated_images/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.pt.png) +![scatterplot 2](../../../../../translated_images/pt/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.png) Com esta mudança de esquema de cores, é possível ver claramente uma forte progressão ao longo dos anos no preço do mel por libra. De facto, ao verificar um conjunto de amostra nos dados (escolha um estado, como o Arizona), pode-se observar um padrão de aumento de preço ano após ano, com poucas exceções: @@ -92,7 +92,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ``` Pode ver o tamanho dos pontos aumentando gradualmente. -![scatterplot 3](../../../../../translated_images/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.pt.png) +![scatterplot 3](../../../../../translated_images/pt/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.png) Será este um caso simples de oferta e procura? Devido a fatores como mudanças climáticas e colapso de colónias, há menos mel disponível para compra ano após ano, e assim o preço aumenta? @@ -107,7 +107,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab ``` Resposta: Sim, com algumas exceções por volta do ano de 2003: -![line chart 1](../../../../../translated_images/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.pt.png) +![line chart 1](../../../../../translated_images/pt/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.png) Pergunta: Bem, em 2003 também podemos ver um pico na oferta de mel? E se observarmos a produção total ano após ano? @@ -115,7 +115,7 @@ Pergunta: Bem, em 2003 também podemos ver um pico na oferta de mel? E se observ qplot(honey$year,honey$totalprod, geom='smooth', span =0.5, xlab = "year",ylab = "totalprod") ``` -![line chart 2](../../../../../translated_images/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.pt.png) +![line chart 2](../../../../../translated_images/pt/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.png) Resposta: Não exatamente. Se observar a produção total, parece que ela realmente aumentou nesse ano específico, embora, de forma geral, a quantidade de mel produzida esteja em declínio durante esses anos. @@ -135,7 +135,7 @@ ggplot(honey, aes(x=yieldpercol, y = numcol,group = 1)) + ``` Nesta visualização, pode comparar o rendimento por colónia e o número de colónias ano após ano, lado a lado, com uma disposição de 3 colunas: -![facet grid](../../../../../translated_images/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.pt.png) +![facet grid](../../../../../translated_images/pt/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.png) Para este conjunto de dados, nada particularmente se destaca em relação ao número de colónias e ao seu rendimento, ano após ano e estado por estado. Existe uma forma diferente de encontrar uma correlação entre estas duas variáveis? @@ -152,7 +152,7 @@ plot(honey$year, honey$yieldpercol, pch = 17, col = 3, axis(side = 4, at = pretty(range(y2))) mtext("colony yield", side = 4, line = 3) ``` -![superimposed plots](../../../../../translated_images/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.pt.png) +![superimposed plots](../../../../../translated_images/pt/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.png) Embora nada salte aos olhos em torno do ano de 2003, isso permite terminar esta lição com uma nota um pouco mais feliz: embora o número de colónias esteja em declínio geral, ele está a estabilizar, mesmo que o rendimento por colónia esteja a diminuir. diff --git a/translations/pt/3-Data-Visualization/R/13-meaningful-vizualizations/README.md b/translations/pt/3-Data-Visualization/R/13-meaningful-vizualizations/README.md index 31789299..a4c855dd 100644 --- a/translations/pt/3-Data-Visualization/R/13-meaningful-vizualizations/README.md +++ b/translations/pt/3-Data-Visualization/R/13-meaningful-vizualizations/README.md @@ -47,25 +47,25 @@ Em lições anteriores, você experimentou criar diversos tipos de visualizaçõ Mesmo que um cientista de dados seja cuidadoso ao escolher o gráfico certo para os dados certos, existem muitas maneiras de exibir dados de forma a provar um ponto, muitas vezes às custas de comprometer os próprios dados. Há muitos exemplos de gráficos e infográficos enganosos! -[![Como os Gráficos Enganam por Alberto Cairo](../../../../../translated_images/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.pt.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "Como os gráficos enganam") +[![Como os Gráficos Enganam por Alberto Cairo](../../../../../translated_images/pt/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "Como os gráficos enganam") > 🎥 Clique na imagem acima para assistir a uma palestra sobre gráficos enganosos Este gráfico inverte o eixo X para mostrar o oposto da verdade, com base na data: -![gráfico ruim 1](../../../../../translated_images/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.pt.png) +![gráfico ruim 1](../../../../../translated_images/pt/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.png) [Este gráfico](https://media.firstcoastnews.com/assets/WTLV/images/170ae16f-4643-438f-b689-50d66ca6a8d8/170ae16f-4643-438f-b689-50d66ca6a8d8_1140x641.jpg) é ainda mais enganoso, pois o olhar é atraído para a direita, levando à conclusão de que, ao longo do tempo, os casos de COVID diminuíram nos vários condados. Na verdade, ao observar atentamente as datas, percebe-se que elas foram reorganizadas para criar essa tendência descendente enganosa. -![gráfico ruim 2](../../../../../translated_images/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.pt.jpg) +![gráfico ruim 2](../../../../../translated_images/pt/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.jpg) Este exemplo notório usa cor E um eixo Y invertido para enganar: em vez de concluir que as mortes por armas aumentaram após a aprovação de uma legislação favorável às armas, o olhar é enganado para pensar que o oposto é verdadeiro: -![gráfico ruim 3](../../../../../translated_images/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.pt.jpg) +![gráfico ruim 3](../../../../../translated_images/pt/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.jpg) Este gráfico estranho mostra como a proporção pode ser manipulada, com efeito hilário: -![gráfico ruim 4](../../../../../translated_images/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.pt.jpg) +![gráfico ruim 4](../../../../../translated_images/pt/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.jpg) Comparar o incomparável é mais um truque duvidoso. Existe um [site maravilhoso](https://tylervigen.com/spurious-correlations) dedicado a 'correlações espúrias', exibindo 'fatos' que correlacionam coisas como a taxa de divórcio no Maine e o consumo de margarina. Um grupo no Reddit também coleta os [usos feios](https://www.reddit.com/r/dataisugly/top/?t=all) de dados. @@ -100,13 +100,13 @@ Rotule os seus eixos, forneça uma legenda, se necessário, e ofereça tooltips Se os seus dados forem textuais e extensos no eixo X, você pode inclinar o texto para melhorar a legibilidade. [plot3D](https://cran.r-project.org/web/packages/plot3D/index.html) oferece gráficos em 3D, se os seus dados suportarem. Visualizações de dados sofisticadas podem ser produzidas usando esta biblioteca. -![gráficos 3D](../../../../../translated_images/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.pt.png) +![gráficos 3D](../../../../../translated_images/pt/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.png) ## Exibição de gráficos animados e em 3D Algumas das melhores visualizações de dados hoje em dia são animadas. Shirley Wu tem exemplos incríveis feitos com D3, como '[film flowers](http://bl.ocks.org/sxywu/raw/d612c6c653fb8b4d7ff3d422be164a5d/)', onde cada flor é uma visualização de um filme. Outro exemplo para o Guardian é 'bussed out', uma experiência interativa que combina visualizações com Greensock e D3, além de um formato de artigo com narrativa para mostrar como NYC lida com o problema dos sem-teto, enviando pessoas para fora da cidade. -![busing](../../../../../translated_images/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.pt.png) +![busing](../../../../../translated_images/pt/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.png) > "Bussed Out: Como a América Move os Sem-Teto" do [Guardian](https://www.theguardian.com/us-news/ng-interactive/2017/dec/20/bussed-out-america-moves-homeless-people-country-study). Visualizações por Nadieh Bremer & Shirley Wu @@ -116,7 +116,7 @@ Embora esta lição não seja suficiente para ensinar em profundidade essas pode Você completará um aplicativo web que exibirá uma visão animada dessa rede social. Ele utiliza uma biblioteca criada para gerar uma [visualização de uma rede](https://github.com/emiliorizzo/vue-d3-network) usando Vue.js e D3. Quando o aplicativo estiver em execução, você pode mover os nós na tela para reorganizar os dados. -![liaisons](../../../../../translated_images/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.pt.png) +![liaisons](../../../../../translated_images/pt/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.png) ## Projeto: Crie um gráfico para mostrar uma rede usando D3.js diff --git a/translations/pt/3-Data-Visualization/README.md b/translations/pt/3-Data-Visualization/README.md index aaadb039..a4813d4f 100644 --- a/translations/pt/3-Data-Visualization/README.md +++ b/translations/pt/3-Data-Visualization/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Visualizações -![uma abelha numa flor de lavanda](../../../translated_images/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.pt.jpg) +![uma abelha numa flor de lavanda](../../../translated_images/pt/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.jpg) > Foto de Jenna Lee no Unsplash Visualizar dados é uma das tarefas mais importantes de um cientista de dados. Imagens valem mais do que mil palavras, e uma visualização pode ajudá-lo a identificar vários aspetos interessantes dos seus dados, como picos, valores atípicos, agrupamentos, tendências e muito mais, que podem ajudá-lo a compreender a história que os seus dados estão a tentar contar. diff --git a/translations/pt/4-Data-Science-Lifecycle/14-Introduction/README.md b/translations/pt/4-Data-Science-Lifecycle/14-Introduction/README.md index 6d60a728..bbb6763d 100644 --- a/translations/pt/4-Data-Science-Lifecycle/14-Introduction/README.md +++ b/translations/pt/4-Data-Science-Lifecycle/14-Introduction/README.md @@ -25,7 +25,7 @@ Neste ponto, provavelmente já percebeu que a ciência de dados é um processo. Esta lição foca-se em 3 partes do ciclo de vida: captura, processamento e manutenção. -![Diagrama do ciclo de vida da ciência de dados](../../../../translated_images/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.pt.jpg) +![Diagrama do ciclo de vida da ciência de dados](../../../../translated_images/pt/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.jpg) > Foto por [Berkeley School of Information](https://ischoolonline.berkeley.edu/data-science/what-is-data-science/) ## Captura @@ -101,7 +101,7 @@ Explore o [Ciclo de Vida do Processo de Ciência de Dados em Equipa](https://doc |Processo de Ciência de Dados em Equipa (TDSP)|Processo padrão da indústria para mineração de dados (CRISP-DM)| |--|--| -|![Ciclo de Vida do Processo de Ciência de Dados em Equipa](../../../../translated_images/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.pt.png) | ![Imagem do Processo de Ciência de Dados](../../../../translated_images/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.pt.png) | +|![Ciclo de Vida do Processo de Ciência de Dados em Equipa](../../../../translated_images/pt/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.png) | ![Imagem do Processo de Ciência de Dados](../../../../translated_images/pt/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.png) | | Imagem por [Microsoft](https://docs.microsoft.comazure/architecture/data-science-process/lifecycle) | Imagem por [Data Science Process Alliance](https://www.datascience-pm.com/crisp-dm-2/) | ## [Questionário Pós-Aula](https://ff-quizzes.netlify.app/en/ds/quiz/27) diff --git a/translations/pt/4-Data-Science-Lifecycle/README.md b/translations/pt/4-Data-Science-Lifecycle/README.md index 42acfb5b..e1317a8e 100644 --- a/translations/pt/4-Data-Science-Lifecycle/README.md +++ b/translations/pt/4-Data-Science-Lifecycle/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # O Ciclo de Vida da Ciência de Dados -![comunicação](../../../translated_images/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.pt.jpg) +![comunicação](../../../translated_images/pt/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.jpg) > Foto por Headway no Unsplash Nestes conteúdos, vais explorar alguns dos aspetos do ciclo de vida da Ciência de Dados, incluindo análise e comunicação de dados. diff --git a/translations/pt/5-Data-Science-In-Cloud/README.md b/translations/pt/5-Data-Science-In-Cloud/README.md index 09d6fb13..50002682 100644 --- a/translations/pt/5-Data-Science-In-Cloud/README.md +++ b/translations/pt/5-Data-Science-In-Cloud/README.md @@ -9,13 +9,13 @@ CO_OP_TRANSLATOR_METADATA: --> # Ciência de Dados na Cloud -![cloud-picture](../../../translated_images/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.pt.jpg) +![cloud-picture](../../../translated_images/pt/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.jpg) > Foto de [Jelleke Vanooteghem](https://unsplash.com/@ilumire) no [Unsplash](https://unsplash.com/s/photos/cloud?orientation=landscape) Quando se trata de fazer ciência de dados com big data, a cloud pode ser um divisor de águas. Nas próximas três lições, vamos explorar o que é a cloud e por que ela pode ser tão útil. Também vamos analisar um conjunto de dados sobre insuficiência cardíaca e construir um modelo para ajudar a avaliar a probabilidade de alguém sofrer de insuficiência cardíaca. Utilizaremos o poder da cloud para treinar, implementar e consumir um modelo de duas formas diferentes. Uma forma será utilizando apenas a interface de utilizador, num estilo de "Low code/No code", e a outra será através do Azure Machine Learning Software Developer Kit (Azure ML SDK). -![project-schema](../../../translated_images/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.pt.png) +![project-schema](../../../translated_images/pt/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.png) ### Tópicos diff --git a/translations/pt/6-Data-Science-In-Wild/20-Real-World-Examples/README.md b/translations/pt/6-Data-Science-In-Wild/20-Real-World-Examples/README.md index e43390ab..ca4bbc1b 100644 --- a/translations/pt/6-Data-Science-In-Wild/20-Real-World-Examples/README.md +++ b/translations/pt/6-Data-Science-In-Wild/20-Real-World-Examples/README.md @@ -41,7 +41,7 @@ Graças à democratização da IA, os desenvolvedores estão a encontrar formas * [Ciência de Dados na Saúde](https://data-flair.training/blogs/data-science-in-healthcare/) - destaca aplicações como imagiologia médica (e.g., ressonância magnética, raio-X, tomografia), genómica (sequenciamento de DNA), desenvolvimento de medicamentos (avaliação de risco, previsão de sucesso), análise preditiva (cuidados ao paciente e logística de fornecimento), rastreamento e prevenção de doenças, etc. -![Aplicações de Ciência de Dados no Mundo Real](../../../../translated_images/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.pt.png) Crédito da Imagem: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) +![Aplicações de Ciência de Dados no Mundo Real](../../../../translated_images/pt/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.png) Crédito da Imagem: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) A figura mostra outros domínios e exemplos de aplicação de técnicas de ciência de dados. Queres explorar outras aplicações? Consulta a secção [Revisão e Autoestudo](../../../../6-Data-Science-In-Wild/20-Real-World-Examples) abaixo. diff --git a/translations/pt/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md b/translations/pt/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md index fa5a7cc6..e7a834e5 100644 --- a/translations/pt/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md +++ b/translations/pt/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md @@ -22,7 +22,7 @@ A interface do Explorer (mostrada na imagem abaixo) permite-te selecionar um con 2. Explorar o [Catálogo](https://planetarycomputer.microsoft.com/catalog) de conjuntos de dados - aprender o propósito de cada um. 3. Usar o Explorer - escolher um conjunto de dados do teu interesse, selecionar uma consulta relevante e uma opção de renderização. -![O Explorer do Planetary Computer](../../../../translated_images/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.pt.png) +![O Explorer do Planetary Computer](../../../../translated_images/pt/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.png) `A Tua Tarefa:` Agora analisa a visualização que foi gerada no navegador e responde às seguintes questões: diff --git a/translations/pt/CONTRIBUTING.md b/translations/pt/CONTRIBUTING.md index 62426c68..c49d3ac3 100644 --- a/translations/pt/CONTRIBUTING.md +++ b/translations/pt/CONTRIBUTING.md @@ -316,7 +316,7 @@ Inclua na descrição do seu PR: ``` ```` -- Adicione texto alternativo às imagens: `![Texto alternativo](../../translated_images/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.pt.png)` +- Adicione texto alternativo às imagens: `![Texto alternativo](../../translated_images/pt/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.png)` - Mantenha comprimentos de linha razoáveis (cerca de 80-100 caracteres) ### Python diff --git a/translations/pt/README.md b/translations/pt/README.md index 24608ca9..ea76e54a 100644 --- a/translations/pt/README.md +++ b/translations/pt/README.md @@ -28,7 +28,7 @@ Os Azure Cloud Advocates da Microsoft têm o prazer de oferecer um currículo de **🙏 Agradecimentos especiais 🙏 aos nossos autores, revisores e contribuidores de conteúdo do [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/),** nomeadamente Aaryan Arora, [Aditya Garg](https://github.com/AdityaGarg00), [Alondra Sanchez](https://www.linkedin.com/in/alondra-sanchez-molina/), [Ankita Singh](https://www.linkedin.com/in/ankitasingh007), [Anupam Mishra](https://www.linkedin.com/in/anupam--mishra/), [Arpita Das](https://www.linkedin.com/in/arpitadas01/), ChhailBihari Dubey, [Dibri Nsofor](https://www.linkedin.com/in/dibrinsofor), [Dishita Bhasin](https://www.linkedin.com/in/dishita-bhasin-7065281bb), [Majd Safi](https://www.linkedin.com/in/majd-s/), [Max Blum](https://www.linkedin.com/in/max-blum-6036a1186/), [Miguel Correa](https://www.linkedin.com/in/miguelmque/), [Mohamma Iftekher (Iftu) Ebne Jalal](https://twitter.com/iftu119), [Nawrin Tabassum](https://www.linkedin.com/in/nawrin-tabassum), [Raymond Wangsa Putra](https://www.linkedin.com/in/raymond-wp/), [Rohit Yadav](https://www.linkedin.com/in/rty2423), Samridhi Sharma, [Sanya Sinha](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200), [Sheena Narula](https://www.linkedin.com/in/sheena-narua-n/), [Tauqeer Ahmad](https://www.linkedin.com/in/tauqeerahmad5201/), Yogendrasingh Pawar , [Vidushi Gupta](https://www.linkedin.com/in/vidushi-gupta07/), [Jasleen Sondhi](https://www.linkedin.com/in/jasleen-sondhi/) -|![Sketchnote por @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.pt.png)| +|![Sketchnote por @sketchthedocs https://sketchthedocs.dev](../../translated_images/pt/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.png)| |:---:| | Ciência de Dados para Iniciantes - _Sketchnote por [@nitya](https://twitter.com/nitya)_ | @@ -47,7 +47,7 @@ Os Azure Cloud Advocates da Microsoft têm o prazer de oferecer um currículo de Temos uma série Discord "Learn with AI" em curso — saiba mais e junte-se a nós em [Learn with AI Series](https://aka.ms/learnwithai/discord) de 18 a 30 de setembro de 2025. Vai receber dicas e truques sobre como usar o GitHub Copilot para Ciência de Dados. -![Série Aprender com IA](../../translated_images/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.pt.jpg) +![Série Aprender com IA](../../translated_images/pt/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.jpg) # És estudante? @@ -127,7 +127,7 @@ Cada exemplo inclui comentários detalhados que explicam cada passo, tornando-o ## Lições -|![ Sketchnote por @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.pt.png)| +|![ Sketchnote por @sketchthedocs https://sketchthedocs.dev](../../translated_images/pt/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png)| |:---:| | Ciência de Dados para Iniciantes: Roteiro - _Sketchnote por [@nitya](https://twitter.com/nitya)_ | diff --git a/translations/pt/sketchnotes/README.md b/translations/pt/sketchnotes/README.md index 97259238..0a2e90d4 100644 --- a/translations/pt/sketchnotes/README.md +++ b/translations/pt/sketchnotes/README.md @@ -13,7 +13,7 @@ Encontre todas as sketchnotes aqui! Nitya Narasimhan, artista -![sketchnote do roadmap](../../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.pt.png) +![sketchnote do roadmap](../../../translated_images/pt/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png) **Aviso Legal**: Este documento foi traduzido utilizando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, esteja ciente de que traduções automáticas podem conter erros ou imprecisões. O documento original no seu idioma nativo deve ser considerado a fonte autoritária. Para informações críticas, recomenda-se uma tradução profissional realizada por humanos. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações incorretas resultantes do uso desta tradução. \ No newline at end of file diff --git a/translations/ro/1-Introduction/01-defining-data-science/README.md b/translations/ro/1-Introduction/01-defining-data-science/README.md index 0a028cdf..e98f15ef 100644 --- a/translations/ro/1-Introduction/01-defining-data-science/README.md +++ b/translations/ro/1-Introduction/01-defining-data-science/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: --- -[![Video despre Definirea Științei Datelor](../../../../translated_images/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.ro.png)](https://youtu.be/beZ7Mb_oz9I) +[![Video despre Definirea Științei Datelor](../../../../translated_images/ro/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.png)](https://youtu.be/beZ7Mb_oz9I) ## [Chestionar înainte de curs](https://ff-quizzes.netlify.app/en/ds/quiz/0) @@ -153,7 +153,7 @@ Dacă dorim să complicăm și mai mult lucrurile, putem reprezenta grafic timpu În această provocare, vom încerca să identificăm concepte relevante pentru domeniul Științei Datelor analizând texte. Vom lua un articol de pe Wikipedia despre Știința Datelor, vom descărca și procesa textul, iar apoi vom construi un nor de cuvinte asemănător cu acesta: -![Nor de cuvinte pentru Știința Datelor](../../../../translated_images/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.ro.png) +![Nor de cuvinte pentru Știința Datelor](../../../../translated_images/ro/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.png) Vizitează [`notebook.ipynb`](../../../../1-Introduction/01-defining-data-science/notebook.ipynb ':ignore') pentru a parcurge codul. Poți, de asemenea, să rulezi codul și să vezi cum efectuează toate transformările de date în timp real. diff --git a/translations/ro/1-Introduction/04-stats-and-probability/README.md b/translations/ro/1-Introduction/04-stats-and-probability/README.md index 3a4ce75a..30e5cb79 100644 --- a/translations/ro/1-Introduction/04-stats-and-probability/README.md +++ b/translations/ro/1-Introduction/04-stats-and-probability/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: Teoria statisticii și probabilității sunt două domenii strâns legate ale matematicii, extrem de relevante pentru știința datelor. Este posibil să lucrăm cu date fără o cunoaștere profundă a matematicii, dar este totuși mai bine să știm cel puțin câteva concepte de bază. Aici vom prezenta o scurtă introducere care te va ajuta să începi. -[![Video introductiv](../../../../translated_images/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.ro.png)](https://youtu.be/Z5Zy85g4Yjw) +[![Video introductiv](../../../../translated_images/ro/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.png)](https://youtu.be/Z5Zy85g4Yjw) ## [Chestionar înainte de lecție](https://ff-quizzes.netlify.app/en/ds/quiz/6) @@ -39,7 +39,7 @@ Este mai dificil să descriem distribuția probabilității unei variabile conti Putem vorbi doar despre probabilitatea ca o variabilă să se încadreze într-un anumit interval de valori, de exemplu P(t1≤X2). În acest caz, distribuția probabilității este descrisă printr-o **funcție de densitate a probabilității** p(x), astfel încât -![P(t_1\le X Mai multe exemple de corelație și covarianță pot fi găsite în [notebook-ul asociat](notebook.ipynb). diff --git a/translations/ro/1-Introduction/README.md b/translations/ro/1-Introduction/README.md index dc427af7..9dc885b6 100644 --- a/translations/ro/1-Introduction/README.md +++ b/translations/ro/1-Introduction/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Introducere în Știința Datelor -![date în acțiune](../../../translated_images/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.ro.jpg) +![date în acțiune](../../../translated_images/ro/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.jpg) > Fotografie de Stephen Dawson pe Unsplash În aceste lecții, vei descoperi cum este definită Știința Datelor și vei învăța despre considerațiile etice pe care trebuie să le ia în calcul un specialist în date. De asemenea, vei învăța cum sunt definite datele și vei afla câte ceva despre statistică și probabilitate, domeniile academice de bază ale Științei Datelor. diff --git a/translations/ro/2-Working-With-Data/07-python/README.md b/translations/ro/2-Working-With-Data/07-python/README.md index 2736c4f1..259c379f 100644 --- a/translations/ro/2-Working-With-Data/07-python/README.md +++ b/translations/ro/2-Working-With-Data/07-python/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: | :-------------------------------------------------------------------------------------------------------: | | Lucrul cu Python - _Sketchnote de [@nitya](https://twitter.com/nitya)_ | -[![Video Introductiv](../../../../translated_images/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.ro.png)](https://youtu.be/dZjWOGbsN4Y) +[![Video Introductiv](../../../../translated_images/ro/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.png)](https://youtu.be/dZjWOGbsN4Y) Deși bazele de date oferă modalități foarte eficiente de stocare a datelor și de interogare a acestora folosind limbaje de interogare, cea mai flexibilă metodă de procesare a datelor este scrierea propriului program pentru manipularea datelor. În multe cazuri, o interogare SQL ar fi o metodă mai eficientă. Totuși, în unele situații, când este necesară o procesare mai complexă a datelor, aceasta nu poate fi realizată ușor folosind SQL. Procesarea datelor poate fi programată în orice limbaj de programare, dar există anumite limbaje care sunt mai potrivite pentru lucrul cu date. De obicei, oamenii de știință în domeniul datelor preferă unul dintre următoarele limbaje: @@ -75,7 +75,7 @@ print(f"Length of index is {len(idx)}") items_sold = pd.Series(np.random.randint(25,50,size=len(idx)),index=idx) items_sold.plot() ``` -![Grafic Serii Temporale](../../../../translated_images/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.ro.png) +![Grafic Serii Temporale](../../../../translated_images/ro/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.png) Acum să presupunem că în fiecare săptămână organizăm o petrecere pentru prieteni și luăm 10 pachete suplimentare de înghețată pentru petrecere. Putem crea un alt series, indexat pe săptămână, pentru a demonstra acest lucru: ```python @@ -86,7 +86,7 @@ Când adunăm cele două series, obținem numărul total: total_items = items_sold.add(additional_items,fill_value=0) total_items.plot() ``` -![Grafic Serii Temporale](../../../../translated_images/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.ro.png) +![Grafic Serii Temporale](../../../../translated_images/ro/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.png) > **Notă** că nu folosim sintaxa simplă `total_items+additional_items`. Dacă am fi făcut acest lucru, am fi obținut multe valori `NaN` (*Not a Number*) în series-ul rezultat. Acest lucru se întâmplă deoarece lipsesc valori pentru unele puncte de index în series-ul `additional_items`, iar adunarea `NaN` cu orice altceva rezultă în `NaN`. Astfel, trebuie să specificăm parametrul `fill_value` în timpul adunării. @@ -95,7 +95,7 @@ Cu serii temporale, putem **resampla** series-ul cu intervale de timp diferite. monthly = total_items.resample("1M").mean() ax = monthly.plot(kind='bar') ``` -![Medii Lunare Serii Temporale](../../../../translated_images/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.ro.png) +![Medii Lunare Serii Temporale](../../../../translated_images/ro/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.png) ### DataFrame @@ -221,7 +221,7 @@ Prima problemă pe care ne vom concentra este modelarea răspândirii epidemiei Deoarece dorim să demonstrăm cum să lucrăm cu datele, vă invităm să deschideți [`notebook-covidspread.ipynb`](notebook-covidspread.ipynb) și să îl citiți de la început până la sfârșit. Puteți, de asemenea, să executați celulele și să rezolvați unele provocări pe care le-am lăsat pentru voi la final. -![COVID Spread](../../../../translated_images/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.ro.png) +![COVID Spread](../../../../translated_images/ro/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.png) > Dacă nu știți cum să rulați codul în Jupyter Notebook, consultați [acest articol](https://soshnikov.com/education/how-to-execute-notebooks-from-github/). @@ -243,7 +243,7 @@ Un exemplu complet de analiză a acestui set de date folosind serviciul cognitiv Deschideți [`notebook-papers.ipynb`](notebook-papers.ipynb) și citiți-l de la început până la sfârșit. Puteți, de asemenea, să executați celulele și să rezolvați unele provocări pe care le-am lăsat pentru voi la final. -![Covid Medical Treatment](../../../../translated_images/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.ro.png) +![Covid Medical Treatment](../../../../translated_images/ro/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.png) ## Procesarea Datelor din Imagini diff --git a/translations/ro/2-Working-With-Data/README.md b/translations/ro/2-Working-With-Data/README.md index 5fc4ebd2..5189badf 100644 --- a/translations/ro/2-Working-With-Data/README.md +++ b/translations/ro/2-Working-With-Data/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Lucrul cu Date -![data love](../../../translated_images/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.ro.jpg) +![data love](../../../translated_images/ro/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.jpg) > Fotografie de Alexander Sinn pe Unsplash În aceste lecții, vei învăța câteva dintre modurile în care datele pot fi gestionate, manipulate și utilizate în aplicații. Vei învăța despre bazele de date relaționale și non-relaționale și despre cum pot fi stocate datele în acestea. De asemenea, vei învăța elementele de bază ale lucrului cu Python pentru a gestiona datele și vei descoperi câteva dintre numeroasele moduri în care poți folosi Python pentru a gestiona și analiza datele. diff --git a/translations/ro/3-Data-Visualization/12-visualization-relationships/README.md b/translations/ro/3-Data-Visualization/12-visualization-relationships/README.md index a9e306fe..343f0561 100644 --- a/translations/ro/3-Data-Visualization/12-visualization-relationships/README.md +++ b/translations/ro/3-Data-Visualization/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ Creează o diagramă scatter de bază pentru a arăta relația dintre prețul pe ```python sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ``` -![scatterplot 1](../../../../translated_images/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.ro.png) +![scatterplot 1](../../../../translated_images/ro/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.png) Acum, afișează aceleași date cu o schemă de culori inspirată de miere pentru a arăta cum evoluează prețul de-a lungul anilor. Poți face acest lucru adăugând un parametru 'hue' pentru a evidenția schimbările anuale: @@ -60,7 +60,7 @@ Acum, afișează aceleași date cu o schemă de culori inspirată de miere pentr ```python sns.relplot(x="priceperlb", y="state", hue="year", palette="YlOrBr", data=honey, height=15, aspect=.5); ``` -![scatterplot 2](../../../../translated_images/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.ro.png) +![scatterplot 2](../../../../translated_images/ro/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.png) Cu această schimbare de culori, poți observa clar o progresie puternică de-a lungul anilor în ceea ce privește prețul pe liră al mierii. De fapt, dacă verifici un set de date eșantion (de exemplu, statul Arizona), poți observa un model de creștere a prețului an de an, cu câteva excepții: @@ -89,7 +89,7 @@ sns.relplot(x="priceperlb", y="state", size="year", data=honey, height=15, aspec ``` Poți observa cum dimensiunea punctelor crește treptat. -![scatterplot 3](../../../../translated_images/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.ro.png) +![scatterplot 3](../../../../translated_images/ro/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.png) Este acesta un caz simplu de cerere și ofertă? Din cauza unor factori precum schimbările climatice și colapsul coloniilor, există mai puțină miere disponibilă pentru cumpărare an de an, ceea ce duce la creșterea prețului? @@ -104,7 +104,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); ``` Răspuns: Da, cu câteva excepții în jurul anului 2003: -![line chart 1](../../../../translated_images/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.ro.png) +![line chart 1](../../../../translated_images/ro/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.png) ✅ Deoarece Seaborn agregă datele într-o singură linie, afișează "măsurătorile multiple pentru fiecare valoare x prin reprezentarea mediei și a intervalului de încredere de 95% în jurul mediei". [Sursa](https://seaborn.pydata.org/tutorial/relational.html). Acest comportament consumator de timp poate fi dezactivat adăugând `ci=None`. @@ -114,7 +114,7 @@ Răspuns: Da, cu câteva excepții în jurul anului 2003: sns.relplot(x="year", y="totalprod", kind="line", data=honey); ``` -![line chart 2](../../../../translated_images/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.ro.png) +![line chart 2](../../../../translated_images/ro/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.png) Răspuns: Nu chiar. Dacă te uiți la producția totală, aceasta pare să fi crescut în acel an, deși, în general, cantitatea de miere produsă este în scădere în acești ani. @@ -139,7 +139,7 @@ sns.relplot( ``` În această vizualizare, poți compara producția per colonie și numărul de colonii an de an, alăturat, cu o împărțire pe 3 coloane: -![facet grid](../../../../translated_images/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.ro.png) +![facet grid](../../../../translated_images/ro/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.png) Pentru acest set de date, nimic nu iese în evidență în mod special în ceea ce privește numărul de colonii și producția lor, an de an și stat de stat. Există o altă modalitate de a analiza corelația dintre aceste două variabile? @@ -162,7 +162,7 @@ sns.despine(right=False) plt.ylabel('colony yield') ax.figure.legend(); ``` -![superimposed plots](../../../../translated_images/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.ro.png) +![superimposed plots](../../../../translated_images/ro/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.png) Deși nimic nu sare în ochi în jurul anului 2003, acest grafic ne permite să încheiem lecția pe o notă mai optimistă: deși numărul coloniilor este în scădere, acesta pare să se stabilizeze, chiar dacă producția per colonie este în scădere. diff --git a/translations/ro/3-Data-Visualization/R/09-visualization-quantities/README.md b/translations/ro/3-Data-Visualization/R/09-visualization-quantities/README.md index ae72d5ad..bc0a8479 100644 --- a/translations/ro/3-Data-Visualization/R/09-visualization-quantities/README.md +++ b/translations/ro/3-Data-Visualization/R/09-visualization-quantities/README.md @@ -66,7 +66,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` Aici, instalezi pachetul `ggplot2` și apoi îl importi în spațiul de lucru folosind comanda `library("ggplot2")`. Pentru a crea orice grafic în ggplot, se folosește funcția `ggplot()` și specifici setul de date, variabilele x și y ca atribute. În acest caz, folosim funcția `geom_line()` deoarece dorim să creăm un grafic liniar. -![MaxWingspan-lineplot](../../../../../translated_images/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.ro.png) +![MaxWingspan-lineplot](../../../../../translated_images/ro/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.png) Ce observi imediat? Pare să existe cel puțin un outlier - ce anvergură impresionantă! O anvergură de peste 2000 de centimetri înseamnă mai mult de 20 de metri - sunt Pterodactili care zboară prin Minnesota? Să investigăm. @@ -84,7 +84,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` Specificăm unghiul în `theme` și specificăm etichetele axelor x și y în `xlab()` și `ylab()` respectiv. Funcția `ggtitle()` oferă un nume graficului. -![MaxWingspan-lineplot-improved](../../../../../translated_images/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.ro.png) +![MaxWingspan-lineplot-improved](../../../../../translated_images/ro/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.png) Chiar și cu rotația etichetelor setată la 45 de grade, sunt prea multe pentru a fi citite. Să încercăm o strategie diferită: etichetează doar outlierii și setează etichetele în interiorul graficului. Poți folosi un grafic scatter pentru a face mai mult loc etichetării: @@ -100,7 +100,7 @@ Ce se întâmplă aici? Ai folosit funcția `geom_point()` pentru a reprezenta p Ce descoperi? -![MaxWingspan-scatterplot](../../../../../translated_images/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.ro.png) +![MaxWingspan-scatterplot](../../../../../translated_images/ro/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.png) ## Filtrează datele tale @@ -119,7 +119,7 @@ ggplot(data=birds_filtered, aes(x=Name, y=MaxWingspan,group=1)) + ``` Am creat un nou cadru de date `birds_filtered` și apoi am reprezentat un grafic scatter. Prin filtrarea outlierilor, datele tale sunt acum mai coerente și mai ușor de înțeles. -![MaxWingspan-scatterplot-improved](../../../../../translated_images/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.ro.png) +![MaxWingspan-scatterplot-improved](../../../../../translated_images/ro/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.png) Acum că avem un set de date mai curat, cel puțin în ceea ce privește anvergura aripilor, să descoperim mai multe despre aceste păsări. @@ -160,7 +160,7 @@ birds_filtered %>% group_by(Category) %>% ``` În următorul fragment, instalăm pachetele [dplyr](https://www.rdocumentation.org/packages/dplyr/versions/0.7.8) și [lubridate](https://www.rdocumentation.org/packages/lubridate/versions/1.8.0) pentru a ajuta la manipularea și gruparea datelor în vederea creării unui grafic cu bare stivuite. Mai întâi, grupăm datele după `Category` și apoi sumarizăm coloanele `MinLength`, `MaxLength`, `MinBodyMass`, `MaxBodyMass`, `MinWingspan`, `MaxWingspan`. Apoi, reprezentăm graficul cu bare folosind pachetul `ggplot2` și specificăm culorile pentru diferitele categorii și etichetele. -![Stacked bar chart](../../../../../translated_images/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.ro.png) +![Stacked bar chart](../../../../../translated_images/ro/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.png) Acest grafic cu bare, totuși, este greu de citit deoarece există prea multe date negrupate. Trebuie să selectezi doar datele pe care vrei să le reprezinți grafic, așa că să analizăm lungimea păsărilor în funcție de categoria lor. @@ -175,7 +175,7 @@ ggplot(birds_count,aes(Category,n))+geom_bar(stat="identity")+coord_flip() ``` Mai întâi numeri valorile unice din coloana `Category` și apoi le sortezi într-un nou cadru de date `birds_count`. Aceste date sortate sunt apoi factorizate la același nivel pentru a fi reprezentate grafic în mod ordonat. Folosind `ggplot2`, reprezinți grafic datele într-un grafic cu bare. Funcția `coord_flip()` afișează barele orizontal. -![category-length](../../../../../translated_images/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.ro.png) +![category-length](../../../../../translated_images/ro/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.png) Acest grafic cu bare oferă o vedere bună asupra numărului de păsări din fiecare categorie. Dintr-o privire, vezi că cel mai mare număr de păsări din această regiune sunt în categoria Rațe/Gâște/Păsări acvatice. Minnesota este 'țara celor 10.000 de lacuri', așa că acest lucru nu este surprinzător! @@ -198,7 +198,7 @@ ggplot(birds_grouped,aes(Category,MaxLength))+geom_bar(stat="identity")+coord_fl ``` Grupăm datele `birds_filtered` după `Category` și apoi reprezentăm grafic un grafic cu bare. -![comparing data](../../../../../translated_images/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.ro.png) +![comparing data](../../../../../translated_images/ro/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.png) Nimic nu este surprinzător aici: colibrii au cea mai mică LungimeMaximă comparativ cu Pelicanii sau Gâștele. Este bine când datele au sens logic! @@ -210,7 +210,7 @@ ggplot(data=birds_grouped, aes(x=Category)) + geom_bar(aes(y=MinLength), stat="identity", position="identity", fill='orange')+ coord_flip() ``` -![super-imposed values](../../../../../translated_images/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.ro.png) +![super-imposed values](../../../../../translated_images/ro/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.png) ## 🚀 Provocare diff --git a/translations/ro/3-Data-Visualization/R/10-visualization-distributions/README.md b/translations/ro/3-Data-Visualization/R/10-visualization-distributions/README.md index 21905071..6b70b4f1 100644 --- a/translations/ro/3-Data-Visualization/R/10-visualization-distributions/README.md +++ b/translations/ro/3-Data-Visualization/R/10-visualization-distributions/README.md @@ -45,7 +45,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + geom_point() + ggtitle("Max Length per order") + coord_flip() ``` -![lungime maximă per ordin](../../../../../translated_images/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.ro.png) +![lungime maximă per ordin](../../../../../translated_images/ro/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.png) Acest grafic oferă o privire de ansamblu asupra distribuției generale a lungimii corpului per Ordin de păsări, dar nu este cea mai bună modalitate de a afișa distribuțiile reale. Această sarcină este de obicei realizată prin crearea unui histogramă. ## Lucrul cu histograme @@ -56,7 +56,7 @@ Acest grafic oferă o privire de ansamblu asupra distribuției generale a lungim ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=10)+ylab('Frequency') ``` -![distribuție pe întregul set de date](../../../../../translated_images/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.ro.png) +![distribuție pe întregul set de date](../../../../../translated_images/ro/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.png) După cum poți vedea, majoritatea celor 400+ păsări din acest set de date se încadrează în intervalul de sub 2000 pentru masa corporală maximă. Obține mai multe informații despre date modificând parametrul `bins` la un număr mai mare, cum ar fi 30: @@ -64,7 +64,7 @@ După cum poți vedea, majoritatea celor 400+ păsări din acest set de date se ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![distribuție-30bins](../../../../../translated_images/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.ro.png) +![distribuție-30bins](../../../../../translated_images/ro/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.png) Acest grafic arată distribuția într-un mod puțin mai detaliat. Un grafic mai puțin înclinat spre stânga ar putea fi creat asigurându-te că selectezi doar datele dintr-un anumit interval: @@ -76,7 +76,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![histogramă filtrată](../../../../../translated_images/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.ro.png) +![histogramă filtrată](../../../../../translated_images/ro/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.png) ✅ Încearcă alte filtre și puncte de date. Pentru a vedea distribuția completă a datelor, elimină filtrul `['MaxBodyMass']` pentru a afișa distribuțiile etichetate. @@ -90,7 +90,7 @@ ggplot(data=birds_filtered_1, aes(x=MaxBodyMass, y=MaxLength) ) + ``` Pare să existe o corelație așteptată între aceste două elemente de-a lungul unui ax previzibil, cu un punct de convergență deosebit de puternic: -![grafic 2d](../../../../../translated_images/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.ro.png) +![grafic 2d](../../../../../translated_images/ro/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.png) Histogramele funcționează bine în mod implicit pentru date numerice. Ce se întâmplă dacă trebuie să vezi distribuții în funcție de date text? ## Explorează setul de date pentru distribuții utilizând date text @@ -121,7 +121,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + scale_fill_manual(name="Conservation Status",values=c("red","green","blue","pink"),labels=c("Endangered","Near Threathened","Vulnerable","Least Concern")) ``` -![anvergură și status conservare](../../../../../translated_images/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.ro.png) +![anvergură și status conservare](../../../../../translated_images/ro/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.png) Nu pare să existe o corelație bună între anvergura minimă a aripilor și statusul de conservare. Testează alte elemente ale setului de date utilizând această metodă. Poți încerca diferite filtre. Găsești vreo corelație? @@ -135,7 +135,7 @@ Să lucrăm acum cu grafice de densitate! ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + geom_density() ``` -![grafic de densitate](../../../../../translated_images/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.ro.png) +![grafic de densitate](../../../../../translated_images/ro/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.png) Poți vedea cum graficul reflectă cel anterior pentru datele despre anvergura minimă a aripilor; este doar puțin mai lin. Dacă ai vrea să revizitezi acea linie „în trepte” a masei corporale maxime din al doilea grafic pe care l-ai construit, ai putea să o netezești foarte bine recreând-o utilizând această metodă: @@ -143,7 +143,7 @@ Poți vedea cum graficul reflectă cel anterior pentru datele despre anvergura m ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density() ``` -![densitate masă corporală](../../../../../translated_images/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.ro.png) +![densitate masă corporală](../../../../../translated_images/ro/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.png) Dacă ai vrea o linie netedă, dar nu prea netedă, editează parametrul `adjust`: @@ -151,7 +151,7 @@ Dacă ai vrea o linie netedă, dar nu prea netedă, editează parametrul `adjust ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density(adjust = 1/5) ``` -![masă corporală mai puțin netedă](../../../../../translated_images/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.ro.png) +![masă corporală mai puțin netedă](../../../../../translated_images/ro/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.png) ✅ Citește despre parametrii disponibili pentru acest tip de grafic și experimentează! @@ -161,7 +161,7 @@ Acest tip de grafic oferă vizualizări explicative frumoase. Cu câteva linii d ggplot(data=birds_filtered_1,aes(x = MaxBodyMass, fill = Order)) + geom_density(alpha=0.5) ``` -![masă corporală per ordin](../../../../../translated_images/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.ro.png) +![masă corporală per ordin](../../../../../translated_images/ro/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.png) ## 🚀 Provocare diff --git a/translations/ro/3-Data-Visualization/R/11-visualization-proportions/README.md b/translations/ro/3-Data-Visualization/R/11-visualization-proportions/README.md index 2f5bec1c..49646b5d 100644 --- a/translations/ro/3-Data-Visualization/R/11-visualization-proportions/README.md +++ b/translations/ro/3-Data-Visualization/R/11-visualization-proportions/README.md @@ -93,7 +93,7 @@ pie(grouped$count,grouped$class, main="Edible?") ``` Voila, un grafic de tip plăcintă care arată proporțiile acestor date conform celor două clase de ciuperci. Este foarte important să obții ordinea corectă a etichetelor, mai ales aici, așa că asigură-te că verifici ordinea în care este construită matricea de etichete! -![grafic plăcintă](../../../../../translated_images/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.ro.png) +![grafic plăcintă](../../../../../translated_images/ro/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.png) ## Gogoși! @@ -127,7 +127,7 @@ library(webr) PieDonut(habitat, aes(habitat, count=count)) ``` -![grafic gogoașă](../../../../../translated_images/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.ro.png) +![grafic gogoașă](../../../../../translated_images/ro/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.png) Acest cod folosește două biblioteci - ggplot2 și webr. Folosind funcția PieDonut din biblioteca webr, putem crea ușor un grafic de tip gogoașă! @@ -165,7 +165,7 @@ waffle((cap_color$count/10), rows = 7, title = "Waffle Chart")+scale_fill_manual Folosind un grafic de tip vafă, poți vedea clar proporțiile culorilor pălăriilor din acest set de date despre ciuperci. Interesant, există multe ciuperci cu pălării verzi! -![grafic vafă](../../../../../translated_images/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.ro.png) +![grafic vafă](../../../../../translated_images/ro/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.png) În această lecție, ai învățat trei modalități de a vizualiza proporțiile. Mai întâi, trebuie să grupezi datele în categorii și apoi să decizi care este cea mai bună modalitate de a afișa datele - plăcintă, gogoașă sau vafă. Toate sunt delicioase și oferă utilizatorului o imagine instantanee a unui set de date. diff --git a/translations/ro/3-Data-Visualization/R/12-visualization-relationships/README.md b/translations/ro/3-Data-Visualization/R/12-visualization-relationships/README.md index 079d4106..8c1d0819 100644 --- a/translations/ro/3-Data-Visualization/R/12-visualization-relationships/README.md +++ b/translations/ro/3-Data-Visualization/R/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ library(ggplot2) ggplot(honey, aes(x = priceperlb, y = state)) + geom_point(colour = "blue") ``` -![scatterplot 1](../../../../../translated_images/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.ro.png) +![scatterplot 1](../../../../../translated_images/ro/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.png) Acum, afișează aceleași date cu o schemă de culori inspirată de miere pentru a arăta cum evoluează prețul de-a lungul anilor. Poți face acest lucru adăugând un parametru 'scale_color_gradientn' pentru a arăta schimbarea, an după an: @@ -61,7 +61,7 @@ Acum, afișează aceleași date cu o schemă de culori inspirată de miere pentr ggplot(honey, aes(x = priceperlb, y = state, color=year)) + geom_point()+scale_color_gradientn(colours = colorspace::heat_hcl(7)) ``` -![scatterplot 2](../../../../../translated_images/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.ro.png) +![scatterplot 2](../../../../../translated_images/ro/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.png) Cu această schimbare de schemă de culori, poți observa clar o progresie puternică de-a lungul anilor în ceea ce privește prețul mierii per kilogram. De fapt, dacă analizezi un set de date exemplu pentru a verifica (alege un stat, Arizona, de exemplu), poți observa un model de creștere a prețului an după an, cu câteva excepții: @@ -92,7 +92,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ``` Poți observa cum dimensiunea punctelor crește treptat. -![scatterplot 3](../../../../../translated_images/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.ro.png) +![scatterplot 3](../../../../../translated_images/ro/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.png) Este acesta un caz simplu de cerere și ofertă? Din cauza unor factori precum schimbările climatice și colapsul coloniilor, există mai puțină miere disponibilă pentru cumpărare an după an, iar astfel prețul crește? @@ -107,7 +107,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab ``` Răspuns: Da, cu câteva excepții în jurul anului 2003: -![line chart 1](../../../../../translated_images/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.ro.png) +![line chart 1](../../../../../translated_images/ro/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.png) Întrebare: Ei bine, în 2003 putem observa și o creștere a stocului de miere? Ce se întâmplă dacă analizezi producția totală an după an? @@ -115,7 +115,7 @@ Răspuns: Da, cu câteva excepții în jurul anului 2003: qplot(honey$year,honey$totalprod, geom='smooth', span =0.5, xlab = "year",ylab = "totalprod") ``` -![line chart 2](../../../../../translated_images/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.ro.png) +![line chart 2](../../../../../translated_images/ro/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.png) Răspuns: Nu chiar. Dacă te uiți la producția totală, pare să fi crescut în acel an, deși, în general, cantitatea de miere produsă este în scădere în acești ani. @@ -135,7 +135,7 @@ ggplot(honey, aes(x=yieldpercol, y = numcol,group = 1)) + ``` În această vizualizare, poți compara producția per colonie și numărul de colonii an după an, alăturate, cu o grilă setată la 3 coloane: -![facet grid](../../../../../translated_images/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.ro.png) +![facet grid](../../../../../translated_images/ro/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.png) Pentru acest set de date, nimic nu pare să iasă în evidență în ceea ce privește numărul de colonii și producția lor, an după an și stat după stat. Există o altă modalitate de a găsi o corelație între aceste două variabile? @@ -152,7 +152,7 @@ plot(honey$year, honey$yieldpercol, pch = 17, col = 3, axis(side = 4, at = pretty(range(y2))) mtext("colony yield", side = 4, line = 3) ``` -![superimposed plots](../../../../../translated_images/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.ro.png) +![superimposed plots](../../../../../translated_images/ro/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.png) Deși nimic nu sare în ochi în jurul anului 2003, acest lucru ne permite să încheiem lecția pe o notă puțin mai optimistă: deși numărul de colonii este în scădere, acesta pare să se stabilizeze, chiar dacă producția per colonie este în scădere. diff --git a/translations/ro/3-Data-Visualization/R/13-meaningful-vizualizations/README.md b/translations/ro/3-Data-Visualization/R/13-meaningful-vizualizations/README.md index cb878131..a4ef0b25 100644 --- a/translations/ro/3-Data-Visualization/R/13-meaningful-vizualizations/README.md +++ b/translations/ro/3-Data-Visualization/R/13-meaningful-vizualizations/README.md @@ -47,25 +47,25 @@ Una dintre abilitățile de bază ale unui specialist în date este capacitatea Chiar dacă un specialist în date este atent să aleagă graficul potrivit pentru datele potrivite, există multe moduri în care datele pot fi afișate pentru a susține un punct de vedere, adesea în detrimentul datelor în sine. Există numeroase exemple de grafice și infografice înșelătoare! -[![Cum Mint Graficele de Alberto Cairo](../../../../../translated_images/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.ro.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "Cum mint graficele") +[![Cum Mint Graficele de Alberto Cairo](../../../../../translated_images/ro/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "Cum mint graficele") > 🎥 Fă clic pe imaginea de mai sus pentru o prezentare despre graficele înșelătoare Acest grafic inversează axa X pentru a arăta opusul adevărului, bazat pe date: -![grafic prost 1](../../../../../translated_images/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.ro.png) +![grafic prost 1](../../../../../translated_images/ro/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.png) [Acest grafic](https://media.firstcoastnews.com/assets/WTLV/images/170ae16f-4643-438f-b689-50d66ca6a8d8/170ae16f-4643-438f-b689-50d66ca6a8d8_1140x641.jpg) este chiar mai înșelător, deoarece privirea este atrasă spre dreapta pentru a concluziona că, în timp, cazurile de COVID au scăzut în diverse județe. De fapt, dacă te uiți atent la date, vei descoperi că acestea au fost rearanjate pentru a crea acea tendință descendentă înșelătoare. -![grafic prost 2](../../../../../translated_images/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.ro.jpg) +![grafic prost 2](../../../../../translated_images/ro/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.jpg) Acest exemplu notoriu folosește culoarea ȘI o axă Y inversată pentru a înșela: în loc să concluzionezi că decesele cauzate de arme au crescut după adoptarea legislației favorabile armelor, privirea este păcălită să creadă că opusul este adevărat: -![grafic prost 3](../../../../../translated_images/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.ro.jpg) +![grafic prost 3](../../../../../translated_images/ro/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.jpg) Acest grafic ciudat arată cum proporțiile pot fi manipulate, cu efecte hilare: -![grafic prost 4](../../../../../translated_images/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.ro.jpg) +![grafic prost 4](../../../../../translated_images/ro/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.jpg) Compararea incomparabilului este o altă tactică dubioasă. Există un [site web minunat](https://tylervigen.com/spurious-correlations) dedicat 'corelațiilor false', care afișează 'fapte' ce corelează lucruri precum rata divorțurilor din Maine și consumul de margarină. Un grup Reddit colectează, de asemenea, [utilizările urâte](https://www.reddit.com/r/dataisugly/top/?t=all) ale datelor. @@ -100,13 +100,13 @@ Etichetează axele, oferă o legendă dacă este necesar și oferă tooltips pen Dacă datele tale sunt textuale și verbose pe axa X, poți înclina textul pentru o mai bună lizibilitate. [plot3D](https://cran.r-project.org/web/packages/plot3D/index.html) oferă graficare 3D, dacă datele tale o susțin. Vizualizări sofisticate de date pot fi produse folosind această metodă. -![grafice 3D](../../../../../translated_images/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.ro.png) +![grafice 3D](../../../../../translated_images/ro/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.png) ## Afișarea animată și graficarea 3D Unele dintre cele mai bune vizualizări de date de astăzi sunt animate. Shirley Wu are exemple uimitoare realizate cu D3, cum ar fi '[film flowers](http://bl.ocks.org/sxywu/raw/d612c6c653fb8b4d7ff3d422be164a5d/)', unde fiecare floare este o vizualizare a unui film. Un alt exemplu pentru Guardian este 'bussed out', o experiență interactivă care combină vizualizările cu Greensock și D3 plus un format de articol narativ pentru a arăta cum NYC gestionează problema persoanelor fără adăpost, trimițându-le cu autobuzul în afara orașului. -![busing](../../../../../translated_images/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.ro.png) +![busing](../../../../../translated_images/ro/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.png) > "Bussed Out: Cum America își mută persoanele fără adăpost" de la [Guardian](https://www.theguardian.com/us-news/ng-interactive/2017/dec/20/bussed-out-america-moves-homeless-people-country-study). Vizualizări de Nadieh Bremer & Shirley Wu @@ -116,7 +116,7 @@ Deși această lecție nu este suficientă pentru a intra în detalii despre ace Vei finaliza o aplicație web care va afișa o vizualizare animată a acestei rețele sociale. Folosește o bibliotecă construită pentru a crea o [vizualizare a unei rețele](https://github.com/emiliorizzo/vue-d3-network) utilizând Vue.js și D3. Când aplicația rulează, poți trage nodurile pe ecran pentru a rearanja datele. -![liaisons](../../../../../translated_images/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.ro.png) +![liaisons](../../../../../translated_images/ro/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.png) ## Proiect: Construiește un grafic pentru a arăta o rețea folosind D3.js diff --git a/translations/ro/3-Data-Visualization/README.md b/translations/ro/3-Data-Visualization/README.md index 50fa03a4..ad6e6a67 100644 --- a/translations/ro/3-Data-Visualization/README.md +++ b/translations/ro/3-Data-Visualization/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Vizualizări -![o albină pe o floare de lavandă](../../../translated_images/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.ro.jpg) +![o albină pe o floare de lavandă](../../../translated_images/ro/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.jpg) > Fotografie de Jenna Lee pe Unsplash Vizualizarea datelor este una dintre cele mai importante sarcini ale unui specialist în date. Imaginile valorează cât 1000 de cuvinte, iar o vizualizare te poate ajuta să identifici tot felul de aspecte interesante ale datelor tale, cum ar fi vârfuri, valori extreme, grupări, tendințe și multe altele, care te pot ajuta să înțelegi povestea pe care datele tale încearcă să o spună. diff --git a/translations/ro/4-Data-Science-Lifecycle/14-Introduction/README.md b/translations/ro/4-Data-Science-Lifecycle/14-Introduction/README.md index ce50e96c..41adcf9e 100644 --- a/translations/ro/4-Data-Science-Lifecycle/14-Introduction/README.md +++ b/translations/ro/4-Data-Science-Lifecycle/14-Introduction/README.md @@ -25,7 +25,7 @@ Până în acest moment, probabil ai realizat că știința datelor este un proc Această lecție se concentrează pe 3 părți ale ciclului de viață: capturare, procesare și mentenanță. -![Diagramă a ciclului de viață al științei datelor](../../../../translated_images/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.ro.jpg) +![Diagramă a ciclului de viață al științei datelor](../../../../translated_images/ro/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.jpg) > Fotografie de [Berkeley School of Information](https://ischoolonline.berkeley.edu/data-science/what-is-data-science/) ## Capturare @@ -101,7 +101,7 @@ Explorează [Ciclul de viață al Procesului de Știința Datelor al Echipei](ht |Procesul de Știința Datelor al Echipei (TDSP)|Procesul standard inter-industrial pentru mineritul datelor (CRISP-DM)| |--|--| -|![Ciclul de viață al echipei de știința datelor](../../../../translated_images/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.ro.png) | ![Imaginea Procesului de Știința Datelor](../../../../translated_images/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.ro.png) | +|![Ciclul de viață al echipei de știința datelor](../../../../translated_images/ro/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.png) | ![Imaginea Procesului de Știința Datelor](../../../../translated_images/ro/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.png) | | Imagine de [Microsoft](https://docs.microsoft.comazure/architecture/data-science-process/lifecycle) | Imagine de [Data Science Process Alliance](https://www.datascience-pm.com/crisp-dm-2/) | ## [Chestionar după lecție](https://ff-quizzes.netlify.app/en/ds/quiz/27) diff --git a/translations/ro/4-Data-Science-Lifecycle/README.md b/translations/ro/4-Data-Science-Lifecycle/README.md index 45627644..af3c9ba5 100644 --- a/translations/ro/4-Data-Science-Lifecycle/README.md +++ b/translations/ro/4-Data-Science-Lifecycle/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Ciclu de Viață în Știința Datelor -![communication](../../../translated_images/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.ro.jpg) +![communication](../../../translated_images/ro/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.jpg) > Fotografie de Headway pe Unsplash În aceste lecții, vei explora câteva aspecte ale ciclului de viață în Știința Datelor, inclusiv analiza și comunicarea legate de date. diff --git a/translations/ro/5-Data-Science-In-Cloud/README.md b/translations/ro/5-Data-Science-In-Cloud/README.md index 7ac2515e..29f72fb4 100644 --- a/translations/ro/5-Data-Science-In-Cloud/README.md +++ b/translations/ro/5-Data-Science-In-Cloud/README.md @@ -9,13 +9,13 @@ CO_OP_TRANSLATOR_METADATA: --> # Știința Datelor în Cloud -![cloud-picture](../../../translated_images/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.ro.jpg) +![cloud-picture](../../../translated_images/ro/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.jpg) > Fotografie de [Jelleke Vanooteghem](https://unsplash.com/@ilumire) de pe [Unsplash](https://unsplash.com/s/photos/cloud?orientation=landscape) Când vine vorba de realizarea științei datelor cu seturi mari de date, cloud-ul poate fi o schimbare majoră. În următoarele trei lecții, vom vedea ce este cloud-ul și de ce poate fi foarte util. De asemenea, vom explora un set de date despre insuficiența cardiacă și vom construi un model pentru a ajuta la evaluarea probabilității ca cineva să sufere de insuficiență cardiacă. Vom folosi puterea cloud-ului pentru a antrena, implementa și utiliza un model în două moduri diferite. Un mod folosind doar interfața utilizatorului într-un stil Low code/No code, iar celălalt mod folosind Azure Machine Learning Software Developer Kit (Azure ML SDK). -![project-schema](../../../translated_images/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.ro.png) +![project-schema](../../../translated_images/ro/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.png) ### Subiecte diff --git a/translations/ro/6-Data-Science-In-Wild/20-Real-World-Examples/README.md b/translations/ro/6-Data-Science-In-Wild/20-Real-World-Examples/README.md index 2d45b2b8..f0205fc3 100644 --- a/translations/ro/6-Data-Science-In-Wild/20-Real-World-Examples/README.md +++ b/translations/ro/6-Data-Science-In-Wild/20-Real-World-Examples/README.md @@ -41,7 +41,7 @@ Datorită democratizării AI, dezvoltatorii găsesc acum mai ușor să proiectez * [Știința datelor în sănătate](https://data-flair.training/blogs/data-science-in-healthcare/) - evidențiază aplicații precum imagistica medicală (de exemplu, RMN, radiografie, tomografie), genomica (secvențierea ADN-ului), dezvoltarea medicamentelor (evaluarea riscurilor, predicția succesului), analitica predictivă (îngrijirea pacienților și logistica aprovizionării), urmărirea și prevenirea bolilor etc. -![Aplicații ale Științei Datelor în Lumea Reală](../../../../translated_images/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.ro.png) Credit imagine: [Data Flair: 6 Aplicații Uimitoare ale Științei Datelor ](https://data-flair.training/blogs/data-science-applications/) +![Aplicații ale Științei Datelor în Lumea Reală](../../../../translated_images/ro/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.png) Credit imagine: [Data Flair: 6 Aplicații Uimitoare ale Științei Datelor ](https://data-flair.training/blogs/data-science-applications/) Figura arată alte domenii și exemple de aplicare a tehnicilor de știința datelor. Vrei să explorezi alte aplicații? Consultă secțiunea [Revizuire & Studiu Individual](../../../../6-Data-Science-In-Wild/20-Real-World-Examples) de mai jos. diff --git a/translations/ro/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md b/translations/ro/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md index 85b1c744..55adbf4e 100644 --- a/translations/ro/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md +++ b/translations/ro/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md @@ -22,7 +22,7 @@ Interfața Explorer (prezentată în captura de ecran de mai jos) îți permite 2. Explorezi [Catalogul](https://planetarycomputer.microsoft.com/catalog) seturilor de date - află scopul fiecăruia. 3. Utilizezi Explorer - alegi un set de date de interes, selectezi o interogare și o opțiune de redare relevante. -![The Planetary Computer Explorer](../../../../translated_images/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.ro.png) +![The Planetary Computer Explorer](../../../../translated_images/ro/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.png) `Sarcina ta:` Acum studiază vizualizarea afișată în browser și răspunde la următoarele întrebări: diff --git a/translations/ro/CONTRIBUTING.md b/translations/ro/CONTRIBUTING.md index edffd08e..69bec4ce 100644 --- a/translations/ro/CONTRIBUTING.md +++ b/translations/ro/CONTRIBUTING.md @@ -318,7 +318,7 @@ Includeți în descrierea PR-ului: ``` ```` -- Adăugați text alternativ la imagini: `![Alt text](../../translated_images/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.ro.png)` +- Adăugați text alternativ la imagini: `![Alt text](../../translated_images/ro/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.png)` - Mențineți lungimea liniilor rezonabilă (aproximativ 80-100 de caractere) ### Python diff --git a/translations/ro/README.md b/translations/ro/README.md index 1906b398..b8180f54 100644 --- a/translations/ro/README.md +++ b/translations/ro/README.md @@ -33,7 +33,7 @@ Azure Cloud Advocates de la Microsoft sunt încântați să ofere un curriculum **🙏 Mulțumiri speciale 🙏 autorilor, recenzorilor și contribuitorilor de conținut din rândul [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/),** în special Aaryan Arora, [Aditya Garg](https://github.com/AdityaGarg00), [Alondra Sanchez](https://www.linkedin.com/in/alondra-sanchez-molina/), [Ankita Singh](https://www.linkedin.com/in/ankitasingh007), [Anupam Mishra](https://www.linkedin.com/in/anupam--mishra/), [Arpita Das](https://www.linkedin.com/in/arpitadas01/), ChhailBihari Dubey, [Dibri Nsofor](https://www.linkedin.com/in/dibrinsofor), [Dishita Bhasin](https://www.linkedin.com/in/dishita-bhasin-7065281bb), [Majd Safi](https://www.linkedin.com/in/majd-s/), [Max Blum](https://www.linkedin.com/in/max-blum-6036a1186/), [Miguel Correa](https://www.linkedin.com/in/miguelmque/), [Mohamma Iftekher (Iftu) Ebne Jalal](https://twitter.com/iftu119), [Nawrin Tabassum](https://www.linkedin.com/in/nawrin-tabassum), [Raymond Wangsa Putra](https://www.linkedin.com/in/raymond-wp/), [Rohit Yadav](https://www.linkedin.com/in/rty2423), Samridhi Sharma, [Sanya Sinha](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200), [Sheena Narula](https://www.linkedin.com/in/sheena-narua-n/), [Tauqeer Ahmad](https://www.linkedin.com/in/tauqeerahmad5201/), Yogendrasingh Pawar , [Vidushi Gupta](https://www.linkedin.com/in/vidushi-gupta07/), [Jasleen Sondhi](https://www.linkedin.com/in/jasleen-sondhi/) -|![Sketchnote realizat de @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.ro.png)| +|![Sketchnote realizat de @sketchthedocs https://sketchthedocs.dev](../../translated_images/ro/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.png)| |:---:| | Știința datelor pentru începători - _Sketchnote realizat de [@nitya](https://twitter.com/nitya)_ | @@ -52,7 +52,7 @@ Azure Cloud Advocates de la Microsoft sunt încântați să ofere un curriculum Avem o serie pe Discord "Learn with AI" în desfășurare; aflați mai multe și alăturați-vă la [Learn with AI Series](https://aka.ms/learnwithai/discord) în perioada 18 - 30 septembrie 2025. Veți primi sfaturi și trucuri pentru folosirea GitHub Copilot în Știința Datelor. -![Seria Learn with AI](../../translated_images/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.ro.jpg) +![Seria Learn with AI](../../translated_images/ro/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.jpg) # Ești student? @@ -132,7 +132,7 @@ Fiecare exemplu include comentarii detaliate care explică fiecare pas, fiind pe ## Lecții -|![ Sketchnote de @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.ro.png)| +|![ Sketchnote de @sketchthedocs https://sketchthedocs.dev](../../translated_images/ro/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png)| |:---:| | Știința datelor pentru începători: Plan de parcurs - _Sketchnote de [@nitya](https://twitter.com/nitya)_ | diff --git a/translations/ro/sketchnotes/README.md b/translations/ro/sketchnotes/README.md index 810143b5..3b3cd645 100644 --- a/translations/ro/sketchnotes/README.md +++ b/translations/ro/sketchnotes/README.md @@ -13,7 +13,7 @@ Găsește toate notițele schițate aici! Nitya Narasimhan, artist -![notiță schițată cu planificare](../../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.ro.png) +![notiță schițată cu planificare](../../../translated_images/ro/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png) --- diff --git a/translations/ru/1-Introduction/01-defining-data-science/README.md b/translations/ru/1-Introduction/01-defining-data-science/README.md index 9075bf0d..a2bf3624 100644 --- a/translations/ru/1-Introduction/01-defining-data-science/README.md +++ b/translations/ru/1-Introduction/01-defining-data-science/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: --- -[![Видео: Определение науки о данных](../../../../translated_images/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.ru.png)](https://youtu.be/beZ7Mb_oz9I) +[![Видео: Определение науки о данных](../../../../translated_images/ru/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.png)](https://youtu.be/beZ7Mb_oz9I) ## [Тест перед лекцией](https://ff-quizzes.netlify.app/en/ds/quiz/0) @@ -153,7 +153,7 @@ CO_OP_TRANSLATOR_METADATA: В этом задании мы попробуем найти концепции, связанные с областью Data Science, анализируя тексты. Мы возьмем статью из Википедии о Data Science, загрузим и обработаем текст, а затем создадим облако слов, похожее на это: -![Облако слов для Data Science](../../../../translated_images/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.ru.png) +![Облако слов для Data Science](../../../../translated_images/ru/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.png) Посетите [`notebook.ipynb`](../../../../1-Introduction/01-defining-data-science/notebook.ipynb ':ignore'), чтобы ознакомиться с кодом. Вы также можете запустить код и увидеть, как он выполняет все преобразования данных в реальном времени. diff --git a/translations/ru/1-Introduction/04-stats-and-probability/README.md b/translations/ru/1-Introduction/04-stats-and-probability/README.md index b0224da6..9bca718e 100644 --- a/translations/ru/1-Introduction/04-stats-and-probability/README.md +++ b/translations/ru/1-Introduction/04-stats-and-probability/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: Статистика и теория вероятностей — это две тесно связанные области математики, которые имеют большое значение для анализа данных. Можно работать с данными без глубоких знаний математики, но лучше знать хотя бы основные концепции. Здесь мы представим краткое введение, которое поможет вам начать. -[![Вводное видео](../../../../translated_images/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.ru.png)](https://youtu.be/Z5Zy85g4Yjw) +[![Вводное видео](../../../../translated_images/ru/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.png)](https://youtu.be/Z5Zy85g4Yjw) ## [Тест перед лекцией](https://ff-quizzes.netlify.app/en/ds/quiz/6) @@ -39,7 +39,7 @@ CO_OP_TRANSLATOR_METADATA: Мы можем говорить только о вероятности того, что переменная попадёт в заданный интервал значений, например P(t1≤X2). В этом случае распределение вероятностей описывается **функцией плотности вероятности** p(x), такой, что -![P(t_1\le X Больше примеров корреляции и ковариации можно найти в [сопроводительном ноутбуке](notebook.ipynb). diff --git a/translations/ru/1-Introduction/README.md b/translations/ru/1-Introduction/README.md index ae0ab0bd..a3b95d47 100644 --- a/translations/ru/1-Introduction/README.md +++ b/translations/ru/1-Introduction/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Введение в науку о данных -![данные в действии](../../../translated_images/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.ru.jpg) +![данные в действии](../../../translated_images/ru/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.jpg) > Фото сделано Стивеном Доусоном на Unsplash В этих уроках вы узнаете, как определяется наука о данных, а также познакомитесь с этическими аспектами, которые должен учитывать специалист по данным. Вы также узнаете, что такое данные, и получите общее представление о статистике и теории вероятностей — ключевых академических областях науки о данных. diff --git a/translations/ru/2-Working-With-Data/07-python/README.md b/translations/ru/2-Working-With-Data/07-python/README.md index a727ca97..ee3dbf9a 100644 --- a/translations/ru/2-Working-With-Data/07-python/README.md +++ b/translations/ru/2-Working-With-Data/07-python/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: | :-------------------------------------------------------------------------------------------------------: | | Работа с Python - _Скетчноут от [@nitya](https://twitter.com/nitya)_ | -[![Вводное видео](../../../../translated_images/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.ru.png)](https://youtu.be/dZjWOGbsN4Y) +[![Вводное видео](../../../../translated_images/ru/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.png)](https://youtu.be/dZjWOGbsN4Y) Хотя базы данных предоставляют очень эффективные способы хранения данных и их запросов с использованием языков запросов, наиболее гибким способом обработки данных является написание собственной программы для их манипуляции. Во многих случаях запрос к базе данных будет более эффективным. Однако в некоторых случаях, когда требуется более сложная обработка данных, это не так просто сделать с помощью SQL. Обработка данных может быть запрограммирована на любом языке программирования, но существуют языки, которые считаются более высокоуровневыми для работы с данными. Специалисты по данным обычно предпочитают один из следующих языков: @@ -73,7 +73,7 @@ print(f"Length of index is {len(idx)}") items_sold = pd.Series(np.random.randint(25,50,size=len(idx)),index=idx) items_sold.plot() ``` -![График временного ряда](../../../../translated_images/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.ru.png) +![График временного ряда](../../../../translated_images/ru/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.png) Теперь предположим, что каждую неделю мы устраиваем вечеринку для друзей и берем дополнительно 10 упаковок мороженого для вечеринки. Мы можем создать еще один Series, индексированный по неделям, чтобы это показать: ```python @@ -84,7 +84,7 @@ additional_items = pd.Series(10,index=pd.date_range(start_date,end_date,freq="W" total_items = items_sold.add(additional_items,fill_value=0) total_items.plot() ``` -![График временного ряда](../../../../translated_images/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.ru.png) +![График временного ряда](../../../../translated_images/ru/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.png) > **Примечание**: Мы не используем простую запись `total_items+additional_items`. Если бы мы это сделали, то получили бы много значений `NaN` (*Not a Number*) в результирующем Series. Это происходит из-за отсутствующих значений для некоторых точек индекса в Series `additional_items`, а сложение `NaN` с чем-либо дает `NaN`. Поэтому необходимо указать параметр `fill_value` при сложении. @@ -93,7 +93,7 @@ total_items.plot() monthly = total_items.resample("1M").mean() ax = monthly.plot(kind='bar') ``` -![Средние значения временного ряда за месяц](../../../../translated_images/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.ru.png) +![Средние значения временного ряда за месяц](../../../../translated_images/ru/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.png) ### DataFrame @@ -219,7 +219,7 @@ df = pd.read_csv('file.csv') Поскольку мы хотим продемонстрировать, как работать с данными, мы приглашаем вас открыть [`notebook-covidspread.ipynb`](notebook-covidspread.ipynb) и прочитать его от начала до конца. Вы также можете выполнить ячейки и выполнить задания, которые мы оставили для вас в конце. -![COVID Spread](../../../../translated_images/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.ru.png) +![COVID Spread](../../../../translated_images/ru/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.png) > Если вы не знаете, как запускать код в Jupyter Notebook, ознакомьтесь с [этой статьей](https://soshnikov.com/education/how-to-execute-notebooks-from-github/). @@ -241,7 +241,7 @@ df = pd.read_csv('file.csv') Откройте [`notebook-papers.ipynb`](notebook-papers.ipynb) и прочитайте его от начала до конца. Вы также можете выполнить ячейки и выполнить задания, которые мы оставили для вас в конце. -![Covid Medical Treatment](../../../../translated_images/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.ru.png) +![Covid Medical Treatment](../../../../translated_images/ru/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.png) ## Обработка данных изображений diff --git a/translations/ru/2-Working-With-Data/README.md b/translations/ru/2-Working-With-Data/README.md index cc6ec7a3..7d8ee1e8 100644 --- a/translations/ru/2-Working-With-Data/README.md +++ b/translations/ru/2-Working-With-Data/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Работа с данными -![data love](../../../translated_images/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.ru.jpg) +![data love](../../../translated_images/ru/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.jpg) > Фото Александра Синна на Unsplash В этих уроках вы узнаете о различных способах управления, обработки и использования данных в приложениях. Вы познакомитесь с реляционными и нереляционными базами данных и узнаете, как данные могут храниться в них. Вы изучите основы работы с Python для управления данными и откроете для себя множество способов использования Python для обработки и анализа данных. diff --git a/translations/ru/3-Data-Visualization/12-visualization-relationships/README.md b/translations/ru/3-Data-Visualization/12-visualization-relationships/README.md index c23fb7b6..f69b3eb5 100644 --- a/translations/ru/3-Data-Visualization/12-visualization-relationships/README.md +++ b/translations/ru/3-Data-Visualization/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ honey.head() ```python sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ``` -![scatterplot 1](../../../../translated_images/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.ru.png) +![scatterplot 1](../../../../translated_images/ru/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.png) Теперь покажите те же данные с цветовой схемой, напоминающей цвет мёда, чтобы отобразить, как цена меняется год за годом. Вы можете сделать это, добавив параметр 'hue', чтобы показать изменения: @@ -60,7 +60,7 @@ sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ```python sns.relplot(x="priceperlb", y="state", hue="year", palette="YlOrBr", data=honey, height=15, aspect=.5); ``` -![scatterplot 2](../../../../translated_images/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.ru.png) +![scatterplot 2](../../../../translated_images/ru/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.png) С изменением цветовой схемы становится очевидным, что цена за фунт мёда значительно увеличивается с годами. Если вы проверите это на выборке данных (например, выберите штат Аризона), вы увидите закономерность роста цен год за годом, за редкими исключениями: @@ -89,7 +89,7 @@ sns.relplot(x="priceperlb", y="state", size="year", data=honey, height=15, aspec ``` Вы можете заметить, как размер точек постепенно увеличивается. -![scatterplot 3](../../../../translated_images/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.ru.png) +![scatterplot 3](../../../../translated_images/ru/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.png) Это простой случай спроса и предложения? Из-за таких факторов, как изменение климата и разрушение колоний, становится ли мёда меньше с каждым годом, что приводит к росту цен? @@ -104,7 +104,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); ``` Ответ: Да, с некоторыми исключениями около 2003 года: -![line chart 1](../../../../translated_images/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.ru.png) +![line chart 1](../../../../translated_images/ru/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.png) ✅ Поскольку Seaborn агрегирует данные в одну линию, он отображает "несколько измерений для каждого значения x, строя среднее значение и 95% доверительный интервал вокруг среднего". [Источник](https://seaborn.pydata.org/tutorial/relational.html). Это трудоёмкое поведение можно отключить, добавив `ci=None`. @@ -114,7 +114,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); sns.relplot(x="year", y="totalprod", kind="line", data=honey); ``` -![line chart 2](../../../../translated_images/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.ru.png) +![line chart 2](../../../../translated_images/ru/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.png) Ответ: Не совсем. Если посмотреть на общий объём производства, он, наоборот, кажется увеличившимся в этот год, хотя в целом объём производства мёда снижается в течение этих лет. @@ -139,7 +139,7 @@ sns.relplot( ``` В этой визуализации вы можете сравнить урожайность на колонию и количество колоний год за годом, рядом друг с другом, с wrap, установленным на 3 для столбцов: -![facet grid](../../../../translated_images/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.ru.png) +![facet grid](../../../../translated_images/ru/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.png) Для этого набора данных ничего особенно не выделяется в отношении количества колоний и их урожайности год за годом и штат за штатом. Есть ли другой способ найти корреляцию между этими двумя переменными? @@ -162,7 +162,7 @@ sns.despine(right=False) plt.ylabel('colony yield') ax.figure.legend(); ``` -![superimposed plots](../../../../translated_images/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.ru.png) +![superimposed plots](../../../../translated_images/ru/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.png) Хотя ничего явно не выделяется около 2003 года, это позволяет завершить урок на более позитивной ноте: несмотря на общее снижение количества колоний, их число стабилизируется, даже если урожайность на колонию уменьшается. diff --git a/translations/ru/3-Data-Visualization/R/09-visualization-quantities/README.md b/translations/ru/3-Data-Visualization/R/09-visualization-quantities/README.md index 9eadc028..61b5a175 100644 --- a/translations/ru/3-Data-Visualization/R/09-visualization-quantities/README.md +++ b/translations/ru/3-Data-Visualization/R/09-visualization-quantities/README.md @@ -66,7 +66,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` Здесь вы устанавливаете пакет `ggplot2`, а затем импортируете его в рабочую область с помощью команды `library("ggplot2")`. Для построения любого графика в ggplot используется функция `ggplot()`, где вы указываете набор данных, переменные x и y в качестве атрибутов. В данном случае мы используем функцию `geom_line()`, так как хотим построить линейный график. -![MaxWingspan-lineplot](../../../../../translated_images/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.ru.png) +![MaxWingspan-lineplot](../../../../../translated_images/ru/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.png) Что вы замечаете сразу? Кажется, есть как минимум один выброс — это впечатляющий размах крыльев! Размах крыльев более 2000 сантиметров равен более чем 20 метрам — неужели в Миннесоте летают птеродактили? Давайте разберемся. @@ -84,7 +84,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` Мы задаем угол в `theme` и указываем подписи осей x и y в `xlab()` и `ylab()` соответственно. Функция `ggtitle()` задает название графика. -![MaxWingspan-lineplot-improved](../../../../../translated_images/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.ru.png) +![MaxWingspan-lineplot-improved](../../../../../translated_images/ru/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.png) Даже с поворотом подписей на 45 градусов их слишком много, чтобы прочитать. Попробуем другой подход: подпишем только выбросы и разместим подписи внутри графика. Вы можете использовать точечный график, чтобы освободить место для подписей: @@ -100,7 +100,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + Что вы обнаруживаете? -![MaxWingspan-scatterplot](../../../../../translated_images/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.ru.png) +![MaxWingspan-scatterplot](../../../../../translated_images/ru/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.png) ## Фильтрация данных @@ -119,7 +119,7 @@ ggplot(data=birds_filtered, aes(x=Name, y=MaxWingspan,group=1)) + ``` Мы создали новый датафрейм `birds_filtered`, а затем построили точечный график. Исключив выбросы, ваши данные стали более согласованными и понятными. -![MaxWingspan-scatterplot-improved](../../../../../translated_images/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.ru.png) +![MaxWingspan-scatterplot-improved](../../../../../translated_images/ru/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.png) Теперь, когда у нас есть более чистый набор данных, по крайней мере, в отношении размаха крыльев, давайте узнаем больше об этих птицах. @@ -161,7 +161,7 @@ birds_filtered %>% group_by(Category) %>% ``` В следующем фрагменте мы устанавливаем пакеты [dplyr](https://www.rdocumentation.org/packages/dplyr/versions/0.7.8) и [lubridate](https://www.rdocumentation.org/packages/lubridate/versions/1.8.0), чтобы помочь манипулировать и группировать данные для построения составной столбчатой диаграммы. Сначала вы группируете данные по `Category` птиц, а затем суммируете столбцы `MinLength`, `MaxLength`, `MinBodyMass`, `MaxBodyMass`, `MinWingspan`, `MaxWingspan`. Затем строите столбчатую диаграмму с помощью пакета `ggplot2`, указывая цвета для разных категорий и подписи. -![Stacked bar chart](../../../../../translated_images/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.ru.png) +![Stacked bar chart](../../../../../translated_images/ru/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.png) Эта столбчатая диаграмма, однако, нечитаема из-за слишком большого количества негруппированных данных. Вам нужно выбрать только те данные, которые вы хотите отобразить, поэтому давайте посмотрим на длину птиц в зависимости от их категории. @@ -176,7 +176,7 @@ ggplot(birds_count,aes(Category,n))+geom_bar(stat="identity")+coord_flip() ``` Сначала вы подсчитываете уникальные значения в столбце `Category`, а затем сортируете их в новый датафрейм `birds_count`. Эти отсортированные данные затем учитываются на том же уровне, чтобы они были построены в отсортированном порядке. Используя `ggplot2`, вы затем строите данные в виде столбчатой диаграммы. Функция `coord_flip()` строит горизонтальные столбцы. -![category-length](../../../../../translated_images/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.ru.png) +![category-length](../../../../../translated_images/ru/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.png) Эта столбчатая диаграмма дает хороший обзор количества птиц в каждой категории. С первого взгляда видно, что наибольшее количество птиц в этом регионе относится к категории Утки/Гуси/Водоплавающие. Миннесота — это "земля 10,000 озер", так что это неудивительно! @@ -199,7 +199,7 @@ ggplot(birds_grouped,aes(Category,MaxLength))+geom_bar(stat="identity")+coord_fl ``` Мы группируем данные `birds_filtered` по `Category`, а затем строим столбчатую диаграмму. -![comparing data](../../../../../translated_images/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.ru.png) +![comparing data](../../../../../translated_images/ru/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.png) Здесь ничего удивительного: колибри имеют наименьший MaxLength по сравнению с пеликанами или гусями. Хорошо, когда данные логически понятны! @@ -211,7 +211,7 @@ ggplot(data=birds_grouped, aes(x=Category)) + geom_bar(aes(y=MinLength), stat="identity", position="identity", fill='orange')+ coord_flip() ``` -![super-imposed values](../../../../../translated_images/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.ru.png) +![super-imposed values](../../../../../translated_images/ru/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.png) ## 🚀 Задание diff --git a/translations/ru/3-Data-Visualization/R/10-visualization-distributions/README.md b/translations/ru/3-Data-Visualization/R/10-visualization-distributions/README.md index 13001982..1a20d154 100644 --- a/translations/ru/3-Data-Visualization/R/10-visualization-distributions/README.md +++ b/translations/ru/3-Data-Visualization/R/10-visualization-distributions/README.md @@ -45,7 +45,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + geom_point() + ggtitle("Max Length per order") + coord_flip() ``` -![максимальная длина по отрядам](../../../../../translated_images/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.ru.png) +![максимальная длина по отрядам](../../../../../translated_images/ru/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.png) Это дает обзор общего распределения длины тела по отрядам птиц, но это не оптимальный способ отображения истинных распределений. Для этой задачи обычно создается гистограмма. @@ -57,7 +57,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=10)+ylab('Frequency') ``` -![распределение по всему набору данных](../../../../../translated_images/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.ru.png) +![распределение по всему набору данных](../../../../../translated_images/ru/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.png) Как видно, большинство из 400+ птиц в этом наборе данных имеют Max Body Mass менее 2000. Получите больше информации о данных, изменив параметр `bins` на большее число, например, 30: @@ -65,7 +65,7 @@ ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![распределение-30 интервалов](../../../../../translated_images/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.ru.png) +![распределение-30 интервалов](../../../../../translated_images/ru/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.png) Эта диаграмма показывает распределение в более детализированном виде. Менее смещенную влево диаграмму можно создать, выбрав данные только в заданном диапазоне: @@ -77,7 +77,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![отфильтрованная гистограмма](../../../../../translated_images/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.ru.png) +![отфильтрованная гистограмма](../../../../../translated_images/ru/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.png) ✅ Попробуйте другие фильтры и точки данных. Чтобы увидеть полное распределение данных, удалите фильтр `['MaxBodyMass']`, чтобы показать распределения с метками. @@ -91,7 +91,7 @@ ggplot(data=birds_filtered_1, aes(x=MaxBodyMass, y=MaxLength) ) + ``` Кажется, существует ожидаемая корреляция между этими двумя элементами вдоль ожидаемой оси, с одной особенно сильной точкой сходимости: -![2D диаграмма](../../../../../translated_images/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.ru.png) +![2D диаграмма](../../../../../translated_images/ru/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.png) Гистограммы хорошо работают по умолчанию для числовых данных. А что, если вам нужно увидеть распределения по текстовым данным? ## Исследуем набор данных для распределений с использованием текстовых данных @@ -122,7 +122,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + scale_fill_manual(name="Conservation Status",values=c("red","green","blue","pink"),labels=c("Endangered","Near Threathened","Vulnerable","Least Concern")) ``` -![размах крыльев и статус сохранности](../../../../../translated_images/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.ru.png) +![размах крыльев и статус сохранности](../../../../../translated_images/ru/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.png) Не кажется, что существует хорошая корреляция между минимальным размахом крыльев и статусом сохранности. Проверьте другие элементы набора данных, используя этот метод. Вы можете попробовать разные фильтры. Найдете ли вы какую-либо корреляцию? @@ -136,7 +136,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + geom_density() ``` -![плотностный график](../../../../../translated_images/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.ru.png) +![плотностный график](../../../../../translated_images/ru/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.png) Вы можете увидеть, как график повторяет предыдущий для данных о минимальном размахе крыльев; он просто немного более плавный. Если вы захотите вернуться к той зубчатой линии MaxBodyMass на втором графике, который вы построили, вы сможете сгладить ее, используя этот метод: @@ -144,7 +144,7 @@ ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density() ``` -![плотность массы тела](../../../../../translated_images/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.ru.png) +![плотность массы тела](../../../../../translated_images/ru/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.png) Если вы хотите получить плавную, но не слишком плавную линию, измените параметр `adjust`: @@ -152,7 +152,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density(adjust = 1/5) ``` -![менее плавная масса тела](../../../../../translated_images/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.ru.png) +![менее плавная масса тела](../../../../../translated_images/ru/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.png) ✅ Прочитайте о доступных параметрах для этого типа графика и экспериментируйте! @@ -162,7 +162,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + ggplot(data=birds_filtered_1,aes(x = MaxBodyMass, fill = Order)) + geom_density(alpha=0.5) ``` -![масса тела по отрядам](../../../../../translated_images/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.ru.png) +![масса тела по отрядам](../../../../../translated_images/ru/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.png) ## 🚀 Задание diff --git a/translations/ru/3-Data-Visualization/R/11-visualization-proportions/README.md b/translations/ru/3-Data-Visualization/R/11-visualization-proportions/README.md index 3fe405a5..e0017746 100644 --- a/translations/ru/3-Data-Visualization/R/11-visualization-proportions/README.md +++ b/translations/ru/3-Data-Visualization/R/11-visualization-proportions/README.md @@ -93,7 +93,7 @@ pie(grouped$count,grouped$class, main="Edible?") ``` Вуаля, круговая диаграмма, показывающая пропорции данных в соответствии с двумя классами грибов. Очень важно правильно указать порядок меток, особенно здесь, поэтому обязательно проверьте порядок, в котором создается массив меток! -![круговая диаграмма](../../../../../translated_images/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.ru.png) +![круговая диаграмма](../../../../../translated_images/ru/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.png) ## Пончики! @@ -127,7 +127,7 @@ library(webr) PieDonut(habitat, aes(habitat, count=count)) ``` -![диаграмма в виде пончика](../../../../../translated_images/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.ru.png) +![диаграмма в виде пончика](../../../../../translated_images/ru/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.png) Этот код использует две библиотеки - ggplot2 и webr. С помощью функции PieDonut из библиотеки webr можно легко создать диаграмму в виде пончика! @@ -165,7 +165,7 @@ waffle((cap_color$count/10), rows = 7, title = "Waffle Chart")+scale_fill_manual С помощью вафельной диаграммы вы можете четко увидеть пропорции цветов шляпок грибов в этом наборе данных. Интересно, что есть много грибов с зелеными шляпками! -![вафельная диаграмма](../../../../../translated_images/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.ru.png) +![вафельная диаграмма](../../../../../translated_images/ru/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.png) В этом уроке вы узнали три способа визуализации пропорций. Сначала нужно сгруппировать данные в категории, а затем решить, какой способ отображения данных — круг, пончик или вафля — подходит лучше всего. Все они аппетитны и дают пользователю мгновенное представление о наборе данных. diff --git a/translations/ru/3-Data-Visualization/R/12-visualization-relationships/README.md b/translations/ru/3-Data-Visualization/R/12-visualization-relationships/README.md index 7d1a5908..b072dd6a 100644 --- a/translations/ru/3-Data-Visualization/R/12-visualization-relationships/README.md +++ b/translations/ru/3-Data-Visualization/R/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ library(ggplot2) ggplot(honey, aes(x = priceperlb, y = state)) + geom_point(colour = "blue") ``` -![scatterplot 1](../../../../../translated_images/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.ru.png) +![scatterplot 1](../../../../../translated_images/ru/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.png) Теперь покажите те же данные с цветовой схемой, напоминающей цвет мёда, чтобы отобразить, как цена меняется год за годом. Вы можете сделать это, добавив параметр 'scale_color_gradientn', чтобы показать изменения: @@ -61,7 +61,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ggplot(honey, aes(x = priceperlb, y = state, color=year)) + geom_point()+scale_color_gradientn(colours = colorspace::heat_hcl(7)) ``` -![scatterplot 2](../../../../../translated_images/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.ru.png) +![scatterplot 2](../../../../../translated_images/ru/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.png) С изменением цветовой схемы становится очевидным, что цена за фунт мёда значительно увеличивается с течением времени. Если вы проверите выборку данных (например, для штата Аризона), вы увидите закономерность увеличения цен год за годом, с редкими исключениями: @@ -92,7 +92,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ``` Вы можете заметить, как размер точек постепенно увеличивается. -![scatterplot 3](../../../../../translated_images/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.ru.png) +![scatterplot 3](../../../../../translated_images/ru/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.png) Это простой случай спроса и предложения? Из-за таких факторов, как изменение климата и разрушение колоний, становится ли мёда меньше с каждым годом, что приводит к увеличению цены? @@ -107,7 +107,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab ``` Ответ: Да, с некоторыми исключениями около 2003 года: -![line chart 1](../../../../../translated_images/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.ru.png) +![line chart 1](../../../../../translated_images/ru/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.png) Вопрос: А в 2003 году можно ли также увидеть всплеск предложения мёда? Что если посмотреть на общий объём производства год за годом? @@ -115,7 +115,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab qplot(honey$year,honey$totalprod, geom='smooth', span =0.5, xlab = "year",ylab = "totalprod") ``` -![line chart 2](../../../../../translated_images/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.ru.png) +![line chart 2](../../../../../translated_images/ru/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.png) Ответ: Не совсем. Если посмотреть на общий объём производства, он, наоборот, кажется увеличившимся в этот год, хотя в целом объём производства мёда снижается в течение этих лет. @@ -135,7 +135,7 @@ ggplot(honey, aes(x=yieldpercol, y = numcol,group = 1)) + ``` В этой визуализации вы можете сравнить урожайность на одну колонию и количество колоний год за годом, бок о бок, с настройкой wrap на 3 для столбцов: -![facet grid](../../../../../translated_images/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.ru.png) +![facet grid](../../../../../translated_images/ru/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.png) Для этого набора данных ничего особенно не выделяется в отношении количества колоний и их урожайности год за годом и штат за штатом. Можно ли найти другой способ выявить корреляцию между этими двумя переменными? @@ -152,7 +152,7 @@ plot(honey$year, honey$yieldpercol, pch = 17, col = 3, axis(side = 4, at = pretty(range(y2))) mtext("colony yield", side = 4, line = 3) ``` -![superimposed plots](../../../../../translated_images/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.ru.png) +![superimposed plots](../../../../../translated_images/ru/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.png) Хотя ничего явно не выделяется около 2003 года, это позволяет завершить урок на более позитивной ноте: несмотря на общее снижение количества колоний, их число стабилизируется, даже если урожайность на одну колонию уменьшается. diff --git a/translations/ru/3-Data-Visualization/R/13-meaningful-vizualizations/README.md b/translations/ru/3-Data-Visualization/R/13-meaningful-vizualizations/README.md index b78470b2..91a26a39 100644 --- a/translations/ru/3-Data-Visualization/R/13-meaningful-vizualizations/README.md +++ b/translations/ru/3-Data-Visualization/R/13-meaningful-vizualizations/README.md @@ -47,25 +47,25 @@ CO_OP_TRANSLATOR_METADATA: Даже если дата-сайентист тщательно выбирает подходящую диаграмму для данных, существует множество способов представить данные так, чтобы доказать определенную точку зрения, часто в ущерб самим данным. Существует множество примеров обманчивых диаграмм и инфографики! -[![Как диаграммы вводят в заблуждение, Альберто Кайро](../../../../../translated_images/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.ru.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "Как диаграммы вводят в заблуждение") +[![Как диаграммы вводят в заблуждение, Альберто Кайро](../../../../../translated_images/ru/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "Как диаграммы вводят в заблуждение") > 🎥 Нажмите на изображение выше, чтобы посмотреть доклад о вводящих в заблуждение диаграммах Эта диаграмма переворачивает ось X, чтобы показать противоположное истине, основываясь на дате: -![плохая диаграмма 1](../../../../../translated_images/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.ru.png) +![плохая диаграмма 1](../../../../../translated_images/ru/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.png) [Эта диаграмма](https://media.firstcoastnews.com/assets/WTLV/images/170ae16f-4643-438f-b689-50d66ca6a8d8/170ae16f-4643-438f-b689-50d66ca6a8d8_1140x641.jpg) еще более обманчива, так как взгляд автоматически устремляется вправо, чтобы сделать вывод, что со временем случаи COVID сократились в различных округах. Однако, если внимательно посмотреть на даты, можно заметить, что они были переставлены, чтобы создать ложное впечатление нисходящего тренда. -![плохая диаграмма 2](../../../../../translated_images/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.ru.jpg) +![плохая диаграмма 2](../../../../../translated_images/ru/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.jpg) Этот печально известный пример использует цвет И перевернутую ось Y, чтобы ввести в заблуждение: вместо вывода о том, что количество смертей от огнестрельного оружия возросло после принятия закона, благоприятного для оружия, взгляд обманчиво воспринимает противоположное: -![плохая диаграмма 3](../../../../../translated_images/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.ru.jpg) +![плохая диаграмма 3](../../../../../translated_images/ru/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.jpg) Эта странная диаграмма показывает, как можно манипулировать пропорциями, вызывая комический эффект: -![плохая диаграмма 4](../../../../../translated_images/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.ru.jpg) +![плохая диаграмма 4](../../../../../translated_images/ru/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.jpg) Сравнение несравнимого — еще один сомнительный прием. Существует [замечательный веб-сайт](https://tylervigen.com/spurious-correlations), посвященный "ложным корреляциям", где представлены "факты", связывающие, например, уровень разводов в штате Мэн и потребление маргарина. На Reddit также есть группа, собирающая [некрасивые примеры](https://www.reddit.com/r/dataisugly/top/?t=all) использования данных. @@ -100,13 +100,13 @@ CO_OP_TRANSLATOR_METADATA: Если ваши данные текстовые и содержат длинные подписи на оси X, вы можете наклонить текст для улучшения читаемости. [plot3D](https://cran.r-project.org/web/packages/plot3D/index.html) предлагает 3D-графики, если ваши данные это поддерживают. С его помощью можно создавать сложные визуализации данных. -![3D графики](../../../../../translated_images/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.ru.png) +![3D графики](../../../../../translated_images/ru/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.png) ## Анимация и 3D-отображение диаграмм Некоторые из лучших визуализаций данных сегодня являются анимационными. Ширли Ву создала потрясающие примеры с использованием D3, такие как '[цветы фильмов](http://bl.ocks.org/sxywu/raw/d612c6c653fb8b4d7ff3d422be164a5d/)', где каждый цветок представляет собой визуализацию фильма. Другой пример для Guardian — 'Bussed Out', интерактивный проект, сочетающий визуализации с Greensock и D3, а также формат статьи с прокруткой, чтобы показать, как Нью-Йорк решает проблему бездомных, отправляя их за пределы города. -![перевозка](../../../../../translated_images/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.ru.png) +![перевозка](../../../../../translated_images/ru/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.png) > "Bussed Out: Как Америка перемещает своих бездомных" от [The Guardian](https://www.theguardian.com/us-news/ng-interactive/2017/dec/20/bussed-out-america-moves-homeless-people-country-study). Визуализации Надии Бремер и Ширли Ву @@ -116,7 +116,7 @@ CO_OP_TRANSLATOR_METADATA: Вы завершите веб-приложение, которое будет отображать анимированный вид этой социальной сети. Оно использует библиотеку, созданную для [визуализации сети](https://github.com/emiliorizzo/vue-d3-network) с использованием Vue.js и D3. Когда приложение запущено, вы можете перемещать узлы на экране, чтобы изменять расположение данных. -![связи](../../../../../translated_images/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.ru.png) +![связи](../../../../../translated_images/ru/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.png) ## Проект: Создание диаграммы для отображения сети с использованием D3.js diff --git a/translations/ru/3-Data-Visualization/README.md b/translations/ru/3-Data-Visualization/README.md index 938ebbd0..4f32403d 100644 --- a/translations/ru/3-Data-Visualization/README.md +++ b/translations/ru/3-Data-Visualization/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Визуализации -![пчела на цветке лаванды](../../../translated_images/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.ru.jpg) +![пчела на цветке лаванды](../../../translated_images/ru/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.jpg) > Фото от Jenna Lee на Unsplash Визуализация данных — одна из самых важных задач для специалиста по данным. Изображения говорят больше, чем тысяча слов, а визуализация может помочь вам выявить множество интересных аспектов ваших данных, таких как пики, выбросы, группировки, тенденции и многое другое, что поможет вам понять историю, которую ваши данные пытаются рассказать. diff --git a/translations/ru/4-Data-Science-Lifecycle/14-Introduction/README.md b/translations/ru/4-Data-Science-Lifecycle/14-Introduction/README.md index ac6c9891..0925001b 100644 --- a/translations/ru/4-Data-Science-Lifecycle/14-Introduction/README.md +++ b/translations/ru/4-Data-Science-Lifecycle/14-Introduction/README.md @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: Этот урок сосредоточен на трех частях жизненного цикла: сборе данных, обработке и поддержке. -![Диаграмма жизненного цикла Data Science](../../../../translated_images/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.ru.jpg) +![Диаграмма жизненного цикла Data Science](../../../../translated_images/ru/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.jpg) > Фото от [Berkeley School of Information](https://ischoolonline.berkeley.edu/data-science/what-is-data-science/) ## Сбор данных @@ -101,7 +101,7 @@ CO_OP_TRANSLATOR_METADATA: |Процесс Team Data Science (TDSP)|Стандартный процесс для анализа данных в различных отраслях (CRISP-DM)| |--|--| -|![Жизненный цикл Team Data Science](../../../../translated_images/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.ru.png) | ![Изображение процесса CRISP-DM](../../../../translated_images/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.ru.png) | +|![Жизненный цикл Team Data Science](../../../../translated_images/ru/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.png) | ![Изображение процесса CRISP-DM](../../../../translated_images/ru/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.png) | | Изображение от [Microsoft](https://docs.microsoft.comazure/architecture/data-science-process/lifecycle) | Изображение от [Data Science Process Alliance](https://www.datascience-pm.com/crisp-dm-2/) | ## [Тест после лекции](https://ff-quizzes.netlify.app/en/ds/quiz/27) diff --git a/translations/ru/4-Data-Science-Lifecycle/README.md b/translations/ru/4-Data-Science-Lifecycle/README.md index 79c65577..1b4e05fb 100644 --- a/translations/ru/4-Data-Science-Lifecycle/README.md +++ b/translations/ru/4-Data-Science-Lifecycle/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Жизненный цикл Data Science -![communication](../../../translated_images/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.ru.jpg) +![communication](../../../translated_images/ru/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.jpg) > Фото от Headway на Unsplash В этих уроках вы изучите некоторые аспекты жизненного цикла Data Science, включая анализ и коммуникацию, связанные с данными. diff --git a/translations/ru/5-Data-Science-In-Cloud/README.md b/translations/ru/5-Data-Science-In-Cloud/README.md index 92569f7d..ab71317e 100644 --- a/translations/ru/5-Data-Science-In-Cloud/README.md +++ b/translations/ru/5-Data-Science-In-Cloud/README.md @@ -9,13 +9,13 @@ CO_OP_TRANSLATOR_METADATA: --> # Наука о данных в облаке -![cloud-picture](../../../translated_images/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.ru.jpg) +![cloud-picture](../../../translated_images/ru/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.jpg) > Фото [Jelleke Vanooteghem](https://unsplash.com/@ilumire) с [Unsplash](https://unsplash.com/s/photos/cloud?orientation=landscape) Когда речь идет о работе с большими данными в области науки о данных, облако может стать настоящим прорывом. В следующих трех уроках мы узнаем, что такое облако и почему оно может быть очень полезным. Мы также изучим набор данных о сердечной недостаточности и создадим модель, которая поможет оценить вероятность сердечной недостаточности у человека. Мы будем использовать возможности облака для обучения, развертывания и использования модели двумя различными способами. Один способ — используя только пользовательский интерфейс в стиле Low code/No code, другой — с помощью набора инструментов разработчика Azure Machine Learning (Azure ML SDK). -![project-schema](../../../translated_images/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.ru.png) +![project-schema](../../../translated_images/ru/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.png) ### Темы diff --git a/translations/ru/6-Data-Science-In-Wild/20-Real-World-Examples/README.md b/translations/ru/6-Data-Science-In-Wild/20-Real-World-Examples/README.md index 32277b85..b33d93cb 100644 --- a/translations/ru/6-Data-Science-In-Wild/20-Real-World-Examples/README.md +++ b/translations/ru/6-Data-Science-In-Wild/20-Real-World-Examples/README.md @@ -41,7 +41,7 @@ CO_OP_TRANSLATOR_METADATA: * [Наука о данных в здравоохранении](https://data-flair.training/blogs/data-science-in-healthcare/) - выделяет такие приложения, как медицинская визуализация (например, МРТ, рентген, КТ-сканирование), геномика (секвенирование ДНК), разработка лекарств (оценка рисков, прогнозирование успеха), предиктивная аналитика (уход за пациентами и логистика поставок), отслеживание и предотвращение заболеваний и т.д. -![Приложения науки о данных в реальном мире](../../../../translated_images/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.ru.png) Источник изображения: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) +![Приложения науки о данных в реальном мире](../../../../translated_images/ru/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.png) Источник изображения: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) На рисунке показаны другие области и примеры применения методов науки о данных. Хотите изучить другие приложения? Ознакомьтесь с разделом [Обзор и самостоятельное изучение](../../../../6-Data-Science-In-Wild/20-Real-World-Examples) ниже. diff --git a/translations/ru/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md b/translations/ru/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md index 3b3a2bf4..b4d2feae 100644 --- a/translations/ru/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md +++ b/translations/ru/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md @@ -22,7 +22,7 @@ CO_OP_TRANSLATOR_METADATA: 2. Изучить [каталог наборов данных](https://planetarycomputer.microsoft.com/catalog) — узнать назначение каждого набора. 3. Использовать Explorer — выбрать интересующий набор данных, подходящий запрос и вариант визуализации. -![The Planetary Computer Explorer](../../../../translated_images/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.ru.png) +![The Planetary Computer Explorer](../../../../translated_images/ru/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.png) `Ваше задание:` Теперь изучите визуализацию, которая отображается в браузере, и ответьте на следующие вопросы: diff --git a/translations/ru/CONTRIBUTING.md b/translations/ru/CONTRIBUTING.md index c162c59d..a29d66fa 100644 --- a/translations/ru/CONTRIBUTING.md +++ b/translations/ru/CONTRIBUTING.md @@ -318,7 +318,7 @@ def calculate_mean(data): ``` ```` -- Добавляйте альтернативный текст к изображениям: `![Alt text](../../translated_images/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.ru.png)` +- Добавляйте альтернативный текст к изображениям: `![Alt text](../../translated_images/ru/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.png)` - Сохраняйте разумную длину строк (около 80-100 символов) ### Python diff --git a/translations/ru/README.md b/translations/ru/README.md index 7d73ebf4..523b827c 100644 --- a/translations/ru/README.md +++ b/translations/ru/README.md @@ -33,7 +33,7 @@ Azure Cloud Advocates в Microsoft рады предложить 10-недель **🙏 Особая благодарность 🙏 нашим авторам, рецензентам и вкладчикам контента из числа [Студенческих послов Microsoft](https://studentambassadors.microsoft.com/),** в частности Aaryan Arora, [Aditya Garg](https://github.com/AdityaGarg00), [Alondra Sanchez](https://www.linkedin.com/in/alondra-sanchez-molina/), [Ankita Singh](https://www.linkedin.com/in/ankitasingh007), [Anupam Mishra](https://www.linkedin.com/in/anupam--mishra/), [Arpita Das](https://www.linkedin.com/in/arpitadas01/), ChhailBihari Dubey, [Dibri Nsofor](https://www.linkedin.com/in/dibrinsofor), [Dishita Bhasin](https://www.linkedin.com/in/dishita-bhasin-7065281bb), [Majd Safi](https://www.linkedin.com/in/majd-s/), [Max Blum](https://www.linkedin.com/in/max-blum-6036a1186/), [Miguel Correa](https://www.linkedin.com/in/miguelmque/), [Mohamma Iftekher (Iftu) Ebne Jalal](https://twitter.com/iftu119), [Nawrin Tabassum](https://www.linkedin.com/in/nawrin-tabassum), [Raymond Wangsa Putra](https://www.linkedin.com/in/raymond-wp/), [Rohit Yadav](https://www.linkedin.com/in/rty2423), Samridhi Sharma, [Sanya Sinha](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200), [Sheena Narula](https://www.linkedin.com/in/sheena-narua-n/), [Tauqeer Ahmad](https://www.linkedin.com/in/tauqeerahmad5201/), Yogendrasingh Pawar , [Vidushi Gupta](https://www.linkedin.com/in/vidushi-gupta07/), [Jasleen Sondhi](https://www.linkedin.com/in/jasleen-sondhi/) -|![Скетчноут от @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.ru.png)| +|![Скетчноут от @sketchthedocs https://sketchthedocs.dev](../../translated_images/ru/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.png)| |:---:| | Наука о данных для начинающих - _Скетчноут от [@nitya](https://twitter.com/nitya)_ | @@ -52,7 +52,7 @@ Azure Cloud Advocates в Microsoft рады предложить 10-недель У нас идёт серия в Discord «Учимся с ИИ», узнайте больше и присоединяйтесь к нам на [Learn with AI Series](https://aka.ms/learnwithai/discord) с 18 по 30 сентября 2025 года. Вы получите советы и приёмы по использованию GitHub Copilot для науки о данных. -![Серия «Учимся с ИИ»](../../translated_images/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.ru.jpg) +![Серия «Учимся с ИИ»](../../translated_images/ru/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.jpg) # Вы студент? @@ -132,7 +132,7 @@ Azure Cloud Advocates в Microsoft рады предложить 10-недель ## Уроки -|![ Скетчноут от @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.ru.png)| +|![ Скетчноут от @sketchthedocs https://sketchthedocs.dev](../../translated_images/ru/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png)| |:---:| | Наука о данных для начинающих: Дорожная карта - _Скетчноут от [@nitya](https://twitter.com/nitya)_ | diff --git a/translations/ru/sketchnotes/README.md b/translations/ru/sketchnotes/README.md index 80f81695..7dcd761f 100644 --- a/translations/ru/sketchnotes/README.md +++ b/translations/ru/sketchnotes/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: Нития Нарасимхан, художник -![скетчнота дорожной карты](../../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.ru.png) +![скетчнота дорожной карты](../../../translated_images/ru/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png) --- diff --git a/translations/sk/1-Introduction/01-defining-data-science/README.md b/translations/sk/1-Introduction/01-defining-data-science/README.md index 4d8be864..a2a1a447 100644 --- a/translations/sk/1-Introduction/01-defining-data-science/README.md +++ b/translations/sk/1-Introduction/01-defining-data-science/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: --- -[![Video o definícii dátovej vedy](../../../../translated_images/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.sk.png)](https://youtu.be/beZ7Mb_oz9I) +[![Video o definícii dátovej vedy](../../../../translated_images/sk/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.png)](https://youtu.be/beZ7Mb_oz9I) ## [Kvíz pred prednáškou](https://ff-quizzes.netlify.app/en/ds/quiz/0) @@ -151,7 +151,7 @@ Ak chceme ísť ešte ďalej, môžeme vykresliť čas potrebný na dokončenie V tejto výzve sa pokúsime nájsť koncepty relevantné pre oblasť dátovej vedy tým, že sa pozrieme na texty. Vezmeme článok z Wikipédie o dátovej vede, stiahneme a spracujeme text, a potom vytvoríme oblak slov, ako je tento: -![Oblak slov pre dátovú vedu](../../../../translated_images/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.sk.png) +![Oblak slov pre dátovú vedu](../../../../translated_images/sk/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.png) Navštívte [`notebook.ipynb`](../../../../1-Introduction/01-defining-data-science/notebook.ipynb ':ignore'), aby ste si prečítali kód. Môžete tiež spustiť kód a vidieť, ako vykonáva všetky transformácie dát v reálnom čase. diff --git a/translations/sk/1-Introduction/04-stats-and-probability/README.md b/translations/sk/1-Introduction/04-stats-and-probability/README.md index 030891cc..55bec872 100644 --- a/translations/sk/1-Introduction/04-stats-and-probability/README.md +++ b/translations/sk/1-Introduction/04-stats-and-probability/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: Teória štatistiky a pravdepodobnosti sú dve úzko prepojené oblasti matematiky, ktoré sú veľmi dôležité pre dátovú vedu. Dá sa pracovať s dátami aj bez hlbokých matematických znalostí, ale je lepšie poznať aspoň základné koncepty. Tu vám predstavíme krátky úvod, ktorý vám pomôže začať. -[![Úvodné video](../../../../translated_images/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.sk.png)](https://youtu.be/Z5Zy85g4Yjw) +[![Úvodné video](../../../../translated_images/sk/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.png)](https://youtu.be/Z5Zy85g4Yjw) ## [Kvíz pred prednáškou](https://ff-quizzes.netlify.app/en/ds/quiz/6) @@ -39,7 +39,7 @@ Je ťažšie opísať pravdepodobnostné rozdelenie spojitej premennej, ktorej h Môžeme hovoriť iba o pravdepodobnosti, že premenná spadne do daného intervalu hodnôt, napr. P(t1≤X2). V tomto prípade je pravdepodobnostné rozdelenie opísané pomocou **hustoty pravdepodobnosti** p(x), pričom -![P(t_1\le X Viac príkladov korelácie a kovariancie nájdete v [sprievodnom notebooku](notebook.ipynb). diff --git a/translations/sk/1-Introduction/README.md b/translations/sk/1-Introduction/README.md index 72fe7d96..9e79125f 100644 --- a/translations/sk/1-Introduction/README.md +++ b/translations/sk/1-Introduction/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Úvod do dátovej vedy -![dáta v akcii](../../../translated_images/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.sk.jpg) +![dáta v akcii](../../../translated_images/sk/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.jpg) > Fotografia od Stephena Dawsona na Unsplash V týchto lekciách objavíte, ako je definovaná dátová veda, a oboznámite sa s etickými otázkami, ktoré musí dátový vedec zohľadniť. Taktiež sa dozviete, ako sú definované dáta, a získate základné poznatky o štatistike a pravdepodobnosti, ktoré sú jadrom akademických oblastí dátovej vedy. diff --git a/translations/sk/2-Working-With-Data/07-python/README.md b/translations/sk/2-Working-With-Data/07-python/README.md index 6f821ad2..0bcb6528 100644 --- a/translations/sk/2-Working-With-Data/07-python/README.md +++ b/translations/sk/2-Working-With-Data/07-python/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: | :-------------------------------------------------------------------------------------------------------: | | Práca s Pythonom - _Sketchnote od [@nitya](https://twitter.com/nitya)_ | -[![Úvodné video](../../../../translated_images/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.sk.png)](https://youtu.be/dZjWOGbsN4Y) +[![Úvodné video](../../../../translated_images/sk/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.png)](https://youtu.be/dZjWOGbsN4Y) Databázy ponúkajú veľmi efektívne spôsoby ukladania dát a ich dotazovania pomocou dotazovacích jazykov, no najflexibilnejším spôsobom spracovania dát je napísanie vlastného programu na manipuláciu s dátami. V mnohých prípadoch by bolo efektívnejšie použiť dotaz do databázy. Avšak v prípadoch, keď je potrebné zložitejšie spracovanie dát, to nie je možné jednoducho vykonať pomocou SQL. Spracovanie dát je možné programovať v akomkoľvek programovacom jazyku, ale existujú určité jazyky, ktoré sú na prácu s dátami na vyššej úrovni. Dátoví vedci zvyčajne preferujú jeden z nasledujúcich jazykov: @@ -73,7 +73,7 @@ print(f"Length of index is {len(idx)}") items_sold = pd.Series(np.random.randint(25,50,size=len(idx)),index=idx) items_sold.plot() ``` -![Graf časovej série](../../../../translated_images/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.sk.png) +![Graf časovej série](../../../../translated_images/sk/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.png) Teraz predpokladajme, že každý týždeň organizujeme párty pre priateľov a berieme na párty ďalších 10 balení zmrzliny. Môžeme vytvoriť ďalšiu sériu, indexovanú podľa týždňov, aby sme to ukázali: ```python @@ -84,7 +84,7 @@ Keď sčítame dve série, dostaneme celkový počet: total_items = items_sold.add(additional_items,fill_value=0) total_items.plot() ``` -![Graf časovej série](../../../../translated_images/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.sk.png) +![Graf časovej série](../../../../translated_images/sk/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.png) > **Poznámka**: Nepoužívame jednoduchú syntax `total_items+additional_items`. Ak by sme to urobili, dostali by sme veľa hodnôt `NaN` (*Not a Number*) v výslednej sérii. Je to preto, že pre niektoré indexové body v sérii `additional_items` chýbajú hodnoty, a sčítanie `NaN` s čímkoľvek vedie k `NaN`. Preto musíme počas sčítania špecifikovať parameter `fill_value`. @@ -93,7 +93,7 @@ Pri časových sériách môžeme tiež **preukladať** sériu s rôznymi časov monthly = total_items.resample("1M").mean() ax = monthly.plot(kind='bar') ``` -![Mesačné priemery časovej série](../../../../translated_images/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.sk.png) +![Mesačné priemery časovej série](../../../../translated_images/sk/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.png) ### DataFrame @@ -219,7 +219,7 @@ Prvým problémom, na ktorý sa zameriame, je modelovanie epidémie šírenia CO Keďže chceme demonštrovať, ako pracovať s dátami, pozývame vás otvoriť [`notebook-covidspread.ipynb`](notebook-covidspread.ipynb) a prečítať si ho od začiatku do konca. Môžete tiež spustiť bunky a splniť niektoré výzvy, ktoré sme pre vás nechali na konci. -![COVID Spread](../../../../translated_images/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.sk.png) +![COVID Spread](../../../../translated_images/sk/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.png) > Ak neviete, ako spustiť kód v Jupyter Notebooku, pozrite si [tento článok](https://soshnikov.com/education/how-to-execute-notebooks-from-github/). @@ -241,7 +241,7 @@ Kompletný príklad analýzy tohto datasetu pomocou [Text Analytics for Health]( Otvorte [`notebook-papers.ipynb`](notebook-papers.ipynb) a prečítajte si ho od začiatku do konca. Môžete tiež spustiť bunky a splniť niektoré výzvy, ktoré sme pre vás nechali na konci. -![Covid Medical Treatment](../../../../translated_images/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.sk.png) +![Covid Medical Treatment](../../../../translated_images/sk/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.png) ## Spracovanie obrazových dát diff --git a/translations/sk/2-Working-With-Data/README.md b/translations/sk/2-Working-With-Data/README.md index 24a5f983..129f0db7 100644 --- a/translations/sk/2-Working-With-Data/README.md +++ b/translations/sk/2-Working-With-Data/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Práca s dátami -![data love](../../../translated_images/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.sk.jpg) +![data love](../../../translated_images/sk/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.jpg) > Fotografia od Alexandra Sinna na Unsplash V týchto lekciách sa naučíte niektoré spôsoby, ako je možné dáta spravovať, manipulovať s nimi a používať ich v aplikáciách. Dozviete sa o relačných a nerelačných databázach a o tom, ako v nich môžu byť dáta uložené. Naučíte sa základy práce s Pythonom na správu dát a objavíte niektoré z mnohých spôsobov, ako môžete s Pythonom pracovať na správe a analýze dát. diff --git a/translations/sk/3-Data-Visualization/12-visualization-relationships/README.md b/translations/sk/3-Data-Visualization/12-visualization-relationships/README.md index 0958b5a0..5d53c6e9 100644 --- a/translations/sk/3-Data-Visualization/12-visualization-relationships/README.md +++ b/translations/sk/3-Data-Visualization/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ Vytvorte základný bodový graf na zobrazenie vzťahu medzi cenou za libru medu ```python sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ``` -![bodový graf 1](../../../../translated_images/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.sk.png) +![bodový graf 1](../../../../translated_images/sk/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.png) Teraz zobrazte tie isté údaje s farebnou schémou pripomínajúcou med, aby ste ukázali, ako sa cena vyvíja v priebehu rokov. Môžete to dosiahnuť pridaním parametra 'hue', ktorý ukáže zmenu rok čo rok: @@ -60,7 +60,7 @@ Teraz zobrazte tie isté údaje s farebnou schémou pripomínajúcou med, aby st ```python sns.relplot(x="priceperlb", y="state", hue="year", palette="YlOrBr", data=honey, height=15, aspect=.5); ``` -![bodový graf 2](../../../../translated_images/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.sk.png) +![bodový graf 2](../../../../translated_images/sk/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.png) S touto zmenou farebnej schémy môžete vidieť, že v priebehu rokov existuje zjavný silný progres v cene medu za libru. Ak sa pozriete na vzorku údajov na overenie (napríklad vyberte štát Arizona), môžete vidieť vzor zvyšovania cien rok čo rok, s niekoľkými výnimkami: @@ -89,7 +89,7 @@ sns.relplot(x="priceperlb", y="state", size="year", data=honey, height=15, aspec ``` Vidíte, že veľkosť bodov sa postupne zväčšuje. -![bodový graf 3](../../../../translated_images/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.sk.png) +![bodový graf 3](../../../../translated_images/sk/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.png) Je to jednoduchý prípad ponuky a dopytu? Kvôli faktorom, ako je klimatická zmena a kolaps kolónií, je k dispozícii menej medu na predaj rok čo rok, a preto cena rastie? @@ -104,7 +104,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); ``` Odpoveď: Áno, s niekoľkými výnimkami okolo roku 2003: -![čiarový graf 1](../../../../translated_images/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.sk.png) +![čiarový graf 1](../../../../translated_images/sk/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.png) ✅ Pretože Seaborn agreguje údaje okolo jednej čiary, zobrazuje "viacero meraní pri každej hodnote x tým, že vykresľuje priemer a 95% interval spoľahlivosti okolo priemeru". [Zdroj](https://seaborn.pydata.org/tutorial/relational.html). Toto časovo náročné správanie môžete vypnúť pridaním `ci=None`. @@ -114,7 +114,7 @@ Otázka: No, v roku 2003 môžeme tiež vidieť nárast v zásobách medu? Čo a sns.relplot(x="year", y="totalprod", kind="line", data=honey); ``` -![čiarový graf 2](../../../../translated_images/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.sk.png) +![čiarový graf 2](../../../../translated_images/sk/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.png) Odpoveď: Nie celkom. Ak sa pozriete na celkovú produkciu, zdá sa, že v tom konkrétnom roku skutočne vzrástla, aj keď všeobecne množstvo vyprodukovaného medu v týchto rokoch klesá. @@ -139,7 +139,7 @@ sns.relplot( ``` V tejto vizualizácii môžete porovnať výnos na kolóniu a počet kolónií rok čo rok, vedľa seba, s nastavením wrap na 3 pre stĺpce: -![mriežka aspektov](../../../../translated_images/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.sk.png) +![mriežka aspektov](../../../../translated_images/sk/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.png) Pre tento dataset nič zvlášť nevyniká, pokiaľ ide o počet kolónií a ich výnos, rok čo rok a štát po štáte. Existuje iný spôsob, ako nájsť koreláciu medzi týmito dvoma premennými? @@ -162,7 +162,7 @@ sns.despine(right=False) plt.ylabel('colony yield') ax.figure.legend(); ``` -![prekryté grafy](../../../../translated_images/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.sk.png) +![prekryté grafy](../../../../translated_images/sk/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.png) Aj keď okolo roku 2003 nič zvlášť nevyniká, umožňuje nám to ukončiť túto lekciu na trochu pozitívnejšiu nôtu: aj keď počet kolónií celkovo klesá, ich počet sa stabilizuje, aj keď ich výnos na kolóniu klesá. diff --git a/translations/sk/3-Data-Visualization/R/09-visualization-quantities/README.md b/translations/sk/3-Data-Visualization/R/09-visualization-quantities/README.md index aa4f1265..db4f95d6 100644 --- a/translations/sk/3-Data-Visualization/R/09-visualization-quantities/README.md +++ b/translations/sk/3-Data-Visualization/R/09-visualization-quantities/README.md @@ -67,7 +67,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` Tu nainštalujete balík `ggplot2` a potom ho importujete do pracovného priestoru pomocou príkazu `library("ggplot2")`. Na vykreslenie akéhokoľvek grafu v ggplot sa používa funkcia `ggplot()` a špecifikujete dataset, premenné x a y ako atribúty. V tomto prípade používame funkciu `geom_line()`, pretože chceme vykresliť čiarový graf. -![MaxRozpätie-čiarový graf](../../../../../translated_images/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.sk.png) +![MaxRozpätie-čiarový graf](../../../../../translated_images/sk/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.png) Čo si všimnete okamžite? Zdá sa, že existuje aspoň jeden extrémny údaj - to je poriadne rozpätie krídel! Rozpätie krídel viac ako 2000 centimetrov znamená viac ako 20 metrov - potulujú sa v Minnesote pterodaktyly? Poďme to preskúmať. @@ -85,7 +85,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` Špecifikujeme uhol v `theme` a špecifikujeme štítky osí x a y v `xlab()` a `ylab()` respektíve. Funkcia `ggtitle()` dáva grafu názov. -![MaxRozpätie-čiarový graf-vylepšený](../../../../../translated_images/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.sk.png) +![MaxRozpätie-čiarový graf-vylepšený](../../../../../translated_images/sk/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.png) Aj s rotáciou štítkov nastavenou na 45 stupňov je ich príliš veľa na čítanie. Skúsme inú stratégiu: označme iba tie extrémne hodnoty a nastavme štítky priamo v grafe. Môžete použiť bodový graf, aby ste získali viac priestoru na označovanie: @@ -101,7 +101,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + Čo objavíte? -![MaxRozpätie-bodový graf](../../../../../translated_images/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.sk.png) +![MaxRozpätie-bodový graf](../../../../../translated_images/sk/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.png) ## Filtrovanie údajov @@ -120,7 +120,7 @@ ggplot(data=birds_filtered, aes(x=Name, y=MaxWingspan,group=1)) + ``` Vytvorili sme nový dataframe `birds_filtered` a potom vykreslili bodový graf. Filtrovaním extrémnych hodnôt sú vaše údaje teraz súdržnejšie a zrozumiteľnejšie. -![MaxRozpätie-bodový graf-vylepšený](../../../../../translated_images/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.sk.png) +![MaxRozpätie-bodový graf-vylepšený](../../../../../translated_images/sk/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.png) Teraz, keď máme čistejší dataset aspoň z hľadiska rozpätia krídel, poďme objaviť viac o týchto vtákoch. @@ -162,7 +162,7 @@ birds_filtered %>% group_by(Category) %>% ``` V nasledujúcom úryvku inštalujeme balíky [dplyr](https://www.rdocumentation.org/packages/dplyr/versions/0.7.8) a [lubridate](https://www.rdocumentation.org/packages/lubridate/versions/1.8.0), ktoré pomáhajú manipulovať a zoskupovať údaje na vykreslenie zoskupeného stĺpcového grafu. Najprv zoskupíte údaje podľa `Category` vtákov a potom sumarizujete stĺpce `MinLength`, `MaxLength`, `MinBodyMass`, `MaxBodyMass`, `MinWingspan`, `MaxWingspan`. Potom vykreslíte stĺpcový graf pomocou balíka `ggplot2` a špecifikujete farby pre rôzne kategórie a štítky. -![Zoskupený stĺpcový graf](../../../../../translated_images/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.sk.png) +![Zoskupený stĺpcový graf](../../../../../translated_images/sk/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.png) Tento stĺpcový graf je však nečitateľný, pretože obsahuje príliš veľa nezoskupených údajov. Musíte vybrať iba údaje, ktoré chcete vykresliť, takže sa pozrime na dĺžku vtákov podľa ich kategórie. @@ -177,7 +177,7 @@ ggplot(birds_count,aes(Category,n))+geom_bar(stat="identity")+coord_flip() ``` Najprv spočítate unikátne hodnoty v stĺpci `Category` a potom ich zoradíte do nového dataframe `birds_count`. Tieto zoradené údaje sú potom faktorizované na rovnakej úrovni, aby boli vykreslené v zoradenom poradí. Pomocou `ggplot2` potom vykreslíte údaje v stĺpcovom grafe. Funkcia `coord_flip()` vykreslí horizontálne stĺpce. -![kategória-dĺžka](../../../../../translated_images/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.sk.png) +![kategória-dĺžka](../../../../../translated_images/sk/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.png) Tento stĺpcový graf poskytuje dobrý prehľad o počte vtákov v každej kategórii. Na prvý pohľad vidíte, že najväčší počet vtákov v tomto regióne patrí do kategórie Kačice/Husi/Vodné vtáky. Minnesota je "krajina 10 000 jazier", takže to nie je prekvapujúce! @@ -200,7 +200,7 @@ ggplot(birds_grouped,aes(Category,MaxLength))+geom_bar(stat="identity")+coord_fl ``` Zoskupíme údaje `birds_filtered` podľa `Category` a potom vykreslíme stĺpcový graf. -![porovnávanie údajov](../../../../../translated_images/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.sk.png) +![porovnávanie údajov](../../../../../translated_images/sk/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.png) Tu nie je nič prekvapujúce: kolibríky majú najmenšiu MaxDĺžku v porovnaní s pelikánmi alebo husami. Je dobré, keď údaje dávajú logický zmysel! @@ -212,7 +212,7 @@ ggplot(data=birds_grouped, aes(x=Category)) + geom_bar(aes(y=MinLength), stat="identity", position="identity", fill='orange')+ coord_flip() ``` -![prekryté hodnoty](../../../../../translated_images/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.sk.png) +![prekryté hodnoty](../../../../../translated_images/sk/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.png) ## 🚀 Výzva diff --git a/translations/sk/3-Data-Visualization/R/10-visualization-distributions/README.md b/translations/sk/3-Data-Visualization/R/10-visualization-distributions/README.md index 503efc9f..2e3d0086 100644 --- a/translations/sk/3-Data-Visualization/R/10-visualization-distributions/README.md +++ b/translations/sk/3-Data-Visualization/R/10-visualization-distributions/README.md @@ -45,7 +45,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + geom_point() + ggtitle("Max Length per order") + coord_flip() ``` -![max dĺžka podľa radu](../../../../../translated_images/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.sk.png) +![max dĺžka podľa radu](../../../../../translated_images/sk/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.png) Toto poskytuje prehľad o všeobecnej distribúcii dĺžky tela podľa radu vtákov, ale nie je to optimálny spôsob zobrazenia skutočných distribúcií. Táto úloha sa zvyčajne rieši vytvorením histogramu. @@ -57,7 +57,7 @@ Toto poskytuje prehľad o všeobecnej distribúcii dĺžky tela podľa radu vtá ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=10)+ylab('Frequency') ``` -![distribúcia pre celý dataset](../../../../../translated_images/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.sk.png) +![distribúcia pre celý dataset](../../../../../translated_images/sk/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.png) Ako vidíte, väčšina z viac ako 400 vtákov v tomto datasete spadá do rozsahu pod 2000 pre ich maximálnu telesnú hmotnosť. Získajte viac informácií o údajoch zmenou parametra `bins` na vyššie číslo, napríklad 30: @@ -65,7 +65,7 @@ Ako vidíte, väčšina z viac ako 400 vtákov v tomto datasete spadá do rozsah ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![distribúcia-30bins](../../../../../translated_images/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.sk.png) +![distribúcia-30bins](../../../../../translated_images/sk/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.png) Tento graf ukazuje distribúciu o niečo podrobnejšie. Graf menej posunutý doľava by sa dal vytvoriť zabezpečením, že vyberiete iba údaje v danom rozsahu: @@ -77,7 +77,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![filtrovaný histogram](../../../../../translated_images/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.sk.png) +![filtrovaný histogram](../../../../../translated_images/sk/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.png) ✅ Vyskúšajte iné filtre a dátové body. Ak chcete vidieť úplnú distribúciu údajov, odstráňte filter `['MaxBodyMass']`, aby sa zobrazili označené distribúcie. @@ -91,7 +91,7 @@ ggplot(data=birds_filtered_1, aes(x=MaxBodyMass, y=MaxLength) ) + ``` Zdá sa, že medzi týmito dvoma prvkami existuje očakávaná korelácia pozdĺž očakávanej osi, s jedným obzvlášť silným bodom konvergencie: -![2d graf](../../../../../translated_images/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.sk.png) +![2d graf](../../../../../translated_images/sk/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.png) Histogramy fungujú dobre predvolene pre číselné údaje. Čo ak potrebujete vidieť distribúcie podľa textových údajov? ## Preskúmajte dataset pre distribúcie pomocou textových údajov @@ -122,7 +122,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + scale_fill_manual(name="Conservation Status",values=c("red","green","blue","pink"),labels=c("Endangered","Near Threathened","Vulnerable","Least Concern")) ``` -![rozpätie krídel a stav ochrany](../../../../../translated_images/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.sk.png) +![rozpätie krídel a stav ochrany](../../../../../translated_images/sk/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.png) Zdá sa, že medzi minimálnym rozpätím krídel a stavom ochrany neexistuje dobrá korelácia. Otestujte iné prvky datasetu pomocou tejto metódy. Môžete vyskúšať aj rôzne filtre. Nájdete nejakú koreláciu? @@ -136,7 +136,7 @@ Poďme teraz pracovať s hustotnými grafmi! ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + geom_density() ``` -![hustotný graf](../../../../../translated_images/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.sk.png) +![hustotný graf](../../../../../translated_images/sk/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.png) Vidíte, ako graf odráža ten predchádzajúci pre údaje o minimálnom rozpätí krídel; je len o niečo plynulejší. Ak by ste chceli znovu navštíviť tú zubatú čiaru MaxBodyMass v druhom grafe, ktorý ste vytvorili, mohli by ste ju veľmi dobre vyhladiť opätovným vytvorením pomocou tejto metódy: @@ -144,7 +144,7 @@ Vidíte, ako graf odráža ten predchádzajúci pre údaje o minimálnom rozpät ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density() ``` -![hustota telesnej hmotnosti](../../../../../translated_images/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.sk.png) +![hustota telesnej hmotnosti](../../../../../translated_images/sk/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.png) Ak by ste chceli hladkú, ale nie príliš hladkú čiaru, upravte parameter `adjust`: @@ -152,7 +152,7 @@ Ak by ste chceli hladkú, ale nie príliš hladkú čiaru, upravte parameter `ad ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density(adjust = 1/5) ``` -![menej hladká telesná hmotnosť](../../../../../translated_images/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.sk.png) +![menej hladká telesná hmotnosť](../../../../../translated_images/sk/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.png) ✅ Prečítajte si o parametroch dostupných pre tento typ grafu a experimentujte! @@ -162,7 +162,7 @@ Tento typ grafu ponúka krásne vysvetľujúce vizualizácie. S niekoľkými ria ggplot(data=birds_filtered_1,aes(x = MaxBodyMass, fill = Order)) + geom_density(alpha=0.5) ``` -![telesná hmotnosť podľa radu](../../../../../translated_images/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.sk.png) +![telesná hmotnosť podľa radu](../../../../../translated_images/sk/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.png) ## 🚀 Výzva diff --git a/translations/sk/3-Data-Visualization/R/11-visualization-proportions/README.md b/translations/sk/3-Data-Visualization/R/11-visualization-proportions/README.md index 9f72e483..349803cc 100644 --- a/translations/sk/3-Data-Visualization/R/11-visualization-proportions/README.md +++ b/translations/sk/3-Data-Visualization/R/11-visualization-proportions/README.md @@ -97,7 +97,7 @@ pie(grouped$count,grouped$class, main="Edible?") ``` Voila, koláčový graf zobrazujúci proporcie týchto údajov podľa dvoch tried húb. Je veľmi dôležité správne zoradiť poradie štítkov, najmä tu, preto si overte poradie, v akom je pole štítkov vytvorené! -![koláčový graf](../../../../../translated_images/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.sk.png) +![koláčový graf](../../../../../translated_images/sk/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.png) ## Donuty! @@ -132,7 +132,7 @@ library(webr) PieDonut(habitat, aes(habitat, count=count)) ``` -![donutový graf](../../../../../translated_images/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.sk.png) +![donutový graf](../../../../../translated_images/sk/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.png) Tento kód používa dve knižnice - ggplot2 a webr. Pomocou funkcie PieDonut z knižnice webr môžeme ľahko vytvoriť donutový graf! @@ -169,7 +169,7 @@ waffle((cap_color$count/10), rows = 7, title = "Waffle Chart")+scale_fill_manual Pomocou waflového grafu môžete jasne vidieť proporcie farieb klobúkov v tomto datasete húb. Zaujímavé je, že existuje veľa húb so zelenými klobúkmi! -![waflový graf](../../../../../translated_images/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.sk.png) +![waflový graf](../../../../../translated_images/sk/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.png) V tejto lekcii ste sa naučili tri spôsoby vizualizácie proporcií. Najprv musíte zoskupiť svoje údaje do kategórií a potom sa rozhodnúť, ktorý spôsob zobrazenia údajov je najlepší - koláč, donut alebo waffle. Všetky sú chutné a poskytujú používateľovi okamžitý prehľad o datasete. diff --git a/translations/sk/3-Data-Visualization/R/12-visualization-relationships/README.md b/translations/sk/3-Data-Visualization/R/12-visualization-relationships/README.md index 78444e6c..d2545972 100644 --- a/translations/sk/3-Data-Visualization/R/12-visualization-relationships/README.md +++ b/translations/sk/3-Data-Visualization/R/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ library(ggplot2) ggplot(honey, aes(x = priceperlb, y = state)) + geom_point(colour = "blue") ``` -![bodový graf 1](../../../../../translated_images/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.sk.png) +![bodový graf 1](../../../../../translated_images/sk/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.png) Teraz zobrazte tie isté údaje s farebnou schémou medu, aby ste ukázali, ako sa cena vyvíja v priebehu rokov. Môžete to urobiť pridaním parametra 'scale_color_gradientn', ktorý ukazuje zmenu rok po roku: @@ -61,7 +61,7 @@ Teraz zobrazte tie isté údaje s farebnou schémou medu, aby ste ukázali, ako ggplot(honey, aes(x = priceperlb, y = state, color=year)) + geom_point()+scale_color_gradientn(colours = colorspace::heat_hcl(7)) ``` -![bodový graf 2](../../../../../translated_images/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.sk.png) +![bodový graf 2](../../../../../translated_images/sk/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.png) S touto zmenou farebnej schémy môžete vidieť, že v priebehu rokov existuje zjavný silný nárast ceny za libru medu. Ak si overíte vzorku údajov (napríklad pre štát Arizona), môžete vidieť vzor zvyšovania cien rok po roku, s niekoľkými výnimkami: @@ -92,7 +92,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ``` Vidíte, že veľkosť bodov sa postupne zväčšuje. -![bodový graf 3](../../../../../translated_images/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.sk.png) +![bodový graf 3](../../../../../translated_images/sk/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.png) Je to jednoduchý prípad ponuky a dopytu? Kvôli faktorom, ako je zmena klímy a kolaps kolónií, je k dispozícii menej medu na predaj rok po roku, a preto cena stúpa? @@ -107,7 +107,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab ``` Odpoveď: Áno, s niektorými výnimkami okolo roku 2003: -![čiarový graf 1](../../../../../translated_images/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.sk.png) +![čiarový graf 1](../../../../../translated_images/sk/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.png) Otázka: No, v roku 2003 môžeme tiež vidieť nárast v zásobách medu? Čo ak sa pozriete na celkovú produkciu rok po roku? @@ -115,7 +115,7 @@ Otázka: No, v roku 2003 môžeme tiež vidieť nárast v zásobách medu? Čo a qplot(honey$year,honey$totalprod, geom='smooth', span =0.5, xlab = "year",ylab = "totalprod") ``` -![čiarový graf 2](../../../../../translated_images/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.sk.png) +![čiarový graf 2](../../../../../translated_images/sk/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.png) Odpoveď: Nie celkom. Ak sa pozriete na celkovú produkciu, zdá sa, že v tomto konkrétnom roku skutočne vzrástla, aj keď všeobecne produkcia medu v týchto rokoch klesá. @@ -135,7 +135,7 @@ ggplot(honey, aes(x=yieldpercol, y = numcol,group = 1)) + ``` V tejto vizualizácii môžete porovnať výnos na kolóniu a počet kolónií rok po roku, vedľa seba, s nastavením wrap na 3 pre stĺpce: -![mriežka s viacerými grafmi](../../../../../translated_images/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.sk.png) +![mriežka s viacerými grafmi](../../../../../translated_images/sk/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.png) Pre tento dataset nič konkrétne nevyniká, pokiaľ ide o počet kolónií a ich výnos rok po roku a štát po štáte. Existuje iný spôsob, ako nájsť koreláciu medzi týmito dvoma premennými? @@ -152,7 +152,7 @@ plot(honey$year, honey$yieldpercol, pch = 17, col = 3, axis(side = 4, at = pretty(range(y2))) mtext("colony yield", side = 4, line = 3) ``` -![prekryté grafy](../../../../../translated_images/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.sk.png) +![prekryté grafy](../../../../../translated_images/sk/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.png) Aj keď nič výrazné nevyniká okolo roku 2003, umožňuje nám to ukončiť túto lekciu na trochu pozitívnejšiu nôtu: aj keď celkový počet kolónií klesá, počet kolónií sa stabilizuje, aj keď ich výnos na kolóniu klesá. diff --git a/translations/sk/3-Data-Visualization/R/13-meaningful-vizualizations/README.md b/translations/sk/3-Data-Visualization/R/13-meaningful-vizualizations/README.md index c76fdc42..b4ff24c8 100644 --- a/translations/sk/3-Data-Visualization/R/13-meaningful-vizualizations/README.md +++ b/translations/sk/3-Data-Visualization/R/13-meaningful-vizualizations/README.md @@ -47,25 +47,25 @@ V predchádzajúcich lekciách ste experimentovali s vytváraním rôznych zauj Aj keď dátový vedec starostlivo vyberie správny graf pre správne dáta, existuje mnoho spôsobov, ako môžu byť dáta prezentované tak, aby podporili určitý názor, často na úkor samotných dát. Existuje veľa príkladov zavádzajúcich grafov a infografík! -[![Ako grafy klamú od Alberta Caira](../../../../../translated_images/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.sk.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "Ako grafy klamú") +[![Ako grafy klamú od Alberta Caira](../../../../../translated_images/sk/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "Ako grafy klamú") > 🎥 Kliknite na obrázok vyššie pre konferenčný prejav o zavádzajúcich grafoch Tento graf prevracia os X, aby ukázal opak pravdy na základe dátumu: -![zlý graf 1](../../../../../translated_images/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.sk.png) +![zlý graf 1](../../../../../translated_images/sk/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.png) [Tento graf](https://media.firstcoastnews.com/assets/WTLV/images/170ae16f-4643-438f-b689-50d66ca6a8d8/170ae16f-4643-438f-b689-50d66ca6a8d8_1140x641.jpg) je ešte zavádzajúcejší, pretože oko je vedené k záveru, že počet prípadov COVID klesal v rôznych okresoch. Ak sa však pozriete bližšie na dátumy, zistíte, že boli preusporiadané, aby vytvorili tento zavádzajúci klesajúci trend. -![zlý graf 2](../../../../../translated_images/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.sk.jpg) +![zlý graf 2](../../../../../translated_images/sk/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.jpg) Tento notoricky známy príklad používa farbu A prevrátenú os Y na zavádzanie: namiesto záveru, že počet úmrtí na strelné zbrane vzrástol po prijatí legislatívy podporujúcej zbrane, oko je oklamané, aby si myslelo opak: -![zlý graf 3](../../../../../translated_images/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.sk.jpg) +![zlý graf 3](../../../../../translated_images/sk/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.jpg) Tento zvláštny graf ukazuje, ako môže byť proporcia manipulovaná, a to až do komického efektu: -![zlý graf 4](../../../../../translated_images/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.sk.jpg) +![zlý graf 4](../../../../../translated_images/sk/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.jpg) Porovnávanie neporovnateľného je ďalší pochybný trik. Existuje [úžasná webová stránka](https://tylervigen.com/spurious-correlations) venovaná 'falošným koreláciám', ktorá zobrazuje 'fakty' korelujúce veci ako rozvodovosť v Maine a spotrebu margarínu. Skupina na Reddite tiež zbiera [škaredé použitia](https://www.reddit.com/r/dataisugly/top/?t=all) dát. @@ -100,13 +100,13 @@ Označte svoje osi, poskytnite legendu, ak je to potrebné, a ponúknite tooltip Ak sú vaše dáta textové a na osi X príliš dlhé, môžete text nakloniť pre lepšiu čitateľnosť. [plot3D](https://cran.r-project.org/web/packages/plot3D/index.html) ponúka 3D vykresľovanie, ak to vaše dáta podporujú. Pomocou neho je možné vytvoriť sofistikované vizualizácie dát. -![3D grafy](../../../../../translated_images/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.sk.png) +![3D grafy](../../../../../translated_images/sk/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.png) ## Animácia a 3D zobrazenie grafov Niektoré z najlepších vizualizácií dát sú dnes animované. Shirley Wu vytvorila úžasné vizualizácie pomocou D3, ako napríklad '[film flowers](http://bl.ocks.org/sxywu/raw/d612c6c653fb8b4d7ff3d422be164a5d/)', kde každý kvet predstavuje vizualizáciu filmu. Ďalším príkladom pre Guardian je 'bussed out', interaktívny zážitok kombinujúci vizualizácie s Greensock a D3 spolu s článkom vo formáte scrollytelling, ktorý ukazuje, ako NYC rieši problém bezdomovcov tým, že ich presúva mimo mesta. -![busing](../../../../../translated_images/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.sk.png) +![busing](../../../../../translated_images/sk/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.png) > "Bussed Out: Ako Amerika presúva svojich bezdomovcov" od [Guardian](https://www.theguardian.com/us-news/ng-interactive/2017/dec/20/bussed-out-america-moves-homeless-people-country-study). Vizualizácie od Nadieh Bremer & Shirley Wu @@ -116,7 +116,7 @@ Hoci táto lekcia nie je dostatočne podrobná na to, aby vás naučila tieto v Dokončíte webovú aplikáciu, ktorá zobrazí animovaný pohľad na túto sociálnu sieť. Používa knižnicu, ktorá bola vytvorená na [vizualizáciu siete](https://github.com/emiliorizzo/vue-d3-network) pomocou Vue.js a D3. Keď aplikácia beží, môžete uzly na obrazovke presúvať a meniť usporiadanie dát. -![liaisons](../../../../../translated_images/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.sk.png) +![liaisons](../../../../../translated_images/sk/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.png) ## Projekt: Vytvorte graf na zobrazenie siete pomocou D3.js diff --git a/translations/sk/3-Data-Visualization/README.md b/translations/sk/3-Data-Visualization/README.md index 4a9cd81a..86ecc0b9 100644 --- a/translations/sk/3-Data-Visualization/README.md +++ b/translations/sk/3-Data-Visualization/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Vizualizácie -![včela na kvete levandule](../../../translated_images/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.sk.jpg) +![včela na kvete levandule](../../../translated_images/sk/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.jpg) > Fotografia od Jenna Lee na Unsplash Vizualizácia dát je jednou z najdôležitejších úloh dátového vedca. Obrázky majú hodnotu tisíc slov a vizualizácia vám môže pomôcť identifikovať rôzne zaujímavé časti vašich dát, ako sú výkyvy, odľahlé hodnoty, zoskupenia, tendencie a ďalšie, ktoré vám pomôžu pochopiť príbeh, ktorý vaše dáta rozprávajú. diff --git a/translations/sk/4-Data-Science-Lifecycle/14-Introduction/README.md b/translations/sk/4-Data-Science-Lifecycle/14-Introduction/README.md index 85c702fe..3c909617 100644 --- a/translations/sk/4-Data-Science-Lifecycle/14-Introduction/README.md +++ b/translations/sk/4-Data-Science-Lifecycle/14-Introduction/README.md @@ -25,7 +25,7 @@ V tomto bode ste si pravdepodobne uvedomili, že dátová veda je proces. Tento Táto lekcia sa zameriava na 3 časti životného cyklu: získavanie, spracovanie a údržbu. -![Diagram životného cyklu dátovej vedy](../../../../translated_images/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.sk.jpg) +![Diagram životného cyklu dátovej vedy](../../../../translated_images/sk/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.jpg) > Foto od [Berkeley School of Information](https://ischoolonline.berkeley.edu/data-science/what-is-data-science/) ## Získavanie @@ -101,7 +101,7 @@ Preskúmajte [životný cyklus procesu tímovej dátovej vedy](https://docs.micr |Proces tímovej dátovej vedy (TDSP)|Štandardný proces pre dolovanie dát naprieč odvetviami (CRISP-DM)| |--|--| -|![Životný cyklus tímovej dátovej vedy](../../../../translated_images/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.sk.png) | ![Obrázok od Data Science Process Alliance](../../../../translated_images/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.sk.png) | +|![Životný cyklus tímovej dátovej vedy](../../../../translated_images/sk/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.png) | ![Obrázok od Data Science Process Alliance](../../../../translated_images/sk/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.png) | | Obrázok od [Microsoft](https://docs.microsoft.comazure/architecture/data-science-process/lifecycle) | Obrázok od [Data Science Process Alliance](https://www.datascience-pm.com/crisp-dm-2/) | ## [Kvíz po prednáške](https://ff-quizzes.netlify.app/en/ds/quiz/27) diff --git a/translations/sk/4-Data-Science-Lifecycle/README.md b/translations/sk/4-Data-Science-Lifecycle/README.md index 82e8a7b2..0b8b4b8e 100644 --- a/translations/sk/4-Data-Science-Lifecycle/README.md +++ b/translations/sk/4-Data-Science-Lifecycle/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Životný cyklus dátovej vedy -![communication](../../../translated_images/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.sk.jpg) +![communication](../../../translated_images/sk/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.jpg) > Foto od Headway na Unsplash V týchto lekciách preskúmate niektoré aspekty životného cyklu dátovej vedy, vrátane analýzy a komunikácie o dátach. diff --git a/translations/sk/5-Data-Science-In-Cloud/README.md b/translations/sk/5-Data-Science-In-Cloud/README.md index 8d551efb..8be31ce0 100644 --- a/translations/sk/5-Data-Science-In-Cloud/README.md +++ b/translations/sk/5-Data-Science-In-Cloud/README.md @@ -9,13 +9,13 @@ CO_OP_TRANSLATOR_METADATA: --> # Data Science v cloude -![cloud-picture](../../../translated_images/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.sk.jpg) +![cloud-picture](../../../translated_images/sk/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.jpg) > Foto od [Jelleke Vanooteghem](https://unsplash.com/@ilumire) z [Unsplash](https://unsplash.com/s/photos/cloud?orientation=landscape) Keď ide o prácu s veľkými dátami v oblasti dátovej vedy, cloud môže byť prelomovým riešením. V nasledujúcich troch lekciách si vysvetlíme, čo je to cloud a prečo môže byť veľmi užitočný. Taktiež preskúmame dataset o zlyhaní srdca a vytvoríme model, ktorý pomôže odhadnúť pravdepodobnosť, že niekto zažije zlyhanie srdca. Využijeme silu cloudu na trénovanie, nasadenie a používanie modelu dvoma rôznymi spôsobmi. Jeden spôsob bude využívať iba používateľské rozhranie v štýle Low code/No code, druhý spôsob bude využívať Azure Machine Learning Software Developer Kit (Azure ML SDK). -![project-schema](../../../translated_images/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.sk.png) +![project-schema](../../../translated_images/sk/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.png) ### Témy diff --git a/translations/sk/6-Data-Science-In-Wild/20-Real-World-Examples/README.md b/translations/sk/6-Data-Science-In-Wild/20-Real-World-Examples/README.md index 4465dc26..3b39a286 100644 --- a/translations/sk/6-Data-Science-In-Wild/20-Real-World-Examples/README.md +++ b/translations/sk/6-Data-Science-In-Wild/20-Real-World-Examples/README.md @@ -41,7 +41,7 @@ Vďaka demokratizácii AI je pre vývojárov teraz jednoduchšie navrhovať a in * [Dátová veda v zdravotníctve](https://data-flair.training/blogs/data-science-in-healthcare/) - zdôrazňuje aplikácie ako medicínske zobrazovanie (napr. MRI, röntgen, CT-sken), genomika (sekvenovanie DNA), vývoj liekov (hodnotenie rizík, predikcia úspechu), prediktívna analytika (starostlivosť o pacientov a logistika zásob), sledovanie a prevencia chorôb atď. -![Aplikácie dátovej vedy v reálnom svete](../../../../translated_images/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.sk.png) Zdroj obrázku: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) +![Aplikácie dátovej vedy v reálnom svete](../../../../translated_images/sk/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.png) Zdroj obrázku: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) Obrázok ukazuje ďalšie oblasti a príklady aplikácie techník dátovej vedy. Chcete preskúmať ďalšie aplikácie? Pozrite si sekciu [Recenzia a samostatné štúdium](../../../../6-Data-Science-In-Wild/20-Real-World-Examples) nižšie. diff --git a/translations/sk/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md b/translations/sk/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md index c15d8b62..b9d3c9ee 100644 --- a/translations/sk/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md +++ b/translations/sk/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md @@ -22,7 +22,7 @@ Rozhranie Explorer (zobrazené na snímke obrazovky nižšie) vám umožňuje vy 2. Preskúmajte dataset [Katalóg](https://planetarycomputer.microsoft.com/catalog) – zistite účel každého datasetu. 3. Použite Explorer – vyberte dataset, ktorý vás zaujíma, zvoľte relevantný dotaz a možnosť vykreslenia. -![Explorer Planetary Computer](../../../../translated_images/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.sk.png) +![Explorer Planetary Computer](../../../../translated_images/sk/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.png) `Vaša úloha:` Teraz preskúmajte vizualizáciu, ktorá sa zobrazí v prehliadači, a odpovedzte na nasledujúce otázky: diff --git a/translations/sk/CONTRIBUTING.md b/translations/sk/CONTRIBUTING.md index a7eb7869..b5e2ff87 100644 --- a/translations/sk/CONTRIBUTING.md +++ b/translations/sk/CONTRIBUTING.md @@ -312,7 +312,7 @@ Zahrňte do popisu PR: import pandas as pd ``` ```` -- Pridajte alt text k obrázkom: `![Alt text](../../translated_images/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.sk.png)` +- Pridajte alt text k obrázkom: `![Alt text](../../translated_images/sk/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.png)` - Udržujte rozumnú dĺžku riadkov (okolo 80-100 znakov) ### Python diff --git a/translations/sk/README.md b/translations/sk/README.md index 309ca08c..d76977b5 100644 --- a/translations/sk/README.md +++ b/translations/sk/README.md @@ -33,7 +33,7 @@ Azure Cloud Advocates v Microsoft s radosťou ponúkajú 10-týždňový, 20-lek **🙏 Špeciálne poďakovanie 🙏 našim autorom, recenzentom a prispievateľom obsahu z [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/),** predovšetkým Aaryan Arora, [Aditya Garg](https://github.com/AdityaGarg00), [Alondra Sanchez](https://www.linkedin.com/in/alondra-sanchez-molina/), [Ankita Singh](https://www.linkedin.com/in/ankitasingh007), [Anupam Mishra](https://www.linkedin.com/in/anupam--mishra/), [Arpita Das](https://www.linkedin.com/in/arpitadas01/), ChhailBihari Dubey, [Dibri Nsofor](https://www.linkedin.com/in/dibrinsofor), [Dishita Bhasin](https://www.linkedin.com/in/dishita-bhasin-7065281bb), [Majd Safi](https://www.linkedin.com/in/majd-s/), [Max Blum](https://www.linkedin.com/in/max-blum-6036a1186/), [Miguel Correa](https://www.linkedin.com/in/miguelmque/), [Mohamma Iftekher (Iftu) Ebne Jalal](https://twitter.com/iftu119), [Nawrin Tabassum](https://www.linkedin.com/in/nawrin-tabassum), [Raymond Wangsa Putra](https://www.linkedin.com/in/raymond-wp/), [Rohit Yadav](https://www.linkedin.com/in/rty2423), Samridhi Sharma, [Sanya Sinha](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200), [Sheena Narula](https://www.linkedin.com/in/sheena-narua-n/), [Tauqeer Ahmad](https://www.linkedin.com/in/tauqeerahmad5201/), Yogendrasingh Pawar , [Vidushi Gupta](https://www.linkedin.com/in/vidushi-gupta07/), [Jasleen Sondhi](https://www.linkedin.com/in/jasleen-sondhi/) -|![Sketchnota od @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.sk.png)| +|![Sketchnota od @sketchthedocs https://sketchthedocs.dev](../../translated_images/sk/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.png)| |:---:| | Dátová veda pre začiatočníkov - _Sketchnota od [@nitya](https://twitter.com/nitya)_ | @@ -52,7 +52,7 @@ Azure Cloud Advocates v Microsoft s radosťou ponúkajú 10-týždňový, 20-lek Máme prebiehajúcu sériu „Učte sa s AI“ na Discorde, dozviete sa viac a pripojiť sa k nám môžete na [Séria Learn with AI](https://aka.ms/learnwithai/discord) od 18. do 30. septembra 2025. Získate tipy a triky, ako používať GitHub Copilot pre Dátovú vedu. -![Séria Učte sa s AI](../../translated_images/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.sk.jpg) +![Séria Učte sa s AI](../../translated_images/sk/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.jpg) # Ste študent? @@ -132,7 +132,7 @@ Každý príklad obsahuje podrobné komentáre vysvetľujúce každý krok, čo ## Lekcie -|![ Sketchnote od @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.sk.png)| +|![ Sketchnote od @sketchthedocs https://sketchthedocs.dev](../../translated_images/sk/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png)| |:---:| | Dátová veda pre začiatočníkov: Plán cesty - _Sketchnote od [@nitya](https://twitter.com/nitya)_ | diff --git a/translations/sk/sketchnotes/README.md b/translations/sk/sketchnotes/README.md index 6eaa3750..ad2514d7 100644 --- a/translations/sk/sketchnotes/README.md +++ b/translations/sk/sketchnotes/README.md @@ -13,7 +13,7 @@ Nájdite všetky sketchnoty tu! Nitya Narasimhan, umelkyňa -![sketchnota cestovnej mapy](../../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.sk.png) +![sketchnota cestovnej mapy](../../../translated_images/sk/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png) --- diff --git a/translations/sl/1-Introduction/01-defining-data-science/README.md b/translations/sl/1-Introduction/01-defining-data-science/README.md index f6b4d3de..1fd1b871 100644 --- a/translations/sl/1-Introduction/01-defining-data-science/README.md +++ b/translations/sl/1-Introduction/01-defining-data-science/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: --- -[![Video o definiranju podatkovne znanosti](../../../../translated_images/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.sl.png)](https://youtu.be/beZ7Mb_oz9I) +[![Video o definiranju podatkovne znanosti](../../../../translated_images/sl/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.png)](https://youtu.be/beZ7Mb_oz9I) ## [Predavanje - kviz](https://ff-quizzes.netlify.app/en/ds/quiz/0) @@ -153,7 +153,7 @@ Ko začnemo analizirati rezultate testov z več izbirami, lahko poskusimo ugotov V tem izzivu bomo poskušali najti koncepte, povezane s področjem podatkovne znanosti, tako da bomo analizirali besedila. Vzeli bomo članek iz Wikipedije o podatkovni znanosti, prenesli in obdelali besedilo ter nato ustvarili oblak besed, kot je ta: -![Oblak besed za podatkovno znanost](../../../../translated_images/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.sl.png) +![Oblak besed za podatkovno znanost](../../../../translated_images/sl/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.png) Obiščite [`notebook.ipynb`](../../../../1-Introduction/01-defining-data-science/notebook.ipynb ':ignore'), da si ogledate kodo. Prav tako lahko zaženete kodo in vidite, kako v realnem času izvaja vse transformacije podatkov. diff --git a/translations/sl/1-Introduction/04-stats-and-probability/README.md b/translations/sl/1-Introduction/04-stats-and-probability/README.md index b788cc6b..feb9b9da 100644 --- a/translations/sl/1-Introduction/04-stats-and-probability/README.md +++ b/translations/sl/1-Introduction/04-stats-and-probability/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: Teorija statistike in verjetnosti sta dve tesno povezani področji matematike, ki sta zelo pomembni za podatkovno znanost. Čeprav je mogoče delati s podatki brez poglobljenega matematičnega znanja, je vseeno koristno poznati vsaj osnovne koncepte. Tukaj bomo predstavili kratek uvod, ki vam bo pomagal začeti. -[![Uvodni video](../../../../translated_images/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.sl.png)](https://youtu.be/Z5Zy85g4Yjw) +[![Uvodni video](../../../../translated_images/sl/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.png)](https://youtu.be/Z5Zy85g4Yjw) ## [Predavanje - kviz](https://ff-quizzes.netlify.app/en/ds/quiz/6) @@ -39,7 +39,7 @@ Težje je opisati porazdelitev verjetnosti zvezne spremenljivke, katere vrednost Lahko govorimo le o verjetnosti, da spremenljivka pade v določen interval vrednosti, npr. P(t1≤X2). V tem primeru je porazdelitev verjetnosti opisana z **gostotno funkcijo verjetnosti** p(x), tako da -![P(t_1\le X Več primerov korelacije in kovariance najdete v [priloženem zvezku](notebook.ipynb). diff --git a/translations/sl/1-Introduction/README.md b/translations/sl/1-Introduction/README.md index 601cbbdc..9c87268d 100644 --- a/translations/sl/1-Introduction/README.md +++ b/translations/sl/1-Introduction/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Uvod v podatkovno znanost -![podatki v akciji](../../../translated_images/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.sl.jpg) +![podatki v akciji](../../../translated_images/sl/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.jpg) > Fotografija avtorja Stephen Dawson na Unsplash V teh lekcijah boste odkrili, kako je definirana podatkovna znanost, in spoznali etične vidike, ki jih mora upoštevati podatkovni znanstvenik. Prav tako boste izvedeli, kako so podatki definirani, ter se seznanili z osnovami statistike in verjetnosti, ki sta osrednji akademski področji podatkovne znanosti. diff --git a/translations/sl/2-Working-With-Data/07-python/README.md b/translations/sl/2-Working-With-Data/07-python/README.md index 15d9fd14..ef02ac6f 100644 --- a/translations/sl/2-Working-With-Data/07-python/README.md +++ b/translations/sl/2-Working-With-Data/07-python/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: | :-----------------------------------------------------------------------------------------------------------: | | Delo s Pythonom - _Sketchnote avtorja [@nitya](https://twitter.com/nitya)_ | -[![Uvodni video](../../../../translated_images/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.sl.png)](https://youtu.be/dZjWOGbsN4Y) +[![Uvodni video](../../../../translated_images/sl/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.png)](https://youtu.be/dZjWOGbsN4Y) Medtem ko baze podatkov ponujajo zelo učinkovite načine za shranjevanje podatkov in njihovo poizvedovanje z uporabo jezikov za poizvedbe, je najbolj prilagodljiv način obdelave podatkov pisanje lastnega programa za manipulacijo podatkov. V mnogih primerih bi bila poizvedba v bazi podatkov bolj učinkovita. Vendar pa v nekaterih primerih, ko je potrebna bolj zapletena obdelava podatkov, tega ni mogoče enostavno doseči z uporabo SQL. Obdelavo podatkov je mogoče programirati v katerem koli programskem jeziku, vendar obstajajo določeni jeziki, ki so na višji ravni glede dela s podatki. Podatkovni znanstveniki običajno uporabljajo enega od naslednjih jezikov: @@ -73,7 +73,7 @@ print(f"Length of index is {len(idx)}") items_sold = pd.Series(np.random.randint(25,50,size=len(idx)),index=idx) items_sold.plot() ``` -![Graf časovne serije](../../../../translated_images/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.sl.png) +![Graf časovne serije](../../../../translated_images/sl/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.png) Recimo, da vsak teden organiziramo zabavo za prijatelje in vzamemo dodatnih 10 paketov sladoleda za zabavo. Ustvarimo lahko drugo Series, indeksirano po tednih, da to prikažemo: ```python @@ -84,7 +84,7 @@ Ko seštejemo dve Series, dobimo skupno število: total_items = items_sold.add(additional_items,fill_value=0) total_items.plot() ``` -![Graf časovne serije](../../../../translated_images/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.sl.png) +![Graf časovne serije](../../../../translated_images/sl/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.png) > **Opomba**: Ne uporabljamo preproste sintakse `total_items+additional_items`. Če bi jo, bi dobili veliko vrednosti `NaN` (*Not a Number*) v rezultatni Series. To je zato, ker manjkajo vrednosti za nekatere točke indeksa v Series `additional_items`, in seštevanje `NaN` z nečim rezultira v `NaN`. Zato moramo med seštevanjem določiti parameter `fill_value`. @@ -93,7 +93,7 @@ Pri časovnih serijah lahko tudi **ponovno vzorčimo** serijo z različnimi čas monthly = total_items.resample("1M").mean() ax = monthly.plot(kind='bar') ``` -![Mesečna povprečja časovne serije](../../../../translated_images/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.sl.png) +![Mesečna povprečja časovne serije](../../../../translated_images/sl/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.png) ### DataFrame @@ -219,7 +219,7 @@ Prvi problem, na katerega se bomo osredotočili, je modeliranje širjenja epidem Ker želimo pokazati, kako ravnati s podatki, vas vabimo, da odprete [`notebook-covidspread.ipynb`](notebook-covidspread.ipynb) in ga preberete od začetka do konca. Prav tako lahko zaženete celice in rešite nekaj izzivov, ki smo jih pripravili za vas na koncu. -![Širjenje COVID-a](../../../../translated_images/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.sl.png) +![Širjenje COVID-a](../../../../translated_images/sl/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.png) > Če ne veste, kako zagnati kodo v Jupyter Notebooku, si oglejte [ta članek](https://soshnikov.com/education/how-to-execute-notebooks-from-github/). @@ -241,7 +241,7 @@ Celoten primer analize tega podatkovnega niza z uporabo kognitivne storitve [Tex Odprite [`notebook-papers.ipynb`](notebook-papers.ipynb) in ga preberite od začetka do konca. Prav tako lahko zaženete celice in rešite nekaj izzivov, ki smo jih pripravili za vas na koncu. -![Zdravljenje COVID-a](../../../../translated_images/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.sl.png) +![Zdravljenje COVID-a](../../../../translated_images/sl/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.png) ## Obdelava slikovnih podatkov diff --git a/translations/sl/2-Working-With-Data/README.md b/translations/sl/2-Working-With-Data/README.md index 641d1548..54750fe5 100644 --- a/translations/sl/2-Working-With-Data/README.md +++ b/translations/sl/2-Working-With-Data/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Delo s podatki -![ljubezen do podatkov](../../../translated_images/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.sl.jpg) +![ljubezen do podatkov](../../../translated_images/sl/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.jpg) > Fotografija avtorja Alexander Sinn na Unsplash V teh lekcijah boste spoznali nekatere načine, kako je mogoče podatke upravljati, obdelovati in uporabljati v aplikacijah. Spoznali boste relacijske in nerelacijske baze podatkov ter kako se podatki lahko shranjujejo v njih. Naučili se boste osnov dela s Pythonom za upravljanje podatkov in odkrili številne načine, kako lahko s Pythonom upravljate in analizirate podatke. diff --git a/translations/sl/3-Data-Visualization/12-visualization-relationships/README.md b/translations/sl/3-Data-Visualization/12-visualization-relationships/README.md index af6216aa..a2acc3fe 100644 --- a/translations/sl/3-Data-Visualization/12-visualization-relationships/README.md +++ b/translations/sl/3-Data-Visualization/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ Ustvarite osnovni razpršeni graf za prikaz odnosa med ceno na funt medu in njeg ```python sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ``` -![razpršeni graf 1](../../../../translated_images/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.sl.png) +![razpršeni graf 1](../../../../translated_images/sl/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.png) Sedaj prikažite iste podatke z barvno shemo medu, da pokažete, kako se cena spreminja skozi leta. To lahko storite z dodajanjem parametra 'hue', ki prikazuje spremembe iz leta v leto: @@ -60,7 +60,7 @@ Sedaj prikažite iste podatke z barvno shemo medu, da pokažete, kako se cena sp ```python sns.relplot(x="priceperlb", y="state", hue="year", palette="YlOrBr", data=honey, height=15, aspect=.5); ``` -![razpršeni graf 2](../../../../translated_images/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.sl.png) +![razpršeni graf 2](../../../../translated_images/sl/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.png) S to spremembo barvne sheme lahko jasno vidite močan napredek skozi leta glede cene medu na funt. Če pogledate vzorec podatkov (na primer za določeno državo, recimo Arizono), lahko opazite vzorec naraščanja cen iz leta v leto, z nekaj izjemami: @@ -89,7 +89,7 @@ sns.relplot(x="priceperlb", y="state", size="year", data=honey, height=15, aspec ``` Vidite lahko, da se velikost točk postopoma povečuje. -![razpršeni graf 3](../../../../translated_images/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.sl.png) +![razpršeni graf 3](../../../../translated_images/sl/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.png) Je to preprost primer ponudbe in povpraševanja? Zaradi dejavnikov, kot so podnebne spremembe in propad kolonij, je na voljo manj medu za nakup iz leta v leto, zato se cena povečuje? @@ -104,7 +104,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); ``` Odgovor: Da, z nekaj izjemami okoli leta 2003: -![linijski graf 1](../../../../translated_images/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.sl.png) +![linijski graf 1](../../../../translated_images/sl/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.png) ✅ Ker Seaborn združuje podatke okoli ene črte, prikazuje "več meritev pri vsaki vrednosti x z izrisom povprečja in 95% intervala zaupanja okoli povprečja". [Vir](https://seaborn.pydata.org/tutorial/relational.html). To časovno zahtevno vedenje lahko onemogočite z dodajanjem `ci=None`. @@ -114,7 +114,7 @@ Vprašanje: No, ali lahko leta 2003 opazimo tudi porast zaloge medu? Kaj pa, če sns.relplot(x="year", y="totalprod", kind="line", data=honey); ``` -![linijski graf 2](../../../../translated_images/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.sl.png) +![linijski graf 2](../../../../translated_images/sl/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.png) Odgovor: Ne ravno. Če pogledate skupno proizvodnjo, se zdi, da se je v tem letu dejansko povečala, čeprav na splošno količina proizvedenega medu v teh letih upada. @@ -139,7 +139,7 @@ sns.relplot( ``` V tej vizualizaciji lahko primerjate donos na kolonijo in število kolonij iz leta v leto, ena ob drugi, z nastavitvijo razporeditve na 3 za stolpce: -![mreža faset](../../../../translated_images/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.sl.png) +![mreža faset](../../../../translated_images/sl/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.png) Za to podatkovno zbirko nič posebej ne izstopa glede števila kolonij in njihovega donosa, iz leta v leto in iz države v državo. Ali obstaja drugačen način za iskanje korelacije med tema dvema spremenljivkama? @@ -162,7 +162,7 @@ sns.despine(right=False) plt.ylabel('colony yield') ax.figure.legend(); ``` -![nadgrajeni grafi](../../../../translated_images/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.sl.png) +![nadgrajeni grafi](../../../../translated_images/sl/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.png) Čeprav nič ne izstopa okoli leta 2003, nam to omogoča, da zaključimo to lekcijo na nekoliko bolj pozitivni noti: čeprav se število kolonij na splošno zmanjšuje, se število kolonij stabilizira, tudi če njihov donos na kolonijo upada. diff --git a/translations/sl/3-Data-Visualization/R/09-visualization-quantities/README.md b/translations/sl/3-Data-Visualization/R/09-visualization-quantities/README.md index 59bdd8df..3e08da8f 100644 --- a/translations/sl/3-Data-Visualization/R/09-visualization-quantities/README.md +++ b/translations/sl/3-Data-Visualization/R/09-visualization-quantities/README.md @@ -66,7 +66,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` Tukaj namestite paket `ggplot2` in ga nato uvozite v delovni prostor z ukazom `library("ggplot2")`. Za risanje grafa v ggplot se uporablja funkcija `ggplot()`, kjer določite nabor podatkov ter spremenljivki x in y kot atribute. V tem primeru uporabimo funkcijo `geom_line()`, saj želimo narisati črtni graf. -![MaxWingspan-lineplot](../../../../../translated_images/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.sl.png) +![MaxWingspan-lineplot](../../../../../translated_images/sl/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.png) Kaj takoj opazite? Zdi se, da obstaja vsaj en odstopajoč podatek - to je kar razpon kril! Razpon kril več kot 2000 centimetrov pomeni več kot 20 metrov - ali v Minnesoti živijo pterodaktili? Raziščimo. @@ -84,7 +84,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` Kot oznak določimo v `theme` in določimo oznake osi x in y v `xlab()` in `ylab()`. Funkcija `ggtitle()` doda ime grafu. -![MaxWingspan-lineplot-improved](../../../../../translated_images/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.sl.png) +![MaxWingspan-lineplot-improved](../../../../../translated_images/sl/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.png) Tudi z rotacijo oznak na 45 stopinj je preveč podatkov za branje. Poskusimo drugačno strategijo: označimo samo odstopajoče podatke in postavimo oznake znotraj grafa. Uporabite razsevni diagram, da ustvarite več prostora za označevanje: @@ -100,7 +100,7 @@ Kaj se tukaj dogaja? Uporabili ste funkcijo `geom_point()` za risanje razsevnih Kaj odkrijete? -![MaxWingspan-scatterplot](../../../../../translated_images/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.sl.png) +![MaxWingspan-scatterplot](../../../../../translated_images/sl/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.png) ## Filtrirajte svoje podatke @@ -119,7 +119,7 @@ ggplot(data=birds_filtered, aes(x=Name, y=MaxWingspan,group=1)) + ``` Ustvarili smo nov podatkovni okvir `birds_filtered` in nato narisali razsevni diagram. Z odstranitvijo odstopajočih podatkov so vaši podatki zdaj bolj skladni in razumljivi. -![MaxWingspan-scatterplot-improved](../../../../../translated_images/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.sl.png) +![MaxWingspan-scatterplot-improved](../../../../../translated_images/sl/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.png) Zdaj, ko imamo vsaj očiščen nabor podatkov glede razpona kril, odkrijmo več o teh pticah. @@ -160,7 +160,7 @@ birds_filtered %>% group_by(Category) %>% ``` V naslednjem odlomku namestimo paketa [dplyr](https://www.rdocumentation.org/packages/dplyr/versions/0.7.8) in [lubridate](https://www.rdocumentation.org/packages/lubridate/versions/1.8.0), da pomagata pri manipulaciji in grupiranju podatkov za risanje zloženega stolpičnega grafa. Najprej grupirate podatke po `Category` ptic in nato povzamete stolpce `MinLength`, `MaxLength`, `MinBodyMass`, `MaxBodyMass`, `MinWingspan`, `MaxWingspan`. Nato narišete stolpični graf z uporabo paketa `ggplot2` in določite barve za različne kategorije ter oznake. -![Stacked bar chart](../../../../../translated_images/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.sl.png) +![Stacked bar chart](../../../../../translated_images/sl/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.png) Ta stolpični graf je neberljiv, ker je preveč nepovezanih podatkov. Izbrati morate samo podatke, ki jih želite prikazati, zato si oglejmo dolžino ptic glede na njihovo kategorijo. @@ -175,7 +175,7 @@ ggplot(birds_count,aes(Category,n))+geom_bar(stat="identity")+coord_flip() ``` Najprej preštejete unikatne vrednosti v stolpcu `Category` in jih nato razvrstite v nov podatkovni okvir `birds_count`. Te razvrščene podatke nato razvrstite na isti ravni, da so narisani v razvrščenem vrstnem redu. Z uporabo `ggplot2` nato narišete podatke v stolpičnem grafu. Funkcija `coord_flip()` nariše horizontalne stolpce. -![category-length](../../../../../translated_images/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.sl.png) +![category-length](../../../../../translated_images/sl/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.png) Ta stolpični graf prikazuje dober pogled na število ptic v vsaki kategoriji. Na prvi pogled vidite, da je največ ptic v tej regiji v kategoriji Race/Gosi/Vodna ptica. Minnesota je 'dežela 10.000 jezer', zato to ni presenetljivo! @@ -198,7 +198,7 @@ ggplot(birds_grouped,aes(Category,MaxLength))+geom_bar(stat="identity")+coord_fl ``` Grupiramo podatke `birds_filtered` po `Category` in nato narišemo stolpični graf. -![comparing data](../../../../../translated_images/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.sl.png) +![comparing data](../../../../../translated_images/sl/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.png) Tukaj ni nič presenetljivega: kolibriji imajo najmanjšo največjo dolžino v primerjavi s pelikani ali gosmi. Dobro je, ko podatki logično ustrezajo! @@ -210,7 +210,7 @@ ggplot(data=birds_grouped, aes(x=Category)) + geom_bar(aes(y=MinLength), stat="identity", position="identity", fill='orange')+ coord_flip() ``` -![super-imposed values](../../../../../translated_images/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.sl.png) +![super-imposed values](../../../../../translated_images/sl/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.png) ## 🚀 Izziv diff --git a/translations/sl/3-Data-Visualization/R/10-visualization-distributions/README.md b/translations/sl/3-Data-Visualization/R/10-visualization-distributions/README.md index c2d02af1..10fb9081 100644 --- a/translations/sl/3-Data-Visualization/R/10-visualization-distributions/README.md +++ b/translations/sl/3-Data-Visualization/R/10-visualization-distributions/README.md @@ -45,7 +45,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + geom_point() + ggtitle("Max Length per order") + coord_flip() ``` -![največja dolžina po redu](../../../../../translated_images/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.sl.png) +![največja dolžina po redu](../../../../../translated_images/sl/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.png) To daje pregled splošne porazdelitve dolžine telesa po redih ptic, vendar to ni optimalen način za prikaz resničnih porazdelitev. Ta naloga se običajno opravi z ustvarjanjem histograma. @@ -57,7 +57,7 @@ To daje pregled splošne porazdelitve dolžine telesa po redih ptic, vendar to n ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=10)+ylab('Frequency') ``` -![porazdelitev po celotnem naboru podatkov](../../../../../translated_images/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.sl.png) +![porazdelitev po celotnem naboru podatkov](../../../../../translated_images/sl/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.png) Kot lahko vidite, večina od 400+ ptic v tem naboru spada v razpon pod 2000 za njihovo največjo telesno maso. Pridobite več vpogleda v podatke tako, da spremenite parameter `bins` na višjo številko, na primer 30: @@ -65,7 +65,7 @@ Kot lahko vidite, večina od 400+ ptic v tem naboru spada v razpon pod 2000 za n ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![porazdelitev-30bins](../../../../../translated_images/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.sl.png) +![porazdelitev-30bins](../../../../../translated_images/sl/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.png) Ta graf prikazuje porazdelitev na nekoliko bolj podroben način. Manj pristranski graf na levo bi lahko ustvarili tako, da izberete samo podatke znotraj določenega razpona: @@ -77,7 +77,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![filtriran histogram](../../../../../translated_images/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.sl.png) +![filtriran histogram](../../../../../translated_images/sl/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.png) ✅ Preizkusite nekaj drugih filtrov in podatkovnih točk. Če želite videti celotno porazdelitev podatkov, odstranite filter `['MaxBodyMass']`, da prikažete označene porazdelitve. @@ -91,7 +91,7 @@ ggplot(data=birds_filtered_1, aes(x=MaxBodyMass, y=MaxLength) ) + ``` Zdi se, da obstaja pričakovana korelacija med tema dvema elementoma vzdolž pričakovane osi, z eno posebej močno točko konvergence: -![2d graf](../../../../../translated_images/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.sl.png) +![2d graf](../../../../../translated_images/sl/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.png) Histograme je privzeto mogoče dobro uporabiti za številske podatke. Kaj pa, če morate videti porazdelitve glede na besedilne podatke? ## Raziskovanje porazdelitev v naboru podatkov z uporabo besedilnih podatkov @@ -122,7 +122,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + scale_fill_manual(name="Conservation Status",values=c("red","green","blue","pink"),labels=c("Endangered","Near Threathened","Vulnerable","Least Concern")) ``` -![razpon kril in stanje ohranitve](../../../../../translated_images/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.sl.png) +![razpon kril in stanje ohranitve](../../../../../translated_images/sl/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.png) Zdi se, da ni dobre korelacije med minimalnim razponom kril in stanjem ohranitve. Preizkusite druge elemente nabora podatkov z uporabo te metode. Poskusite tudi različne filtre. Ali najdete kakšno korelacijo? @@ -136,7 +136,7 @@ Zdaj se lotimo grafov gostote! ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + geom_density() ``` -![graf gostote](../../../../../translated_images/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.sl.png) +![graf gostote](../../../../../translated_images/sl/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.png) Vidite lahko, kako graf odraža prejšnjega za podatke o minimalnem razponu kril; le da je nekoliko bolj gladek. Če bi želeli ponovno pregledati tisto nazobčano črto MaxBodyMass v drugem grafu, ki ste ga ustvarili, bi jo lahko zelo dobro zgladili z uporabo te metode: @@ -144,7 +144,7 @@ Vidite lahko, kako graf odraža prejšnjega za podatke o minimalnem razponu kril ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density() ``` -![gostota telesne mase](../../../../../translated_images/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.sl.png) +![gostota telesne mase](../../../../../translated_images/sl/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.png) Če želite gladko, a ne preveč gladko črto, uredite parameter `adjust`: @@ -152,7 +152,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density(adjust = 1/5) ``` -![manj gladka telesna masa](../../../../../translated_images/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.sl.png) +![manj gladka telesna masa](../../../../../translated_images/sl/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.png) ✅ Preberite o parametrih, ki so na voljo za to vrsto grafa, in eksperimentirajte! @@ -162,7 +162,7 @@ Ta vrsta grafa ponuja čudovito razlagalne vizualizacije. Na primer, z nekaj vrs ggplot(data=birds_filtered_1,aes(x = MaxBodyMass, fill = Order)) + geom_density(alpha=0.5) ``` -![telesna masa po redu](../../../../../translated_images/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.sl.png) +![telesna masa po redu](../../../../../translated_images/sl/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.png) ## 🚀 Izziv diff --git a/translations/sl/3-Data-Visualization/R/11-visualization-proportions/README.md b/translations/sl/3-Data-Visualization/R/11-visualization-proportions/README.md index 318e7283..f11ee1f8 100644 --- a/translations/sl/3-Data-Visualization/R/11-visualization-proportions/README.md +++ b/translations/sl/3-Data-Visualization/R/11-visualization-proportions/README.md @@ -97,7 +97,7 @@ pie(grouped$count,grouped$class, main="Edible?") ``` Voila, tortni graf, ki prikazuje deleže teh podatkov glede na ti dve kategoriji gob. Zelo pomembno je, da je vrstni red oznak pravilen, še posebej tukaj, zato preverite vrstni red, s katerim je ustvarjen niz oznak! -![tortni graf](../../../../../translated_images/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.sl.png) +![tortni graf](../../../../../translated_images/sl/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.png) ## Krofi! @@ -132,7 +132,7 @@ library(webr) PieDonut(habitat, aes(habitat, count=count)) ``` -![krofni graf](../../../../../translated_images/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.sl.png) +![krofni graf](../../../../../translated_images/sl/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.png) Ta koda uporablja dve knjižnici - ggplot2 in webr. Z uporabo funkcije PieDonut iz knjižnice webr lahko enostavno ustvarimo krofni graf! @@ -169,7 +169,7 @@ waffle((cap_color$count/10), rows = 7, title = "Waffle Chart")+scale_fill_manual Z uporabo vafeljnega grafa lahko jasno vidite deleže barv klobukov v tem naboru podatkov o gobah. Zanimivo je, da je veliko gob z zelenimi klobuki! -![vafeljni graf](../../../../../translated_images/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.sl.png) +![vafeljni graf](../../../../../translated_images/sl/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.png) V tej lekciji ste se naučili treh načinov vizualizacije deležev. Najprej morate razvrstiti svoje podatke v kategorije in nato odločiti, kateri je najboljši način za prikaz podatkov - torta, krof ali vafelj. Vsi so okusni in uporabniku takoj ponudijo vpogled v nabor podatkov. diff --git a/translations/sl/3-Data-Visualization/R/12-visualization-relationships/README.md b/translations/sl/3-Data-Visualization/R/12-visualization-relationships/README.md index d13350f1..5ac7107c 100644 --- a/translations/sl/3-Data-Visualization/R/12-visualization-relationships/README.md +++ b/translations/sl/3-Data-Visualization/R/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ library(ggplot2) ggplot(honey, aes(x = priceperlb, y = state)) + geom_point(colour = "blue") ``` -![razpršeni graf 1](../../../../../translated_images/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.sl.png) +![razpršeni graf 1](../../../../../translated_images/sl/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.png) Zdaj prikažite iste podatke z barvno shemo medu, da pokažete, kako se cena spreminja skozi leta. To lahko storite z dodajanjem parametra 'scale_color_gradientn', ki prikazuje spremembe iz leta v leto: @@ -61,7 +61,7 @@ Zdaj prikažite iste podatke z barvno shemo medu, da pokažete, kako se cena spr ggplot(honey, aes(x = priceperlb, y = state, color=year)) + geom_point()+scale_color_gradientn(colours = colorspace::heat_hcl(7)) ``` -![razpršeni graf 2](../../../../../translated_images/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.sl.png) +![razpršeni graf 2](../../../../../translated_images/sl/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.png) S to spremembo barvne sheme lahko vidite, da je očitno močan napredek skozi leta glede cene medu na funt. Če pogledate vzorec podatkov za preverjanje (izberite določeno državo, na primer Arizono), lahko opazite vzorec naraščanja cen iz leta v leto, z nekaj izjemami: @@ -92,7 +92,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ``` Vidite lahko, da se velikost točk postopoma povečuje. -![razpršeni graf 3](../../../../../translated_images/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.sl.png) +![razpršeni graf 3](../../../../../translated_images/sl/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.png) Je to preprost primer ponudbe in povpraševanja? Zaradi dejavnikov, kot so podnebne spremembe in propad kolonij, je na voljo manj medu za nakup iz leta v leto, zato se cena povečuje? @@ -107,7 +107,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab ``` Odgovor: Da, z nekaj izjemami okoli leta 2003: -![črtni graf 1](../../../../../translated_images/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.sl.png) +![črtni graf 1](../../../../../translated_images/sl/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.png) Vprašanje: No, ali lahko leta 2003 opazimo tudi porast zaloge medu? Kaj pa, če pogledate skupno proizvodnjo iz leta v leto? @@ -115,7 +115,7 @@ Vprašanje: No, ali lahko leta 2003 opazimo tudi porast zaloge medu? Kaj pa, če qplot(honey$year,honey$totalprod, geom='smooth', span =0.5, xlab = "year",ylab = "totalprod") ``` -![črtni graf 2](../../../../../translated_images/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.sl.png) +![črtni graf 2](../../../../../translated_images/sl/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.png) Odgovor: Ne ravno. Če pogledate skupno proizvodnjo, se zdi, da se je v tem letu dejansko povečala, čeprav na splošno količina proizvedenega medu v teh letih upada. @@ -135,7 +135,7 @@ ggplot(honey, aes(x=yieldpercol, y = numcol,group = 1)) + ``` V tej vizualizaciji lahko primerjate donos na kolonijo in število kolonij iz leta v leto, drug ob drugem, z nastavitvijo wrap na 3 za stolpce: -![mreža faset](../../../../../translated_images/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.sl.png) +![mreža faset](../../../../../translated_images/sl/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.png) Za to podatkovno zbirko nič posebej ne izstopa glede števila kolonij in njihovega donosa iz leta v leto ter med državami. Ali obstaja drugačen način za iskanje korelacije med tema dvema spremenljivkama? @@ -152,7 +152,7 @@ plot(honey$year, honey$yieldpercol, pch = 17, col = 3, axis(side = 4, at = pretty(range(y2))) mtext("colony yield", side = 4, line = 3) ``` -![nadgrajeni grafi](../../../../../translated_images/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.sl.png) +![nadgrajeni grafi](../../../../../translated_images/sl/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.png) Čeprav nič ne izstopa okoli leta 2003, nam to omogoča, da zaključimo to lekcijo na nekoliko bolj veseli noti: čeprav je skupno število kolonij v upadu, se število kolonij stabilizira, tudi če njihov donos na kolonijo upada. diff --git a/translations/sl/3-Data-Visualization/R/13-meaningful-vizualizations/README.md b/translations/sl/3-Data-Visualization/R/13-meaningful-vizualizations/README.md index 42de41c7..d6652e6d 100644 --- a/translations/sl/3-Data-Visualization/R/13-meaningful-vizualizations/README.md +++ b/translations/sl/3-Data-Visualization/R/13-meaningful-vizualizations/README.md @@ -47,25 +47,25 @@ V prejšnjih lekcijah ste eksperimentirali z ustvarjanjem različnih zanimivih v Tudi če podatkovni znanstvenik skrbno izbere pravi graf za prave podatke, obstaja veliko načinov, kako lahko podatke prikažemo tako, da podpirajo določeno točko, pogosto na račun resničnosti podatkov. Obstaja veliko primerov zavajajočih grafov in infografik! -[![Kako grafi lažejo avtorja Alberta Caira](../../../../../translated_images/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.sl.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "Kako grafi lažejo") +[![Kako grafi lažejo avtorja Alberta Caira](../../../../../translated_images/sl/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "Kako grafi lažejo") > 🎥 Kliknite zgornjo sliko za konferenčno predavanje o zavajajočih grafih Ta graf obrne os X, da prikaže nasprotje resnice, glede na datum: -![slab graf 1](../../../../../translated_images/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.sl.png) +![slab graf 1](../../../../../translated_images/sl/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.png) [Ta graf](https://media.firstcoastnews.com/assets/WTLV/images/170ae16f-4643-438f-b689-50d66ca6a8d8/170ae16f-4643-438f-b689-50d66ca6a8d8_1140x641.jpg) je še bolj zavajajoč, saj pogled pritegne desno stran, kar daje vtis, da so se primeri COVID-a skozi čas zmanjšali v različnih okrožjih. Če pa natančno pogledate datume, ugotovite, da so bili preurejeni, da bi ustvarili zavajajoč trend navzdol. -![slab graf 2](../../../../../translated_images/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.sl.jpg) +![slab graf 2](../../../../../translated_images/sl/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.jpg) Ta zloglasni primer uporablja barvo IN obrnjeno os Y za zavajanje: namesto da bi sklepali, da so se smrti zaradi orožja povečale po sprejetju zakonodaje, prijazne do orožja, je pogled zaveden, da misli, da je resnica nasprotna: -![slab graf 3](../../../../../translated_images/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.sl.jpg) +![slab graf 3](../../../../../translated_images/sl/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.jpg) Ta nenavaden graf prikazuje, kako je mogoče manipulirati s proporci, kar vodi do smešnih rezultatov: -![slab graf 4](../../../../../translated_images/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.sl.jpg) +![slab graf 4](../../../../../translated_images/sl/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.jpg) Primerjanje neprimerljivega je še en dvomljiv trik. Obstaja [odlična spletna stran](https://tylervigen.com/spurious-correlations), ki prikazuje 'lažne korelacije', kot so 'dejstva', ki povezujejo stopnjo ločitev v Mainu in porabo margarine. Skupina na Redditu prav tako zbira [grde primere](https://www.reddit.com/r/dataisugly/top/?t=all) uporabe podatkov. @@ -100,13 +100,13 @@ Označite svoje osi, zagotovite legendo, če je potrebno, in ponudite orodja za Če so vaši podatki besedilni in obsežni na osi X, lahko besedilo nagnite za boljšo berljivost. [plot3D](https://cran.r-project.org/web/packages/plot3D/index.html) omogoča 3D risanje, če vaši podatki to podpirajo. Z njim je mogoče ustvariti napredne vizualizacije podatkov. -![3d grafi](../../../../../translated_images/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.sl.png) +![3d grafi](../../../../../translated_images/sl/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.png) ## Animacija in prikaz 3D grafov Nekatere najboljše vizualizacije podatkov danes so animirane. Shirley Wu je ustvarila neverjetne vizualizacije z D3, kot je '[film flowers](http://bl.ocks.org/sxywu/raw/d612c6c653fb8b4d7ff3d422be164a5d/)', kjer vsak cvet predstavlja vizualizacijo filma. Drug primer za Guardian je 'bussed out', interaktivna izkušnja, ki združuje vizualizacije z Greensock in D3 ter format članka za prikaz, kako NYC rešuje problem brezdomcev z njihovim prevozom iz mesta. -![bussing](../../../../../translated_images/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.sl.png) +![bussing](../../../../../translated_images/sl/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.png) > "Bussed Out: How America Moves its Homeless" iz [Guardiana](https://www.theguardian.com/us-news/ng-interactive/2017/dec/20/bussed-out-america-moves-homeless-people-country-study). Vizualizacije avtorjev Nadieh Bremer & Shirley Wu @@ -116,7 +116,7 @@ Nekatere najboljše vizualizacije podatkov danes so animirane. Shirley Wu je ust Dokončali boste spletno aplikacijo, ki bo prikazala animiran pogled tega socialnega omrežja. Uporablja knjižnico, zasnovano za ustvarjanje [vizualizacije omrežja](https://github.com/emiliorizzo/vue-d3-network) z uporabo Vue.js in D3. Ko aplikacija deluje, lahko premikate vozlišča po zaslonu in premešate podatke. -![liaisons](../../../../../translated_images/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.sl.png) +![liaisons](../../../../../translated_images/sl/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.png) ## Projekt: Ustvarite graf za prikaz omrežja z uporabo D3.js diff --git a/translations/sl/3-Data-Visualization/README.md b/translations/sl/3-Data-Visualization/README.md index 21047a96..7e673d9d 100644 --- a/translations/sl/3-Data-Visualization/README.md +++ b/translations/sl/3-Data-Visualization/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Vizualizacije -![čebela na cvetu sivke](../../../translated_images/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.sl.jpg) +![čebela na cvetu sivke](../../../translated_images/sl/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.jpg) > Fotografija avtorice Jenna Lee na Unsplash Vizualizacija podatkov je ena najpomembnejših nalog podatkovnega znanstvenika. Slike povedo več kot 1000 besed, vizualizacija pa vam lahko pomaga prepoznati različne zanimive vidike vaših podatkov, kot so konice, odstopanja, skupine, tendence in še več, kar vam omogoča, da bolje razumete zgodbo, ki jo vaši podatki pripovedujejo. diff --git a/translations/sl/4-Data-Science-Lifecycle/14-Introduction/README.md b/translations/sl/4-Data-Science-Lifecycle/14-Introduction/README.md index 226ac2e7..5f107de0 100644 --- a/translations/sl/4-Data-Science-Lifecycle/14-Introduction/README.md +++ b/translations/sl/4-Data-Science-Lifecycle/14-Introduction/README.md @@ -25,7 +25,7 @@ Do zdaj ste verjetno že ugotovili, da je podatkovna znanost proces. Ta proces l Ta lekcija se osredotoča na 3 dele življenjskega cikla: zajemanje, obdelavo in vzdrževanje. -![Diagram življenjskega cikla podatkovne znanosti](../../../../translated_images/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.sl.jpg) +![Diagram življenjskega cikla podatkovne znanosti](../../../../translated_images/sl/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.jpg) > Fotografija avtorja [Berkeley School of Information](https://ischoolonline.berkeley.edu/data-science/what-is-data-science/) ## Zajemanje @@ -101,7 +101,7 @@ Raziščite [življenjski cikel procesa podatkovne znanosti ekipe](https://docs. |Proces podatkovne znanosti ekipe (TDSP)|Križnoindustrijski standardni proces za rudarjenje podatkov (CRISP-DM)| |--|--| -|![Življenjski cikel procesa podatkovne znanosti ekipe](../../../../translated_images/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.sl.png) | ![Slika procesa CRISP-DM](../../../../translated_images/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.sl.png) | +|![Življenjski cikel procesa podatkovne znanosti ekipe](../../../../translated_images/sl/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.png) | ![Slika procesa CRISP-DM](../../../../translated_images/sl/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.png) | | Slika avtorja [Microsoft](https://docs.microsoft.comazure/architecture/data-science-process/lifecycle) | Slika avtorja [Data Science Process Alliance](https://www.datascience-pm.com/crisp-dm-2/) | ## [Kviz po predavanju](https://ff-quizzes.netlify.app/en/ds/quiz/27) diff --git a/translations/sl/4-Data-Science-Lifecycle/README.md b/translations/sl/4-Data-Science-Lifecycle/README.md index feb4fb35..4e4d319a 100644 --- a/translations/sl/4-Data-Science-Lifecycle/README.md +++ b/translations/sl/4-Data-Science-Lifecycle/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Življenjski cikel podatkovne znanosti -![communication](../../../translated_images/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.sl.jpg) +![communication](../../../translated_images/sl/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.jpg) > Fotografija avtorja Headway na Unsplash V teh lekcijah boste raziskali nekatere vidike življenjskega cikla podatkovne znanosti, vključno z analizo in komunikacijo podatkov. diff --git a/translations/sl/5-Data-Science-In-Cloud/README.md b/translations/sl/5-Data-Science-In-Cloud/README.md index c6d76238..d371fec3 100644 --- a/translations/sl/5-Data-Science-In-Cloud/README.md +++ b/translations/sl/5-Data-Science-In-Cloud/README.md @@ -9,13 +9,13 @@ CO_OP_TRANSLATOR_METADATA: --> # Podatkovna znanost v oblaku -![cloud-picture](../../../translated_images/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.sl.jpg) +![cloud-picture](../../../translated_images/sl/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.jpg) > Fotografija avtorja [Jelleke Vanooteghem](https://unsplash.com/@ilumire) na [Unsplash](https://unsplash.com/s/photos/cloud?orientation=landscape) Ko gre za izvajanje podatkovne znanosti z velikimi podatki, je oblak lahko prava sprememba igre. V naslednjih treh lekcijah bomo raziskali, kaj oblak je in zakaj je lahko zelo koristen. Prav tako bomo preučili podatkovni niz o srčnem popuščanju in zgradili model, ki pomaga oceniti verjetnost, da ima nekdo srčno popuščanje. Uporabili bomo moč oblaka za treniranje, uvajanje in uporabo modela na dva različna načina. En način bo uporaba samo uporabniškega vmesnika v načinu "Low code/No code", drugi način pa bo uporaba Azure Machine Learning Software Developer Kit (Azure ML SDK). -![project-schema](../../../translated_images/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.sl.png) +![project-schema](../../../translated_images/sl/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.png) ### Teme diff --git a/translations/sl/6-Data-Science-In-Wild/20-Real-World-Examples/README.md b/translations/sl/6-Data-Science-In-Wild/20-Real-World-Examples/README.md index 8b769a5b..fe161588 100644 --- a/translations/sl/6-Data-Science-In-Wild/20-Real-World-Examples/README.md +++ b/translations/sl/6-Data-Science-In-Wild/20-Real-World-Examples/README.md @@ -41,7 +41,7 @@ Zaradi demokratizacije umetne inteligence razvijalci zdaj lažje oblikujejo in v * [Podatkovna znanost v zdravstvu](https://data-flair.training/blogs/data-science-in-healthcare/) - poudarja aplikacije, kot so medicinsko slikanje (npr. MRI, rentgen, CT-skeniranje), genomika (sekvenciranje DNK), razvoj zdravil (ocena tveganja, napoved uspeha), napovedna analitika (oskrba pacientov in logistika oskrbe), sledenje boleznim in preprečevanje itd. -![Aplikacije podatkovne znanosti v resničnem svetu](../../../../translated_images/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.sl.png) Vir slike: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) +![Aplikacije podatkovne znanosti v resničnem svetu](../../../../translated_images/sl/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.png) Vir slike: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) Slika prikazuje druge domene in primere uporabe tehnik podatkovne znanosti. Želite raziskati druge aplikacije? Oglejte si razdelek [Pregled in samostojno učenje](../../../../6-Data-Science-In-Wild/20-Real-World-Examples) spodaj. diff --git a/translations/sl/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md b/translations/sl/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md index a9906843..2cbbd958 100644 --- a/translations/sl/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md +++ b/translations/sl/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md @@ -22,7 +22,7 @@ Vmesnik Explorer (prikazan na spodnjem posnetku zaslona) vam omogoča izbiro pod 2. Raziščite [katalog podatkovnih zbirk](https://planetarycomputer.microsoft.com/catalog) – spoznajte namen vsake zbirke. 3. Uporabite Explorer – izberite podatkovno zbirko, ki vas zanima, izberite ustrezno poizvedbo in možnost upodabljanja. -![Explorer Planetary Computer](../../../../translated_images/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.sl.png) +![Explorer Planetary Computer](../../../../translated_images/sl/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.png) `Vaša naloga:` Zdaj preučite vizualizacijo, ki je prikazana v brskalniku, in odgovorite na naslednje: diff --git a/translations/sl/CONTRIBUTING.md b/translations/sl/CONTRIBUTING.md index c455f5e5..17b3ddda 100644 --- a/translations/sl/CONTRIBUTING.md +++ b/translations/sl/CONTRIBUTING.md @@ -312,7 +312,7 @@ Vključite v opis PR: import pandas as pd ``` ```` -- Dodajte alt besedilo slikam: `![Alt besedilo](../../translated_images/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.sl.png)` +- Dodajte alt besedilo slikam: `![Alt besedilo](../../translated_images/sl/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.png)` - Ohranjajte dolžino vrstic razumno (približno 80-100 znakov) ### Python diff --git a/translations/sl/README.md b/translations/sl/README.md index a4c13821..aaf9d1d2 100644 --- a/translations/sl/README.md +++ b/translations/sl/README.md @@ -33,7 +33,7 @@ Azure Cloud Advocates pri Microsoftu z veseljem ponujamo 10-tedenski, 20-brisni **🙏 Posebna zahvala 🙏 našim avtorjem, recenzentom in prispevalcem vsebin iz [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/),** zlasti Aaryan Arora, [Aditya Garg](https://github.com/AdityaGarg00), [Alondra Sanchez](https://www.linkedin.com/in/alondra-sanchez-molina/), [Ankita Singh](https://www.linkedin.com/in/ankitasingh007), [Anupam Mishra](https://www.linkedin.com/in/anupam--mishra/), [Arpita Das](https://www.linkedin.com/in/arpitadas01/), ChhailBihari Dubey, [Dibri Nsofor](https://www.linkedin.com/in/dibrinsofor), [Dishita Bhasin](https://www.linkedin.com/in/dishita-bhasin-7065281bb), [Majd Safi](https://www.linkedin.com/in/majd-s/), [Max Blum](https://www.linkedin.com/in/max-blum-6036a1186/), [Miguel Correa](https://www.linkedin.com/in/miguelmque/), [Mohamma Iftekher (Iftu) Ebne Jalal](https://twitter.com/iftu119), [Nawrin Tabassum](https://www.linkedin.com/in/nawrin-tabassum), [Raymond Wangsa Putra](https://www.linkedin.com/in/raymond-wp/), [Rohit Yadav](https://www.linkedin.com/in/rty2423), Samridhi Sharma, [Sanya Sinha](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200), [Sheena Narula](https://www.linkedin.com/in/sheena-narua-n/), [Tauqeer Ahmad](https://www.linkedin.com/in/tauqeerahmad5201/), Yogendrasingh Pawar , [Vidushi Gupta](https://www.linkedin.com/in/vidushi-gupta07/), [Jasleen Sondhi](https://www.linkedin.com/in/jasleen-sondhi/) -|![Sketchnote by @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.sl.png)| +|![Sketchnote by @sketchthedocs https://sketchthedocs.dev](../../translated_images/sl/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.png)| |:---:| | Podatkovna znanost za začetnike - _Sketchnote avtorja [@nitya](https://twitter.com/nitya)_ | @@ -52,7 +52,7 @@ Azure Cloud Advocates pri Microsoftu z veseljem ponujamo 10-tedenski, 20-brisni Imamo tekočo serijo "Learn with AI" na Discordu, izveste več in se nam pridružite na [Learn with AI Series](https://aka.ms/learnwithai/discord) od 18. do 30. septembra 2025. Prejeli boste nasvete in trike za uporabo GitHub Copilot pri podatkovni znanosti. -![Serija Učenje z AI](../../translated_images/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.sl.jpg) +![Serija Učenje z AI](../../translated_images/sl/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.jpg) # Ste študent? @@ -132,7 +132,7 @@ Vsak primer vsebuje podrobne komentarje, ki pojasnjujejo vsak korak, zato je kot ## Lekcije -|![ Sketchnote avtorja @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.sl.png)| +|![ Sketchnote avtorja @sketchthedocs https://sketchthedocs.dev](../../translated_images/sl/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png)| |:---:| | Data Science For Beginners: Roadmap - _Sketchnote by [@nitya](https://twitter.com/nitya)_ | diff --git a/translations/sl/sketchnotes/README.md b/translations/sl/sketchnotes/README.md index a5a1b835..7229727b 100644 --- a/translations/sl/sketchnotes/README.md +++ b/translations/sl/sketchnotes/README.md @@ -13,7 +13,7 @@ Poiščite vse skicopise tukaj! Nitya Narasimhan, umetnica -![skicopis načrta](../../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.sl.png) +![skicopis načrta](../../../translated_images/sl/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png) --- diff --git a/translations/sr/1-Introduction/01-defining-data-science/README.md b/translations/sr/1-Introduction/01-defining-data-science/README.md index fb51bb10..33a60866 100644 --- a/translations/sr/1-Introduction/01-defining-data-science/README.md +++ b/translations/sr/1-Introduction/01-defining-data-science/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: --- -[![Видео о дефинисању науке о подацима](../../../../translated_images/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.sr.png)](https://youtu.be/beZ7Mb_oz9I) +[![Видео о дефинисању науке о подацима](../../../../translated_images/sr/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.png)](https://youtu.be/beZ7Mb_oz9I) ## [Квиз пре предавања](https://ff-quizzes.netlify.app/en/ds/quiz/0) @@ -149,7 +149,7 @@ CO_OP_TRANSLATOR_METADATA: У овом изазову, покушаћемо да пронађемо концепте релевантне за област науке о подацима анализирајући текстове. Узет ћемо Википедијски чланак о науци о подацима, преузети и обрадити текст, а затим направити облак речи попут овог: -![Облак речи за науку о подацима](../../../../translated_images/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.sr.png) +![Облак речи за науку о подацима](../../../../translated_images/sr/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.png) Посетите [`notebook.ipynb`](../../../../1-Introduction/01-defining-data-science/notebook.ipynb ':ignore') да бисте прочитали код. Такође можете покренути код и видети како у реалном времену врши све трансформације података. diff --git a/translations/sr/1-Introduction/04-stats-and-probability/README.md b/translations/sr/1-Introduction/04-stats-and-probability/README.md index bdb4bce7..958dbb41 100644 --- a/translations/sr/1-Introduction/04-stats-and-probability/README.md +++ b/translations/sr/1-Introduction/04-stats-and-probability/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: Теорија статистике и вероватноће су две блиско повезане области математике које су веома значајне за науку о подацима. Могуће је радити са подацима без дубоког познавања математике, али је ипак боље знати барем основне концепте. Овде ћемо представити кратак увод који ће вам помоћи да започнете. -[![Уводни видео](../../../../translated_images/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.sr.png)](https://youtu.be/Z5Zy85g4Yjw) +[![Уводни видео](../../../../translated_images/sr/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.png)](https://youtu.be/Z5Zy85g4Yjw) ## [Квиз пре предавања](https://ff-quizzes.netlify.app/en/ds/quiz/6) @@ -39,7 +39,7 @@ CO_OP_TRANSLATOR_METADATA: Можемо говорити само о вероватноћи да променљива падне у дати интервал вредности, нпр. P(t1≤X2). У овом случају, расподела вероватноће описује се **функцијом густине вероватноће** p(x), тако да -![P(t_1\le X Више примера корелације и коваријансе можете пронаћи у [пратећем нотебуку](notebook.ipynb). diff --git a/translations/sr/1-Introduction/README.md b/translations/sr/1-Introduction/README.md index e583c06d..bc3f36d3 100644 --- a/translations/sr/1-Introduction/README.md +++ b/translations/sr/1-Introduction/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Увод у науку о подацима -![подаци у акцији](../../../translated_images/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.sr.jpg) +![подаци у акцији](../../../translated_images/sr/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.jpg) > Фотографија од Стивена Досона на Unsplash У овим лекцијама открићете како се наука о подацима дефинише и научићете о етичким аспектима које сваки научник о подацима мора узети у обзир. Такође ћете научити како се подаци дефинишу и добити основно знање о статистици и вероватноћи, кључним академским областима науке о подацима. diff --git a/translations/sr/2-Working-With-Data/07-python/README.md b/translations/sr/2-Working-With-Data/07-python/README.md index 10b2a2d8..ca1f241a 100644 --- a/translations/sr/2-Working-With-Data/07-python/README.md +++ b/translations/sr/2-Working-With-Data/07-python/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: | :-------------------------------------------------------------------------------------------------------: | | Рад са Python-ом - _Скетч од [@nitya](https://twitter.com/nitya)_ | -[![Уводни видео](../../../../translated_images/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.sr.png)](https://youtu.be/dZjWOGbsN4Y) +[![Уводни видео](../../../../translated_images/sr/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.png)](https://youtu.be/dZjWOGbsN4Y) Иако базе података нуде веома ефикасне начине за складиштење података и њихово претраживање помоћу језика за упите, најфлексибилнији начин обраде података је писање сопственог програма за манипулацију подацима. У многим случајевима, коришћење упита у бази података би било ефикасније. Међутим, у неким случајевима када је потребна сложенија обрада података, то се не може лако урадити помоћу SQL-а. Обрада података може се програмирати у било ком програмском језику, али постоје одређени језици који су на вишем нивоу када је у питању рад са подацима. Научници који се баве подацима обично преферирају један од следећих језика: @@ -75,7 +75,7 @@ print(f"Length of index is {len(idx)}") items_sold = pd.Series(np.random.randint(25,50,size=len(idx)),index=idx) items_sold.plot() ``` -![График временског низа](../../../../translated_images/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.sr.png) +![График временског низа](../../../../translated_images/sr/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.png) Сада претпоставимо да сваке недеље организујемо журку за пријатеље и узимамо додатних 10 пакета сладоледа за журку. Можемо креирати други низ, индексиран по недељама, да то покажемо: ```python @@ -86,7 +86,7 @@ additional_items = pd.Series(10,index=pd.date_range(start_date,end_date,freq="W" total_items = items_sold.add(additional_items,fill_value=0) total_items.plot() ``` -![График временског низа](../../../../translated_images/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.sr.png) +![График временског низа](../../../../translated_images/sr/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.png) > **Напомена** да не користимо једноставну синтаксу `total_items+additional_items`. Да јесмо, добили бисмо много `NaN` (*Not a Number*) вредности у резултујућем низу. То је зато што недостају вредности за неке тачке индекса у низу `additional_items`, а додавање `NaN` било чему резултира у `NaN`. Због тога морамо да наведемо параметар `fill_value` током сабирања. @@ -95,7 +95,7 @@ total_items.plot() monthly = total_items.resample("1M").mean() ax = monthly.plot(kind='bar') ``` -![Месечни просеци временског низа](../../../../translated_images/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.sr.png) +![Месечни просеци временског низа](../../../../translated_images/sr/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.png) ### DataFrame @@ -221,7 +221,7 @@ Data Scientist често мора да истражује податке, па Пошто желимо да покажемо како се ради са подацима, позивамо вас да отворите [`notebook-covidspread.ipynb`](notebook-covidspread.ipynb) и прочитате га од почетка до краја. Такође можете извршавати ћелије и решавати неке изазове које смо оставили за вас на крају. -![Ширење COVID-а](../../../../translated_images/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.sr.png) +![Ширење COVID-а](../../../../translated_images/sr/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.png) > Ако не знате како да покренете код у Jupyter Notebook-у, погледајте [овај чланак](https://soshnikov.com/education/how-to-execute-notebooks-from-github/). @@ -243,7 +243,7 @@ Data Scientist често мора да истражује податке, па Отворите [`notebook-papers.ipynb`](notebook-papers.ipynb) и прочитајте га од почетка до краја. Такође можете извршавати ћелије и решавати неке изазове које смо оставили за вас на крају. -![Лечење COVID-а](../../../../translated_images/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.sr.png) +![Лечење COVID-а](../../../../translated_images/sr/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.png) ## Обрада сликовних података diff --git a/translations/sr/2-Working-With-Data/README.md b/translations/sr/2-Working-With-Data/README.md index 422452c3..5868f4e4 100644 --- a/translations/sr/2-Working-With-Data/README.md +++ b/translations/sr/2-Working-With-Data/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Рад са подацима -![љубав према подацима](../../../translated_images/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.sr.jpg) +![љубав према подацима](../../../translated_images/sr/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.jpg) > Фотографија од Александра Сина на Unsplash У овим лекцијама ћете научити неке од начина на које се подаци могу управљати, манипулисати и користити у апликацијама. Научићете о релационим и нерелационим базама података и како се подаци могу чувати у њима. Упознаћете основе рада са Python-ом за управљање подацима и открићете неке од многих начина на које можете користити Python за управљање и анализу података. diff --git a/translations/sr/3-Data-Visualization/12-visualization-relationships/README.md b/translations/sr/3-Data-Visualization/12-visualization-relationships/README.md index f255ebf7..e5ac1df4 100644 --- a/translations/sr/3-Data-Visualization/12-visualization-relationships/README.md +++ b/translations/sr/3-Data-Visualization/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ honey.head() ```python sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ``` -![scatterplot 1](../../../../translated_images/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.sr.png) +![scatterplot 1](../../../../translated_images/sr/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.png) Сада прикажите исте податке са шемом боја меда како бисте приказали како се цена мењала током година. Ово можете урадити додавањем параметра 'hue' да прикажете промену из године у годину: @@ -60,7 +60,7 @@ sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ```python sns.relplot(x="priceperlb", y="state", hue="year", palette="YlOrBr", data=honey, height=15, aspect=.5); ``` -![scatterplot 2](../../../../translated_images/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.sr.png) +![scatterplot 2](../../../../translated_images/sr/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.png) Са овом променом шеме боја, можете видети да постоји очигледан снажан напредак током година у погледу цене меда по фунти. Заиста, ако погледате узорак података (на пример, државу Аризону), можете уочити образац повећања цена из године у годину, са неколико изузетака: @@ -89,7 +89,7 @@ sns.relplot(x="priceperlb", y="state", size="year", data=honey, height=15, aspec ``` Можете видети како се величина тачака постепено повећава. -![scatterplot 3](../../../../translated_images/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.sr.png) +![scatterplot 3](../../../../translated_images/sr/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.png) Да ли је ово једноставан случај понуде и потражње? Због фактора као што су климатске промене и колапс кошница, да ли је мање меда доступно за куповину из године у годину, па самим тим цена расте? @@ -104,7 +104,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); ``` Одговор: Да, са неким изузецима око 2003. године: -![line chart 1](../../../../translated_images/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.sr.png) +![line chart 1](../../../../translated_images/sr/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.png) ✅ Пошто Seaborn агрегира податке око једне линије, приказује "више мерења за сваку x вредност тако што приказује просек и 95% интервал поверења око просека". [Извор](https://seaborn.pydata.org/tutorial/relational.html). Ово временски захтевно понашање може се онемогућити додавањем `ci=None`. @@ -114,7 +114,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); sns.relplot(x="year", y="totalprod", kind="line", data=honey); ``` -![line chart 2](../../../../translated_images/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.sr.png) +![line chart 2](../../../../translated_images/sr/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.png) Одговор: Не баш. Ако погледате укупну производњу, чини се да је она заправо порасла те године, иако генерално количина произведеног меда опада током ових година. @@ -139,7 +139,7 @@ sns.relplot( ``` У овој визуелизацији можете упоредити принос по кошници и број кошница из године у годину, један поред другог, са wrap подешеним на 3 за колоне: -![facet grid](../../../../translated_images/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.sr.png) +![facet grid](../../../../translated_images/sr/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.png) За овај скуп података, ништа посебно се не издваја у погледу броја кошница и њиховог приноса, из године у годину и из државе у државу. Постоји ли другачији начин да се пронађе корелација између ове две променљиве? @@ -162,7 +162,7 @@ sns.despine(right=False) plt.ylabel('colony yield') ax.figure.legend(); ``` -![superimposed plots](../../../../translated_images/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.sr.png) +![superimposed plots](../../../../translated_images/sr/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.png) Иако ништа не упада у очи око 2003. године, ово нам омогућава да завршимо лекцију на мало срећнијој ноти: иако укупан број кошница опада, број кошница се стабилизује, чак и ако њихов принос по кошници опада. diff --git a/translations/sr/3-Data-Visualization/R/09-visualization-quantities/README.md b/translations/sr/3-Data-Visualization/R/09-visualization-quantities/README.md index 14788c0d..ae3dbd3b 100644 --- a/translations/sr/3-Data-Visualization/R/09-visualization-quantities/README.md +++ b/translations/sr/3-Data-Visualization/R/09-visualization-quantities/README.md @@ -66,7 +66,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` Овде инсталирате `ggplot2` пакет и затим га увозите у радни простор користећи команду `library("ggplot2")`. За креирање било ког графикона у ggplot-у користи се функција `ggplot()` и ви одређујете базу података, x и y променљиве као атрибуте. У овом случају, користимо функцију `geom_line()` јер желимо да креирамо линијски графикон. -![MaxWingspan-lineplot](../../../../../translated_images/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.sr.png) +![MaxWingspan-lineplot](../../../../../translated_images/sr/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.png) Шта одмах примећујете? Чини се да постоји бар један изузетак - то је прилично велики распон крила! Распон крила од преко 2000 центиметара је више од 20 метара - да ли Птеродактили лете изнад Минесоте? Хајде да истражимо. @@ -84,7 +84,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` Одређујемо угао у `theme` и одређујемо ознаке за x и y осе у `xlab()` и `ylab()` респективно. `ggtitle()` даје назив графикону. -![MaxWingspan-lineplot-improved](../../../../../translated_images/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.sr.png) +![MaxWingspan-lineplot-improved](../../../../../translated_images/sr/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.png) Чак и са ротацијом ознака постављеном на 45 степени, има их превише за читање. Хајде да пробамо другачију стратегију: означимо само те изузетке и поставимо ознаке унутар графикона. Можете користити расејани графикон да бисте направили више простора за означавање: @@ -100,7 +100,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + Шта откривате? -![MaxWingspan-scatterplot](../../../../../translated_images/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.sr.png) +![MaxWingspan-scatterplot](../../../../../translated_images/sr/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.png) ## Филтрирање података @@ -119,7 +119,7 @@ ggplot(data=birds_filtered, aes(x=Name, y=MaxWingspan,group=1)) + ``` Креирали смо нови датафрејм `birds_filtered` и затим креирали расејани графикон. Филтрирањем изузетака, ваши подаци су сада кохезивнији и разумљивији. -![MaxWingspan-scatterplot-improved](../../../../../translated_images/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.sr.png) +![MaxWingspan-scatterplot-improved](../../../../../translated_images/sr/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.png) Сада када имамо чистију базу података барем у смислу распона крила, хајде да откријемо више о овим птицама. @@ -160,7 +160,7 @@ birds_filtered %>% group_by(Category) %>% ``` У следећем исечку, инсталирамо [dplyr](https://www.rdocumentation.org/packages/dplyr/versions/0.7.8) и [lubridate](https://www.rdocumentation.org/packages/lubridate/versions/1.8.0) пакете како бисмо помогли у манипулацији и груписању података ради креирања сложеног стубног графикона. Прво групишете податке по `Category` птица и затим сумирате колоне `MinLength`, `MaxLength`, `MinBodyMass`, `MaxBodyMass`, `MinWingspan`, `MaxWingspan`. Затим креирате стубни графикон користећи `ggplot2` пакет и одређујете боје за различите категорије и ознаке. -![Stacked bar chart](../../../../../translated_images/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.sr.png) +![Stacked bar chart](../../../../../translated_images/sr/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.png) Овај стубни графикон, међутим, није читљив јер има превише неконсолидованих података. Потребно је да изаберете само податке које желите да прикажете, па хајде да погледамо дужину птица на основу њихове категорије. @@ -175,7 +175,7 @@ ggplot(birds_count,aes(Category,n))+geom_bar(stat="identity")+coord_flip() ``` Прво бројите јединствене вредности у колони `Category` и затим их сортирате у нови датафрејм `birds_count`. Ови сортирани подаци се затим факторизују на истом нивоу како би били приказани у сортираном редоследу. Користећи `ggplot2` затим креирате графикон у облику стубног графикона. `coord_flip()` приказује хоризонталне стубове. -![category-length](../../../../../translated_images/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.sr.png) +![category-length](../../../../../translated_images/sr/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.png) Овај стубни графикон пружа добар приказ броја птица у свакој категорији. На први поглед видите да је највећи број птица у овом региону у категорији Патке/Гуске/Водене птице. Минесота је 'земља 10,000 језера', па то није изненађујуће! @@ -198,7 +198,7 @@ ggplot(birds_grouped,aes(Category,MaxLength))+geom_bar(stat="identity")+coord_fl ``` Групишемо `birds_filtered` податке по `Category` и затим креирамо стубни графикон. -![comparing data](../../../../../translated_images/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.sr.png) +![comparing data](../../../../../translated_images/sr/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.png) Ништа није изненађујуће овде: колибри имају најмању максималну дужину у поређењу са пеликанима или гускама. Добро је када подаци имају логичан смисао! @@ -210,7 +210,7 @@ ggplot(data=birds_grouped, aes(x=Category)) + geom_bar(aes(y=MinLength), stat="identity", position="identity", fill='orange')+ coord_flip() ``` -![super-imposed values](../../../../../translated_images/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.sr.png) +![super-imposed values](../../../../../translated_images/sr/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.png) ## 🚀 Изазов diff --git a/translations/sr/3-Data-Visualization/R/10-visualization-distributions/README.md b/translations/sr/3-Data-Visualization/R/10-visualization-distributions/README.md index 64904591..6407a724 100644 --- a/translations/sr/3-Data-Visualization/R/10-visualization-distributions/README.md +++ b/translations/sr/3-Data-Visualization/R/10-visualization-distributions/README.md @@ -45,7 +45,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + geom_point() + ggtitle("Max Length per order") + coord_flip() ``` -![макс дужина по реду](../../../../../translated_images/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.sr.png) +![макс дужина по реду](../../../../../translated_images/sr/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.png) Ово даје преглед опште дистрибуције дужине тела по реду птица, али није оптималан начин за приказивање правих дистрибуција. Тај задатак се обично обавља креирањем хистограма. ## Рад са хистограмима @@ -56,7 +56,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=10)+ylab('Frequency') ``` -![дистрибуција преко целог скупа података](../../../../../translated_images/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.sr.png) +![дистрибуција преко целог скупа података](../../../../../translated_images/sr/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.png) Као што видите, већина од 400+ птица у овом скупу података спада у опсег испод 2000 за њихову максималну телесну масу. Стеците више увида у податке променом параметра `bins` на већи број, нешто попут 30: @@ -64,7 +64,7 @@ ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![дистрибуција-30 сегмената](../../../../../translated_images/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.sr.png) +![дистрибуција-30 сегмената](../../../../../translated_images/sr/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.png) Овај графикон приказује дистрибуцију на мало детаљнији начин. Мање искривљен графикон могао би се направити тако што бисте осигурали да одаберете само податке унутар датог опсега: @@ -76,7 +76,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![филтрирани хистограм](../../../../../translated_images/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.sr.png) +![филтрирани хистограм](../../../../../translated_images/sr/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.png) ✅ Испробајте неке друге филтере и тачке података. Да бисте видели пуну дистрибуцију података, уклоните филтер `['MaxBodyMass']` да бисте приказали означене дистрибуције. @@ -90,7 +90,7 @@ ggplot(data=birds_filtered_1, aes(x=MaxBodyMass, y=MaxLength) ) + ``` Чини се да постоји очекивана корелација између ова два елемента дуж очекиване осе, са једном посебно снажном тачком конвергенције: -![2d графикон](../../../../../translated_images/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.sr.png) +![2d графикон](../../../../../translated_images/sr/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.png) Хистограми добро функционишу по подразумеваним подешавањима за нумеричке податке. Шта ако треба да видите дистрибуције према текстуалним подацима? ## Истражите скуп података за дистрибуције користећи текстуалне податке @@ -121,7 +121,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + scale_fill_manual(name="Conservation Status",values=c("red","green","blue","pink"),labels=c("Endangered","Near Threathened","Vulnerable","Least Concern")) ``` -![распон крила и статус очувања](../../../../../translated_images/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.sr.png) +![распон крила и статус очувања](../../../../../translated_images/sr/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.png) Чини се да не постоји добра корелација између минималног распона крила и статуса очувања. Тестирајте друге елементе скупа података користећи овај метод. Можете испробати различите филтере. Да ли налазите неку корелацију? @@ -135,7 +135,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + geom_density() ``` -![густински графикон](../../../../../translated_images/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.sr.png) +![густински графикон](../../../../../translated_images/sr/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.png) Можете видети како графикон одражава претходни за податке о минималном распону крила; само је мало глаткији. Ако желите да поново погледате ту назубљену линију MaxBodyMass у другом графикону који сте направили, могли бисте је веома добро изгладити поновним креирањем користећи овај метод: @@ -143,7 +143,7 @@ ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density() ``` -![густина телесне масе](../../../../../translated_images/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.sr.png) +![густина телесне масе](../../../../../translated_images/sr/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.png) Ако желите глатку, али не превише глатку линију, измените параметар `adjust`: @@ -151,7 +151,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density(adjust = 1/5) ``` -![мање глатка телесна маса](../../../../../translated_images/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.sr.png) +![мање глатка телесна маса](../../../../../translated_images/sr/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.png) ✅ Прочитајте о доступним параметрима за овај тип графикона и експериментишите! @@ -161,7 +161,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + ggplot(data=birds_filtered_1,aes(x = MaxBodyMass, fill = Order)) + geom_density(alpha=0.5) ``` -![телесна маса по реду](../../../../../translated_images/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.sr.png) +![телесна маса по реду](../../../../../translated_images/sr/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.png) ## 🚀 Изазов diff --git a/translations/sr/3-Data-Visualization/R/11-visualization-proportions/README.md b/translations/sr/3-Data-Visualization/R/11-visualization-proportions/README.md index 2cdad1d5..e1fc366c 100644 --- a/translations/sr/3-Data-Visualization/R/11-visualization-proportions/README.md +++ b/translations/sr/3-Data-Visualization/R/11-visualization-proportions/README.md @@ -92,7 +92,7 @@ pie(grouped$count,grouped$class, main="Edible?") ``` Ево га, пита графикон који приказује пропорције ових података према ове две класе печурака. Веома је важно добити редослед етикета исправно, посебно овде, па обавезно проверите редослед којим је низ етикета направљен! -![пита графикон](../../../../../translated_images/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.sr.png) +![пита графикон](../../../../../translated_images/sr/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.png) ## Крофне! @@ -126,7 +126,7 @@ library(webr) PieDonut(habitat, aes(habitat, count=count)) ``` -![крофна графикон](../../../../../translated_images/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.sr.png) +![крофна графикон](../../../../../translated_images/sr/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.png) Овај код користи две библиотеке - ggplot2 и webr. Користећи функцију PieDonut из webr библиотеке, лако можемо направити крофна графикон! @@ -164,7 +164,7 @@ waffle((cap_color$count/10), rows = 7, title = "Waffle Chart")+scale_fill_manual Користећи вафл графикон, јасно можете видети пропорције боја шешира у овом сету података о печуркама. Интересантно је да постоји много печурака са зеленим шеширима! -![вафл графикон](../../../../../translated_images/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.sr.png) +![вафл графикон](../../../../../translated_images/sr/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.png) У овој лекцији, научили сте три начина за визуализацију пропорција. Прво, потребно је да групишете своје податке у категорије, а затим одлучите који је најбољи начин за приказивање података - пита, крофна или вафл. Сви су укусни и пружају кориснику тренутни увид у сет података. diff --git a/translations/sr/3-Data-Visualization/R/12-visualization-relationships/README.md b/translations/sr/3-Data-Visualization/R/12-visualization-relationships/README.md index e0dea913..334d949d 100644 --- a/translations/sr/3-Data-Visualization/R/12-visualization-relationships/README.md +++ b/translations/sr/3-Data-Visualization/R/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ library(ggplot2) ggplot(honey, aes(x = priceperlb, y = state)) + geom_point(colour = "blue") ``` -![scatterplot 1](../../../../../translated_images/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.sr.png) +![scatterplot 1](../../../../../translated_images/sr/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.png) Сада, прикажите исте податке са шемом боја меда да покажете како се цена развија током година. То можете урадити додавањем параметра 'scale_color_gradientn' да прикажете промену из године у годину: @@ -61,7 +61,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ggplot(honey, aes(x = priceperlb, y = state, color=year)) + geom_point()+scale_color_gradientn(colours = colorspace::heat_hcl(7)) ``` -![scatterplot 2](../../../../../translated_images/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.sr.png) +![scatterplot 2](../../../../../translated_images/sr/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.png) Са овом променом шеме боја, можете видети да постоји очигледан напредак током година у погледу цене меда по фунти. У ствари, ако погледате узорак података да бисте проверили (на пример, изаберите државу Аризону), можете видети образац повећања цена из године у годину, са неколико изузетака: @@ -92,7 +92,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ``` Можете видети да се величина тачака постепено повећава. -![scatterplot 3](../../../../../translated_images/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.sr.png) +![scatterplot 3](../../../../../translated_images/sr/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.png) Да ли је ово једноставан случај понуде и потражње? Због фактора као што су климатске промене и колапс колонија, да ли је мање меда доступно за куповину из године у годину, па самим тим цена расте? @@ -107,7 +107,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab ``` Одговор: Да, са неким изузецима око 2003. године: -![line chart 1](../../../../../translated_images/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.sr.png) +![line chart 1](../../../../../translated_images/sr/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.png) Питање: Па, да ли можемо видети скок у залихама меда 2003. године? Шта ако погледате укупну производњу из године у годину? @@ -115,7 +115,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab qplot(honey$year,honey$totalprod, geom='smooth', span =0.5, xlab = "year",ylab = "totalprod") ``` -![line chart 2](../../../../../translated_images/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.sr.png) +![line chart 2](../../../../../translated_images/sr/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.png) Одговор: Не баш. Ако погледате укупну производњу, чини се да је она заправо повећана те године, иако генерално количина произведеног меда опада током ових година. @@ -135,7 +135,7 @@ ggplot(honey, aes(x=yieldpercol, y = numcol,group = 1)) + ``` У овој визуализацији можете упоредити принос по колонији и број колонија из године у годину, један поред другог, са подешавањем wrap на 3 за колоне: -![facet grid](../../../../../translated_images/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.sr.png) +![facet grid](../../../../../translated_images/sr/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.png) За овај скуп података, ништа посебно не одскаче у погледу броја колонија и њиховог приноса, из године у годину и из државе у државу. Да ли постоји другачији начин да се пронађе корелација између ове две променљиве? @@ -152,7 +152,7 @@ plot(honey$year, honey$yieldpercol, pch = 17, col = 3, axis(side = 4, at = pretty(range(y2))) mtext("colony yield", side = 4, line = 3) ``` -![superimposed plots](../../../../../translated_images/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.sr.png) +![superimposed plots](../../../../../translated_images/sr/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.png) Иако ништа не упада у очи око 2003. године, ово нам омогућава да завршимо лекцију на мало срећнијој ноти: иако је укупан број колонија у опадању, број колонија се стабилизује, чак и ако њихов принос по колонији опада. diff --git a/translations/sr/3-Data-Visualization/R/13-meaningful-vizualizations/README.md b/translations/sr/3-Data-Visualization/R/13-meaningful-vizualizations/README.md index b340a5d6..017834e9 100644 --- a/translations/sr/3-Data-Visualization/R/13-meaningful-vizualizations/README.md +++ b/translations/sr/3-Data-Visualization/R/13-meaningful-vizualizations/README.md @@ -47,25 +47,25 @@ CO_OP_TRANSLATOR_METADATA: Чак и ако дата научник пажљиво одабере прави графикон за праве податке, постоји много начина на које подаци могу бити приказани да би се доказала одређена теза, често на штету самих података. Постоји много примера обмањујућих графикона и инфографика! -[![Како графикони лажу од Алберта Каира](../../../../../translated_images/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.sr.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "Како графикони лажу") +[![Како графикони лажу од Алберта Каира](../../../../../translated_images/sr/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "Како графикони лажу") > 🎥 Кликните на слику изнад за конференцијски говор о обмањујућим графиконима Овај графикон обрће X осу да би приказао супротно од истине, на основу датума: -![лош графикон 1](../../../../../translated_images/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.sr.png) +![лош графикон 1](../../../../../translated_images/sr/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.png) [Овај графикон](https://media.firstcoastnews.com/assets/WTLV/images/170ae16f-4643-438f-b689-50d66ca6a8d8/170ae16f-4643-438f-b689-50d66ca6a8d8_1140x641.jpg) је још обмањујући, јер око прати десну страну и закључује да су током времена случајеви COVID-а опали у различитим окрузима. У ствари, ако пажљиво погледате датуме, открићете да су они преуређени да би се приказао обмањујући тренд опадања. -![лош графикон 2](../../../../../translated_images/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.sr.jpg) +![лош графикон 2](../../../../../translated_images/sr/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.jpg) Овај озлоглашени пример користи боју И обрнуту Y осу да би обмануо: уместо закључка да су смртни случајеви од оружја нагло порасли након усвајања закона који подржава оружје, око је преварено да мисли супротно: -![лош графикон 3](../../../../../translated_images/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.sr.jpg) +![лош графикон 3](../../../../../translated_images/sr/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.jpg) Овај необичан графикон показује како се пропорција може манипулисати, на комичан начин: -![лош графикон 4](../../../../../translated_images/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.sr.jpg) +![лош графикон 4](../../../../../translated_images/sr/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.jpg) Упоређивање неупоредивог је још један сумњив трик. Постоји [диван веб сајт](https://tylervigen.com/spurious-correlations) посвећен 'лажним корелацијама' који приказује 'чињенице' које корелирају ствари попут стопе развода у Мејну и потрошње маргарина. Реддит група такође сакупља [ружне употребе](https://www.reddit.com/r/dataisugly/top/?t=all) података. @@ -100,13 +100,13 @@ CO_OP_TRANSLATOR_METADATA: Ако су ваши подаци текстуални и опширни на X оси, можете нагнути текст ради боље читљивости. [plot3D](https://cran.r-project.org/web/packages/plot3D/index.html) нуди 3Д графиконе, ако ваши подаци то подржавају. Софистициране визуализације података могу се произвести помоћу њега. -![3д графикони](../../../../../translated_images/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.sr.png) +![3д графикони](../../../../../translated_images/sr/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.png) ## Анимација и приказ 3Д графикона Неки од најбољих визуализација података данас су анимирани. Ширли Ву има невероватне примере направљене са D3, као што је '[цветови филма](http://bl.ocks.org/sxywu/raw/d612c6c653fb8b4d7ff3d422be164a5d/)', где је сваки цвет визуализација филма. Још један пример за Guardian је 'бусед оут', интерактивно искуство које комбинује визуализације са Greensock и D3 плус формат чланка са скроловањем за приказ како Њујорк решава проблем бескућника тако што их шаље ван града. -![бусинг](../../../../../translated_images/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.sr.png) +![бусинг](../../../../../translated_images/sr/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.png) > "Бусед Оут: Како Америка премешта своје бескућнике" из [Guardian-а](https://www.theguardian.com/us-news/ng-interactive/2017/dec/20/bussed-out-america-moves-homeless-people-country-study). Визуализације од Надие Бремер и Ширли Ву @@ -116,7 +116,7 @@ CO_OP_TRANSLATOR_METADATA: Завршићете веб апликацију која ће приказати анимирани приказ ове друштвене мреже. Користи библиотеку која је направљена за креирање [визуализације мреже](https://github.com/emiliorizzo/vue-d3-network) користећи Vue.js и D3. Када апликација ради, можете померати чворове по екрану да бисте прераспоредили податке. -![везе](../../../../../translated_images/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.sr.png) +![везе](../../../../../translated_images/sr/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.png) ## Пројекат: Направите графикон за приказ мреже користећи D3.js diff --git a/translations/sr/3-Data-Visualization/README.md b/translations/sr/3-Data-Visualization/README.md index fb00ea23..66d54d3f 100644 --- a/translations/sr/3-Data-Visualization/README.md +++ b/translations/sr/3-Data-Visualization/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Визуализације -![пчела на цвету лаванде](../../../translated_images/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.sr.jpg) +![пчела на цвету лаванде](../../../translated_images/sr/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.jpg) > Фотографија од Џене Ли на Unsplash Визуелизација података је један од најважнијих задатака научника за податке. Слика вреди хиљаду речи, а визуелизација вам може помоћи да уочите све врсте занимљивих аспеката ваших података, као што су врхови, одступања, груписања, тенденције и још много тога, што вам може помоћи да разумете причу коју ваши подаци покушавају да испричају. diff --git a/translations/sr/4-Data-Science-Lifecycle/14-Introduction/README.md b/translations/sr/4-Data-Science-Lifecycle/14-Introduction/README.md index 39910a91..c6d125a2 100644 --- a/translations/sr/4-Data-Science-Lifecycle/14-Introduction/README.md +++ b/translations/sr/4-Data-Science-Lifecycle/14-Introduction/README.md @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: Ова лекција се фокусира на 3 дела животног циклуса: прикупљање, обрада и одржавање. -![Дијаграм животног циклуса науке о подацима](../../../../translated_images/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.sr.jpg) +![Дијаграм животног циклуса науке о подацима](../../../../translated_images/sr/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.jpg) > Фотографија од [Berkeley School of Information](https://ischoolonline.berkeley.edu/data-science/what-is-data-science/) ## Прикупљање @@ -101,7 +101,7 @@ CO_OP_TRANSLATOR_METADATA: |Team Data Science Process (TDSP)|Cross-industry standard process for data mining (CRISP-DM)| |--|--| -|![Team Data Science Lifecycle](../../../../translated_images/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.sr.png) | ![Data Science Process Alliance Image](../../../../translated_images/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.sr.png) | +|![Team Data Science Lifecycle](../../../../translated_images/sr/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.png) | ![Data Science Process Alliance Image](../../../../translated_images/sr/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.png) | | Слика од [Microsoft](https://docs.microsoft.comazure/architecture/data-science-process/lifecycle) | Слика од [Data Science Process Alliance](https://www.datascience-pm.com/crisp-dm-2/) | ## [Квиз после предавања](https://ff-quizzes.netlify.app/en/ds/quiz/27) diff --git a/translations/sr/4-Data-Science-Lifecycle/README.md b/translations/sr/4-Data-Science-Lifecycle/README.md index 80469d61..f662ad53 100644 --- a/translations/sr/4-Data-Science-Lifecycle/README.md +++ b/translations/sr/4-Data-Science-Lifecycle/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Животни циклус науке о подацима -![communication](../../../translated_images/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.sr.jpg) +![communication](../../../translated_images/sr/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.jpg) > Фотографија од Headway на Unsplash У овим лекцијама истражићете неке аспекте животног циклуса науке о подацима, укључујући анализу и комуникацију у вези са подацима. diff --git a/translations/sr/5-Data-Science-In-Cloud/README.md b/translations/sr/5-Data-Science-In-Cloud/README.md index 9ce1b57b..28c1cf4a 100644 --- a/translations/sr/5-Data-Science-In-Cloud/README.md +++ b/translations/sr/5-Data-Science-In-Cloud/README.md @@ -9,13 +9,13 @@ CO_OP_TRANSLATOR_METADATA: --> # Наука о подацима у облаку -![cloud-picture](../../../translated_images/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.sr.jpg) +![cloud-picture](../../../translated_images/sr/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.jpg) > Фотографија од [Јелеке Ванутегем](https://unsplash.com/@ilumire) са [Unsplash](https://unsplash.com/s/photos/cloud?orientation=landscape) Када је реч о примени науке о подацима на велике скупове података, облак може бити револуционарно решење. У наредне три лекције, видећемо шта је облак и зашто може бити веома користан. Такође ћемо истражити скуп података о срчаној инсуфицијенцији и изградити модел који ће помоћи у процени вероватноће да неко има срчану инсуфицијенцију. Искористићемо моћ облака да обучимо, применимо и користимо модел на два различита начина. Један начин користи само кориснички интерфејс у стилу "Low code/No code", а други начин користи Azure Machine Learning Software Developer Kit (Azure ML SDK). -![project-schema](../../../translated_images/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.sr.png) +![project-schema](../../../translated_images/sr/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.png) ### Теме diff --git a/translations/sr/6-Data-Science-In-Wild/20-Real-World-Examples/README.md b/translations/sr/6-Data-Science-In-Wild/20-Real-World-Examples/README.md index 2d73ed63..1d7bebd3 100644 --- a/translations/sr/6-Data-Science-In-Wild/20-Real-World-Examples/README.md +++ b/translations/sr/6-Data-Science-In-Wild/20-Real-World-Examples/README.md @@ -41,7 +41,7 @@ CO_OP_TRANSLATOR_METADATA: * [Наука о подацима у здравству](https://data-flair.training/blogs/data-science-in-healthcare/) - истиче апликације као што су медицинска сликања (нпр. MRI, X-Ray, CT-Scan), геномика (секвенцирање ДНК), развој лекова (процена ризика, предвиђање успеха), предиктивна аналитика (нега пацијената и логистика снабдевања), праћење и превенција болести итд. -![Примене науке о подацима у стварном свету](../../../../translated_images/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.sr.png) Извор слике: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) +![Примене науке о подацима у стварном свету](../../../../translated_images/sr/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.png) Извор слике: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) Слика приказује друге домене и примере примене техника науке о подацима. Желите да истражите друге апликације? Погледајте секцију [Преглед и самостално учење](../../../../6-Data-Science-In-Wild/20-Real-World-Examples) испод. diff --git a/translations/sr/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md b/translations/sr/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md index 3b46340a..8ebff98d 100644 --- a/translations/sr/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md +++ b/translations/sr/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md @@ -22,7 +22,7 @@ CO_OP_TRANSLATOR_METADATA: 2. Истражите [Каталог](https://planetarycomputer.microsoft.com/catalog) скупа података - упознајте се са сврхом сваког скупа. 3. Користите Explorer - изаберите скуп података који вас интересује, изаберите релевантан упит и опцију приказа. -![The Planetary Computer Explorer](../../../../translated_images/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.sr.png) +![The Planetary Computer Explorer](../../../../translated_images/sr/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.png) `Ваш задатак:` Сада проучите визуализацију која је приказана у прегледачу и одговорите на следеће: diff --git a/translations/sr/CONTRIBUTING.md b/translations/sr/CONTRIBUTING.md index 285c826b..ed697976 100644 --- a/translations/sr/CONTRIBUTING.md +++ b/translations/sr/CONTRIBUTING.md @@ -318,7 +318,7 @@ def calculate_mean(data): ``` ```` -- Додајте alt текст сликама: `![Alt text](../../translated_images/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.sr.png)` +- Додајте alt текст сликама: `![Alt text](../../translated_images/sr/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.png)` - Одржавајте разумну дужину линија (око 80-100 карактера) ### Python diff --git a/translations/sr/README.md b/translations/sr/README.md index ca6d2efc..9a3ca69a 100644 --- a/translations/sr/README.md +++ b/translations/sr/README.md @@ -33,7 +33,7 @@ Azure Cloud Advocates у компанији Microsoft са задовољств **🙏 Посебне захвалности 🙏 нашим ауторима, рецензентима и сарадницима садржаја који су део [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/),** нарочито Aaryan Arora, [Aditya Garg](https://github.com/AdityaGarg00), [Alondra Sanchez](https://www.linkedin.com/in/alondra-sanchez-molina/), [Ankita Singh](https://www.linkedin.com/in/ankitasingh007), [Anupam Mishra](https://www.linkedin.com/in/anupam--mishra/), [Arpita Das](https://www.linkedin.com/in/arpitadas01/), ChhailBihari Dubey, [Dibri Nsofor](https://www.linkedin.com/in/dibrinsofor), [Dishita Bhasin](https://www.linkedin.com/in/dishita-bhasin-7065281bb), [Majd Safi](https://www.linkedin.com/in/majd-s/), [Max Blum](https://www.linkedin.com/in/max-blum-6036a1186/), [Miguel Correa](https://www.linkedin.com/in/miguelmque/), [Mohamma Iftekher (Iftu) Ebne Jalal](https://twitter.com/iftu119), [Nawrin Tabassum](https://www.linkedin.com/in/nawrin-tabassum), [Raymond Wangsa Putra](https://www.linkedin.com/in/raymond-wp/), [Rohit Yadav](https://www.linkedin.com/in/rty2423), Samridhi Sharma, [Sanya Sinha](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200), [Sheena Narula](https://www.linkedin.com/in/sheena-narua-n/), [Tauqeer Ahmad](https://www.linkedin.com/in/tauqeerahmad5201/), Yogendrasingh Pawar , [Vidushi Gupta](https://www.linkedin.com/in/vidushi-gupta07/), [Jasleen Sondhi](https://www.linkedin.com/in/jasleen-sondhi/) -|![Скетчнот од @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.sr.png)| +|![Скетчнот од @sketchthedocs https://sketchthedocs.dev](../../translated_images/sr/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.png)| |:---:| | Наука о подацима за почетнике - _Скетчнот од [@nitya](https://twitter.com/nitya)_ | @@ -52,7 +52,7 @@ Azure Cloud Advocates у компанији Microsoft са задовољств Имамо текућу Discord серију Learn with AI, сазнајте више и придружите нам се на [Серија Learn with AI](https://aka.ms/learnwithai/discord) од 18. до 30. септембра 2025. године. Добићете савете и трикове за коришћење GitHub Copilot за Науку о подацима. -![Серија Learn with AI](../../translated_images/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.sr.jpg) +![Серија Learn with AI](../../translated_images/sr/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.jpg) # Да ли сте студент? @@ -132,7 +132,7 @@ Azure Cloud Advocates у компанији Microsoft са задовољств ## Лекције -|![ Sketchnote by @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.sr.png)| +|![ Sketchnote by @sketchthedocs https://sketchthedocs.dev](../../translated_images/sr/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png)| |:---:| | Наука о подацима за почетнике: План пута - _Скетчнот од [@nitya](https://twitter.com/nitya)_ | diff --git a/translations/sr/sketchnotes/README.md b/translations/sr/sketchnotes/README.md index 59c1b4ec..58940b17 100644 --- a/translations/sr/sketchnotes/README.md +++ b/translations/sr/sketchnotes/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: Нитија Нарасимхан, уметница -![скицнота путање](../../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.sr.png) +![скицнота путање](../../../translated_images/sr/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png) --- diff --git a/translations/sv/1-Introduction/01-defining-data-science/README.md b/translations/sv/1-Introduction/01-defining-data-science/README.md index 636bfd3e..42110beb 100644 --- a/translations/sv/1-Introduction/01-defining-data-science/README.md +++ b/translations/sv/1-Introduction/01-defining-data-science/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: --- -[![Video om att definiera Data Science](../../../../translated_images/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.sv.png)](https://youtu.be/beZ7Mb_oz9I) +[![Video om att definiera Data Science](../../../../translated_images/sv/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.png)](https://youtu.be/beZ7Mb_oz9I) ## [Quiz före föreläsningen](https://ff-quizzes.netlify.app/en/ds/quiz/0) @@ -153,7 +153,7 @@ Om vi vill göra det ännu mer komplicerat kan vi plotta den tid som krävs för I denna utmaning ska vi försöka hitta koncept som är relevanta för området Data Science genom att analysera texter. Vi kommer att ta en Wikipedia-artikel om Data Science, ladda ner och bearbeta texten, och sedan skapa ett ordmoln som detta: -![Ordmoln för Data Science](../../../../translated_images/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.sv.png) +![Ordmoln för Data Science](../../../../translated_images/sv/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.png) Besök [`notebook.ipynb`](../../../../1-Introduction/01-defining-data-science/notebook.ipynb ':ignore') för att läsa igenom koden. Du kan också köra koden och se hur den utför alla datatransformationer i realtid. diff --git a/translations/sv/1-Introduction/04-stats-and-probability/README.md b/translations/sv/1-Introduction/04-stats-and-probability/README.md index 4cb47dab..629b5fb4 100644 --- a/translations/sv/1-Introduction/04-stats-and-probability/README.md +++ b/translations/sv/1-Introduction/04-stats-and-probability/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: Statistik och sannolikhetsteori är två nära besläktade områden inom matematiken som är mycket relevanta för datavetenskap. Det är möjligt att arbeta med data utan djupa kunskaper i matematik, men det är ändå bättre att känna till åtminstone några grundläggande begrepp. Här presenterar vi en kort introduktion som hjälper dig att komma igång. -[![Introduktionsvideo](../../../../translated_images/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.sv.png)](https://youtu.be/Z5Zy85g4Yjw) +[![Introduktionsvideo](../../../../translated_images/sv/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.png)](https://youtu.be/Z5Zy85g4Yjw) ## [Quiz före föreläsningen](https://ff-quizzes.netlify.app/en/ds/quiz/6) @@ -39,7 +39,7 @@ Det är svårare att beskriva sannolikhetsfördelningen för en kontinuerlig var Vi kan bara prata om sannolikheten att en variabel faller inom ett givet intervall av värden, t.ex. P(t1≤X2). I detta fall beskrivs sannolikhetsfördelningen av en **sannolikhetstäthetsfunktion** p(x), sådan att -![P(t_1\le X Fler exempel på korrelation och kovarians finns i [medföljande anteckningsbok](notebook.ipynb). diff --git a/translations/sv/1-Introduction/README.md b/translations/sv/1-Introduction/README.md index eb569ece..a29416ce 100644 --- a/translations/sv/1-Introduction/README.md +++ b/translations/sv/1-Introduction/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Introduktion till Data Science -![data i aktion](../../../translated_images/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.sv.jpg) +![data i aktion](../../../translated_images/sv/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.jpg) > Foto av Stephen Dawson på Unsplash I dessa lektioner kommer du att upptäcka hur Data Science definieras och lära dig om etiska överväganden som en dataanalytiker måste ta hänsyn till. Du kommer också att lära dig hur data definieras och få en introduktion till statistik och sannolikhet, de centrala akademiska områdena inom Data Science. diff --git a/translations/sv/2-Working-With-Data/07-python/README.md b/translations/sv/2-Working-With-Data/07-python/README.md index cc031685..1cd74bc3 100644 --- a/translations/sv/2-Working-With-Data/07-python/README.md +++ b/translations/sv/2-Working-With-Data/07-python/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: | :-------------------------------------------------------------------------------------------------------: | | Arbeta med Python - _Sketchnote av [@nitya](https://twitter.com/nitya)_ | -[![Introduktionsvideo](../../../../translated_images/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.sv.png)](https://youtu.be/dZjWOGbsN4Y) +[![Introduktionsvideo](../../../../translated_images/sv/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.png)](https://youtu.be/dZjWOGbsN4Y) Även om databaser erbjuder mycket effektiva sätt att lagra och söka data med hjälp av frågespråk, är det mest flexibla sättet att bearbeta data att skriva sitt eget program för att manipulera data. I många fall är en databasfråga ett mer effektivt sätt. Men i vissa fall, när mer komplex databehandling behövs, kan det inte enkelt göras med SQL. Databehandling kan programmeras i vilket programmeringsspråk som helst, men det finns vissa språk som är mer högspecialiserade för att arbeta med data. Dataforskare föredrar vanligtvis ett av följande språk: @@ -73,7 +73,7 @@ print(f"Length of index is {len(idx)}") items_sold = pd.Series(np.random.randint(25,50,size=len(idx)),index=idx) items_sold.plot() ``` -![Tidsserieplott](../../../../translated_images/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.sv.png) +![Tidsserieplott](../../../../translated_images/sv/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.png) Anta nu att vi varje vecka organiserar en fest för vänner och tar med ytterligare 10 paket glass till festen. Vi kan skapa en annan Series, indexerad per vecka, för att visa detta: ```python @@ -84,7 +84,7 @@ När vi adderar två Series får vi det totala antalet: total_items = items_sold.add(additional_items,fill_value=0) total_items.plot() ``` -![Tidsserieplott](../../../../translated_images/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.sv.png) +![Tidsserieplott](../../../../translated_images/sv/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.png) > **Notera** att vi inte använder den enkla syntaxen `total_items+additional_items`. Om vi gjorde det skulle vi få många `NaN` (*Not a Number*)-värden i den resulterande serien. Detta beror på att det saknas värden för vissa indexpunkter i serien `additional_items`, och att addera `NaN` till något resulterar i `NaN`. Därför måste vi specificera parametern `fill_value` under additionen. @@ -93,7 +93,7 @@ Med tidsserier kan vi också **resampla** serien med olika tidsintervall. Till e monthly = total_items.resample("1M").mean() ax = monthly.plot(kind='bar') ``` -![Månadsvisa tidsseriegenomsnitt](../../../../translated_images/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.sv.png) +![Månadsvisa tidsseriegenomsnitt](../../../../translated_images/sv/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.png) ### DataFrame @@ -220,7 +220,7 @@ Det första problemet vi kommer att fokusera på är modellering av epidemisk sp Eftersom vi vill demonstrera hur man hanterar data, uppmanar vi dig att öppna [`notebook-covidspread.ipynb`](notebook-covidspread.ipynb) och läsa den från början till slut. Du kan också köra cellerna och göra några utmaningar som vi har lämnat åt dig i slutet. -![COVID-spridning](../../../../translated_images/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.sv.png) +![COVID-spridning](../../../../translated_images/sv/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.png) > Om du inte vet hur man kör kod i Jupyter Notebook, ta en titt på [denna artikel](https://soshnikov.com/education/how-to-execute-notebooks-from-github/). @@ -242,7 +242,7 @@ Ett komplett exempel på att analysera detta dataset med hjälp av [Text Analyti Öppna [`notebook-papers.ipynb`](notebook-papers.ipynb) och läs den från början till slut. Du kan också köra cellerna och göra några utmaningar som vi har lämnat åt dig i slutet. -![Covid Medicinsk Behandling](../../../../translated_images/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.sv.png) +![Covid Medicinsk Behandling](../../../../translated_images/sv/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.png) ## Bearbeta bilddata diff --git a/translations/sv/2-Working-With-Data/README.md b/translations/sv/2-Working-With-Data/README.md index 99fa7786..f5168ddf 100644 --- a/translations/sv/2-Working-With-Data/README.md +++ b/translations/sv/2-Working-With-Data/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Arbeta med data -![data love](../../../translated_images/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.sv.jpg) +![data love](../../../translated_images/sv/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.jpg) > Foto av Alexander Sinn på Unsplash I dessa lektioner kommer du att lära dig några av de sätt som data kan hanteras, manipuleras och användas i applikationer. Du kommer att lära dig om relations- och icke-relationsdatabaser och hur data kan lagras i dem. Du kommer att lära dig grunderna i att arbeta med Python för att hantera data, och du kommer att upptäcka några av de många sätten du kan använda Python för att hantera och analysera data. diff --git a/translations/sv/3-Data-Visualization/12-visualization-relationships/README.md b/translations/sv/3-Data-Visualization/12-visualization-relationships/README.md index 8a17fa79..bc78e6d7 100644 --- a/translations/sv/3-Data-Visualization/12-visualization-relationships/README.md +++ b/translations/sv/3-Data-Visualization/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ Skapa ett grundläggande spridningsdiagram för att visa relationen mellan prise ```python sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ``` -![spridningsdiagram 1](../../../../translated_images/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.sv.png) +![spridningsdiagram 1](../../../../translated_images/sv/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.png) Visa nu samma data med ett honungsfärgschema för att visa hur priset utvecklas över åren. Du kan göra detta genom att lägga till en 'hue'-parameter för att visa förändringen år för år: @@ -60,7 +60,7 @@ Visa nu samma data med ett honungsfärgschema för att visa hur priset utvecklas ```python sns.relplot(x="priceperlb", y="state", hue="year", palette="YlOrBr", data=honey, height=15, aspect=.5); ``` -![spridningsdiagram 2](../../../../translated_images/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.sv.png) +![spridningsdiagram 2](../../../../translated_images/sv/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.png) Med denna färgschemaförändring kan du tydligt se en stark progression över åren när det gäller priset på honung per pound. Om du tittar på ett urval av data för att verifiera (välj en viss delstat, till exempel Arizona) kan du se ett mönster av prisökningar år för år, med få undantag: @@ -89,7 +89,7 @@ sns.relplot(x="priceperlb", y="state", size="year", data=honey, height=15, aspec ``` Du kan se att storleken på punkterna gradvis ökar. -![spridningsdiagram 3](../../../../translated_images/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.sv.png) +![spridningsdiagram 3](../../../../translated_images/sv/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.png) Är detta ett enkelt fall av utbud och efterfrågan? På grund av faktorer som klimatförändringar och kollaps av bisamhällen, finns det mindre honung tillgänglig för köp år för år, och därmed ökar priset? @@ -104,7 +104,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); ``` Svar: Ja, med vissa undantag runt år 2003: -![linjediagram 1](../../../../translated_images/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.sv.png) +![linjediagram 1](../../../../translated_images/sv/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.png) ✅ Eftersom Seaborn aggregerar data runt en linje, visar den "de flera mätningarna vid varje x-värde genom att plotta medelvärdet och 95 % konfidensintervallet runt medelvärdet". [Källa](https://seaborn.pydata.org/tutorial/relational.html). Detta tidskrävande beteende kan inaktiveras genom att lägga till `ci=None`. @@ -114,7 +114,7 @@ Fråga: Kan vi också se en topp i honungstillgången runt 2003? Vad händer om sns.relplot(x="year", y="totalprod", kind="line", data=honey); ``` -![linjediagram 2](../../../../translated_images/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.sv.png) +![linjediagram 2](../../../../translated_images/sv/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.png) Svar: Inte riktigt. Om du tittar på den totala produktionen verkar den faktiskt ha ökat det året, även om mängden producerad honung generellt sett minskar under dessa år. @@ -139,7 +139,7 @@ sns.relplot( ``` I denna visualisering kan du jämföra avkastning per samhälle och antal samhällen år för år, sida vid sida med en wrap inställd på 3 för kolumnerna: -![facet grid](../../../../translated_images/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.sv.png) +![facet grid](../../../../translated_images/sv/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.png) För detta dataset framträder inget särskilt med avseende på antalet samhällen och deras avkastning, år för år och delstat för delstat. Finns det ett annat sätt att hitta en korrelation mellan dessa två variabler? @@ -162,7 +162,7 @@ sns.despine(right=False) plt.ylabel('colony yield') ax.figure.legend(); ``` -![överlagrade diagram](../../../../translated_images/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.sv.png) +![överlagrade diagram](../../../../translated_images/sv/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.png) Även om inget särskilt framträder runt år 2003, låter det oss avsluta denna lektion på en lite gladare not: även om antalet samhällen totalt sett minskar, stabiliseras antalet samhällen även om deras avkastning per samhälle minskar. diff --git a/translations/sv/3-Data-Visualization/R/09-visualization-quantities/README.md b/translations/sv/3-Data-Visualization/R/09-visualization-quantities/README.md index 6e77029e..877c1856 100644 --- a/translations/sv/3-Data-Visualization/R/09-visualization-quantities/README.md +++ b/translations/sv/3-Data-Visualization/R/09-visualization-quantities/README.md @@ -66,7 +66,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` Här installerar du `ggplot2`-paketet och importerar det sedan till arbetsytan med kommandot `library("ggplot2")`. För att plotta ett diagram i ggplot används funktionen `ggplot()` och du specificerar datasetet, x- och y-variabler som attribut. I detta fall använder vi funktionen `geom_line()` eftersom vi vill plotta ett linjediagram. -![MaxVingbredd-linjediagram](../../../../../translated_images/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.sv.png) +![MaxVingbredd-linjediagram](../../../../../translated_images/sv/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.png) Vad märker du direkt? Det verkar finnas åtminstone en avvikelse - det är en ganska imponerande vingbredd! En vingbredd på över 2000 centimeter motsvarar mer än 20 meter - finns det Pterodactyler som strövar omkring i Minnesota? Låt oss undersöka. @@ -84,7 +84,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` Vi specificerar vinkeln i `theme` och anger x- och y-axelns etiketter i `xlab()` och `ylab()` respektive. `ggtitle()` ger diagrammet/plotten ett namn. -![MaxVingbredd-linjediagram-förbättrat](../../../../../translated_images/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.sv.png) +![MaxVingbredd-linjediagram-förbättrat](../../../../../translated_images/sv/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.png) Även med rotationen av etiketterna inställd på 45 grader är det för många för att läsa. Låt oss prova en annan strategi: märk endast avvikelserna och placera etiketterna inom diagrammet. Du kan använda ett spridningsdiagram för att skapa mer utrymme för märkningen: @@ -100,7 +100,7 @@ Vad händer här? Du använde funktionen `geom_point()` för att plotta spridnin Vad upptäcker du? -![MaxVingbredd-spridningsdiagram](../../../../../translated_images/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.sv.png) +![MaxVingbredd-spridningsdiagram](../../../../../translated_images/sv/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.png) ## Filtrera din data @@ -119,7 +119,7 @@ ggplot(data=birds_filtered, aes(x=Name, y=MaxWingspan,group=1)) + ``` Vi skapade en ny dataframe `birds_filtered` och plottade sedan ett spridningsdiagram. Genom att filtrera bort avvikelser är din data nu mer sammanhängande och begriplig. -![MaxVingbredd-spridningsdiagram-förbättrat](../../../../../translated_images/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.sv.png) +![MaxVingbredd-spridningsdiagram-förbättrat](../../../../../translated_images/sv/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.png) Nu när vi har ett renare dataset åtminstone när det gäller vingbredd, låt oss upptäcka mer om dessa fåglar. @@ -161,7 +161,7 @@ birds_filtered %>% group_by(Category) %>% ``` I följande kodsnutt installerar vi paketen [dplyr](https://www.rdocumentation.org/packages/dplyr/versions/0.7.8) och [lubridate](https://www.rdocumentation.org/packages/lubridate/versions/1.8.0) för att hjälpa till att manipulera och gruppera data för att plotta ett staplat stapeldiagram. Först grupperar du datan efter fåglarnas `Category` och summerar sedan kolumnerna `MinLength`, `MaxLength`, `MinBodyMass`, `MaxBodyMass`, `MinWingspan`, `MaxWingspan`. Sedan plotter du stapeldiagrammet med `ggplot2`-paketet och specificerar färgerna för de olika kategorierna och etiketterna. -![Staplat stapeldiagram](../../../../../translated_images/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.sv.png) +![Staplat stapeldiagram](../../../../../translated_images/sv/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.png) Detta stapeldiagram är dock oläsligt eftersom det finns för mycket icke-grupperad data. Du behöver välja endast den data du vill plotta, så låt oss titta på fåglarnas längd baserat på deras kategori. @@ -176,7 +176,7 @@ ggplot(birds_count,aes(Category,n))+geom_bar(stat="identity")+coord_flip() ``` Du räknar först unika värden i kolumnen `Category` och sorterar dem sedan i en ny dataframe `birds_count`. Denna sorterade data faktoreras sedan på samma nivå så att den plottas på ett sorterat sätt. Med `ggplot2` plotter du sedan datan i ett stapeldiagram. `coord_flip()` plotter horisontella staplar. -![kategori-längd](../../../../../translated_images/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.sv.png) +![kategori-längd](../../../../../translated_images/sv/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.png) Detta stapeldiagram ger en bra översikt över antalet fåglar i varje kategori. Vid en snabb blick ser du att det största antalet fåglar i denna region tillhör kategorin Ankor/Gäss/Vattenfåglar. Minnesota är "landet med 10 000 sjöar", så detta är inte förvånande! @@ -199,7 +199,7 @@ ggplot(birds_grouped,aes(Category,MaxLength))+geom_bar(stat="identity")+coord_fl ``` Vi grupperar `birds_filtered`-datan efter `Category` och plotter sedan ett stapeldiagram. -![jämföra data](../../../../../translated_images/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.sv.png) +![jämföra data](../../../../../translated_images/sv/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.png) Inget är förvånande här: kolibrier har den minsta MaxLängden jämfört med pelikaner eller gäss. Det är bra när data är logisk! @@ -211,7 +211,7 @@ ggplot(data=birds_grouped, aes(x=Category)) + geom_bar(aes(y=MinLength), stat="identity", position="identity", fill='orange')+ coord_flip() ``` -![överlagrade värden](../../../../../translated_images/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.sv.png) +![överlagrade värden](../../../../../translated_images/sv/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.png) ## 🚀 Utmaning diff --git a/translations/sv/3-Data-Visualization/R/10-visualization-distributions/README.md b/translations/sv/3-Data-Visualization/R/10-visualization-distributions/README.md index 57a6ebf1..efa74d44 100644 --- a/translations/sv/3-Data-Visualization/R/10-visualization-distributions/README.md +++ b/translations/sv/3-Data-Visualization/R/10-visualization-distributions/README.md @@ -45,7 +45,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + geom_point() + ggtitle("Max Length per order") + coord_flip() ``` -![max längd per ordning](../../../../../translated_images/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.sv.png) +![max längd per ordning](../../../../../translated_images/sv/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.png) Detta ger en översikt över den generella fördelningen av kroppslängd per fågelordning, men det är inte det optimala sättet att visa verkliga fördelningar. Den uppgiften hanteras vanligtvis genom att skapa ett histogram. ## Arbeta med histogram @@ -56,7 +56,7 @@ Detta ger en översikt över den generella fördelningen av kroppslängd per få ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=10)+ylab('Frequency') ``` -![fördelning över hela datasetet](../../../../../translated_images/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.sv.png) +![fördelning över hela datasetet](../../../../../translated_images/sv/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.png) Som du kan se, faller de flesta av de 400+ fåglarna i detta dataset inom intervallet under 2000 för deras Max Kroppsmassa. Få mer insikt i datan genom att ändra `bins`-parametern till ett högre nummer, något som 30: @@ -64,7 +64,7 @@ Som du kan se, faller de flesta av de 400+ fåglarna i detta dataset inom interv ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![fördelning-30bins](../../../../../translated_images/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.sv.png) +![fördelning-30bins](../../../../../translated_images/sv/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.png) Detta diagram visar fördelningen på ett lite mer detaljerat sätt. Ett diagram som är mindre snedvridet åt vänster kan skapas genom att säkerställa att du endast väljer data inom ett visst intervall: @@ -76,7 +76,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![filtrerat histogram](../../../../../translated_images/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.sv.png) +![filtrerat histogram](../../../../../translated_images/sv/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.png) ✅ Prova några andra filter och datapunkter. För att se den fullständiga fördelningen av datan, ta bort `['MaxBodyMass']`-filtret för att visa märkta fördelningar. @@ -90,7 +90,7 @@ ggplot(data=birds_filtered_1, aes(x=MaxBodyMass, y=MaxLength) ) + ``` Det verkar finnas en förväntad korrelation mellan dessa två element längs en förväntad axel, med en särskilt stark konvergenspunkt: -![2d diagram](../../../../../translated_images/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.sv.png) +![2d diagram](../../../../../translated_images/sv/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.png) Histogram fungerar bra som standard för numerisk data. Vad händer om du behöver se fördelningar enligt textdata? ## Utforska datasetet för fördelningar med hjälp av textdata @@ -121,7 +121,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + scale_fill_manual(name="Conservation Status",values=c("red","green","blue","pink"),labels=c("Endangered","Near Threathened","Vulnerable","Least Concern")) ``` -![vingbredd och bevarande](../../../../../translated_images/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.sv.png) +![vingbredd och bevarande](../../../../../translated_images/sv/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.png) Det verkar inte finnas någon bra korrelation mellan minsta vingbredd och bevarandestatus. Testa andra element i datasetet med denna metod. Du kan prova olika filter också. Hittar du någon korrelation? @@ -135,7 +135,7 @@ Låt oss arbeta med täthetsdiagram nu! ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + geom_density() ``` -![täthetsdiagram](../../../../../translated_images/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.sv.png) +![täthetsdiagram](../../../../../translated_images/sv/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.png) Du kan se hur diagrammet speglar det tidigare för Minsta Vingbredd-data; det är bara lite smidigare. Om du ville återbesöka den hackiga MaxKroppsmassa-linjen i det andra diagrammet du byggde, kunde du jämna ut den mycket väl genom att återskapa den med denna metod: @@ -143,7 +143,7 @@ Du kan se hur diagrammet speglar det tidigare för Minsta Vingbredd-data; det ä ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density() ``` -![kroppsmassa täthet](../../../../../translated_images/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.sv.png) +![kroppsmassa täthet](../../../../../translated_images/sv/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.png) Om du ville ha en smidig, men inte alltför smidig linje, redigera `adjust`-parametern: @@ -151,7 +151,7 @@ Om du ville ha en smidig, men inte alltför smidig linje, redigera `adjust`-para ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density(adjust = 1/5) ``` -![mindre smidig kroppsmassa](../../../../../translated_images/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.sv.png) +![mindre smidig kroppsmassa](../../../../../translated_images/sv/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.png) ✅ Läs om de parametrar som finns tillgängliga för denna typ av diagram och experimentera! @@ -161,7 +161,7 @@ Denna typ av diagram erbjuder vackert förklarande visualiseringar. Med några f ggplot(data=birds_filtered_1,aes(x = MaxBodyMass, fill = Order)) + geom_density(alpha=0.5) ``` -![kroppsmassa per ordning](../../../../../translated_images/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.sv.png) +![kroppsmassa per ordning](../../../../../translated_images/sv/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.png) ## 🚀 Utmaning diff --git a/translations/sv/3-Data-Visualization/R/11-visualization-proportions/README.md b/translations/sv/3-Data-Visualization/R/11-visualization-proportions/README.md index 73f15e32..c85cbc0d 100644 --- a/translations/sv/3-Data-Visualization/R/11-visualization-proportions/README.md +++ b/translations/sv/3-Data-Visualization/R/11-visualization-proportions/README.md @@ -93,7 +93,7 @@ pie(grouped$count,grouped$class, main="Edible?") ``` Voilà, ett pajdiagram som visar proportionerna av denna data enligt dessa två svampklasser. Det är ganska viktigt att få ordningen på etiketterna rätt, särskilt här, så se till att verifiera ordningen när etikettarrayen byggs! -![pajdiagram](../../../../../translated_images/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.sv.png) +![pajdiagram](../../../../../translated_images/sv/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.png) ## Donuts! @@ -127,7 +127,7 @@ library(webr) PieDonut(habitat, aes(habitat, count=count)) ``` -![donutdiagram](../../../../../translated_images/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.sv.png) +![donutdiagram](../../../../../translated_images/sv/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.png) Denna kod använder de två biblioteken ggplot2 och webr. Med hjälp av funktionen PieDonut i webr-biblioteket kan vi enkelt skapa ett donutdiagram! @@ -165,7 +165,7 @@ waffle((cap_color$count/10), rows = 7, title = "Waffle Chart")+scale_fill_manual Med ett våffeldiagram kan du tydligt se proportionerna av hattfärger i detta svampdataset. Intressant nog finns det många svampar med gröna hattar! -![våffeldiagram](../../../../../translated_images/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.sv.png) +![våffeldiagram](../../../../../translated_images/sv/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.png) I den här lektionen lärde du dig tre sätt att visualisera proportioner. Först behöver du gruppera din data i kategorier och sedan bestämma vilket som är det bästa sättet att visa datan - paj, donut eller våffla. Alla är läckra och ger användaren en omedelbar överblick av ett dataset. diff --git a/translations/sv/3-Data-Visualization/R/12-visualization-relationships/README.md b/translations/sv/3-Data-Visualization/R/12-visualization-relationships/README.md index f6857a71..d3beea00 100644 --- a/translations/sv/3-Data-Visualization/R/12-visualization-relationships/README.md +++ b/translations/sv/3-Data-Visualization/R/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ library(ggplot2) ggplot(honey, aes(x = priceperlb, y = state)) + geom_point(colour = "blue") ``` -![spridningsdiagram 1](../../../../../translated_images/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.sv.png) +![spridningsdiagram 1](../../../../../translated_images/sv/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.png) Visa nu samma data med ett honungsfärgschema för att visa hur priset utvecklas över åren. Du kan göra detta genom att lägga till en 'scale_color_gradientn'-parameter för att visa förändringen år för år: @@ -61,7 +61,7 @@ Visa nu samma data med ett honungsfärgschema för att visa hur priset utvecklas ggplot(honey, aes(x = priceperlb, y = state, color=year)) + geom_point()+scale_color_gradientn(colours = colorspace::heat_hcl(7)) ``` -![spridningsdiagram 2](../../../../../translated_images/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.sv.png) +![spridningsdiagram 2](../../../../../translated_images/sv/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.png) Med denna färgschemaändring kan du tydligt se en stark utveckling över åren när det gäller honungspriset per pund. Om du tittar på ett urval av data för att verifiera (välj en given delstat, till exempel Arizona) kan du se ett mönster av prisökningar år för år, med få undantag: @@ -92,7 +92,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ``` Du kan se att storleken på punkterna gradvis ökar. -![spridningsdiagram 3](../../../../../translated_images/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.sv.png) +![spridningsdiagram 3](../../../../../translated_images/sv/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.png) Är detta ett enkelt fall av utbud och efterfrågan? På grund av faktorer som klimatförändringar och kolonikollaps, finns det mindre honung tillgänglig för köp år för år, och därmed ökar priset? @@ -107,7 +107,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab ``` Svar: Ja, med vissa undantag runt år 2003: -![linjediagram 1](../../../../../translated_images/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.sv.png) +![linjediagram 1](../../../../../translated_images/sv/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.png) Fråga: Kan vi också se en topp i honungstillgången år 2003? Vad händer om du tittar på total produktion år för år? @@ -115,7 +115,7 @@ Fråga: Kan vi också se en topp i honungstillgången år 2003? Vad händer om d qplot(honey$year,honey$totalprod, geom='smooth', span =0.5, xlab = "year",ylab = "totalprod") ``` -![linjediagram 2](../../../../../translated_images/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.sv.png) +![linjediagram 2](../../../../../translated_images/sv/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.png) Svar: Inte riktigt. Om du tittar på total produktion verkar det faktiskt ha ökat det året, även om mängden honung som produceras generellt sett minskar under dessa år. @@ -135,7 +135,7 @@ ggplot(honey, aes(x=yieldpercol, y = numcol,group = 1)) + ``` I denna visualisering kan du jämföra avkastning per koloni och antal kolonier år för år, sida vid sida med en wrap inställd på 3 för kolumnerna: -![facet grid](../../../../../translated_images/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.sv.png) +![facet grid](../../../../../translated_images/sv/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.png) För denna dataset sticker inget särskilt ut när det gäller antalet kolonier och deras avkastning, år för år och delstat för delstat. Finns det ett annat sätt att hitta en korrelation mellan dessa två variabler? @@ -152,7 +152,7 @@ plot(honey$year, honey$yieldpercol, pch = 17, col = 3, axis(side = 4, at = pretty(range(y2))) mtext("colony yield", side = 4, line = 3) ``` -![överlagrade diagram](../../../../../translated_images/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.sv.png) +![överlagrade diagram](../../../../../translated_images/sv/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.png) Även om inget sticker ut runt år 2003, låter det oss avsluta denna lektion med en lite gladare ton: även om det totalt sett är ett minskande antal kolonier, stabiliseras antalet kolonier även om deras avkastning per koloni minskar. diff --git a/translations/sv/3-Data-Visualization/R/13-meaningful-vizualizations/README.md b/translations/sv/3-Data-Visualization/R/13-meaningful-vizualizations/README.md index 24e88fb4..7cce0235 100644 --- a/translations/sv/3-Data-Visualization/R/13-meaningful-vizualizations/README.md +++ b/translations/sv/3-Data-Visualization/R/13-meaningful-vizualizations/README.md @@ -47,25 +47,25 @@ I tidigare lektioner experimenterade du med att skapa olika typer av intressanta Även om en dataanalytiker är noggrann med att välja rätt diagram för rätt data, finns det många sätt att visa data på ett sätt som bevisar en poäng, ofta på bekostnad av att undergräva själva datan. Det finns många exempel på vilseledande diagram och infografik! -[![How Charts Lie av Alberto Cairo](../../../../../translated_images/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.sv.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "How charts lie") +[![How Charts Lie av Alberto Cairo](../../../../../translated_images/sv/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "How charts lie") > 🎥 Klicka på bilden ovan för en konferensföreläsning om vilseledande diagram Detta diagram vänder X-axeln för att visa motsatsen till sanningen, baserat på datum: -![dåligt diagram 1](../../../../../translated_images/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.sv.png) +![dåligt diagram 1](../../../../../translated_images/sv/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.png) [Detta diagram](https://media.firstcoastnews.com/assets/WTLV/images/170ae16f-4643-438f-b689-50d66ca6a8d8/170ae16f-4643-438f-b689-50d66ca6a8d8_1140x641.jpg) är ännu mer vilseledande, eftersom ögat dras till höger för att dra slutsatsen att COVID-fallen har minskat över tid i olika län. Faktum är att om du tittar noga på datumen, upptäcker du att de har omarrangerats för att ge den vilseledande nedåtgående trenden. -![dåligt diagram 2](../../../../../translated_images/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.sv.jpg) +![dåligt diagram 2](../../../../../translated_images/sv/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.jpg) Detta ökända exempel använder färg OCH en vänd Y-axel för att vilseleda: istället för att dra slutsatsen att vapenvåld ökade efter införandet av vapenvänlig lagstiftning, luras ögat att tro att motsatsen är sann: -![dåligt diagram 3](../../../../../translated_images/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.sv.jpg) +![dåligt diagram 3](../../../../../translated_images/sv/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.jpg) Detta märkliga diagram visar hur proportioner kan manipuleras, med komisk effekt: -![dåligt diagram 4](../../../../../translated_images/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.sv.jpg) +![dåligt diagram 4](../../../../../translated_images/sv/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.jpg) Att jämföra det ojämförbara är ytterligare ett skumt knep. Det finns en [fantastisk webbplats](https://tylervigen.com/spurious-correlations) som handlar om 'spurious correlations' och visar 'fakta' som korrelerar saker som skilsmässofrekvensen i Maine och konsumtionen av margarin. En Reddit-grupp samlar också [fula användningar](https://www.reddit.com/r/dataisugly/top/?t=all) av data. @@ -100,13 +100,13 @@ Märk dina axlar, tillhandahåll en legend om det behövs, och erbjud verktygsti Om din data är textbaserad och omfattande på X-axeln, kan du vinkla texten för bättre läsbarhet. [plot3D](https://cran.r-project.org/web/packages/plot3D/index.html) erbjuder 3D-plotting, om din data stödjer det. Sofistikerade datavisualiseringar kan skapas med det. -![3d diagram](../../../../../translated_images/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.sv.png) +![3d diagram](../../../../../translated_images/sv/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.png) ## Animation och 3D-diagram Några av de bästa datavisualiseringarna idag är animerade. Shirley Wu har fantastiska exempel gjorda med D3, såsom '[film flowers](http://bl.ocks.org/sxywu/raw/d612c6c653fb8b4d7ff3d422be164a5d/)', där varje blomma är en visualisering av en film. Ett annat exempel för Guardian är 'bussed out', en interaktiv upplevelse som kombinerar visualiseringar med Greensock och D3 plus ett scrollytelling-artikelformat för att visa hur NYC hanterar sitt hemlöshetsproblem genom att bussa ut människor från staden. -![bussing](../../../../../translated_images/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.sv.png) +![bussing](../../../../../translated_images/sv/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.png) > "Bussed Out: How America Moves its Homeless" från [the Guardian](https://www.theguardian.com/us-news/ng-interactive/2017/dec/20/bussed-out-america-moves-homeless-people-country-study). Visualiseringar av Nadieh Bremer & Shirley Wu @@ -116,7 +116,7 @@ Några av de bästa datavisualiseringarna idag är animerade. Shirley Wu har fan Du kommer att slutföra en webbapp som visar en animerad vy av detta sociala nätverk. Den använder ett bibliotek som skapades för att skapa en [visualisering av ett nätverk](https://github.com/emiliorizzo/vue-d3-network) med Vue.js och D3. När appen körs kan du dra runt noderna på skärmen för att omorganisera datan. -![liaisons](../../../../../translated_images/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.sv.png) +![liaisons](../../../../../translated_images/sv/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.png) ## Projekt: Skapa ett diagram för att visa ett nätverk med D3.js diff --git a/translations/sv/3-Data-Visualization/README.md b/translations/sv/3-Data-Visualization/README.md index 849d634b..abf2c709 100644 --- a/translations/sv/3-Data-Visualization/README.md +++ b/translations/sv/3-Data-Visualization/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Visualiseringar -![en bi på en lavendelblomma](../../../translated_images/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.sv.jpg) +![en bi på en lavendelblomma](../../../translated_images/sv/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.jpg) > Foto av Jenna Lee på Unsplash Att visualisera data är en av de viktigaste uppgifterna för en dataanalytiker. Bilder säger mer än tusen ord, och en visualisering kan hjälpa dig att identifiera olika intressanta delar av din data, såsom toppar, avvikelser, grupperingar, tendenser och mycket mer, som kan hjälpa dig att förstå berättelsen din data försöker förmedla. diff --git a/translations/sv/4-Data-Science-Lifecycle/14-Introduction/README.md b/translations/sv/4-Data-Science-Lifecycle/14-Introduction/README.md index e3aa573b..66b9f97d 100644 --- a/translations/sv/4-Data-Science-Lifecycle/14-Introduction/README.md +++ b/translations/sv/4-Data-Science-Lifecycle/14-Introduction/README.md @@ -25,7 +25,7 @@ Vid det här laget har du förmodligen insett att dataanalys är en process. Den Den här lektionen fokuserar på tre delar av livscykeln: insamling, bearbetning och underhåll. -![Diagram över livscykeln för dataanalys](../../../../translated_images/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.sv.jpg) +![Diagram över livscykeln för dataanalys](../../../../translated_images/sv/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.jpg) > Foto av [Berkeley School of Information](https://ischoolonline.berkeley.edu/data-science/what-is-data-science/) ## Insamling @@ -101,7 +101,7 @@ Utforska [Team Data Science Process livscykel](https://docs.microsoft.com/en-us/ |Team Data Science Process (TDSP)|Cross-industry standard process for data mining (CRISP-DM)| |--|--| -|![Team Data Science Lifecycle](../../../../translated_images/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.sv.png) | ![Data Science Process Alliance Image](../../../../translated_images/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.sv.png) | +|![Team Data Science Lifecycle](../../../../translated_images/sv/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.png) | ![Data Science Process Alliance Image](../../../../translated_images/sv/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.png) | | Bild av [Microsoft](https://docs.microsoft.comazure/architecture/data-science-process/lifecycle) | Bild av [Data Science Process Alliance](https://www.datascience-pm.com/crisp-dm-2/) | ## [Quiz efter föreläsningen](https://ff-quizzes.netlify.app/en/ds/quiz/27) diff --git a/translations/sv/4-Data-Science-Lifecycle/README.md b/translations/sv/4-Data-Science-Lifecycle/README.md index 24dbe9f9..b8da4d37 100644 --- a/translations/sv/4-Data-Science-Lifecycle/README.md +++ b/translations/sv/4-Data-Science-Lifecycle/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Livscykeln för Data Science -![communication](../../../translated_images/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.sv.jpg) +![communication](../../../translated_images/sv/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.jpg) > Foto av Headway på Unsplash I dessa lektioner kommer du att utforska några aspekter av livscykeln för Data Science, inklusive analys och kommunikation kring data. diff --git a/translations/sv/5-Data-Science-In-Cloud/README.md b/translations/sv/5-Data-Science-In-Cloud/README.md index 0801a304..bdc039d2 100644 --- a/translations/sv/5-Data-Science-In-Cloud/README.md +++ b/translations/sv/5-Data-Science-In-Cloud/README.md @@ -9,13 +9,13 @@ CO_OP_TRANSLATOR_METADATA: --> # Data Science i molnet -![cloud-picture](../../../translated_images/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.sv.jpg) +![cloud-picture](../../../translated_images/sv/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.jpg) > Foto av [Jelleke Vanooteghem](https://unsplash.com/@ilumire) från [Unsplash](https://unsplash.com/s/photos/cloud?orientation=landscape) När det gäller att utföra data science med stora datamängder kan molnet vara en riktig game changer. Under de kommande tre lektionerna ska vi titta på vad molnet är och varför det kan vara väldigt användbart. Vi kommer också att utforska en dataset om hjärtsvikt och bygga en modell för att hjälpa till att bedöma sannolikheten för att någon drabbas av hjärtsvikt. Vi kommer att använda molnets kraft för att träna, distribuera och använda en modell på två olika sätt. Ett sätt är att använda endast användargränssnittet på ett Low code/No code-sätt, och det andra sättet är att använda Azure Machine Learning Software Developer Kit (Azure ML SDK). -![project-schema](../../../translated_images/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.sv.png) +![project-schema](../../../translated_images/sv/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.png) ### Ämnen diff --git a/translations/sv/6-Data-Science-In-Wild/20-Real-World-Examples/README.md b/translations/sv/6-Data-Science-In-Wild/20-Real-World-Examples/README.md index 4ed784d8..2d4fd1e9 100644 --- a/translations/sv/6-Data-Science-In-Wild/20-Real-World-Examples/README.md +++ b/translations/sv/6-Data-Science-In-Wild/20-Real-World-Examples/README.md @@ -41,7 +41,7 @@ Tack vare AI:s demokratisering är det nu enklare för utvecklare att designa oc * [Data Science inom Hälsovård](https://data-flair.training/blogs/data-science-in-healthcare/) - lyfter fram tillämpningar som medicinsk bildbehandling (t.ex. MRI, röntgen, CT-skanning), genomik (DNA-sekvensering), läkemedelsutveckling (riskbedömning, framgångsprognoser), prediktiv analys (patientvård och logistik), sjukdomsspårning och förebyggande åtgärder. -![Data Science-tillämpningar i Verkligheten](../../../../translated_images/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.sv.png) Bildkälla: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) +![Data Science-tillämpningar i Verkligheten](../../../../translated_images/sv/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.png) Bildkälla: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) Figuren visar andra områden och exempel på hur data science-tekniker kan tillämpas. Vill du utforska fler tillämpningar? Kolla in [Review & Self Study](../../../../6-Data-Science-In-Wild/20-Real-World-Examples)-avsnittet nedan. diff --git a/translations/sv/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md b/translations/sv/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md index 5c135fd8..fc6b6514 100644 --- a/translations/sv/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md +++ b/translations/sv/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md @@ -22,7 +22,7 @@ Explorer-gränssnittet (visat i skärmbilden nedan) låter dig välja en dataset 2. Utforska datasetens [Katalog](https://planetarycomputer.microsoft.com/catalog) - lär dig syftet med varje dataset. 3. Använd Explorer - välj en dataset som intresserar dig, välj en relevant fråga och ett renderingsalternativ. -![The Planetary Computer Explorer](../../../../translated_images/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.sv.png) +![The Planetary Computer Explorer](../../../../translated_images/sv/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.png) `Din uppgift:` Studera nu visualiseringen som renderas i webbläsaren och svara på följande: diff --git a/translations/sv/CONTRIBUTING.md b/translations/sv/CONTRIBUTING.md index 635167ba..cf978929 100644 --- a/translations/sv/CONTRIBUTING.md +++ b/translations/sv/CONTRIBUTING.md @@ -312,7 +312,7 @@ Inkludera i din PR-beskrivning: import pandas as pd ``` ```` -- Lägg till alt-text till bilder: `![Alt text](../../translated_images/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.sv.png)` +- Lägg till alt-text till bilder: `![Alt text](../../translated_images/sv/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.png)` - Håll linjelängder rimliga (runt 80-100 tecken) ### Python diff --git a/translations/sv/README.md b/translations/sv/README.md index 35fca95b..c476e920 100644 --- a/translations/sv/README.md +++ b/translations/sv/README.md @@ -33,7 +33,7 @@ Azure Cloud Advocates på Microsoft är glada att erbjuda ett 10-veckors, 20-lek **🙏 Särskilt tack 🙏 till våra [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/) författare, granskare och innehållsbidragsgivare,** särskilt Aaryan Arora, [Aditya Garg](https://github.com/AdityaGarg00), [Alondra Sanchez](https://www.linkedin.com/in/alondra-sanchez-molina/), [Ankita Singh](https://www.linkedin.com/in/ankitasingh007), [Anupam Mishra](https://www.linkedin.com/in/anupam--mishra/), [Arpita Das](https://www.linkedin.com/in/arpitadas01/), ChhailBihari Dubey, [Dibri Nsofor](https://www.linkedin.com/in/dibrinsofor), [Dishita Bhasin](https://www.linkedin.com/in/dishita-bhasin-7065281bb), [Majd Safi](https://www.linkedin.com/in/majd-s/), [Max Blum](https://www.linkedin.com/in/max-blum-6036a1186/), [Miguel Correa](https://www.linkedin.com/in/miguelmque/), [Mohamma Iftekher (Iftu) Ebne Jalal](https://twitter.com/iftu119), [Nawrin Tabassum](https://www.linkedin.com/in/nawrin-tabassum), [Raymond Wangsa Putra](https://www.linkedin.com/in/raymond-wp/), [Rohit Yadav](https://www.linkedin.com/in/rty2423), Samridhi Sharma, [Sanya Sinha](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200), [Sheena Narula](https://www.linkedin.com/in/sheena-narua-n/), [Tauqeer Ahmad](https://www.linkedin.com/in/tauqeerahmad5201/), Yogendrasingh Pawar , [Vidushi Gupta](https://www.linkedin.com/in/vidushi-gupta07/), [Jasleen Sondhi](https://www.linkedin.com/in/jasleen-sondhi/) -|![Skissnotering av @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.sv.png)| +|![Skissnotering av @sketchthedocs https://sketchthedocs.dev](../../translated_images/sv/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.png)| |:---:| | Data Science för nybörjare - _Skissnotering av [@nitya](https://twitter.com/nitya)_ | @@ -52,7 +52,7 @@ Azure Cloud Advocates på Microsoft är glada att erbjuda ett 10-veckors, 20-lek Vi har en pågående Discord-serie "Learn with AI", läs mer och gå med oss på [Learn with AI Series](https://aka.ms/learnwithai/discord) från 18 - 30 september 2025. Du får tips och tricks för att använda GitHub Copilot för Data Science. -![Lär dig med AI-serien](../../translated_images/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.sv.jpg) +![Lär dig med AI-serien](../../translated_images/sv/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.jpg) # Är du student? @@ -132,7 +132,7 @@ Varje exempel innehåller detaljerade kommentarer som förklarar varje steg, vil ## Lektioner -|![ Sketchnote by @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.sv.png)| +|![ Sketchnote by @sketchthedocs https://sketchthedocs.dev](../../translated_images/sv/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png)| |:---:| | Data Science för nybörjare: Färdplan - _Sketchnote av [@nitya](https://twitter.com/nitya)_ | diff --git a/translations/sv/sketchnotes/README.md b/translations/sv/sketchnotes/README.md index be701fa6..75aa9fab 100644 --- a/translations/sv/sketchnotes/README.md +++ b/translations/sv/sketchnotes/README.md @@ -13,7 +13,7 @@ Hitta alla sketchnotes här! Nitya Narasimhan, konstnär -![roadmap sketchnote](../../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.sv.png) +![roadmap sketchnote](../../../translated_images/sv/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png) --- diff --git a/translations/sw/1-Introduction/01-defining-data-science/README.md b/translations/sw/1-Introduction/01-defining-data-science/README.md index 6d16ec65..8c9a3bad 100644 --- a/translations/sw/1-Introduction/01-defining-data-science/README.md +++ b/translations/sw/1-Introduction/01-defining-data-science/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: --- -[![Video ya Kufafanua Sayansi ya Takwimu](../../../../translated_images/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.sw.png)](https://youtu.be/beZ7Mb_oz9I) +[![Video ya Kufafanua Sayansi ya Takwimu](../../../../translated_images/sw/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.png)](https://youtu.be/beZ7Mb_oz9I) ## [Jaribio la kabla ya somo](https://ff-quizzes.netlify.app/en/ds/quiz/0) @@ -153,7 +153,7 @@ Ikiwa tunataka kufanya uchambuzi wa kina zaidi, tunaweza kuchora muda unaotumika Katika changamoto hii, tutajaribu kutafuta dhana zinazohusiana na uwanja wa Sayansi ya Takwimu kwa kuangalia maandishi. Tutachukua makala ya Wikipedia kuhusu Sayansi ya Takwimu, kupakua na kuchakata maandishi, kisha kujenga wingu la maneno kama hili: -![Wingu la Maneno kwa Sayansi ya Takwimu](../../../../translated_images/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.sw.png) +![Wingu la Maneno kwa Sayansi ya Takwimu](../../../../translated_images/sw/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.png) Tembelea [`notebook.ipynb`](../../../../1-Introduction/01-defining-data-science/notebook.ipynb ':ignore') kusoma kupitia msimbo. Unaweza pia kuendesha msimbo huo, na kuona jinsi unavyofanya mabadiliko yote ya data kwa wakati halisi. diff --git a/translations/sw/1-Introduction/04-stats-and-probability/README.md b/translations/sw/1-Introduction/04-stats-and-probability/README.md index 7b6701a4..4bcda432 100644 --- a/translations/sw/1-Introduction/04-stats-and-probability/README.md +++ b/translations/sw/1-Introduction/04-stats-and-probability/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: Nadharia ya Takwimu na Uwezekano ni maeneo mawili yanayohusiana sana ya Hisabati ambayo ni muhimu sana kwa Sayansi ya Data. Inawezekana kufanya kazi na data bila uelewa wa kina wa hisabati, lakini ni bora kujua angalau dhana za msingi. Hapa tutatoa utangulizi mfupi ambao utakusaidia kuanza. -[![Video ya Utangulizi](../../../../translated_images/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.sw.png)](https://youtu.be/Z5Zy85g4Yjw) +[![Video ya Utangulizi](../../../../translated_images/sw/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.png)](https://youtu.be/Z5Zy85g4Yjw) ## [Jaribio la Kabla ya Somo](https://ff-quizzes.netlify.app/en/ds/quiz/6) @@ -39,7 +39,7 @@ Ni vigumu zaidi kuelezea usambazaji wa uwezekano wa mabadiliko endelevu, yenye t Tunaweza tu kuzungumzia uwezekano wa mabadiliko kuangukia katika kipengele fulani cha thamani, kwa mfano P(t1≤X2). Katika kesi hii, usambazaji wa uwezekano unaelezewa na **kazi ya msongamano wa uwezekano** p(x), ambapo -![P(t_1\le X Mifano zaidi ya uwiano wa pamoja na uhusiano wa pamoja inaweza kupatikana katika [majarida yanayoambatana](notebook.ipynb). diff --git a/translations/sw/1-Introduction/README.md b/translations/sw/1-Introduction/README.md index 4c42d599..1be682ec 100644 --- a/translations/sw/1-Introduction/README.md +++ b/translations/sw/1-Introduction/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Utangulizi wa Sayansi ya Takwimu -![data in action](../../../translated_images/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.sw.jpg) +![data in action](../../../translated_images/sw/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.jpg) > Picha na Stephen Dawson kwenye Unsplash Katika masomo haya, utagundua jinsi Sayansi ya Takwimu inavyofafanuliwa na kujifunza kuhusu masuala ya kimaadili ambayo lazima yazingatiwe na mwanasayansi wa takwimu. Pia utajifunza jinsi takwimu zinavyofafanuliwa na kupata maarifa kidogo kuhusu takwimu na uwezekano, maeneo ya msingi ya kitaaluma ya Sayansi ya Takwimu. diff --git a/translations/sw/2-Working-With-Data/07-python/README.md b/translations/sw/2-Working-With-Data/07-python/README.md index 7630ef31..29a5b535 100644 --- a/translations/sw/2-Working-With-Data/07-python/README.md +++ b/translations/sw/2-Working-With-Data/07-python/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: | :-------------------------------------------------------------------------------------------------------: | | Kufanya Kazi na Python - _Sketchnote na [@nitya](https://twitter.com/nitya)_ | -[![Video ya Utangulizi](../../../../translated_images/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.sw.png)](https://youtu.be/dZjWOGbsN4Y) +[![Video ya Utangulizi](../../../../translated_images/sw/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.png)](https://youtu.be/dZjWOGbsN4Y) Ingawa hifadhidata zinatoa njia bora za kuhifadhi data na kuziuliza kwa kutumia lugha za maswali, njia inayobadilika zaidi ya kuchakata data ni kuandika programu yako mwenyewe ili kuibadilisha. Katika hali nyingi, kufanya maswali ya hifadhidata kungekuwa njia bora zaidi. Hata hivyo, katika baadhi ya hali ambapo uchakataji wa data changamani unahitajika, haiwezi kufanyika kwa urahisi kwa kutumia SQL. Uchakataji wa data unaweza kupangwa kwa lugha yoyote ya programu, lakini kuna lugha fulani ambazo ni za kiwango cha juu zaidi linapokuja suala la kufanya kazi na data. Wanasayansi wa data mara nyingi hupendelea mojawapo ya lugha zifuatazo: @@ -73,7 +73,7 @@ print(f"Length of index is {len(idx)}") items_sold = pd.Series(np.random.randint(25,50,size=len(idx)),index=idx) items_sold.plot() ``` -![Mchoro wa Mfululizo wa Muda](../../../../translated_images/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.sw.png) +![Mchoro wa Mfululizo wa Muda](../../../../translated_images/sw/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.png) Sasa fikiria kwamba kila wiki tunaandaa sherehe kwa marafiki, na tunachukua pakiti 10 za ziada za ice-cream kwa ajili ya sherehe. Tunaweza kuunda series nyingine, iliyoorodheshwa kwa wiki, kuonyesha hilo: ```python @@ -84,7 +84,7 @@ Tunapoongeza series mbili pamoja, tunapata jumla ya idadi: total_items = items_sold.add(additional_items,fill_value=0) total_items.plot() ``` -![Mchoro wa Mfululizo wa Muda](../../../../translated_images/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.sw.png) +![Mchoro wa Mfululizo wa Muda](../../../../translated_images/sw/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.png) > **Kumbuka** kwamba hatutumii sintaksia rahisi `total_items+additional_items`. Ikiwa tungefanya hivyo, tungepata thamani nyingi za `NaN` (*Not a Number*) katika series inayotokana. Hii ni kwa sababu kuna thamani zinazokosekana kwa baadhi ya pointi za index katika series ya `additional_items`, na kuongeza `NaN` kwa chochote husababisha `NaN`. Kwa hivyo tunahitaji kubainisha parameter ya `fill_value` wakati wa kuongeza. @@ -93,7 +93,7 @@ Kwa mfululizo wa muda, tunaweza pia **kurekebisha upya** series kwa vipindi tofa monthly = total_items.resample("1M").mean() ax = monthly.plot(kind='bar') ``` -![Wastani wa Mfululizo wa Muda wa Kila Mwezi](../../../../translated_images/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.sw.png) +![Wastani wa Mfululizo wa Muda wa Kila Mwezi](../../../../translated_images/sw/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.png) ### DataFrame @@ -219,7 +219,7 @@ Tatizo la kwanza ambalo tutalenga ni uundaji wa kuenea kwa janga la COVID-19. Il Kwa kuwa tunataka kuonyesha jinsi ya kushughulikia data, tunakualika kufungua [`notebook-covidspread.ipynb`](notebook-covidspread.ipynb) na kuisoma kutoka juu hadi chini. Unaweza pia kutekeleza seli, na kufanya changamoto ambazo tumeacha kwa ajili yako mwishoni. -![COVID Spread](../../../../translated_images/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.sw.png) +![COVID Spread](../../../../translated_images/sw/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.png) > Ikiwa hujui jinsi ya kuendesha msimbo katika Jupyter Notebook, angalia [makala hii](https://soshnikov.com/education/how-to-execute-notebooks-from-github/). @@ -241,7 +241,7 @@ Mfano kamili wa kuchambua dataset hii kwa kutumia huduma ya kiakili ya [Text Ana Fungua [`notebook-papers.ipynb`](notebook-papers.ipynb) na isome kutoka juu hadi chini. Unaweza pia kutekeleza seli, na kufanya changamoto ambazo tumeacha kwa ajili yako mwishoni. -![Covid Medical Treatment](../../../../translated_images/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.sw.png) +![Covid Medical Treatment](../../../../translated_images/sw/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.png) ## Usindikaji wa Data ya Picha diff --git a/translations/sw/2-Working-With-Data/README.md b/translations/sw/2-Working-With-Data/README.md index af21f1b1..b434d267 100644 --- a/translations/sw/2-Working-With-Data/README.md +++ b/translations/sw/2-Working-With-Data/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Kufanya Kazi na Data -![data love](../../../translated_images/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.sw.jpg) +![data love](../../../translated_images/sw/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.jpg) > Picha na Alexander Sinn kwenye Unsplash Katika masomo haya, utajifunza baadhi ya njia ambazo data inaweza kusimamiwa, kubadilishwa, na kutumika katika programu. Utajifunza kuhusu hifadhidata za uhusiano (relational) na zisizo za uhusiano (non-relational) na jinsi data inaweza kuhifadhiwa ndani yake. Pia, utajifunza misingi ya kufanya kazi na Python kusimamia data, na kugundua baadhi ya njia nyingi ambazo unaweza kutumia Python kusimamia na kuchimba data. diff --git a/translations/sw/3-Data-Visualization/12-visualization-relationships/README.md b/translations/sw/3-Data-Visualization/12-visualization-relationships/README.md index 50c308f2..4a3f3a9d 100644 --- a/translations/sw/3-Data-Visualization/12-visualization-relationships/README.md +++ b/translations/sw/3-Data-Visualization/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ Unda grafu ya msingi ya alama kuonyesha mahusiano kati ya bei kwa pauni ya asali ```python sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ``` -![scatterplot 1](../../../../translated_images/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.sw.png) +![scatterplot 1](../../../../translated_images/sw/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.png) Sasa, onyesha data hiyo hiyo kwa mpangilio wa rangi ya asali ili kuonyesha jinsi bei inavyobadilika mwaka hadi mwaka. Unaweza kufanya hivi kwa kuongeza kipengele cha 'hue' kuonyesha mabadiliko, mwaka hadi mwaka: @@ -60,7 +60,7 @@ Sasa, onyesha data hiyo hiyo kwa mpangilio wa rangi ya asali ili kuonyesha jinsi ```python sns.relplot(x="priceperlb", y="state", hue="year", palette="YlOrBr", data=honey, height=15, aspect=.5); ``` -![scatterplot 2](../../../../translated_images/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.sw.png) +![scatterplot 2](../../../../translated_images/sw/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.png) Kwa mabadiliko haya ya mpangilio wa rangi, unaweza kuona wazi kuwa kuna mwelekeo wa ongezeko la bei kwa pauni ya asali mwaka hadi mwaka. Kwa kweli, ukichunguza seti ya sampuli katika data ili kuthibitisha (chagua jimbo fulani, Arizona kwa mfano) unaweza kuona mwelekeo wa ongezeko la bei mwaka hadi mwaka, isipokuwa kwa baadhi ya miaka: @@ -89,7 +89,7 @@ sns.relplot(x="priceperlb", y="state", size="year", data=honey, height=15, aspec ``` Unaweza kuona ukubwa wa alama ukiongezeka hatua kwa hatua. -![scatterplot 3](../../../../translated_images/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.sw.png) +![scatterplot 3](../../../../translated_images/sw/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.png) Je, hili ni suala rahisi la mahitaji na usambazaji? Kutokana na mambo kama mabadiliko ya hali ya hewa na kuporomoka kwa makoloni, je, kuna asali kidogo inayopatikana kwa ununuzi mwaka hadi mwaka, na hivyo bei kuongezeka? @@ -104,7 +104,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); ``` Jibu: Ndiyo, isipokuwa kwa baadhi ya miaka kama 2003: -![line chart 1](../../../../translated_images/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.sw.png) +![line chart 1](../../../../translated_images/sw/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.png) ✅ Kwa sababu Seaborn inakusanya data kuzunguka mstari mmoja, inaonyesha "vipimo vingi kwa kila thamani ya x kwa kuonyesha wastani na kipimo cha kujiamini cha 95% kuzunguka wastani". [Chanzo](https://seaborn.pydata.org/tutorial/relational.html). Tabia hii inayochukua muda inaweza kuzimwa kwa kuongeza `ci=None`. @@ -114,7 +114,7 @@ Swali: Vema, mwaka 2003 tunaweza pia kuona ongezeko la usambazaji wa asali? Je, sns.relplot(x="year", y="totalprod", kind="line", data=honey); ``` -![line chart 2](../../../../translated_images/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.sw.png) +![line chart 2](../../../../translated_images/sw/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.png) Jibu: Sio kweli. Ukitazama uzalishaji wa jumla, inaonekana kuwa uliongezeka katika mwaka huo, ingawa kwa ujumla uzalishaji wa asali unaonekana kupungua katika miaka hiyo. @@ -139,7 +139,7 @@ sns.relplot( ``` Katika grafu hii, unaweza kulinganisha mavuno kwa kila koloni na idadi ya makoloni mwaka hadi mwaka, sambamba na mpangilio wa safu 3: -![facet grid](../../../../translated_images/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.sw.png) +![facet grid](../../../../translated_images/sw/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.png) Kwa seti hii ya data, hakuna kitu kinachojitokeza hasa kuhusu idadi ya makoloni na mavuno yao, mwaka hadi mwaka na jimbo hadi jimbo. Je, kuna njia tofauti ya kutazama uhusiano kati ya vigezo hivi viwili? @@ -162,7 +162,7 @@ sns.despine(right=False) plt.ylabel('colony yield') ax.figure.legend(); ``` -![superimposed plots](../../../../translated_images/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.sw.png) +![superimposed plots](../../../../translated_images/sw/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.png) Ingawa hakuna kitu kinachojitokeza kwa macho karibu na mwaka 2003, inaturuhusu kumaliza somo hili kwa maelezo ya furaha kidogo: ingawa kuna idadi inayopungua ya makoloni, idadi ya makoloni inastabilisha hata kama mavuno yao kwa kila koloni yanapungua. diff --git a/translations/sw/3-Data-Visualization/R/09-visualization-quantities/README.md b/translations/sw/3-Data-Visualization/R/09-visualization-quantities/README.md index dfdfd88b..8a0e9bf8 100644 --- a/translations/sw/3-Data-Visualization/R/09-visualization-quantities/README.md +++ b/translations/sw/3-Data-Visualization/R/09-visualization-quantities/README.md @@ -66,7 +66,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` Hapa, unasakinisha kifurushi cha `ggplot2` na kisha unakileta kwenye workspace kwa kutumia amri `library("ggplot2")`. Ili kuchora mchoro wowote katika ggplot, kazi ya `ggplot()` inatumika na unataja seti ya data, vigezo vya x na y kama sifa. Katika kesi hii, tunatumia kazi ya `geom_line()` kwa kuwa tunalenga kuchora mchoro wa mstari. -![MaxWingspan-lineplot](../../../../../translated_images/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.sw.png) +![MaxWingspan-lineplot](../../../../../translated_images/sw/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.png) Unagundua nini mara moja? Inaonekana kuna angalau kipengele kimoja cha nje - huo ni upana wa mabawa wa ajabu! Upana wa mabawa wa zaidi ya sentimita 2000 ni zaidi ya mita 20 - kuna Pterodactyls wanaozunguka Minnesota? Hebu tuchunguze. @@ -84,7 +84,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` Tunataja pembe katika `theme` na tunataja lebo za mhimili wa x na y katika `xlab()` na `ylab()` mtawalia. `ggtitle()` inatoa jina kwa grafu/mchoro. -![MaxWingspan-lineplot-improved](../../../../../translated_images/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.sw.png) +![MaxWingspan-lineplot-improved](../../../../../translated_images/sw/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.png) Hata kwa mzunguko wa lebo uliowekwa kwa digrii 45, kuna nyingi sana kusoma. Hebu jaribu mkakati tofauti: lebo tu kwa vipengele vya nje na weka lebo ndani ya mchoro. Unaweza kutumia mchoro wa scatter ili kutoa nafasi zaidi kwa kuweka lebo: @@ -100,7 +100,7 @@ Nini kinaendelea hapa? Ulitumia kazi ya `geom_point()` kuchora alama za scatter. Unagundua nini? -![MaxWingspan-scatterplot](../../../../../translated_images/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.sw.png) +![MaxWingspan-scatterplot](../../../../../translated_images/sw/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.png) ## Chuja data yako @@ -119,7 +119,7 @@ ggplot(data=birds_filtered, aes(x=Name, y=MaxWingspan,group=1)) + ``` Tulitengeneza dataframe mpya `birds_filtered` na kisha tukachora mchoro wa scatter. Kwa kuchuja vipengele vya nje, data yako sasa ni ya mshikamano zaidi na inayoeleweka. -![MaxWingspan-scatterplot-improved](../../../../../translated_images/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.sw.png) +![MaxWingspan-scatterplot-improved](../../../../../translated_images/sw/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.png) Sasa kwa kuwa tuna seti ya data safi angalau kwa suala la upana wa mabawa, hebu tujifunze zaidi kuhusu ndege hawa. @@ -160,7 +160,7 @@ birds_filtered %>% group_by(Category) %>% ``` Katika kipande kinachofuata, tunasakinisha vifurushi vya [dplyr](https://www.rdocumentation.org/packages/dplyr/versions/0.7.8) na [lubridate](https://www.rdocumentation.org/packages/lubridate/versions/1.8.0) ili kusaidia kudhibiti na kuunda data kwa lengo la kuchora mchoro wa bar uliojaa. Kwanza, unagawanya data kwa `Category` ya ndege na kisha unatoa muhtasari wa safu za `MinLength`, `MaxLength`, `MinBodyMass`, `MaxBodyMass`, `MinWingspan`, `MaxWingspan`. Kisha, unachora mchoro wa bar kwa kutumia kifurushi cha `ggplot2` na kutaja rangi kwa kategoria tofauti na lebo. -![Stacked bar chart](../../../../../translated_images/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.sw.png) +![Stacked bar chart](../../../../../translated_images/sw/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.png) Hata hivyo, mchoro huu wa bar hauwezi kusomeka kwa sababu kuna data nyingi isiyogawanywa. Unahitaji kuchagua tu data unayotaka kuchora, kwa hivyo hebu tuangalie urefu wa ndege kulingana na kategoria yao. @@ -175,7 +175,7 @@ ggplot(birds_count,aes(Category,n))+geom_bar(stat="identity")+coord_flip() ``` Kwanza unahesabu thamani za kipekee katika safu ya `Category` na kisha unazipanga katika dataframe mpya `birds_count`. Data hii iliyopangwa kisha inafanywa kuwa ya kiwango sawa ili iweze kuchorwa kwa mpangilio uliopangwa. Kwa kutumia `ggplot2` unachora data katika mchoro wa bar. `coord_flip()` inachora bar wima. -![category-length](../../../../../translated_images/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.sw.png) +![category-length](../../../../../translated_images/sw/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.png) Mchoro huu wa bar unaonyesha mtazamo mzuri wa idadi ya ndege katika kila kategoria. Kwa haraka, unaona kwamba idadi kubwa ya ndege katika eneo hili wako katika kategoria ya Ducks/Geese/Waterfowl. Minnesota ni 'ardhi ya maziwa 10,000' kwa hivyo hili si la kushangaza! @@ -198,7 +198,7 @@ ggplot(birds_grouped,aes(Category,MaxLength))+geom_bar(stat="identity")+coord_fl ``` Tunagawanya data ya `birds_filtered` kwa `Category` na kisha tunachora mchoro wa bar. -![comparing data](../../../../../translated_images/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.sw.png) +![comparing data](../../../../../translated_images/sw/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.png) Hakuna la kushangaza hapa: hummingbirds wana MaxLength ndogo zaidi ikilinganishwa na Pelicans au Geese. Ni vizuri wakati data ina mantiki! @@ -210,7 +210,7 @@ ggplot(data=birds_grouped, aes(x=Category)) + geom_bar(aes(y=MinLength), stat="identity", position="identity", fill='orange')+ coord_flip() ``` -![super-imposed values](../../../../../translated_images/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.sw.png) +![super-imposed values](../../../../../translated_images/sw/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.png) ## 🚀 Changamoto diff --git a/translations/sw/3-Data-Visualization/R/10-visualization-distributions/README.md b/translations/sw/3-Data-Visualization/R/10-visualization-distributions/README.md index 959d40a0..6cdd7cba 100644 --- a/translations/sw/3-Data-Visualization/R/10-visualization-distributions/README.md +++ b/translations/sw/3-Data-Visualization/R/10-visualization-distributions/README.md @@ -45,7 +45,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + geom_point() + ggtitle("Max Length per order") + coord_flip() ``` -![urefu wa juu kwa oda](../../../../../translated_images/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.sw.png) +![urefu wa juu kwa oda](../../../../../translated_images/sw/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.png) Hii inatoa muhtasari wa usambazaji wa jumla wa urefu wa mwili kwa kila Oda ya ndege, lakini si njia bora ya kuonyesha usambazaji wa kweli. Kazi hii kawaida hufanywa kwa kuunda Histogramu. @@ -57,7 +57,7 @@ Hii inatoa muhtasari wa usambazaji wa jumla wa urefu wa mwili kwa kila Oda ya nd ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=10)+ylab('Frequency') ``` -![usambazaji wa seti nzima ya data](../../../../../translated_images/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.sw.png) +![usambazaji wa seti nzima ya data](../../../../../translated_images/sw/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.png) Kama unavyoona, ndege wengi zaidi ya 400 katika seti hii ya data wanaangukia katika safu ya chini ya 2000 kwa UzitoMkuu wa Mwili wao. Pata ufahamu zaidi kuhusu data kwa kubadilisha kipengele cha `bins` kuwa namba kubwa zaidi, kama 30: @@ -65,7 +65,7 @@ Kama unavyoona, ndege wengi zaidi ya 400 katika seti hii ya data wanaangukia kat ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![usambazaji-30bins](../../../../../translated_images/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.sw.png) +![usambazaji-30bins](../../../../../translated_images/sw/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.png) Mchoro huu unaonyesha usambazaji kwa undani zaidi. Mchoro usioegemea sana upande wa kushoto unaweza kuundwa kwa kuhakikisha kuwa unachagua tu data ndani ya safu fulani: @@ -77,7 +77,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![histogramu iliyochujwa](../../../../../translated_images/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.sw.png) +![histogramu iliyochujwa](../../../../../translated_images/sw/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.png) ✅ Jaribu vichujio vingine na pointi za data. Ili kuona usambazaji kamili wa data, ondoa kichujio cha `['MaxBodyMass']` ili kuonyesha usambazaji ulio na lebo. @@ -91,7 +91,7 @@ ggplot(data=birds_filtered_1, aes(x=MaxBodyMass, y=MaxLength) ) + ``` Inaonekana kuna uhusiano unaotarajiwa kati ya vipengele hivi viwili kwenye mhimili unaotarajiwa, na sehemu moja yenye nguvu ya mwelekeo: -![mchoro wa 2d](../../../../../translated_images/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.sw.png) +![mchoro wa 2d](../../../../../translated_images/sw/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.png) Histogramu hufanya kazi vizuri kwa chaguo-msingi kwa data ya namba. Je, unahitaji kuona usambazaji kulingana na data ya maandishi? @@ -123,7 +123,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + scale_fill_manual(name="Conservation Status",values=c("red","green","blue","pink"),labels=c("Endangered","Near Threathened","Vulnerable","Least Concern")) ``` -![mabawa na hali ya uhifadhi](../../../../../translated_images/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.sw.png) +![mabawa na hali ya uhifadhi](../../../../../translated_images/sw/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.png) Haionekani kuwa na uhusiano mzuri kati ya mabawa madogo na hali ya uhifadhi. Jaribu vipengele vingine vya seti ya data kwa kutumia njia hii. Je, unapata uhusiano wowote? @@ -137,7 +137,7 @@ Hebu tufanye kazi na mchoro wa msongamano sasa! ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + geom_density() ``` -![mchoro wa msongamano](../../../../../translated_images/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.sw.png) +![mchoro wa msongamano](../../../../../translated_images/sw/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.png) Unaweza kuona jinsi mchoro unavyoakisi ule wa awali wa data ya MabawaMdogo; ni laini kidogo tu. Ikiwa ungependa kurejea mstari wa vipande wa UzitoMkuu wa Mwili katika mchoro wa pili uliounda, ungeweza kuulainisha vizuri kwa kuunda upya kwa kutumia njia hii: @@ -145,7 +145,7 @@ Unaweza kuona jinsi mchoro unavyoakisi ule wa awali wa data ya MabawaMdogo; ni l ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density() ``` -![msongamano wa uzito wa mwili](../../../../../translated_images/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.sw.png) +![msongamano wa uzito wa mwili](../../../../../translated_images/sw/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.png) Ikiwa ungependa mstari ulio laini, lakini si laini sana, hariri kipengele cha `adjust`: @@ -153,7 +153,7 @@ Ikiwa ungependa mstari ulio laini, lakini si laini sana, hariri kipengele cha `a ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density(adjust = 1/5) ``` -![msongamano wa uzito wa mwili usio laini sana](../../../../../translated_images/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.sw.png) +![msongamano wa uzito wa mwili usio laini sana](../../../../../translated_images/sw/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.png) ✅ Soma kuhusu vigezo vinavyopatikana kwa aina hii ya mchoro na ujaribu! @@ -163,7 +163,7 @@ Aina hii ya mchoro inatoa vielelezo vya kuelezea vizuri. Kwa mistari michache ya ggplot(data=birds_filtered_1,aes(x = MaxBodyMass, fill = Order)) + geom_density(alpha=0.5) ``` -![uzito wa mwili kwa oda](../../../../../translated_images/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.sw.png) +![uzito wa mwili kwa oda](../../../../../translated_images/sw/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.png) ## 🚀 Changamoto diff --git a/translations/sw/3-Data-Visualization/R/11-visualization-proportions/README.md b/translations/sw/3-Data-Visualization/R/11-visualization-proportions/README.md index 79dc192a..3cb38dd3 100644 --- a/translations/sw/3-Data-Visualization/R/11-visualization-proportions/README.md +++ b/translations/sw/3-Data-Visualization/R/11-visualization-proportions/README.md @@ -97,7 +97,7 @@ pie(grouped$count,grouped$class, main="Edible?") ``` Voila, chati ya pai inayoonyesha uwiano wa data hii kulingana na madarasa haya mawili ya uyoga. Ni muhimu sana kupata mpangilio wa lebo sahihi, hasa hapa, kwa hivyo hakikisha unathibitisha mpangilio ambao safu ya lebo imejengwa! -![chati ya pai](../../../../../translated_images/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.sw.png) +![chati ya pai](../../../../../translated_images/sw/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.png) ## Donati! @@ -132,7 +132,7 @@ library(webr) PieDonut(habitat, aes(habitat, count=count)) ``` -![chati ya donati](../../../../../translated_images/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.sw.png) +![chati ya donati](../../../../../translated_images/sw/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.png) Msimbo huu unatumia maktaba mbili - ggplot2 na webr. Kwa kutumia kipengele cha PieDonut cha maktaba ya webr, tunaweza kuunda chati ya donati kwa urahisi! @@ -170,7 +170,7 @@ waffle((cap_color$count/10), rows = 7, title = "Waffle Chart")+scale_fill_manual Kwa kutumia chati ya waffle, unaweza kuona wazi uwiano wa rangi za kofia za uyoga katika seti hii ya data. Cha kuvutia, kuna uyoga wengi wenye kofia za kijani! -![chati ya waffle](../../../../../translated_images/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.sw.png) +![chati ya waffle](../../../../../translated_images/sw/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.png) Katika somo hili, ulijifunza njia tatu za kuonyesha uwiano. Kwanza, unahitaji kupanga data yako katika kategoria na kisha kuamua ni njia gani bora ya kuonyesha data - pai, donati, au waffle. Zote ni tamu na zinamfurahisha mtumiaji kwa muhtasari wa haraka wa seti ya data. diff --git a/translations/sw/3-Data-Visualization/R/12-visualization-relationships/README.md b/translations/sw/3-Data-Visualization/R/12-visualization-relationships/README.md index d1e6647a..44ff06a8 100644 --- a/translations/sw/3-Data-Visualization/R/12-visualization-relationships/README.md +++ b/translations/sw/3-Data-Visualization/R/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ library(ggplot2) ggplot(honey, aes(x = priceperlb, y = state)) + geom_point(colour = "blue") ``` -![scatterplot 1](../../../../../translated_images/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.sw.png) +![scatterplot 1](../../../../../translated_images/sw/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.png) Sasa, onyesha data hiyo hiyo kwa mpangilio wa rangi ya asali ili kuonyesha jinsi bei inavyobadilika mwaka hadi mwaka. Unaweza kufanya hivi kwa kuongeza kipengele cha 'scale_color_gradientn' kuonyesha mabadiliko, mwaka hadi mwaka: @@ -61,7 +61,7 @@ Sasa, onyesha data hiyo hiyo kwa mpangilio wa rangi ya asali ili kuonyesha jinsi ggplot(honey, aes(x = priceperlb, y = state, color=year)) + geom_point()+scale_color_gradientn(colours = colorspace::heat_hcl(7)) ``` -![scatterplot 2](../../../../../translated_images/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.sw.png) +![scatterplot 2](../../../../../translated_images/sw/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.png) Kwa mabadiliko haya ya mpangilio wa rangi, unaweza kuona wazi kuwa kuna mwelekeo wa kuongezeka kwa bei ya asali kwa pauni mwaka hadi mwaka. Kwa kweli, ukichunguza seti ya sampuli katika data ili kuthibitisha (chagua jimbo fulani, Arizona kwa mfano) unaweza kuona mwelekeo wa ongezeko la bei mwaka hadi mwaka, isipokuwa kwa baadhi ya miaka: @@ -92,7 +92,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ``` Unaweza kuona ukubwa wa alama ukiongezeka polepole. -![scatterplot 3](../../../../../translated_images/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.sw.png) +![scatterplot 3](../../../../../translated_images/sw/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.png) Je, hili ni suala rahisi la mahitaji na usambazaji? Kutokana na sababu kama mabadiliko ya hali ya hewa na kuporomoka kwa makoloni, je, kuna asali kidogo inayopatikana kwa ununuzi mwaka hadi mwaka, na hivyo bei kuongezeka? @@ -107,7 +107,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab ``` Jibu: Ndiyo, isipokuwa kwa baadhi ya miaka kama 2003: -![line chart 1](../../../../../translated_images/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.sw.png) +![line chart 1](../../../../../translated_images/sw/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.png) Swali: Vema, mwaka 2003 tunaweza pia kuona ongezeko la usambazaji wa asali? Je, ukichunguza uzalishaji wa jumla mwaka hadi mwaka? @@ -115,7 +115,7 @@ Swali: Vema, mwaka 2003 tunaweza pia kuona ongezeko la usambazaji wa asali? Je, qplot(honey$year,honey$totalprod, geom='smooth', span =0.5, xlab = "year",ylab = "totalprod") ``` -![line chart 2](../../../../../translated_images/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.sw.png) +![line chart 2](../../../../../translated_images/sw/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.png) Jibu: Sio kweli. Ukichunguza uzalishaji wa jumla, inaonekana kuwa uliongezeka katika mwaka huo, ingawa kwa ujumla uzalishaji wa asali unaonekana kupungua katika miaka hiyo. @@ -135,7 +135,7 @@ ggplot(honey, aes(x=yieldpercol, y = numcol,group = 1)) + ``` Katika mchoro huu, unaweza kulinganisha mavuno kwa koloni na idadi ya makoloni mwaka hadi mwaka, sambamba na mpangilio wa safu 3: -![facet grid](../../../../../translated_images/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.sw.png) +![facet grid](../../../../../translated_images/sw/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.png) Kwa seti hii ya data, hakuna kitu kinachojitokeza hasa kuhusu idadi ya makoloni na mavuno yao, mwaka hadi mwaka na jimbo hadi jimbo. Je, kuna njia tofauti ya kutafuta uhusiano kati ya vigezo hivi viwili? @@ -152,7 +152,7 @@ plot(honey$year, honey$yieldpercol, pch = 17, col = 3, axis(side = 4, at = pretty(range(y2))) mtext("colony yield", side = 4, line = 3) ``` -![superimposed plots](../../../../../translated_images/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.sw.png) +![superimposed plots](../../../../../translated_images/sw/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.png) Ingawa hakuna kitu kinachojitokeza karibu na mwaka 2003, inaturuhusu kumaliza somo hili kwa maelezo ya furaha kidogo: ingawa kuna kupungua kwa idadi ya makoloni kwa ujumla, idadi ya makoloni inaimarika hata kama mavuno yao kwa koloni yanapungua. diff --git a/translations/sw/3-Data-Visualization/R/13-meaningful-vizualizations/README.md b/translations/sw/3-Data-Visualization/R/13-meaningful-vizualizations/README.md index 8a68490d..3333c355 100644 --- a/translations/sw/3-Data-Visualization/R/13-meaningful-vizualizations/README.md +++ b/translations/sw/3-Data-Visualization/R/13-meaningful-vizualizations/README.md @@ -47,25 +47,25 @@ Katika masomo yaliyopita, ulijaribu kujenga aina mbalimbali za uwasilishaji wa t Hata kama mwanasayansi wa takwimu atakuwa makini kuchagua chati sahihi kwa data sahihi, kuna njia nyingi ambazo data inaweza kuwasilishwa kwa njia ya kuthibitisha hoja fulani, mara nyingi kwa gharama ya kudhoofisha data yenyewe. Kuna mifano mingi ya chati na infografiki za kupotosha! -[![Jinsi Chati Zinavyodanganya na Alberto Cairo](../../../../../translated_images/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.sw.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "Jinsi chati zinavyodanganya") +[![Jinsi Chati Zinavyodanganya na Alberto Cairo](../../../../../translated_images/sw/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "Jinsi chati zinavyodanganya") > 🎥 Bofya picha hapo juu kwa mazungumzo ya mkutano kuhusu chati za kupotosha Chati hii inageuza mhimili wa X ili kuonyesha kinyume cha ukweli, kulingana na tarehe: -![chati mbaya 1](../../../../../translated_images/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.sw.png) +![chati mbaya 1](../../../../../translated_images/sw/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.png) [Chati hii](https://media.firstcoastnews.com/assets/WTLV/images/170ae16f-4643-438f-b689-50d66ca6a8d8/170ae16f-4643-438f-b689-50d66ca6a8d8_1140x641.jpg) ni ya kupotosha zaidi, kwani jicho linaelekezwa upande wa kulia kuhitimisha kuwa, kwa muda, kesi za COVID zimepungua katika kaunti mbalimbali. Kwa kweli, ukitazama kwa makini tarehe, utagundua kuwa zimepangwa upya ili kuonyesha mwenendo wa kupungua ambao si wa kweli. -![chati mbaya 2](../../../../../translated_images/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.sw.jpg) +![chati mbaya 2](../../../../../translated_images/sw/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.jpg) Mfano huu maarufu unatumia rangi NA mhimili wa Y uliogeuzwa ili kudanganya: badala ya kuhitimisha kuwa vifo vya bunduki viliongezeka baada ya kupitishwa kwa sheria zinazounga mkono bunduki, jicho linadanganywa kufikiri kinyume chake: -![chati mbaya 3](../../../../../translated_images/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.sw.jpg) +![chati mbaya 3](../../../../../translated_images/sw/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.jpg) Chati hii ya ajabu inaonyesha jinsi uwiano unavyoweza kudanganywa, kwa njia ya kuchekesha: -![chati mbaya 4](../../../../../translated_images/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.sw.jpg) +![chati mbaya 4](../../../../../translated_images/sw/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.jpg) Kulinganisha vitu visivyolinganishika ni mbinu nyingine ya hila. Kuna [tovuti nzuri](https://tylervigen.com/spurious-correlations) inayohusu 'uwiano wa uongo' ikionyesha 'ukweli' unaohusisha mambo kama kiwango cha talaka huko Maine na matumizi ya siagi ya margarine. Kikundi cha Reddit pia hukusanya [matumizi mabaya](https://www.reddit.com/r/dataisugly/top/?t=all) ya data. @@ -100,13 +100,13 @@ Weka lebo kwenye mhimili wako, toa ufafanuzi ikiwa ni lazima, na toa vidokezo vy Ikiwa data yako ni ya maandishi na ndefu kwenye mhimili wa X, unaweza kuipangilia kwa pembe kwa usomaji bora. [plot3D](https://cran.r-project.org/web/packages/plot3D/index.html) inatoa uwezekano wa kuchora kwa 3D, ikiwa data yako inaunga mkono. Uwasilishaji wa takwimu wa hali ya juu unaweza kuzalishwa kwa kutumia maktaba hii. -![chati za 3D](../../../../../translated_images/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.sw.png) +![chati za 3D](../../../../../translated_images/sw/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.png) ## Uhuishaji na maonyesho ya chati za 3D Baadhi ya uwasilishaji bora wa takwimu leo ni wa kuhuishwa. Shirley Wu ana mifano ya kushangaza iliyofanywa na D3, kama '[film flowers](http://bl.ocks.org/sxywu/raw/d612c6c653fb8b4d7ff3d422be164a5d/)', ambapo kila ua ni uwasilishaji wa filamu. Mfano mwingine kwa Guardian ni 'bussed out', uzoefu wa maingiliano unaochanganya uwasilishaji na Greensock na D3 pamoja na makala ya scrollytelling kuonyesha jinsi NYC inavyoshughulikia tatizo la watu wasio na makazi kwa kuwahamisha nje ya jiji. -![busing](../../../../../translated_images/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.sw.png) +![busing](../../../../../translated_images/sw/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.png) > "Bussed Out: Jinsi Marekani Inavyohamisha Watu Wasio na Makazi" kutoka [the Guardian](https://www.theguardian.com/us-news/ng-interactive/2017/dec/20/bussed-out-america-moves-homeless-people-country-study). Uwasilishaji na Nadieh Bremer & Shirley Wu @@ -116,7 +116,7 @@ Ingawa somo hili halitoshi kufundisha kwa kina maktaba hizi zenye nguvu za uwasi Utakamilisha programu ya wavuti ambayo itaonyesha mtazamo wa kuhuishwa wa mtandao huu wa kijamii. Inatumia maktaba iliyojengwa kuunda [uwasilishaji wa mtandao](https://github.com/emiliorizzo/vue-d3-network) kwa kutumia Vue.js na D3. Wakati programu inafanya kazi, unaweza kuvuta nodi kwenye skrini ili kupanga upya data. -![liaisons](../../../../../translated_images/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.sw.png) +![liaisons](../../../../../translated_images/sw/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.png) ## Mradi: Jenga chati kuonyesha mtandao kwa kutumia D3.js diff --git a/translations/sw/3-Data-Visualization/README.md b/translations/sw/3-Data-Visualization/README.md index d46a3c96..2f975e7d 100644 --- a/translations/sw/3-Data-Visualization/README.md +++ b/translations/sw/3-Data-Visualization/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Uakisi -![nyuki juu ya ua la lavenda](../../../translated_images/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.sw.jpg) +![nyuki juu ya ua la lavenda](../../../translated_images/sw/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.jpg) > Picha na Jenna Lee kwenye Unsplash Kuakisi data ni moja ya kazi muhimu zaidi kwa mwanasayansi wa data. Picha zina thamani ya maneno 1000, na uakisi unaweza kukusaidia kutambua aina zote za sehemu za kuvutia katika data yako kama vile miinuko, data zisizo za kawaida, makundi, mwelekeo, na zaidi, ambavyo vinaweza kukusaidia kuelewa hadithi ambayo data yako inajaribu kusimulia. diff --git a/translations/sw/4-Data-Science-Lifecycle/14-Introduction/README.md b/translations/sw/4-Data-Science-Lifecycle/14-Introduction/README.md index 45240b6f..16a68e57 100644 --- a/translations/sw/4-Data-Science-Lifecycle/14-Introduction/README.md +++ b/translations/sw/4-Data-Science-Lifecycle/14-Introduction/README.md @@ -25,7 +25,7 @@ Hadi kufikia hatua hii, labda umebaini kuwa sayansi ya takwimu ni mchakato. Mcha Somo hili linazingatia sehemu tatu za mzunguko wa maisha: kukusanya, kuchakata, na kudumisha. -![Mchoro wa mzunguko wa maisha wa sayansi ya takwimu](../../../../translated_images/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.sw.jpg) +![Mchoro wa mzunguko wa maisha wa sayansi ya takwimu](../../../../translated_images/sw/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.jpg) > Picha na [Berkeley School of Information](https://ischoolonline.berkeley.edu/data-science/what-is-data-science/) ## Kukusanya @@ -101,7 +101,7 @@ Chunguza [Mzunguko wa Maisha wa Mchakato wa Timu ya Sayansi ya Takwimu](https:// |Mchakato wa Timu ya Sayansi ya Takwimu (TDSP)|Mchakato wa Kawaida wa Viwanda kwa Uchimbaji wa Takwimu (CRISP-DM)| |--|--| -|![Mzunguko wa Maisha wa Timu ya Sayansi ya Takwimu](../../../../translated_images/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.sw.png) | ![Picha ya Mchakato wa Sayansi ya Takwimu](../../../../translated_images/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.sw.png) | +|![Mzunguko wa Maisha wa Timu ya Sayansi ya Takwimu](../../../../translated_images/sw/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.png) | ![Picha ya Mchakato wa Sayansi ya Takwimu](../../../../translated_images/sw/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.png) | | Picha na [Microsoft](https://docs.microsoft.comazure/architecture/data-science-process/lifecycle) | Picha na [Data Science Process Alliance](https://www.datascience-pm.com/crisp-dm-2/) | ## [Jaribio la Baada ya Somo](https://ff-quizzes.netlify.app/en/ds/quiz/27) diff --git a/translations/sw/4-Data-Science-Lifecycle/README.md b/translations/sw/4-Data-Science-Lifecycle/README.md index 3b7bcdce..281b4c22 100644 --- a/translations/sw/4-Data-Science-Lifecycle/README.md +++ b/translations/sw/4-Data-Science-Lifecycle/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Mzunguko wa Maisha wa Sayansi ya Takwimu -![communication](../../../translated_images/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.sw.jpg) +![communication](../../../translated_images/sw/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.jpg) > Picha na Headway kwenye Unsplash Katika masomo haya, utachunguza baadhi ya vipengele vya mzunguko wa maisha wa Sayansi ya Takwimu, ikiwa ni pamoja na uchambuzi na mawasiliano kuhusu data. diff --git a/translations/sw/5-Data-Science-In-Cloud/README.md b/translations/sw/5-Data-Science-In-Cloud/README.md index 132ad921..56cbd84c 100644 --- a/translations/sw/5-Data-Science-In-Cloud/README.md +++ b/translations/sw/5-Data-Science-In-Cloud/README.md @@ -9,13 +9,13 @@ CO_OP_TRANSLATOR_METADATA: --> # Sayansi ya Takwimu kwenye Wingu -![cloud-picture](../../../translated_images/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.sw.jpg) +![cloud-picture](../../../translated_images/sw/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.jpg) > Picha na [Jelleke Vanooteghem](https://unsplash.com/@ilumire) kutoka [Unsplash](https://unsplash.com/s/photos/cloud?orientation=landscape) Linapokuja suala la kufanya sayansi ya takwimu na data kubwa, wingu linaweza kuwa mabadiliko makubwa. Katika masomo matatu yajayo, tutaangalia wingu ni nini na kwa nini linaweza kuwa msaada mkubwa. Pia tutaangazia seti ya data ya kushindwa kwa moyo na kujenga mfano wa kusaidia kutathmini uwezekano wa mtu kupata kushindwa kwa moyo. Tutatumia nguvu ya wingu kufundisha, kupeleka, na kutumia mfano kwa njia mbili tofauti. Njia moja ni kwa kutumia tu kiolesura cha mtumiaji kwa mtindo wa "Low code/No code", na njia nyingine ni kwa kutumia Azure Machine Learning Software Developer Kit (Azure ML SDK). -![project-schema](../../../translated_images/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.sw.png) +![project-schema](../../../translated_images/sw/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.png) ### Mada diff --git a/translations/sw/6-Data-Science-In-Wild/20-Real-World-Examples/README.md b/translations/sw/6-Data-Science-In-Wild/20-Real-World-Examples/README.md index ab516b43..9fc30957 100644 --- a/translations/sw/6-Data-Science-In-Wild/20-Real-World-Examples/README.md +++ b/translations/sw/6-Data-Science-In-Wild/20-Real-World-Examples/README.md @@ -41,7 +41,7 @@ Shukrani kwa upatikanaji wa AI kwa urahisi, sasa watengenezaji wanapata urahisi * [Sayansi ya Takwimu katika Afya](https://data-flair.training/blogs/data-science-in-healthcare/) - inaonyesha matumizi kama picha za matibabu (mfano, MRI, X-Ray, CT-Scan), genomics (mpangilio wa DNA), maendeleo ya dawa (tathmini ya hatari, utabiri wa mafanikio), uchambuzi wa utabiri (huduma ya mgonjwa na vifaa vya usambazaji), ufuatiliaji wa magonjwa na kuzuia n.k. -![Matumizi ya Sayansi ya Takwimu Katika Ulimwengu Halisi](../../../../translated_images/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.sw.png) Picha: [Data Flair: Matumizi 6 ya Kushangaza ya Sayansi ya Takwimu ](https://data-flair.training/blogs/data-science-applications/) +![Matumizi ya Sayansi ya Takwimu Katika Ulimwengu Halisi](../../../../translated_images/sw/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.png) Picha: [Data Flair: Matumizi 6 ya Kushangaza ya Sayansi ya Takwimu ](https://data-flair.training/blogs/data-science-applications/) Mchoro unaonyesha maeneo mengine na mifano ya kutumia mbinu za sayansi ya takwimu. Unataka kuchunguza matumizi mengine? Angalia sehemu ya [Mapitio na Kujisomea](../../../../6-Data-Science-In-Wild/20-Real-World-Examples) hapa chini. diff --git a/translations/sw/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md b/translations/sw/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md index b1265a4c..b6d2e2f5 100644 --- a/translations/sw/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md +++ b/translations/sw/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md @@ -22,7 +22,7 @@ Kiolesura cha Explorer (kinachoonyeshwa kwenye picha hapa chini) kinakuruhusu ku 2. Chunguza [Catalog ya dataset](https://planetarycomputer.microsoft.com/catalog) - elewa madhumuni ya kila moja. 3. Tumia Explorer - chagua dataset inayokuvutia, chagua swali linalofaa na chaguo la uwasilishaji. -![The Planetary Computer Explorer](../../../../translated_images/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.sw.png) +![The Planetary Computer Explorer](../../../../translated_images/sw/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.png) `Jukumu Lako:` Sasa chunguza taswira inayotolewa kwenye kivinjari na jibu yafuatayo: diff --git a/translations/sw/CONTRIBUTING.md b/translations/sw/CONTRIBUTING.md index 504dd9ca..3c67cefd 100644 --- a/translations/sw/CONTRIBUTING.md +++ b/translations/sw/CONTRIBUTING.md @@ -312,7 +312,7 @@ Jumuisha katika maelezo ya PR yako: import pandas as pd ``` ```` -- Ongeza maandishi mbadala kwa picha: `![Alt text](../../translated_images/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.sw.png)` +- Ongeza maandishi mbadala kwa picha: `![Alt text](../../translated_images/sw/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.png)` - Dumisha urefu wa mistari unaofaa (karibu herufi 80-100) ### Python diff --git a/translations/sw/README.md b/translations/sw/README.md index 6f1ffad4..a809128a 100644 --- a/translations/sw/README.md +++ b/translations/sw/README.md @@ -33,7 +33,7 @@ Azure Cloud Advocates at Microsoft wanafurahia kutoa mtaala wa wiki 10, masomo 2 **🙏 Shukrani maalum 🙏 kwa waandishi, wakaguzi na wachangiaji wa maudhui wa [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/),** hasa Aaryan Arora, [Aditya Garg](https://github.com/AdityaGarg00), [Alondra Sanchez](https://www.linkedin.com/in/alondra-sanchez-molina/), [Ankita Singh](https://www.linkedin.com/in/ankitasingh007), [Anupam Mishra](https://www.linkedin.com/in/anupam--mishra/), [Arpita Das](https://www.linkedin.com/in/arpitadas01/), ChhailBihari Dubey, [Dibri Nsofor](https://www.linkedin.com/in/dibrinsofor), [Dishita Bhasin](https://www.linkedin.com/in/dishita-bhasin-7065281bb), [Majd Safi](https://www.linkedin.com/in/majd-s/), [Max Blum](https://www.linkedin.com/in/max-blum-6036a1186/), [Miguel Correa](https://www.linkedin.com/in/miguelmque/), [Mohamma Iftekher (Iftu) Ebne Jalal](https://twitter.com/iftu119), [Nawrin Tabassum](https://www.linkedin.com/in/nawrin-tabassum), [Raymond Wangsa Putra](https://www.linkedin.com/in/raymond-wp/), [Rohit Yadav](https://www.linkedin.com/in/rty2423), Samridhi Sharma, [Sanya Sinha](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200), [Sheena Narula](https://www.linkedin.com/in/sheena-narua-n/), [Tauqeer Ahmad](https://www.linkedin.com/in/tauqeerahmad5201/), Yogendrasingh Pawar , [Vidushi Gupta](https://www.linkedin.com/in/vidushi-gupta07/), [Jasleen Sondhi](https://www.linkedin.com/in/jasleen-sondhi/) -|![Sketchnote na @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.sw.png)| +|![Sketchnote na @sketchthedocs https://sketchthedocs.dev](../../translated_images/sw/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.png)| |:---:| | Sayansi ya Data kwa Waanzilishi - _Sketchnote na [@nitya](https://twitter.com/nitya)_ | @@ -52,7 +52,7 @@ Azure Cloud Advocates at Microsoft wanafurahia kutoa mtaala wa wiki 10, masomo 2 Tuna mfululizo wa Discord "Learn with AI" unaoendelea; jifunze zaidi na ujiunge nasi kwenye [Learn with AI Series](https://aka.ms/learnwithai/discord) kuanzia 18 - 30 Septemba, 2025. Utapokea vidokezo na mbinu za kutumia GitHub Copilot kwa Sayansi ya Data. -![Mfululizo wa Jifunze na AI](../../translated_images/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.sw.jpg) +![Mfululizo wa Jifunze na AI](../../translated_images/sw/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.jpg) # Je, wewe ni mwanafunzi? @@ -132,7 +132,7 @@ Kila mfano una maoni ya kina yanayoelezea kila hatua, ukifanya kuwa kamili kwa w ## Lessons -|![ Sketchnote na @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.sw.png)| +|![ Sketchnote na @sketchthedocs https://sketchthedocs.dev](../../translated_images/sw/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png)| |:---:| | Sayansi ya Data kwa Waanziaji: Ramani ya Njia - _Sketchnote na [@nitya](https://twitter.com/nitya)_ | diff --git a/translations/sw/sketchnotes/README.md b/translations/sw/sketchnotes/README.md index 7aa4c2c6..d72520fc 100644 --- a/translations/sw/sketchnotes/README.md +++ b/translations/sw/sketchnotes/README.md @@ -13,7 +13,7 @@ Pata sketchnoti zote hapa! Nitya Narasimhan, msanii -![sketchnoti ya ramani ya barabara](../../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.sw.png) +![sketchnoti ya ramani ya barabara](../../../translated_images/sw/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png) --- diff --git a/translations/ta/1-Introduction/01-defining-data-science/README.md b/translations/ta/1-Introduction/01-defining-data-science/README.md index 5048fc36..d58e3c97 100644 --- a/translations/ta/1-Introduction/01-defining-data-science/README.md +++ b/translations/ta/1-Introduction/01-defining-data-science/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: --- -[![தரவியல் அறிவியல் வீடியோ வரையறை](../../../../translated_images/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.ta.png)](https://youtu.be/beZ7Mb_oz9I) +[![தரவியல் அறிவியல் வீடியோ வரையறை](../../../../translated_images/ta/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.png)](https://youtu.be/beZ7Mb_oz9I) ## [முன்-வகுப்பு வினாடி வினா](https://ff-quizzes.netlify.app/en/ds/quiz/0) @@ -141,7 +141,7 @@ CO_OP_TRANSLATOR_METADATA: இந்த சவாலில், டேட்டா சயின்ஸ் துறைக்கு தொடர்புடைய கருத்துக்களை உரைகளைக் கொண்டு கண்டறிய முயற்சிக்கிறோம். டேட்டா சயின்ஸ் பற்றிய விக்கிபீடியா கட்டுரையை எடுத்து, உரையை பதிவிறக்கம் செய்து செயல்படுத்தி, பின்னர் கீழே உள்ளதைப் போன்ற ஒரு வார்த்தை மேகத்தை உருவாக்குவோம்: -![டேட்டா சயின்ஸ் வார்த்தை மேகம்](../../../../translated_images/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.ta.png) +![டேட்டா சயின்ஸ் வார்த்தை மேகம்](../../../../translated_images/ta/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.png) குறியீட்டை படிக்க [`notebook.ipynb`](../../../../1-Introduction/01-defining-data-science/notebook.ipynb ':ignore') ஐ பார்வையிடவும். நீங்கள் குறியீட்டை இயக்கி, அது நேரடி தரவுப் பரிமாற்றங்களை எவ்வாறு செயல்படுத்துகிறது என்பதைப் பார்க்கலாம். diff --git a/translations/ta/1-Introduction/04-stats-and-probability/README.md b/translations/ta/1-Introduction/04-stats-and-probability/README.md index 882635ff..cd2a3435 100644 --- a/translations/ta/1-Introduction/04-stats-and-probability/README.md +++ b/translations/ta/1-Introduction/04-stats-and-probability/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: புள்ளியியல் மற்றும் சாத்தியக்கூறு கோட்பாடு என்பது கணிதத்தின் இரண்டு தொடர்புடைய பகுதிகள் ஆகும், மேலும் அவை தரவியல் அறிவியலில் மிகவும் முக்கியமானவை. கணிதத்தை ஆழமாக அறியாமல் தரவுடன் செயல்படுவது சாத்தியமானது, ஆனால் குறைந்தபட்சம் சில அடிப்படை கருத்துகளை அறிந்திருப்பது நல்லது. இங்கு உங்களைத் தொடங்க உதவும் ஒரு சுருக்கமான அறிமுகத்தை வழங்குகிறோம். -[![அறிமுக வீடியோ](../../../../translated_images/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.ta.png)](https://youtu.be/Z5Zy85g4Yjw) +[![அறிமுக வீடியோ](../../../../translated_images/ta/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.png)](https://youtu.be/Z5Zy85g4Yjw) ## [முன்-வகுப்பு வினாடி வினா](https://ff-quizzes.netlify.app/en/ds/quiz/6) @@ -39,7 +39,7 @@ CO_OP_TRANSLATOR_METADATA: நாம் ஒரு மாறி ஒரு குறிப்பிட்ட மதிப்புகளின் இடைவெளியில் விழும் சாத்தியக்கூறைப் பற்றி மட்டுமே பேச முடியும், உதாரணமாக P(t1≤X<t2). இந்த சந்தர்ப்பத்தில், சாத்தியக்கூறு பகிர்மானம் **சாத்தியக்கூறு அடர்த்தி செயல்பாடு** p(x) மூலம் விவரிக்கப்படுகிறது, இது: -![P(t_1\le X தொடர்பு மற்றும் Covariance பற்றிய மேலும் உதாரணங்கள் [இணைக்கப்பட்ட நோட்புக்கில்](notebook.ipynb) காணலாம். diff --git a/translations/ta/1-Introduction/README.md b/translations/ta/1-Introduction/README.md index 87b48363..08fb37c2 100644 --- a/translations/ta/1-Introduction/README.md +++ b/translations/ta/1-Introduction/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # தரவியல் அறிவியலுக்கான அறிமுகம் -![தரவு செயல்பாட்டில்](../../../translated_images/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.ta.jpg) +![தரவு செயல்பாட்டில்](../../../translated_images/ta/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.jpg) > புகைப்படம் Stephen Dawson மூலம் Unsplash இல் இந்த பாடங்களில், தரவியல் அறிவியல் எப்படி வரையறுக்கப்படுகிறது என்பதை நீங்கள் கண்டறிந்து, ஒரு தரவியல் விஞ்ஞானி கவனிக்க வேண்டிய நெறிமுறைகள் பற்றிய அறிமுகத்தை பெறுவீர்கள். மேலும், தரவு எப்படி வரையறுக்கப்படுகிறது என்பதைப் பற்றி அறிந்து, தரவியல் அறிவியலின் முக்கிய கல்வி துறைகள் ஆகிய புள்ளியியல் மற்றும் சாத்தியக்கூறுகள் பற்றிய அடிப்படைகளைப் பற்றி சில தகவல்களைப் பெறுவீர்கள். diff --git a/translations/ta/2-Working-With-Data/06-non-relational/README.md b/translations/ta/2-Working-With-Data/06-non-relational/README.md index 16ae985e..b7085107 100644 --- a/translations/ta/2-Working-With-Data/06-non-relational/README.md +++ b/translations/ta/2-Working-With-Data/06-non-relational/README.md @@ -21,7 +21,7 @@ CO_OP_TRANSLATOR_METADATA: ஸ்பிரெட்ஷீட்கள் தரவுகளை சேமிக்கவும், ஆராயவும் பிரபலமான வழியாகும், ஏனெனில் இதை அமைக்கவும் தொடங்கவும் குறைவான வேலை தேவைப்படும். இந்த பாடத்தில் நீங்கள் ஒரு ஸ்பிரெட்ஷீட்டின் அடிப்படை கூறுகள், சூத்திரங்கள் மற்றும் செயல்பாடுகள் ஆகியவற்றைப் பற்றி கற்றுக்கொள்வீர்கள். எடுத்துக்காட்டுகள் Microsoft Excel மூலம் விளக்கப்படும், ஆனால் பிற ஸ்பிரெட்ஷீட் மென்பொருட்களுடன் ஒப்பிடும்போது பெரும்பாலான பகுதிகளுக்கும் தலைப்புகளுக்கும் ஒரே மாதிரியான பெயர்கள் மற்றும் படிகள் இருக்கும். -![இரண்டு வேலைத் தாள்களுடன் ஒரு காலியான Microsoft Excel வேலைப்புத்தகம்](../../../../translated_images/parts-of-spreadsheet.120711c82aa18a45c3e62a491a15bba0a31ab0e9db407ec022702fed8ffd89bf.ta.png) +![இரண்டு வேலைத் தாள்களுடன் ஒரு காலியான Microsoft Excel வேலைப்புத்தகம்](../../../../translated_images/ta/parts-of-spreadsheet.120711c82aa18a45c3e62a491a15bba0a31ab0e9db407ec022702fed8ffd89bf.png) ஒரு ஸ்பிரெட்ஷீட் ஒரு கோப்பாகும் மற்றும் கணினி, சாதனம் அல்லது மேக அடிப்படையிலான கோப்பு அமைப்பில் அணுகக்கூடியதாக இருக்கும். மென்பொருள் தானாகவே உலாவியில் அடிப்படையாகவோ அல்லது கணினியில் நிறுவப்பட வேண்டிய பயன்பாடாகவோ இருக்கலாம். Excel இல் இந்த கோப்புகள் **வேலைப்புத்தகங்கள்** என்று வரையறுக்கப்படுகின்றன, மேலும் இந்த பாடத்தின் மீதமுள்ள பகுதிகளில் இந்த சொல் பயன்படுத்தப்படும். @@ -33,11 +33,11 @@ Excel வேலைப்புத்தகத்தின் இந்த அட "InventoryExample" எனும் ஸ்பிரெட்ஷீட் கோப்பு, ஒரு சரக்கில் உள்ள பொருட்களின் வடிவமைக்கப்பட்ட ஸ்பிரெட்ஷீட் ஆகும், இதில் மூன்று வேலைத் தாள்கள் உள்ளன, அவற்றின் தாவல்கள் "Inventory List", "Inventory Pick List" மற்றும் "Bin Lookup" என குறிக்கப்படுகின்றன. Inventory List வேலைத் தாளத்தின் வரி 4 தலைப்பு ஆகும், இது தலைப்பு நெடுவரிசையில் உள்ள ஒவ்வொரு செலின் மதிப்பையும் விவரிக்கிறது. -![Microsoft Excel இல் ஒரு எடுத்துக்காட்டு சரக்கு பட்டியலிலிருந்து ஒரு ஹைலைட்டான சூத்திரம்](../../../../translated_images/formula-excel.ad1068c220892f5ead570d12f2394897961d31a5043a1dd4e6fc5d7690c7a14e.ta.png) +![Microsoft Excel இல் ஒரு எடுத்துக்காட்டு சரக்கு பட்டியலிலிருந்து ஒரு ஹைலைட்டான சூத்திரம்](../../../../translated_images/ta/formula-excel.ad1068c220892f5ead570d12f2394897961d31a5043a1dd4e6fc5d7690c7a14e.png) சில நேரங்களில், ஒரு செலின் மதிப்பு, அதன் மதிப்பை உருவாக்க மற்ற செல்களின் மதிப்புகளின் மீது சார்ந்திருக்கும். Inventory List ஸ்பிரெட்ஷீட் அதன் சரக்கில் உள்ள ஒவ்வொரு பொருளின் செலவை கண்காணிக்கிறது, ஆனால் சரக்கில் உள்ள அனைத்திற்கும் செலவை எவ்வாறு கண்டறிவது? [**சூத்திரங்கள்**](https://support.microsoft.com/en-us/office/overview-of-formulas-34519a4e-1e8d-4f4b-84d4-d642c4f63263) செலின் தரவுகளில் செயல்பாடுகளைச் செய்ய பயன்படுகிறது, மேலும் இந்த எடுத்துக்காட்டில் சரக்கின் செலவை கணக்கிட பயன்படுத்தப்படுகிறது. Inventory Value நெடுவரிசையில் உள்ள ஒவ்வொரு பொருளின் மதிப்பையும் கணக்கிட இந்த ஸ்பிரெட்ஷீட் QTY தலைப்பின் கீழ் உள்ள அளவை அதன் செலவுடன் (COST தலைப்பின் கீழ் உள்ள செல்கள்) பெருக்கி ஒரு சூத்திரத்தைப் பயன்படுத்தியது. ஒரு செலின் மீது இரட்டை கிளிக் செய்தால் அல்லது அதை ஹைலைட் செய்தால், அந்த சூத்திரத்தை காணலாம். சூத்திரங்கள் சமன்பாடு குறியீட்டுடன் தொடங்கும், அதன் பிறகு கணக்கீடு அல்லது செயல்பாடு வரும். -![Microsoft Excel இல் ஒரு எடுத்துக்காட்டு சரக்கு பட்டியலிலிருந்து ஒரு ஹைலைட்டான செயல்பாடு](../../../../translated_images/function-excel.be2ae4feddc10ca089f3d4363040d93b7fd046c8d4f83ba975ec46483ee99895.ta.png) +![Microsoft Excel இல் ஒரு எடுத்துக்காட்டு சரக்கு பட்டியலிலிருந்து ஒரு ஹைலைட்டான செயல்பாடு](../../../../translated_images/ta/function-excel.be2ae4feddc10ca089f3d4363040d93b7fd046c8d4f83ba975ec46483ee99895.png) சரக்கின் மொத்த மதிப்பை பெற, Inventory Value இன் அனைத்து மதிப்புகளையும் சேர்க்க மற்றொரு சூத்திரத்தைப் பயன்படுத்தலாம். ஒவ்வொரு செலையும் சேர்த்து மொத்தத்தை உருவாக்க இது கணக்கிடப்படலாம், ஆனால் இது ஒரு சிரமமான பணி. Excel இல் [**செயல்பாடுகள்**](https://support.microsoft.com/en-us/office/sum-function-043e1c7d-7726-4e80-8f32-07b23e057f89) அல்லது செல்களின் மதிப்புகளில் கணக்கீடுகளைச் செய்ய முன்பே வரையறுக்கப்பட்ட சூத்திரங்கள் உள்ளன. செயல்பாடுகள் வாதங்களை தேவைப்படும், அவை இந்த கணக்கீடுகளைச் செய்ய தேவையான மதிப்புகள். செயல்பாடுகள் ஒன்றுக்கு மேற்பட்ட வாதங்களைத் தேவைப்படுத்தும் போது, அவை ஒரு குறிப்பிட்ட வரிசையில் பட்டியலிடப்பட வேண்டும், இல்லையெனில் செயல்பாடு சரியான மதிப்பை கணக்கிடாது. இந்த எடுத்துக்காட்டில் SUM செயல்பாடு பயன்படுத்தப்படுகிறது, மேலும் Inventory Value இன் மதிப்புகளை மொத்தமாக சேர்க்க வாதமாக பயன்படுத்துகிறது, இது வரி 3, நெடுவரிசை B (B3 என்றும் குறிப்பிடப்படுகிறது) கீழ் பட்டியலிடப்பட்டுள்ளது. @@ -45,17 +45,17 @@ Excel வேலைப்புத்தகத்தின் இந்த அட NoSQL என்பது தொடர்பற்ற தரவுகளை சேமிக்க பல்வேறு வழிகளை குறிக்கும் ஒரு பொதுப்பெயர் ஆகும், மேலும் இது "non-SQL", "non-relational" அல்லது "not only SQL" என பொருள்படும். இந்த வகை தரவுத்தொகுப்பு அமைப்புகள் 4 வகைகளாக வகைப்படுத்தப்படலாம். -![ஒரு முக்கிய-மதிப்பு தரவுத்தொகுப்பின் கிராஃபிகல் பிரதிநிதித்துவம், 4 தனித்துவமான எண்குறிகள் மற்றும் 4 பல்வேறு மதிப்புகளுடன் இணைக்கப்பட்டுள்ளன](../../../../translated_images/kv-db.e8f2b75686bbdfcba0c827b9272c10ae0821611ea0fe98429b9d13194383afa6.ta.png) +![ஒரு முக்கிய-மதிப்பு தரவுத்தொகுப்பின் கிராஃபிகல் பிரதிநிதித்துவம், 4 தனித்துவமான எண்குறிகள் மற்றும் 4 பல்வேறு மதிப்புகளுடன் இணைக்கப்பட்டுள்ளன](../../../../translated_images/ta/kv-db.e8f2b75686bbdfcba0c827b9272c10ae0821611ea0fe98429b9d13194383afa6.png) > [Michał Białecki Blog](https://www.michalbialecki.com/2018/03/18/azure-cosmos-db-key-value-database-cloud/) மூலம் [Key-value](https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/non-relational-data#keyvalue-data-stores) தரவுத்தொகுப்புகள் தனித்துவமான முக்கியங்களைப் பொருத்துகின்றன, அவை ஒரு மதிப்புடன் இணைக்கப்பட்ட தனித்துவமான அடையாளமாகும். இந்த ஜோடிகள் ஒரு [ஹாஷ் அட்டவணை](https://www.hackerearth.com/practice/data-structures/hash-tables/basics-of-hash-tables/tutorial/) மற்றும் பொருத்தமான ஹாஷிங் செயல்பாட்டைப் பயன்படுத்தி சேமிக்கப்படுகின்றன. -![மக்கள், அவர்களின் விருப்பங்கள் மற்றும் இடங்களுக்கிடையிலான உறவுகளை காட்டும் ஒரு கிராஃப்தின் கிராஃபிகல் பிரதிநிதித்துவம்](../../../../translated_images/graph-db.d13629152f79a9dac895b20fa7d841d4d4d6f6008b1382227c3bbd200fd4cfa1.ta.png) +![மக்கள், அவர்களின் விருப்பங்கள் மற்றும் இடங்களுக்கிடையிலான உறவுகளை காட்டும் ஒரு கிராஃப்தின் கிராஃபிகல் பிரதிநிதித்துவம்](../../../../translated_images/ta/graph-db.d13629152f79a9dac895b20fa7d841d4d4d6f6008b1382227c3bbd200fd4cfa1.png) > [Microsoft](https://docs.microsoft.com/en-us/azure/cosmos-db/graph/graph-introduction#graph-database-by-example) மூலம் [Graph](https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/non-relational-data#graph-data-stores) தரவுத்தொகுப்புகள் தரவுகளில் உள்ள உறவுகளை விவரிக்கின்றன, மேலும் அவை நொடுகள் மற்றும் விளிம்புகளின் தொகுப்பாக பிரதிநிதித்துவம் செய்யப்படுகின்றன. ஒரு நோடு ஒரு நிறுவலைக் குறிக்கிறது, இது ஒரு மாணவர் அல்லது வங்கி அறிக்கை போன்ற உண்மையான உலகில் உள்ள ஏதாவது ஒன்றாக இருக்கலாம். விளிம்புகள் இரண்டு நிறுவனங்களுக்கிடையிலான உறவுகளை பிரதிநிதித்துவம் செய்கின்றன. ஒவ்வொரு நோடு மற்றும் விளிம்புகளுக்கும் கூடுதல் தகவல்களை வழங்கும் பண்புகள் உள்ளன. -![ஒரு வாடிக்கையாளர் தரவுத்தொகுப்புடன் இரண்டு நெடுவரிசை குடும்பங்களை காட்டும் ஒரு நெடுவரிசை தரவுத்தொகுப்பின் கிராஃபிகல் பிரதிநிதித்துவம், அவை Identity மற்றும் Contact Info என அழைக்கப்படுகின்றன](../../../../translated_images/columnar-db.ffcfe73c3e9063a8c8f93f8ace85e1200863584b1e324eb5159d8ca10f62ec04.ta.png) +![ஒரு வாடிக்கையாளர் தரவுத்தொகுப்புடன் இரண்டு நெடுவரிசை குடும்பங்களை காட்டும் ஒரு நெடுவரிசை தரவுத்தொகுப்பின் கிராஃபிகல் பிரதிநிதித்துவம், அவை Identity மற்றும் Contact Info என அழைக்கப்படுகின்றன](../../../../translated_images/ta/columnar-db.ffcfe73c3e9063a8c8f93f8ace85e1200863584b1e324eb5159d8ca10f62ec04.png) [Columnar](https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/non-relational-data#columnar-data-stores) தரவுத்தொகுப்புகள் தரவுகளை நெடுவரிசைகள் மற்றும் வரிசைகளாக ஒழுங்குபடுத்துகின்றன, ஆனால் ஒவ்வொரு நெடுவரிசையும் நெடுவரிசை குடும்பம் எனப்படும் குழுக்களாகப் பிரிக்கப்படுகிறது, அங்கு ஒரு நெடுவரிசையின் கீழ் உள்ள அனைத்து தரவுகளும் தொடர்புடையவை மற்றும் ஒரு அலகில் மீட்டெடுக்கப்படலாம் மற்றும் மாற்றப்படலாம். @@ -88,11 +88,11 @@ Cosmos DB தரவுத்தொகுப்பு "Not Only SQL" என வ எமுலேட்டர் ஒரு உலாவி சாளரத்தைத் தொடங்குகிறது, அங்கு எக்ஸ்ப்ளோரர் பார்வை ஆவணங்களை ஆராய அனுமதிக்கிறது. -![Cosmos DB Emulator இன் எக்ஸ்ப்ளோரர் பார்வை](../../../../translated_images/cosmosdb-emulator-explorer.a1c80b1347206fe2f30f88fc123821636587d04fc5a56a9eb350c7da6b31f361.ta.png) +![Cosmos DB Emulator இன் எக்ஸ்ப்ளோரர் பார்வை](../../../../translated_images/ta/cosmosdb-emulator-explorer.a1c80b1347206fe2f30f88fc123821636587d04fc5a56a9eb350c7da6b31f361.png) நீங்கள் தொடர்ந்தால், "Start with Sample" என்பதைக் கிளிக் செய்து SampleDB எனும் மாதிரி தரவுத்தொகுப்பை உருவாக்கவும். நீங்கள் அம்பை கிளிக் செய்வதன் மூலம் SampleDB ஐ விரிவாக்கினால், `Persons` எனும் ஒரு கொண்டெய்னரை காணலாம். ஒரு கொண்டெய்னர் ஒரு தொகுப்பிலுள்ள உருப்படிகளின் தொகுப்பைக் கொண்டுள்ளது, அவை கொண்டெய்னருக்குள் உள்ள ஆவணங்கள். `Items` கீழ் உள்ள நான்கு தனித்துவமான ஆவணங்களை நீங்கள் ஆராயலாம். -![Cosmos DB Emulator இல் மாதிரி தரவுகளை ஆராய்தல்](../../../../translated_images/cosmosdb-emulator-persons.bf640586a7077c8985dfd3071946465c8e074c722c7c202d6d714de99a93b90a.ta.png) +![Cosmos DB Emulator இல் மாதிரி தரவுகளை ஆராய்தல்](../../../../translated_images/ta/cosmosdb-emulator-persons.bf640586a7077c8985dfd3071946465c8e074c722c7c202d6d714de99a93b90a.png) #### Cosmos DB Emulator உடன் ஆவண தரவுகளை கேட்குதல் @@ -102,7 +102,7 @@ Cosmos DB தரவுத்தொகுப்பு "Not Only SQL" என வ `SELECT * FROM c where c.age < 40` -![Cosmos DB Emulator இல் மாதிரி தரவுகளில் 40 க்கும் குறைவான வயதுடைய ஆவணங்களை கண்டறிய ஒரு SELECT கேள்வியை இயக்குதல்](../../../../translated_images/cosmosdb-emulator-persons-query.6905ebb497e3cd047cd96e55a0a03f69ce1b91b2b3d8c147e617b746b22b7e33.ta.png) +![Cosmos DB Emulator இல் மாதிரி தரவுகளில் 40 க்கும் குறைவான வயதுடைய ஆவணங்களை கண்டறிய ஒரு SELECT கேள்வியை இயக்குதல்](../../../../translated_images/ta/cosmosdb-emulator-persons-query.6905ebb497e3cd047cd96e55a0a03f69ce1b91b2b3d8c147e617b746b22b7e33.png) இந்த கேள்வி இரண்டு ஆவணங்களை திரும்பக் கொடுக்கும், ஒவ்வொரு ஆவணத்திற்கும் வயது மதிப்பு 40 க்கும் குறைவாக உள்ளது என்பதை கவனிக்கவும். diff --git a/translations/ta/2-Working-With-Data/07-python/README.md b/translations/ta/2-Working-With-Data/07-python/README.md index 7ac0bb4b..ee25c480 100644 --- a/translations/ta/2-Working-With-Data/07-python/README.md +++ b/translations/ta/2-Working-With-Data/07-python/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: | :-------------------------------------------------------------------------------------------------------: | | Python உடன் வேலை செய்வது - _Sketchnote by [@nitya](https://twitter.com/nitya)_ | -[![அறிமுக வீடியோ](../../../../translated_images/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.ta.png)](https://youtu.be/dZjWOGbsN4Y) +[![அறிமுக வீடியோ](../../../../translated_images/ta/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.png)](https://youtu.be/dZjWOGbsN4Y) தரவுகளை சேமிக்கவும், கேள்வி மொழிகளைப் பயன்படுத்தி அவற்றை கேள்வி கேட்கவும் தரவுத்தொகுப்புகள் மிகவும் திறமையான வழிகளை வழங்குகின்றன. ஆனால், தரவுகளை செயலாக்குவதற்கான மிகவும் நெகிழ்வான வழி, உங்கள் சொந்த நிரல்களை எழுதுவதுதான். பல சந்தர்ப்பங்களில், தரவுத்தொகுப்பில் கேள்வி கேட்பது மிகவும் பயனுள்ளதாக இருக்கும். ஆனால் சில நேரங்களில், SQL மூலம் எளிதாக செய்ய முடியாத சிக்கலான தரவுச் செயலாக்கம் தேவைப்படும் போது, உங்கள் சொந்த நிரல்களை எழுதுவது சிறந்த தேர்வாக இருக்கும். @@ -74,7 +74,7 @@ print(f"Length of index is {len(idx)}") items_sold = pd.Series(np.random.randint(25,50,size=len(idx)),index=idx) items_sold.plot() ``` -![Time Series Plot](../../../../translated_images/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.ta.png) +![Time Series Plot](../../../../translated_images/ta/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.png) இப்போது ஒவ்வொரு வாரமும் நாங்கள் நண்பர்களுக்காக ஒரு பார்ட்டி ஏற்பாடு செய்கிறோம், மேலும் ஒரு பார்ட்டிக்காக கூடுதல் 10 ice-cream பாக்ஸ் எடுக்கிறோம். இதை காட்ட மற்றொரு series உருவாக்கலாம்: ```python @@ -85,7 +85,7 @@ additional_items = pd.Series(10,index=pd.date_range(start_date,end_date,freq="W" total_items = items_sold.add(additional_items,fill_value=0) total_items.plot() ``` -![Time Series Plot](../../../../translated_images/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.ta.png) +![Time Series Plot](../../../../translated_images/ta/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.png) > **குறிப்பு**: எளிய syntax `total_items+additional_items`-ஐப் பயன்படுத்தவில்லை. அதைச் செய்திருந்தால், resulting series-ல் பல `NaN` (*Not a Number*) மதிப்புகளைப் பெற்றிருப்போம். இது `additional_items` series-ல் சில index புள்ளிகளுக்கு missing values உள்ளதால், மேலும் `NaN`-ஐ எதற்கும் சேர்த்தால் `NaN` ஆகிறது. எனவே addition செய்யும்போது `fill_value` parameter-ஐ குறிப்பிட வேண்டும். @@ -94,7 +94,7 @@ Time series-களுடன், நாம் **resample** செய்யலா monthly = total_items.resample("1M").mean() ax = monthly.plot(kind='bar') ``` -![Monthly Time Series Averages](../../../../translated_images/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.ta.png) +![Monthly Time Series Averages](../../../../translated_images/ta/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.png) ### DataFrame @@ -219,7 +219,7 @@ df = pd.read_csv('file.csv') தரவுகளை எப்படி கையாள வேண்டும் என்பதை விளக்க, [`notebook-covidspread.ipynb`](notebook-covidspread.ipynb) ஐ திறந்து மேலிருந்து கீழ்வரை படிக்குமாறு உங்களை அழைக்கிறோம். நீங்கள் செல்களை இயக்கவும், இறுதியில் நாம் விட்டுச் சென்ற சில சவால்களைச் செய்யவும் முடியும். -![COVID Spread](../../../../translated_images/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.ta.png) +![COVID Spread](../../../../translated_images/ta/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.png) > Jupyter Notebook-ல் குறியீட்டை இயக்குவது எப்படி என்று தெரியவில்லை என்றால், [இந்த கட்டுரை](https://soshnikov.com/education/how-to-execute-notebooks-from-github/) பார்க்கவும். @@ -241,7 +241,7 @@ df = pd.read_csv('file.csv') [`notebook-papers.ipynb`](notebook-papers.ipynb) ஐ திறந்து மேலிருந்து கீழ்வரை படிக்கவும். நீங்கள் செல்களை இயக்கவும், இறுதியில் நாம் விட்டுச் சென்ற சில சவால்களைச் செய்யவும் முடியும். -![Covid Medical Treatment](../../../../translated_images/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.ta.png) +![Covid Medical Treatment](../../../../translated_images/ta/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.png) ## பட தரவுகளை செயலாக்குதல் diff --git a/translations/ta/2-Working-With-Data/README.md b/translations/ta/2-Working-With-Data/README.md index eb0c28a3..a1eac1a0 100644 --- a/translations/ta/2-Working-With-Data/README.md +++ b/translations/ta/2-Working-With-Data/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # தரவுடன் வேலை செய்வது -![data love](../../../translated_images/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.ta.jpg) +![data love](../../../translated_images/ta/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.jpg) > புகைப்படம் அலெக்சாண்டர் சின் மூலம், Unsplash இல் இருந்து இந்த பாடங்களில், தரவுகளை எவ்வாறு மேலாண்மை செய்யலாம், மாற்றலாம் மற்றும் பயன்பாட்டில் பயன்படுத்தலாம் என்பதை நீங்கள் கற்றுக்கொள்வீர்கள். தொடர்புடைய மற்றும் தொடர்பில்லாத தரவுத்தொகுப்புகள் மற்றும் அவற்றில் தரவுகளை எவ்வாறு சேமிக்கலாம் என்பதையும் நீங்கள் கற்றுக்கொள்வீர்கள். Python பயன்படுத்தி தரவுகளை மேலாண்மை செய்வதற்கான அடிப்படைகளை நீங்கள் கற்றுக்கொள்வீர்கள், மேலும் Python பயன்படுத்தி தரவுகளை மேலாண்மை செய்யவும், சுரங்கம் செய்யவும் பல வழிகளை நீங்கள் கண்டறிவீர்கள். diff --git a/translations/ta/3-Data-Visualization/09-visualization-quantities/README.md b/translations/ta/3-Data-Visualization/09-visualization-quantities/README.md index 9e526965..7ce875ff 100644 --- a/translations/ta/3-Data-Visualization/09-visualization-quantities/README.md +++ b/translations/ta/3-Data-Visualization/09-visualization-quantities/README.md @@ -60,7 +60,7 @@ birds.head() wingspan = birds['MaxWingspan'] wingspan.plot() ``` -![Max Wingspan](../../../../translated_images/max-wingspan-02.e79fd847b2640b89e21e340a3a9f4c5d4b224c4fcd65f54385e84f1c9ed26d52.ta.png) +![Max Wingspan](../../../../translated_images/ta/max-wingspan-02.e79fd847b2640b89e21e340a3a9f4c5d4b224c4fcd65f54385e84f1c9ed26d52.png) உடனடியாக நீங்கள் என்ன கவனிக்கிறீர்கள்? குறைந்தது ஒரு outlier உள்ளது போல தெரிகிறது - இது மிகவும் பெரிய இறகுகளின் அகலமாக இருக்கிறது! 2300 செ.மீ. அகலம் 23 மீட்டர் ஆகும் - மினசோட்டாவில் ப்டெரோடாக்டில்கள் சுற்றி வருகிறதா? இதை ஆராய்வோம். @@ -80,7 +80,7 @@ plt.plot(x, y) plt.show() ``` -![wingspan with labels](../../../../translated_images/max-wingspan-labels-02.aa90e826ca49a9d1dde78075e9755c1849ef56a4e9ec60f7e9f3806daf9283e2.ta.png) +![wingspan with labels](../../../../translated_images/ta/max-wingspan-labels-02.aa90e826ca49a9d1dde78075e9755c1849ef56a4e9ec60f7e9f3806daf9283e2.png) லேபிள்களை 45 டிகிரிகளுக்கு சுழற்சியுடன் அமைத்தாலும், அவற்றை படிக்க மிகவும் அதிகமாக உள்ளது. ஒரு வேறு உத்தியை முயற்சிப்போம்: outliers க்கு மட்டும் லேபிள் அமைத்து, லேபிள்களை வரைபடத்திற்குள் அமைக்கவும். லேபிள் இடமளிக்க அதிக இடம் பெற ஒரு சிதறல் வரைபடத்தைப் பயன்படுத்தலாம்: @@ -102,7 +102,7 @@ plt.show() நீங்கள் என்ன கண்டறிந்தீர்கள்? -![outliers](../../../../translated_images/labeled-wingspan-02.6110e2d2401cd5238ccc24dfb6d04a6c19436101f6cec151e3992e719f9f1e1f.ta.png) +![outliers](../../../../translated_images/ta/labeled-wingspan-02.6110e2d2401cd5238ccc24dfb6d04a6c19436101f6cec151e3992e719f9f1e1f.png) ## உங்கள் தரவுகளை வடிகட்டவும் Bald Eagle மற்றும் Prairie Falcon, மிகப்பெரிய பறவைகள் ஆக இருக்கலாம், ஆனால் அவற்றின் அதிகபட்ச இறகுகளின் அகலத்தில் கூடுதல் `0` சேர்க்கப்பட்டுள்ளது. 25 மீட்டர் அகலமுள்ள Bald Eagle ஐ நீங்கள் சந்திக்க வாய்ப்பு இல்லை, ஆனால் சந்திக்கிறீர்கள் என்றால், தயவுசெய்து எங்களுக்கு தெரிவிக்கவும்! இந்த இரண்டு outliers இல்லாமல் ஒரு புதிய dataframe ஐ உருவாக்குவோம்: @@ -122,7 +122,7 @@ plt.show() Outliers ஐ வடிகட்டுவதன் மூலம், உங்கள் தரவுகள் இப்போது மேலும் ஒருங்கிணைந்த மற்றும் புரிந்துகொள்ளக்கூடியதாக உள்ளது. -![scatterplot of wingspans](../../../../translated_images/scatterplot-wingspan-02.1c33790094ce36a75f5fb45b25ed2cf27f0356ea609e43c11e97a2cedd7011a4.ta.png) +![scatterplot of wingspans](../../../../translated_images/ta/scatterplot-wingspan-02.1c33790094ce36a75f5fb45b25ed2cf27f0356ea609e43c11e97a2cedd7011a4.png) இறகுகளின் அகலத்தின் அடிப்படையில் குறைந்தபட்சம் சுத்தமான தரவுத்தொகுப்பு கிடைத்த பிறகு, இந்த பறவைகள் பற்றிய மேலும் தகவல்களை கண்டறிவோம். @@ -148,7 +148,7 @@ birds.plot(x='Category', title='Birds of Minnesota') ``` -![full data as a bar chart](../../../../translated_images/full-data-bar-02.aaa3fda71c63ed564b917841a1886c177dd9a26424142e510c0c0498fd6ca160.ta.png) +![full data as a bar chart](../../../../translated_images/ta/full-data-bar-02.aaa3fda71c63ed564b917841a1886c177dd9a26424142e510c0c0498fd6ca160.png) இந்த பட்டை வரைபடம், எனினும், வாசிக்க முடியாதது, ஏனெனில் குழு செய்யப்படாத தரவுகள் மிகவும் அதிகமாக உள்ளன. நீங்கள் வரைபடத்தில் காட்ட விரும்பும் தரவுகளை மட்டும் தேர்ந்தெடுக்க வேண்டும், எனவே பறவையின் வகையின் அடிப்படையில் பறவையின் நீளத்தைப் பார்ப்போம். @@ -163,7 +163,7 @@ category_count = birds.value_counts(birds['Category'].values, sort=True) plt.rcParams['figure.figsize'] = [6, 12] category_count.plot.barh() ``` -![category and length](../../../../translated_images/category-counts-02.0b9a0a4de42275ae5096d0f8da590d8bf520d9e7e40aad5cc4fc8d276480cc32.ta.png) +![category and length](../../../../translated_images/ta/category-counts-02.0b9a0a4de42275ae5096d0f8da590d8bf520d9e7e40aad5cc4fc8d276480cc32.png) இந்த பட்டை வரைபடம் ஒவ்வொரு வகையில் உள்ள பறவைகளின் எண்ணிக்கையை நல்ல பார்வையை வழங்குகிறது. ஒரு கணத்தில், இந்த பிராந்தியத்தில் உள்ள பறவைகளின் மிகப்பெரிய எண்ணிக்கை Ducks/Geese/Waterfowl வகையில் உள்ளன என்பதை நீங்கள் காணலாம். மினசோட்டா '10,000 ஏரிகளின் நிலம்' என்பதால் இது ஆச்சரியமாக இல்லை! @@ -179,7 +179,7 @@ plt.barh(y=birds['Category'], width=maxlength) plt.rcParams['figure.figsize'] = [6, 12] plt.show() ``` -![comparing data](../../../../translated_images/category-length-02.7304bf519375c9807d8165cc7ec60dd2a60f7b365b23098538e287d89adb7d76.ta.png) +![comparing data](../../../../translated_images/ta/category-length-02.7304bf519375c9807d8165cc7ec60dd2a60f7b365b23098538e287d89adb7d76.png) இங்கே எந்த ஆச்சரியமும் இல்லை: Pelicans அல்லது Geese உடன் ஒப்பிடும்போது hummingbirds க்கு குறைந்த MaxLength உள்ளது. தரவுகள் தர்க்கரீதியாக பொருந்தும்போது நல்லது! @@ -197,7 +197,7 @@ plt.show() ``` இந்த வரைபடத்தில், ஒரு பறவையின் வகையின் அடிப்படையில் குறைந்தபட்ச நீளம் மற்றும் அதிகபட்ச நீளத்தின் வரம்பை நீங்கள் காணலாம். இந்த தரவுகளைப் பார்த்து, பறவை பெரியதாக இருந்தால், அதன் நீள வரம்பும் பெரியதாக இருக்கும் என்று நீங்கள் நம்பிக்கையுடன் கூறலாம். இது மிகவும் சுவாரஸ்யமாக உள்ளது! -![superimposed values](../../../../translated_images/superimposed-02.f03058536baeb2ed7864f01102538464d4c2fd7ade881ddd7d5ba74dc5d2fdae.ta.png) +![superimposed values](../../../../translated_images/ta/superimposed-02.f03058536baeb2ed7864f01102538464d4c2fd7ade881ddd7d5ba74dc5d2fdae.png) ## 🚀 சவால் diff --git a/translations/ta/3-Data-Visualization/10-visualization-distributions/README.md b/translations/ta/3-Data-Visualization/10-visualization-distributions/README.md index ca306464..168acfe5 100644 --- a/translations/ta/3-Data-Visualization/10-visualization-distributions/README.md +++ b/translations/ta/3-Data-Visualization/10-visualization-distributions/README.md @@ -48,7 +48,7 @@ plt.xlabel('Max Length') plt.show() ``` -![max length per order](../../../../translated_images/scatter-wb.9d98b0ed7f0388af979441853361a11df5f518f5307938a503ca7913e986111b.ta.png) +![max length per order](../../../../translated_images/ta/scatter-wb.9d98b0ed7f0388af979441853361a11df5f518f5307938a503ca7913e986111b.png) இது ஒவ்வொரு பறவையின் வரிசையின் உடல் நீளத்தின் பொது விநியோகத்தை ஒரு பார்வையில் காட்டுகிறது, ஆனால் உண்மையான விநியோகங்களை காட்சிப்படுத்த இது சிறந்த வழி அல்ல. இந்த பணியை சாதாரணமாக ஹிஸ்டோகிராம் உருவாக்குவதன் மூலம் செய்யலாம். ## ஹிஸ்டோகிராம்களுடன் வேலை செய்வது @@ -59,7 +59,7 @@ Matplotlib ஹிஸ்டோகிராம்களைப் பயன்ப birds['MaxBodyMass'].plot(kind = 'hist', bins = 10, figsize = (12,12)) plt.show() ``` -![distribution over the entire dataset](../../../../translated_images/dist1-wb.0d0cac82e2974fbbec635826fefead401af795f82e2279e2e2678bf2c117d827.ta.png) +![distribution over the entire dataset](../../../../translated_images/ta/dist1-wb.0d0cac82e2974fbbec635826fefead401af795f82e2279e2e2678bf2c117d827.png) நீங்கள் காணக்கூடியது போல, இந்த தரவுத்தொகுப்பில் உள்ள 400+ பறவைகளில் பெரும்பாலானவை Max Body Mass 2000 க்கும் குறைவாக உள்ள வரம்பில் உள்ளன. `bins` அளவுருவை 30 போன்ற அதிக எண்ணிக்கைக்கு மாற்றுவதன் மூலம் தரவின் மீது மேலும் தெளிவான பார்வையைப் பெறுங்கள்: @@ -67,7 +67,7 @@ plt.show() birds['MaxBodyMass'].plot(kind = 'hist', bins = 30, figsize = (12,12)) plt.show() ``` -![distribution over the entire dataset with larger bins param](../../../../translated_images/dist2-wb.2c0a7a3499b2fbf561e9f93b69f265dfc538dc78f6de15088ba84a88152e26ba.ta.png) +![distribution over the entire dataset with larger bins param](../../../../translated_images/ta/dist2-wb.2c0a7a3499b2fbf561e9f93b69f265dfc538dc78f6de15088ba84a88152e26ba.png) இந்த வரைபடம் விநியோகத்தை மேலும் நுணுக்கமாக காட்டுகிறது. இடது பக்கம் குறைவாக சாய்ந்த ஒரு வரைபடத்தை நீங்கள் ஒரு குறிப்பிட்ட வரம்பில் உள்ள தரவை மட்டும் தேர்ந்தெடுப்பதன் மூலம் உருவாக்கலாம்: @@ -78,7 +78,7 @@ filteredBirds = birds[(birds['MaxBodyMass'] > 1) & (birds['MaxBodyMass'] < 60)] filteredBirds['MaxBodyMass'].plot(kind = 'hist',bins = 40,figsize = (12,12)) plt.show() ``` -![filtered histogram](../../../../translated_images/dist3-wb.64b88db7f9780200bd486a2c2a3252548dd439672dbd3f778193db7f654b100c.ta.png) +![filtered histogram](../../../../translated_images/ta/dist3-wb.64b88db7f9780200bd486a2c2a3252548dd439672dbd3f778193db7f654b100c.png) ✅ மற்ற வடிகட்டல்கள் மற்றும் தரவுப் புள்ளிகளை முயற்சிக்கவும். தரவின் முழு விநியோகத்தைப் பார்க்க, `['MaxBodyMass']` வடிகட்டலை நீக்கி லேபிள் செய்யப்பட்ட விநியோகங்களை காட்சிப்படுத்தவும். @@ -95,7 +95,7 @@ hist = ax.hist2d(x, y) ``` இந்த இரண்டு கூறுகளின் இடையே எதிர்பார்க்கப்பட்ட அச்சில் ஒரு எதிர்பார்க்கப்பட்ட தொடர்பு உள்ளது, குறிப்பாக ஒரு வலுவான ஒருங்கிணைப்பு புள்ளியுடன்: -![2D plot](../../../../translated_images/2D-wb.ae22fdd33936507a41e3af22e11e4903b04a9be973b23a4e05214efaccfd66c8.ta.png) +![2D plot](../../../../translated_images/ta/2D-wb.ae22fdd33936507a41e3af22e11e4903b04a9be973b23a4e05214efaccfd66c8.png) ஹிஸ்டோகிராம்கள் இயல்பாக எண்ணியல் தரவுகளுக்கு நன்றாக வேலை செய்கின்றன. உரை தரவுகளின் அடிப்படையில் விநியோகங்களை நீங்கள் பார்க்க வேண்டியிருந்தால் என்ன செய்வீர்கள்? ## உரை தரவுகளைப் பயன்படுத்தி விநியோகங்களை ஆராயுங்கள் @@ -134,7 +134,7 @@ plt.gca().set(title='Conservation Status', ylabel='Min Wingspan') plt.legend(); ``` -![wingspan and conservation collation](../../../../translated_images/histogram-conservation-wb.3c40450eb072c14de7a1a3ec5c0fcba4995531024760741b392911b567fd8b70.ta.png) +![wingspan and conservation collation](../../../../translated_images/ta/histogram-conservation-wb.3c40450eb072c14de7a1a3ec5c0fcba4995531024760741b392911b567fd8b70.png) குறைந்தபட்ச இறகளவு மற்றும் பாதுகாப்பு நிலைக்கு இடையில் நல்ல தொடர்பு இல்லை என்று தோன்றுகிறது. இந்த முறையைப் பயன்படுத்தி தரவுத்தொகுப்பின் பிற கூறுகளைச் சோதிக்கவும். நீங்கள் வேறு தொடர்புகளை கண்டுபிடிக்கிறீர்களா? @@ -152,7 +152,7 @@ import matplotlib.pyplot as plt sns.kdeplot(filteredBirds['MinWingspan']) plt.show() ``` -![Density plot](../../../../translated_images/density1.8801043bd4af2567b0f706332b5853c7614e5e4b81b457acc27eb4e092a65cbd.ta.png) +![Density plot](../../../../translated_images/ta/density1.8801043bd4af2567b0f706332b5853c7614e5e4b81b457acc27eb4e092a65cbd.png) குறைந்தபட்ச இறகளவு தரவுக்கான முந்தைய வரைபடத்தை இது எவ்வாறு பிரதிபலிக்கிறது என்பதை நீங்கள் காணலாம்; இது கொஞ்சம் மென்மையாக உள்ளது. Seaborn இன் ஆவணங்களின் படி, "ஹிஸ்டோகிராமுடன் ஒப்பிடும்போது, ​​KDE ஒரு குறைவாக குழப்பமான மற்றும் அதிகமாக விளக்கக்கூடிய வரைபடத்தை உருவாக்க முடியும், குறிப்பாக பல விநியோகங்களை வரையும்போது. ஆனால் அடிப்படை விநியோகம் கட்டுப்படுத்தப்பட்ட அல்லது மென்மையானதாக இல்லாவிட்டால், இது விகிதாச்சித்ரங்களை அறிமுகப்படுத்தும் சாத்தியம் உள்ளது. ஹிஸ்டோகிராமைப் போலவே, நல்ல மென்மையான அளவுருக்களைத் தேர்ந்தெடுப்பது பிரதிநிதித்துவத்தின் தரத்தைப் பாதிக்கிறது." [source](https://seaborn.pydata.org/generated/seaborn.kdeplot.html) மற்ற வார்த்தைகளில், எப்போதும் போல புறம்பானவை உங்கள் வரைபடங்களை தவறாக நடக்கச் செய்யும். @@ -162,7 +162,7 @@ plt.show() sns.kdeplot(filteredBirds['MaxBodyMass']) plt.show() ``` -![smooth bodymass line](../../../../translated_images/density2.8e7647257060ff544a1aaded57e8dd1887586bfe340139e9b77ac1e5287f7977.ta.png) +![smooth bodymass line](../../../../translated_images/ta/density2.8e7647257060ff544a1aaded57e8dd1887586bfe340139e9b77ac1e5287f7977.png) மிகவும் மென்மையான, ஆனால் மிக மென்மையான கோடு வேண்டுமெனில், `bw_adjust` அளவுருவைத் திருத்தவும்: @@ -170,7 +170,7 @@ plt.show() sns.kdeplot(filteredBirds['MaxBodyMass'], bw_adjust=.2) plt.show() ``` -![less smooth bodymass line](../../../../translated_images/density3.84ae27da82f31e6b83ad977646f029a1d21186574d7581facd70123b3eb257ee.ta.png) +![less smooth bodymass line](../../../../translated_images/ta/density3.84ae27da82f31e6b83ad977646f029a1d21186574d7581facd70123b3eb257ee.png) ✅ இந்த வகை வரைபடத்திற்கான அளவுருக்களைப் பற்றி படிக்கவும் மற்றும் பரிசோதிக்கவும்! @@ -184,7 +184,7 @@ sns.kdeplot( ) ``` -![bodymass per order](../../../../translated_images/density4.e9d6c033f15c500fd33df94cb592b9f5cf1ed2a3d213c448a3f9e97ba39573ce.ta.png) +![bodymass per order](../../../../translated_images/ta/density4.e9d6c033f15c500fd33df94cb592b9f5cf1ed2a3d213c448a3f9e97ba39573ce.png) ஒரே வரைபடத்தில் பல மாறிகளின் அடர்த்தியை நீங்கள் வரைபடமாக்கலாம். ஒரு பறவையின் MaxLength மற்றும் MinLength ஐ அதன் பாதுகாப்பு நிலைக்கு ஒப்பிடுங்கள்: @@ -192,7 +192,7 @@ sns.kdeplot( sns.kdeplot(data=filteredBirds, x="MinLength", y="MaxLength", hue="ConservationStatus") ``` -![multiple densities, superimposed](../../../../translated_images/multi.56548caa9eae8d0fd9012a8586295538c7f4f426e2abc714ba070e2e4b1fc2c1.ta.png) +![multiple densities, superimposed](../../../../translated_images/ta/multi.56548caa9eae8d0fd9012a8586295538c7f4f426e2abc714ba070e2e4b1fc2c1.png) 'பாதிக்கப்படக்கூடிய' பறவைகள் அதன் நீளத்தின் அடிப்படையில் உள்ள குழு பொருத்தமானதா என்பதை ஆராய்வது மதிப்புமிக்கதா என்பதை ஆராய்வது பொருத்தமாக இருக்கலாம். diff --git a/translations/ta/3-Data-Visualization/11-visualization-proportions/README.md b/translations/ta/3-Data-Visualization/11-visualization-proportions/README.md index fceb8028..b018455f 100644 --- a/translations/ta/3-Data-Visualization/11-visualization-proportions/README.md +++ b/translations/ta/3-Data-Visualization/11-visualization-proportions/README.md @@ -93,7 +93,7 @@ plt.show() ``` வோலா, இந்த இரண்டு வகுப்புகளின் படி தரவின் விகிதங்களை காட்டும் ஒரு பை வரைபடம். குறிப்பாக இங்கு லேபிள் வரிசையின் சரியான ஒழுங்கை பெறுவது மிகவும் முக்கியம், எனவே லேபிள் வரிசை உருவாக்கப்படும் ஒழுங்கை சரிபார்க்க உறுதியாக இருங்கள்! -![பை வரைபடம்](../../../../translated_images/pie1-wb.e201f2fcc335413143ce37650fb7f5f0bb21358e7823a327ed8644dfb84be9db.ta.png) +![பை வரைபடம்](../../../../translated_images/ta/pie1-wb.e201f2fcc335413143ce37650fb7f5f0bb21358e7823a327ed8644dfb84be9db.png) ## டோனட்கள்! @@ -123,7 +123,7 @@ plt.title('Mushroom Habitats') plt.show() ``` -![டோனட் வரைபடம்](../../../../translated_images/donut-wb.be3c12a22712302b5d10c40014d5389d4a1ae4412fe1655b3cf4af57b64f799a.ta.png) +![டோனட் வரைபடம்](../../../../translated_images/ta/donut-wb.be3c12a22712302b5d10c40014d5389d4a1ae4412fe1655b3cf4af57b64f799a.png) இந்த குறியீடு ஒரு வரைபடத்தையும் ஒரு மைய வட்டத்தையும் வரைகிறது, பின்னர் அந்த மைய வட்டத்தை வரைபடத்தில் சேர்க்கிறது. மைய வட்டத்தின் அகலத்தை `0.40` ஐ மற்றொரு மதிப்பாக மாற்றுவதன் மூலம் திருத்துங்கள். @@ -170,7 +170,7 @@ fig = plt.figure( வாஃபிள் வரைபடத்தைப் பயன்படுத்தி, இந்த காளான் தரவுத்தொகுப்பின் தொப்பி நிறங்களின் விகிதங்களை தெளிவாகக் காணலாம். 흥미க்கூடாக, பல பச்சை-தொப்பி காளான்கள் உள்ளன! -![வாஃபிள் வரைபடம்](../../../../translated_images/waffle.5455dbae4ccf17d53bb40ff0a657ecef7b8aa967e27a19cc96325bd81598f65e.ta.png) +![வாஃபிள் வரைபடம்](../../../../translated_images/ta/waffle.5455dbae4ccf17d53bb40ff0a657ecef7b8aa967e27a19cc96325bd81598f65e.png) ✅ Pywaffle [Font Awesome](https://fontawesome.com/) இல் கிடைக்கும் எந்த ஐகானையும் பயன்படுத்தும் வரைபடங்களில் ஐகான்களை ஆதரிக்கிறது. சதுரங்களுக்குப் பதிலாக ஐகான்களைப் பயன்படுத்தி மேலும் 흥미க்கூடிய வாஃபிள் வரைபடத்தை உருவாக்க சில பரிசோதனைகளைச் செய்யுங்கள். diff --git a/translations/ta/3-Data-Visualization/12-visualization-relationships/README.md b/translations/ta/3-Data-Visualization/12-visualization-relationships/README.md index 71af2d10..90fe1e5e 100644 --- a/translations/ta/3-Data-Visualization/12-visualization-relationships/README.md +++ b/translations/ta/3-Data-Visualization/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ honey.head() ```python sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ``` -![scatterplot 1](../../../../translated_images/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.ta.png) +![scatterplot 1](../../../../translated_images/ta/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.png) இப்போது, ஆண்டுதோறும் தேனின் விலை எப்படி மாறுகிறது என்பதை காட்ட தேன் நிறத்துடன் கூடிய ஒரு வண்ணத் திட்டத்தை பயன்படுத்துங்கள். இதை ஆண்டுதோறும் மாற்றத்தை காட்ட 'hue' அளவுருவைச் சேர்த்தால் செய்யலாம்: @@ -60,7 +60,7 @@ sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ```python sns.relplot(x="priceperlb", y="state", hue="year", palette="YlOrBr", data=honey, height=15, aspect=.5); ``` -![scatterplot 2](../../../../translated_images/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.ta.png) +![scatterplot 2](../../../../translated_images/ta/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.png) இந்த வண்ணத் திட்ட மாற்றத்துடன், ஆண்டுகளின் போக்கில் தேனின் ஒரு பவுனுக்கு விலை அதிகரித்துள்ளது என்பது தெளிவாக தெரிகிறது. உண்மையில், தரவுகளில் ஒரு மாதிரியைப் பார்த்தால் (உதாரணமாக, அரிசோனா மாநிலத்தை எடுத்துக்கொள்ளுங்கள்), சில விதிவிலக்குகளைத் தவிர, ஆண்டுதோறும் விலை அதிகரிக்கும் முறைபாட்டை நீங்கள் காணலாம்: @@ -89,7 +89,7 @@ sns.relplot(x="priceperlb", y="state", size="year", data=honey, height=15, aspec ``` புள்ளிகளின் அளவு تدريجமாக அதிகரிப்பதை நீங்கள் காணலாம். -![scatterplot 3](../../../../translated_images/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.ta.png) +![scatterplot 3](../../../../translated_images/ta/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.png) இது ஒரு எளிய வழங்கல் மற்றும் தேவைச் சூழல் ஆக இருக்கிறதா? காலநிலை மாற்றம் மற்றும் தேன்கூட்ட அழிவு போன்ற காரணங்களால், ஆண்டுதோறும் வாங்குவதற்கு குறைவான தேன் கிடைக்கிறதா, எனவே விலை அதிகரிக்கிறதா? @@ -104,7 +104,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); ``` பதில்: ஆம், 2003 ஆம் ஆண்டின் சுற்றியுள்ள சில விதிவிலக்குகளுடன்: -![line chart 1](../../../../translated_images/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.ta.png) +![line chart 1](../../../../translated_images/ta/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.png) ✅ Seaborn தரவுகளை ஒரு கோட்டில் தொகுத்து, "ஒவ்வொரு x மதிப்பில் பல அளவீடுகளை சராசரியாகக் காட்டுகிறது மற்றும் சராசரியின் 95% நம்பகத்தன்மை இடைவெளியை வரைபடத்தில் காட்டுகிறது". [மூலம்](https://seaborn.pydata.org/tutorial/relational.html). இந்த நேரம் எடுத்துக்கொள்ளும் செயல்பாட்டை `ci=None` சேர்த்து முடக்கலாம். @@ -114,7 +114,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); sns.relplot(x="year", y="totalprod", kind="line", data=honey); ``` -![line chart 2](../../../../translated_images/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.ta.png) +![line chart 2](../../../../translated_images/ta/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.png) பதில்: அவ்வளவாக இல்லை. மொத்த உற்பத்தியைப் பார்த்தால், அந்த குறிப்பிட்ட ஆண்டில் அது அதிகரித்தது போல் தெரிகிறது, ஆனால் பொதுவாக, இந்த ஆண்டுகளில் உற்பத்தி குறைந்து கொண்டிருக்கிறது. @@ -139,7 +139,7 @@ sns.relplot( ``` இந்த காட்சிப்படுத்தலில், ஒவ்வொரு ஆண்டும் ஒவ்வொரு மாநிலத்திலும் ஒவ்வொரு தேன்கூட்டத்திற்கான விளைச்சல் மற்றும் தேன்கூட்டங்களின் எண்ணிக்கையை ஒப்பிடலாம், 3 க்கான நெடுவரிசை அமைப்புடன்: -![facet grid](../../../../translated_images/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.ta.png) +![facet grid](../../../../translated_images/ta/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.png) இந்த தரவுத்தொகுப்பில், ஆண்டுதோறும் மற்றும் மாநிலத்திற்கும் தேன்கூட்டங்களின் எண்ணிக்கை மற்றும் அவற்றின் விளைச்சலுடன் தொடர்புடைய எந்தவொரு விஷயமும் குறிப்பிடத்தக்கதாகத் தெரியவில்லை. இந்த இரண்டு மாறிகளுக்கு இடையேயான தொடர்பை கண்டறிய வேறு வழி இருக்கிறதா? @@ -162,7 +162,7 @@ sns.despine(right=False) plt.ylabel('colony yield') ax.figure.legend(); ``` -![superimposed plots](../../../../translated_images/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.ta.png) +![superimposed plots](../../../../translated_images/ta/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.png) 2003 ஆம் ஆண்டின் சுற்றியுள்ள எந்தவொரு விஷயமும் கண்களுக்கு தெளிவாகத் தெரியவில்லை, ஆனால் இது இந்த பாடத்தை ஒரு சிறிய மகிழ்ச்சியான குறிப்பில் முடிக்க அனுமதிக்கிறது: தேன்கூட்டங்களின் எண்ணிக்கை குறைந்து கொண்டிருப்பதுடன், அவற்றின் விளைச்சல் குறைந்தாலும், தேன்கூட்டங்களின் எண்ணிக்கை நிலைத்திருக்கிறது. diff --git a/translations/ta/3-Data-Visualization/13-meaningful-visualizations/README.md b/translations/ta/3-Data-Visualization/13-meaningful-visualizations/README.md index d7212c7f..8a737083 100644 --- a/translations/ta/3-Data-Visualization/13-meaningful-visualizations/README.md +++ b/translations/ta/3-Data-Visualization/13-meaningful-visualizations/README.md @@ -47,25 +47,25 @@ CO_OP_TRANSLATOR_METADATA: ஒரு தரவியல் விஞ்ஞானி சரியான தரவுக்கான சரியான வரைபடத்தைத் தேர்ந்தெடுப்பதில் கவனமாக இருந்தாலும், ஒரு கருத்தை நிரூபிக்க, பெரும்பாலும் தரவின் அடிப்படையை பாதிக்கக்கூடிய வகையில், தரவை காட்சிப்படுத்த பல வழிகள் உள்ளன. மோசமான வரைபடங்கள் மற்றும் தகவல்படங்களின் பல உதாரணங்கள் உள்ளன! -[![How Charts Lie by Alberto Cairo](../../../../translated_images/tornado.9f42168791208f970d6faefc11d1226d7ca89518013b14aa66b1c9edcd7678d2.ta.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "How charts lie") +[![How Charts Lie by Alberto Cairo](../../../../translated_images/ta/tornado.9f42168791208f970d6faefc11d1226d7ca89518013b14aa66b1c9edcd7678d2.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "How charts lie") > 🎥 மோசமான வரைபடங்கள் பற்றிய கருத்தரங்க உரையைப் பார்க்க மேலே உள்ள படத்தை கிளிக் செய்யவும் இந்த வரைபடம் X அச்சை மாற்றி, தேதியின் அடிப்படையில் உண்மையின் எதிர்மறையை காட்டுகிறது: -![bad chart 1](../../../../translated_images/bad-chart-1.93130f495b748bedfb3423d91b1e754d9026e17f94ad967aecdc9ca7203373bf.ta.png) +![bad chart 1](../../../../translated_images/ta/bad-chart-1.93130f495b748bedfb3423d91b1e754d9026e17f94ad967aecdc9ca7203373bf.png) [இந்த வரைபடம்](https://media.firstcoastnews.com/assets/WTLV/images/170ae16f-4643-438f-b689-50d66ca6a8d8/170ae16f-4643-438f-b689-50d66ca6a8d8_1140x641.jpg) இன்னும் மோசமானது, ஏனெனில் கண்கள் வலதுபுறமாக இழுக்கப்பட்டு, காலப்போக்கில் COVID வழக்குகள் குறைந்துவிட்டதாக முடிவு செய்யப்படுகிறது. உண்மையில், தேதிகளை நெருக்கமாகப் பார்த்தால், அவை அந்த மோசமான இறக்குமுகத்தை வழங்குவதற்காக மறுசீரமைக்கப்பட்டுள்ளன. -![bad chart 2](../../../../translated_images/bad-chart-2.c20e36dd4e6f617c0c325878dd421a563885bbf30a394884c147438827254e0e.ta.jpg) +![bad chart 2](../../../../translated_images/ta/bad-chart-2.c20e36dd4e6f617c0c325878dd421a563885bbf30a394884c147438827254e0e.jpg) இந்த பிரபலமான உதாரணம் நிறம் மற்றும் Y அச்சை மாற்றி மோசமாக்குகிறது: துப்பாக்கி-friendly சட்டம் நிறைவேற்றப்பட்ட பிறகு துப்பாக்கி மரணங்கள் அதிகரித்தன என்பதை முடிவெடுக்காமல், கண்கள் எதிர்மறையானது உண்மையாக உள்ளது என்று முடிவு செய்ய ஏமாற்றப்படுகிறது: -![bad chart 3](../../../../translated_images/bad-chart-3.6865d0afac4108d737558d90a61547d23a8722896397ec792264ee51a1be4be5.ta.jpg) +![bad chart 3](../../../../translated_images/ta/bad-chart-3.6865d0afac4108d737558d90a61547d23a8722896397ec792264ee51a1be4be5.jpg) இந்த விசித்திரமான வரைபடம் விகிதத்தை எப்படி மாற்றி அமைக்க முடியும் என்பதை நகைச்சுவையான விளைவாகக் காட்டுகிறது: -![bad chart 4](../../../../translated_images/bad-chart-4.68cfdf4011b454471053ee1231172747e1fbec2403b4443567f1dc678134f4f2.ta.jpg) +![bad chart 4](../../../../translated_images/ta/bad-chart-4.68cfdf4011b454471053ee1231172747e1fbec2403b4443567f1dc678134f4f2.jpg) ஒப்பிட முடியாதவற்றை ஒப்பிடுவது இன்னொரு shady யுக்தியாகும். 'spurious correlations' பற்றிய [அற்புதமான இணையதளம்](https://tylervigen.com/spurious-correlations) உள்ளது, இது மெய்நிகர் 'facts' களை Maine இல் விவாகரத்து விகிதம் மற்றும் மார்ஜரின் நுகர்வு போன்றவற்றை ஒப்பிடுகிறது. ஒரு Reddit குழு [தரவை மோசமாகப் பயன்படுத்தும்](https://www.reddit.com/r/dataisugly/top/?t=all) உதாரணங்களைச் சேகரிக்கிறது. @@ -100,13 +100,13 @@ CO_OP_TRANSLATOR_METADATA: உங்கள் தரவு உரை வடிவிலிருந்தும் X அச்சில் verbose ஆக இருந்தாலும், வாசிக்க எளிதாக உரையை சாய்க்கலாம். [Matplotlib](https://matplotlib.org/stable/tutorials/toolkits/mplot3d.html) உங்கள் தரவை ஆதரிக்குமானால் 3D வரைபடங்களை வழங்குகிறது. `mpl_toolkits.mplot3d` ஐப் பயன்படுத்தி நவீன தரவுக் காட்சிப்படுத்தல்களை உருவாக்கலாம். -![3d plots](../../../../translated_images/3d.0cec12bcc60f0ce7284c63baed1411a843e24716f7d7425de878715ebad54a15.ta.png) +![3d plots](../../../../translated_images/ta/3d.0cec12bcc60f0ce7284c63baed1411a843e24716f7d7425de878715ebad54a15.png) ## அனிமேஷன் மற்றும் 3D வரைபட காட்சி இன்றைய சிறந்த தரவுக் காட்சிப்படுத்தல்கள் சில அனிமேஷன்களாக உள்ளன. Shirley Wu D3 ஐப் பயன்படுத்தி '[film flowers](http://bl.ocks.org/sxywu/raw/d612c6c653fb8b4d7ff3d422be164a5d/)' போன்ற அற்புதமானவற்றை உருவாக்கியுள்ளார், இதில் ஒவ்வொரு பூவும் ஒரு திரைப்படத்தின் காட்சிப்படுத்தலாக உள்ளது. Guardian க்கான மற்றொரு உதாரணம் 'bussed out', Greensock மற்றும் D3 உடன் காட்சிப்படுத்தல்களை இணைக்கும் ஒரு இன்டர்ஆக்டிவ் அனுபவம், மேலும் NYC தனது வீடற்ற பிரச்சினையை நகரத்திலிருந்து மக்களை வெளியேற்றுவதன் மூலம் எப்படி கையாளுகிறது என்பதை காட்ட ஒரு scrollytelling கட்டுரை வடிவத்தை வழங்குகிறது. -![busing](../../../../translated_images/busing.7b9e3b41cd4b981c6d63922cd82004cc1cf18895155536c1d98fcc0999bdd23e.ta.png) +![busing](../../../../translated_images/ta/busing.7b9e3b41cd4b981c6d63922cd82004cc1cf18895155536c1d98fcc0999bdd23e.png) > "Bussed Out: How America Moves its Homeless" from [the Guardian](https://www.theguardian.com/us-news/ng-interactive/2017/dec/20/bussed-out-america-moves-homeless-people-country-study). Visualizations by Nadieh Bremer & Shirley Wu @@ -116,7 +116,7 @@ CO_OP_TRANSLATOR_METADATA: நீங்கள் இந்த சமூக வலையத்தின் அனிமேஷன் காட்சியை காட்டும் ஒரு வலை பயன்பாட்டை முடிக்க வேண்டும். இது Vue.js மற்றும் D3 ஐப் பயன்படுத்தி ஒரு [வலையத்தின் காட்சியை](https://github.com/emiliorizzo/vue-d3-network) உருவாக்க ஒரு நூலகத்தை உருவாக்கியது. பயன்பாடு இயங்கும்போது, நீங்கள் திரையில் nodes ஐ இழுத்து தரவைச் சுழற்றலாம். -![liaisons](../../../../translated_images/liaisons.7b440b28f6d07ea430244fdf1fc4c64ff48f473f143b8e921846eda1c302aeba.ta.png) +![liaisons](../../../../translated_images/ta/liaisons.7b440b28f6d07ea430244fdf1fc4c64ff48f473f143b8e921846eda1c302aeba.png) ## திட்டம்: D3.js ஐப் பயன்படுத்தி ஒரு வலையத்தை காட்டும் வரைபடத்தை உருவாக்கவும் diff --git a/translations/ta/3-Data-Visualization/R/09-visualization-quantities/README.md b/translations/ta/3-Data-Visualization/R/09-visualization-quantities/README.md index 8d094e61..84ce25f8 100644 --- a/translations/ta/3-Data-Visualization/R/09-visualization-quantities/README.md +++ b/translations/ta/3-Data-Visualization/R/09-visualization-quantities/README.md @@ -66,7 +66,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` இங்கே, நீங்கள் `ggplot2` தொகுப்பை நிறுவி, `library("ggplot2")` கட்டளையைப் பயன்படுத்தி அதை workspace இல் இறக்குமதி செய்கிறீர்கள். ggplot இல் எந்தவொரு வரைபடத்தையும் வரைபடமாக்க `ggplot()` செயல்பாடு பயன்படுத்தப்படுகிறது, மேலும் நீங்கள் dataset, x மற்றும் y மாறிகளை பண்புகளாக குறிப்பிடுகிறீர்கள். இந்தக் கட்டத்தில், நாம் line plot ஐ வரைபடமாக்க `geom_line()` செயல்பாட்டைப் பயன்படுத்துகிறோம். -![MaxWingspan-lineplot](../../../../../translated_images/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.ta.png) +![MaxWingspan-lineplot](../../../../../translated_images/ta/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.png) உடனடியாக நீங்கள் என்ன கவனிக்கிறீர்கள்? குறைந்தது ஒரு outlier இருப்பது போல தெரிகிறது - அது ஒரு பெரிய இறகுகளின் அகலமாக இருக்கிறது! 2000+ சென்டிமீட்டர் அகலம் என்பது 20 மீட்டருக்கு மேல் சமமாகும் - மினசோட்டாவில் ப்டெரோடாக்டில்கள் சுற்றி வருகிறதா? ஆராய்வோம். @@ -84,7 +84,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` நாம் `theme` இல் கோணத்தை குறிப்பிடுகிறோம் மற்றும் `xlab()` மற்றும் `ylab()` இல் x மற்றும் y அச்சு labels ஐ குறிப்பிடுகிறோம். `ggtitle()` வரைபடத்திற்கு ஒரு பெயரை வழங்குகிறது. -![MaxWingspan-lineplot-improved](../../../../../translated_images/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.ta.png) +![MaxWingspan-lineplot-improved](../../../../../translated_images/ta/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.png) labels ஐ 45 degree கோணத்தில் சுழற்றியிருந்தாலும், அவற்றை படிக்க மிகவும் அதிகமாக உள்ளது. வேறொரு உத்தியை முயற்சிப்போம்: outliers ஐ மட்டும் label செய்யவும் மற்றும் labels ஐ chart இல் அமைக்கவும். நீங்கள் labeling க்கு இடம் செய்ய scatter chart ஐ பயன்படுத்தலாம்: @@ -100,7 +100,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + நீங்கள் என்ன கண்டுபிடிக்கிறீர்கள்? -![MaxWingspan-scatterplot](../../../../../translated_images/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.ta.png) +![MaxWingspan-scatterplot](../../../../../translated_images/ta/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.png) ## உங்கள் தரவுகளை வடிகட்டவும் @@ -119,7 +119,7 @@ ggplot(data=birds_filtered, aes(x=Name, y=MaxWingspan,group=1)) + ``` நாம் ஒரு புதிய dataframe `birds_filtered` ஐ உருவாக்கி, பின்னர் scatter plot ஐ வரைபடமாக்கினோம். outliers ஐ வடிகட்டுவதன் மூலம், உங்கள் தரவுகள் இப்போது cohesive மற்றும் புரிந்துகொள்ளக்கூடியதாக உள்ளது. -![MaxWingspan-scatterplot-improved](../../../../../translated_images/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.ta.png) +![MaxWingspan-scatterplot-improved](../../../../../translated_images/ta/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.png) இப்போது, குறைந்தது wingspan அடிப்படையில் சுத்தமான dataset உள்ளது, இந்த பறவைகள் பற்றிய மேலும் பல விஷயங்களை கண்டறிவோம். @@ -160,7 +160,7 @@ birds_filtered %>% group_by(Category) %>% ``` கீழே உள்ள snippet இல், [dplyr](https://www.rdocumentation.org/packages/dplyr/versions/0.7.8) மற்றும் [lubridate](https://www.rdocumentation.org/packages/lubridate/versions/1.8.0) தொகுப்புகளை நிறுவி, தரவுகளை manipulate மற்றும் group செய்ய உதவுகிறது, பின்னர் stacked bar chart ஐ plot செய்ய உதவுகிறது. முதலில், நீங்கள் பறவையின் `Category` மூலம் தரவுகளை குழுவாக்கி, பின்னர் `MinLength`, `MaxLength`, `MinBodyMass`, `MaxBodyMass`, `MinWingspan`, `MaxWingspan` களங்களை சுருக்குகிறீர்கள். பின்னர், `ggplot2` தொகுப்பைப் பயன்படுத்தி bar chart ஐ plot செய்து, வெவ்வேறு category க்கான நிறங்களை மற்றும் labels ஐ குறிப்பிடுகிறீர்கள். -![Stacked bar chart](../../../../../translated_images/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.ta.png) +![Stacked bar chart](../../../../../translated_images/ta/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.png) இந்த bar chart, எனினும், படிக்க முடியாதது, ஏனெனில் குழுவாக்கப்படாத தரவுகள் மிகவும் அதிகமாக உள்ளன. நீங்கள் plot செய்ய விரும்பும் தரவுகளை மட்டும் தேர்ந்தெடுக்க வேண்டும், எனவே பறவையின் category அடிப்படையில் length ஐப் பார்ப்போம். @@ -175,7 +175,7 @@ ggplot(birds_count,aes(Category,n))+geom_bar(stat="identity")+coord_flip() ``` முதலில், `Category` column இல் unique values ஐ count செய்து, பின்னர் அவற்றை ஒரு புதிய dataframe `birds_count` இல் sort செய்கிறீர்கள். இந்த sort செய்யப்பட்ட தரவுகள் அதே அளவில் factor செய்யப்படுகிறது, எனவே அது sort செய்யப்பட்ட முறையில் plot செய்யப்படுகிறது. `ggplot2` ஐப் பயன்படுத்தி, பின்னர் bar chart இல் தரவுகளை plot செய்கிறீர்கள். `coord_flip()` horizontal bars ஐ plot செய்கிறது. -![category-length](../../../../../translated_images/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.ta.png) +![category-length](../../../../../translated_images/ta/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.png) இந்த bar chart, ஒவ்வொரு category இல் உள்ள பறவைகளின் எண்ணிக்கையை ஒரு நல்ல பார்வையை வழங்குகிறது. ஒரு கணத்தில், இந்த பகுதியில் உள்ள மிகப்பெரிய பறவைகள் Ducks/Geese/Waterfowl category இல் உள்ளன என்பதை நீங்கள் காணலாம். மினசோட்டா '10,000 ஏரிகளின் நிலம்' என்பதால் இது ஆச்சரியமாக இல்லை! @@ -198,7 +198,7 @@ ggplot(birds_grouped,aes(Category,MaxLength))+geom_bar(stat="identity")+coord_fl ``` நாம் `birds_filtered` தரவுகளை `Category` மூலம் குழுவாக்கி, பின்னர் bar graph ஐ plot செய்கிறோம். -![comparing data](../../../../../translated_images/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.ta.png) +![comparing data](../../../../../translated_images/ta/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.png) இங்கே எந்த ஆச்சரியமும் இல்லை: hummingbirds க்கு Pelicans அல்லது Geese க்கு ஒப்பிட MaxLength மிகவும் குறைவாக உள்ளது. தரவு தர்க்கரீதியாக பொருந்தும் போது நல்லது! @@ -210,7 +210,7 @@ ggplot(data=birds_grouped, aes(x=Category)) + geom_bar(aes(y=MinLength), stat="identity", position="identity", fill='orange')+ coord_flip() ``` -![super-imposed values](../../../../../translated_images/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.ta.png) +![super-imposed values](../../../../../translated_images/ta/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.png) ## 🚀 சவால் diff --git a/translations/ta/3-Data-Visualization/R/10-visualization-distributions/README.md b/translations/ta/3-Data-Visualization/R/10-visualization-distributions/README.md index 8ccc4b13..a93aaa4d 100644 --- a/translations/ta/3-Data-Visualization/R/10-visualization-distributions/README.md +++ b/translations/ta/3-Data-Visualization/R/10-visualization-distributions/README.md @@ -45,7 +45,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + geom_point() + ggtitle("Max Length per order") + coord_flip() ``` -![வரிசை ஒன்றுக்கு அதிகபட்ச நீளம்](../../../../../translated_images/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.ta.png) +![வரிசை ஒன்றுக்கு அதிகபட்ச நீளம்](../../../../../translated_images/ta/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.png) இது ஒவ்வொரு பறவையின் வரிசைக்கு உடல் நீளத்தின் பொது விநியோகத்தை ஒரு பார்வையில் காட்டுகிறது, ஆனால் உண்மையான விநியோகங்களை காட்சிப்படுத்த இது சிறந்த வழி அல்ல. இந்த பணியை சாதாரணமாக ஒரு ஹிஸ்டோகிராம் உருவாக்குவதன் மூலம் கையாளலாம். ## ஹிஸ்டோகிராம்களுடன் வேலை செய்வது @@ -56,7 +56,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=10)+ylab('Frequency') ``` -![முழு தரவுத்தொகுப்பில் விநியோகம்](../../../../../translated_images/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.ta.png) +![முழு தரவுத்தொகுப்பில் விநியோகம்](../../../../../translated_images/ta/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.png) நீங்கள் காணக்கூடியது போல, இந்த தரவுத்தொகுப்பில் உள்ள 400+ பறவைகளில் பெரும்பாலானவை Max Body Mass 2000 க்கும் குறைவாக உள்ளன. `bins` அளவுருவை 30 போன்ற அதிக எண்ணிக்கைக்கு மாற்றுவதன் மூலம் தரவின் மீது மேலும் உள்ளுணர்வு பெறுங்கள்: @@ -64,7 +64,7 @@ ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![distribution-30bins](../../../../../translated_images/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.ta.png) +![distribution-30bins](../../../../../translated_images/ta/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.png) இந்த வரைபடம் விநியோகத்தை மேலும் நுணுக்கமாகக் காட்டுகிறது. இடது பக்கம் குறைவாக சாய்ந்த ஒரு வரைபடத்தை, நீங்கள் குறிப்பிட்ட வரம்பிற்குள் உள்ள தரவுகளை மட்டும் தேர்ந்தெடுப்பதன் மூலம் உருவாக்க முடியும்: @@ -76,7 +76,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![filtered histogram](../../../../../translated_images/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.ta.png) +![filtered histogram](../../../../../translated_images/ta/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.png) ✅ மற்ற வடிகட்டல்கள் மற்றும் தரவுப் புள்ளிகளை முயற்சிக்கவும். தரவின் முழு விநியோகத்தைப் பார்க்க, `['MaxBodyMass']` வடிகட்டலை நீக்கி, லேபிள் செய்யப்பட்ட விநியோகங்களை காட்சிப்படுத்தவும். @@ -90,7 +90,7 @@ ggplot(data=birds_filtered_1, aes(x=MaxBodyMass, y=MaxLength) ) + ``` இந்த இரண்டு கூறுகளின் எதிர்பார்க்கப்பட்ட அச்சில் ஒரு எதிர்பார்க்கப்பட்ட தொடர்பு உள்ளது, குறிப்பாக ஒரு வலுவான இணைப்பு புள்ளியுடன்: -![2d plot](../../../../../translated_images/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.ta.png) +![2d plot](../../../../../translated_images/ta/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.png) ஹிஸ்டோகிராம்கள் எண் தரவுகளுக்கு இயல்பாகவே நன்றாக வேலை செய்கின்றன. உரை தரவுகளின் படி விநியோகங்களைப் பார்க்க வேண்டுமென்றால் என்ன செய்ய வேண்டும்? ## உரை தரவுகளைப் பயன்படுத்தி விநியோகங்களை ஆராயுங்கள் @@ -121,7 +121,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + scale_fill_manual(name="Conservation Status",values=c("red","green","blue","pink"),labels=c("Endangered","Near Threathened","Vulnerable","Least Concern")) ``` -![wingspan and conservation collation](../../../../../translated_images/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.ta.png) +![wingspan and conservation collation](../../../../../translated_images/ta/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.png) குறைந்தபட்ச இறகின் அகலம் மற்றும் பாதுகாப்பு நிலைக்கு இடையில் நல்ல தொடர்பு இல்லை என்று தெரிகிறது. இந்த முறை பயன்படுத்தி தரவுத்தொகுப்பின் பிற கூறுகளை சோதிக்கவும். நீங்கள் எந்த தொடர்பையும் கண்டுபிடிக்கிறீர்களா? @@ -135,7 +135,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + geom_density() ``` -![அடர்த்தி வரைபடம்](../../../../../translated_images/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.ta.png) +![அடர்த்தி வரைபடம்](../../../../../translated_images/ta/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.png) குறைந்தபட்ச இறகின் அகல தரவுகளுக்கான முந்தைய வரைபடத்தை இது எவ்வாறு பிரதிபலிக்கிறது என்பதை நீங்கள் காணலாம்; இது கொஞ்சம் மிருதுவாக உள்ளது. நீங்கள் இரண்டாவது வரைபடத்தில் உள்ள மிருதுவற்ற MaxBodyMass வரியை மீண்டும் பார்க்க விரும்பினால், இந்த முறையைப் பயன்படுத்தி அதை மிகவும் நன்றாக மிருதுவாக்கலாம்: @@ -143,7 +143,7 @@ ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density() ``` -![உடல் எடை அடர்த்தி](../../../../../translated_images/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.ta.png) +![உடல் எடை அடர்த்தி](../../../../../translated_images/ta/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.png) மிகவும் மிருதுவான, ஆனால் மிக மிருதுவாக இல்லாத ஒரு வரியை நீங்கள் விரும்பினால், `adjust` அளவுருவைத் திருத்தவும்: @@ -151,7 +151,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density(adjust = 1/5) ``` -![குறைந்த மிருதுவான உடல் எடை](../../../../../translated_images/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.ta.png) +![குறைந்த மிருதுவான உடல் எடை](../../../../../translated_images/ta/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.png) ✅ இந்த வகை வரைபடத்திற்கான அளவுருக்களைப் பற்றி படித்து, பரிசோதிக்கவும்! @@ -161,7 +161,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + ggplot(data=birds_filtered_1,aes(x = MaxBodyMass, fill = Order)) + geom_density(alpha=0.5) ``` -![வரிசைக்கு உடல் எடை](../../../../../translated_images/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.ta.png) +![வரிசைக்கு உடல் எடை](../../../../../translated_images/ta/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.png) ## 🚀 சவால் diff --git a/translations/ta/3-Data-Visualization/R/11-visualization-proportions/README.md b/translations/ta/3-Data-Visualization/R/11-visualization-proportions/README.md index 0e72633a..c3bd30e5 100644 --- a/translations/ta/3-Data-Visualization/R/11-visualization-proportions/README.md +++ b/translations/ta/3-Data-Visualization/R/11-visualization-proportions/README.md @@ -95,7 +95,7 @@ pie(grouped$count,grouped$class, main="Edible?") ``` வோலா, இந்த இரண்டு வகுப்புகளின் அடிப்படையில் தரவின் விகிதங்களை காட்டும் ஒரு பை வரைபடம். குறிப்பாக இங்கு லேபிள் வரிசை சரியாக இருக்க வேண்டும் என்பதால், லேபிள் வரிசை சரிபார்க்க உறுதியாக இருக்கவும்! -![பை வரைபடம்](../../../../../translated_images/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.ta.png) +![பை வரைபடம்](../../../../../translated_images/ta/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.png) ## டோனட்! @@ -129,7 +129,7 @@ library(webr) PieDonut(habitat, aes(habitat, count=count)) ``` -![டோனட் வரைபடம்](../../../../../translated_images/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.ta.png) +![டோனட் வரைபடம்](../../../../../translated_images/ta/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.png) இந்த குறியீடு இரண்டு நூலகங்களைப் பயன்படுத்துகிறது - ggplot2 மற்றும் webr. webr நூலகத்தின் PieDonut செயல்பாட்டைப் பயன்படுத்தி, டோனட் வரைபடத்தை எளிதாக உருவாக்கலாம்! @@ -167,7 +167,7 @@ waffle((cap_color$count/10), rows = 7, title = "Waffle Chart")+scale_fill_manual வாஃபிள் வரைபடத்தைப் பயன்படுத்தி, இந்த காளான் தரவுத்தொகுப்பின் தொப்பி நிறங்களின் விகிதங்களை தெளிவாகக் காணலாம். சுவாரஸ்யமாக, பச்சை தொப்பி கொண்ட காளான்கள் அதிகமாக உள்ளன! -![வாஃபிள் வரைபடம்](../../../../../translated_images/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.ta.png) +![வாஃபிள் வரைபடம்](../../../../../translated_images/ta/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.png) இந்த பாடத்தில், நீங்கள் விகிதங்களை காட்சிப்படுத்த மூன்று வழிகளை கற்றுக்கொண்டீர்கள். முதலில், உங்கள் தரவுகளை வகைகளாக குழுவாக்க வேண்டும், பின்னர் தரவுகளை காட்சிப்படுத்த சிறந்த வழி எது என்பதைத் தேர்ந்தெடுக்க வேண்டும் - பை, டோனட் அல்லது வாஃபிள். அனைத்தும் சுவையானவை மற்றும் தரவுத்தொகுப்பின் உடனடி கண்ணோட்டத்தை வழங்க பயனரை மகிழ்விக்கின்றன. diff --git a/translations/ta/3-Data-Visualization/R/12-visualization-relationships/README.md b/translations/ta/3-Data-Visualization/R/12-visualization-relationships/README.md index 2318d61a..c06eb0c6 100644 --- a/translations/ta/3-Data-Visualization/R/12-visualization-relationships/README.md +++ b/translations/ta/3-Data-Visualization/R/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ library(ggplot2) ggplot(honey, aes(x = priceperlb, y = state)) + geom_point(colour = "blue") ``` -![scatterplot 1](../../../../../translated_images/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.ta.png) +![scatterplot 1](../../../../../translated_images/ta/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.png) இப்போது, ஆண்டுதோறும் தேனின் விலை எப்படி மாறுகிறது என்பதை தேன் நிறத்துடன் காட்டுங்கள். இதை 'scale_color_gradientn' பராமeter-ஐ சேர்த்து, ஆண்டுதோறும் மாற்றத்தை காட்சிப்படுத்தலாம்: @@ -61,7 +61,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ggplot(honey, aes(x = priceperlb, y = state, color=year)) + geom_point()+scale_color_gradientn(colours = colorspace::heat_hcl(7)) ``` -![scatterplot 2](../../../../../translated_images/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.ta.png) +![scatterplot 2](../../../../../translated_images/ta/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.png) இந்த நிறத்திட்ட மாற்றத்துடன், ஆண்டுதோறும் தேனின் விலை ஒரு பவுண்டுக்கு அதிகரித்திருப்பது தெளிவாக தெரிகிறது. உண்மையில், தரவுகளில் ஒரு மாதிரியைப் பாருங்கள் (அரிசோனா மாநிலத்தை எடுத்துக்கொள்ளுங்கள்), சில விதிவிலக்குகளைத் தவிர, ஆண்டுதோறும் விலை அதிகரிக்கும் முறைபாட்டை நீங்கள் காணலாம்: @@ -92,7 +92,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ``` புள்ளிகளின் அளவு تدريجமாக அதிகரிப்பதை நீங்கள் காணலாம். -![scatterplot 3](../../../../../translated_images/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.ta.png) +![scatterplot 3](../../../../../translated_images/ta/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.png) இது ஒரு எளிய தேவைக்கும் வழங்கலுக்கும் இடையேயான உறவா? காலநிலை மாற்றம் மற்றும் கொள்கலன்களின் அழிவு போன்ற காரணங்களால், ஆண்டுதோறும் வாங்குவதற்கு தேன் குறைவாக உள்ளதா, அதனால் விலை அதிகரிக்கிறதா? @@ -107,7 +107,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab ``` பதில்: ஆம், 2003 ஆம் ஆண்டைச் சுற்றியுள்ள சில விதிவிலக்குகளுடன்: -![line chart 1](../../../../../translated_images/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.ta.png) +![line chart 1](../../../../../translated_images/ta/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.png) கேள்வி: 2003 ஆம் ஆண்டில் தேன் வழங்கலில் ஒரு உச்சம் காண முடியுமா? ஆண்டுதோறும் மொத்த உற்பத்தியைப் பாருங்கள்: @@ -115,7 +115,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab qplot(honey$year,honey$totalprod, geom='smooth', span =0.5, xlab = "year",ylab = "totalprod") ``` -![line chart 2](../../../../../translated_images/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.ta.png) +![line chart 2](../../../../../translated_images/ta/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.png) பதில்: அவ்வாறு இல்லை. மொத்த உற்பத்தியைப் பாருங்கள், குறிப்பிட்ட ஆண்டில் அது அதிகரித்திருப்பது போலவே தெரிகிறது, ஆனால் பொதுவாக இந்த ஆண்டுகளில் தேன் உற்பத்தி குறைந்து கொண்டிருக்கிறது. @@ -135,7 +135,7 @@ ggplot(honey, aes(x=yieldpercol, y = numcol,group = 1)) + ``` இந்த காட்சிப்படுத்தலில், ஒவ்வொரு ஆண்டும் ஒவ்வொரு மாநிலமும் கொள்கலனின் உற்பத்தி மற்றும் எண்ணிக்கையை ஒப்பிடலாம், 3 க்கான நெடுவரிசை அமைப்புடன்: -![facet grid](../../../../../translated_images/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.ta.png) +![facet grid](../../../../../translated_images/ta/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.png) இந்த தரவுத்தொகுப்பில், ஒவ்வொரு ஆண்டும் ஒவ்வொரு மாநிலமும் கொள்கலன்களின் எண்ணிக்கை மற்றும் அவற்றின் உற்பத்தியில் குறிப்பிடத்தக்க மாற்றம் எதுவும் இல்லை. இந்த இரண்டு மாறிகளுக்கு இடையேயான தொடர்பை கண்டறிய வேறொரு வழி இருக்கிறதா? @@ -152,7 +152,7 @@ plot(honey$year, honey$yieldpercol, pch = 17, col = 3, axis(side = 4, at = pretty(range(y2))) mtext("colony yield", side = 4, line = 3) ``` -![superimposed plots](../../../../../translated_images/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.ta.png) +![superimposed plots](../../../../../translated_images/ta/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.png) 2003 ஆம் ஆண்டைச் சுற்றி கண்களுக்கு எதுவும் தெரியவில்லை, ஆனால் இது இந்த பாடத்தை ஒரு சிறிய மகிழ்ச்சியான குறிப்பில் முடிக்க அனுமதிக்கிறது: கொள்கலன்களின் எண்ணிக்கை குறைந்து கொண்டிருப்பதுடன், அவற்றின் உற்பத்தி குறைந்தாலும், கொள்கலன்களின் எண்ணிக்கை நிலைத்திருக்கிறது. diff --git a/translations/ta/3-Data-Visualization/R/13-meaningful-vizualizations/README.md b/translations/ta/3-Data-Visualization/R/13-meaningful-vizualizations/README.md index bafb2b65..a161aa31 100644 --- a/translations/ta/3-Data-Visualization/R/13-meaningful-vizualizations/README.md +++ b/translations/ta/3-Data-Visualization/R/13-meaningful-vizualizations/README.md @@ -47,25 +47,25 @@ CO_OP_TRANSLATOR_METADATA: ஒரு தரவியல் விஞ்ஞானி சரியான தரவுக்கான சரியான வரைபடத்தை தேர்ந்தெடுக்க கவனமாக இருந்தாலும், ஒரு கருத்தை நிரூபிக்க, பெரும்பாலும் தரவின் உண்மையை பாதிக்கக்கூடிய வகையில், தரவுகளை காட்ட பல வழிகள் உள்ளன. மோசடி வரைபடங்கள் மற்றும் தகவல்படங்களின் பல உதாரணங்கள் உள்ளன! -[![அல்பெர்டோ கெய்ரோவின் "How Charts Lie"](../../../../../translated_images/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.ta.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "How charts lie") +[![அல்பெர்டோ கெய்ரோவின் "How Charts Lie"](../../../../../translated_images/ta/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "How charts lie") > 🎥 மோசடி வரைபடங்கள் பற்றிய கருத்தரங்க உரையைப் பார்க்க மேலே உள்ள படத்தை கிளிக் செய்யவும் இந்த வரைபடம் X அச்சை மாற்றி, தேதியின் அடிப்படையில் உண்மைக்கு மாறாக காட்டுகிறது: -![தவறான வரைபடம் 1](../../../../../translated_images/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.ta.png) +![தவறான வரைபடம் 1](../../../../../translated_images/ta/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.png) [இந்த வரைபடம்](https://media.firstcoastnews.com/assets/WTLV/images/170ae16f-4643-438f-b689-50d66ca6a8d8/170ae16f-4643-438f-b689-50d66ca6a8d8_1140x641.jpg) இன்னும் மோசமாக உள்ளது, ஏனெனில் பார்வை வலப்புறமாக ஈர்க்கப்படுகிறது, இது காலப்போக்கில் COVID வழக்குகள் குறைந்துவிட்டன என்று முடிவு செய்யும். உண்மையில், தேதிகளை நெருங்கிப் பார்த்தால், அந்த மோசமான இறக்குமுகப் போக்கை உருவாக்க அவற்றை மறுசீரமைத்துள்ளீர்கள் என்பதை நீங்கள் காணலாம். -![தவறான வரைபடம் 2](../../../../../translated_images/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.ta.jpg) +![தவறான வரைபடம் 2](../../../../../translated_images/ta/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.jpg) இந்த பிரபலமான உதாரணம் நிறத்தையும் ஒரு மாறிய Y அச்சையும் பயன்படுத்தி மோசடியில் ஈடுபடுகிறது: துப்பாக்கி மரணங்கள் திடீரென அதிகரித்தன என்பதை விட, கண் மாறாக உணர்த்தப்படுகிறது: -![தவறான வரைபடம் 3](../../../../../translated_images/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.ta.jpg) +![தவறான வரைபடம் 3](../../../../../translated_images/ta/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.jpg) இந்த விசித்திரமான வரைபடம் விகிதத்தை எப்படி மாற்றி அமைக்க முடியும் என்பதை காட்டுகிறது, அது நகைச்சுவையான விளைவாக உள்ளது: -![தவறான வரைபடம் 4](../../../../../translated_images/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.ta.jpg) +![தவறான வரைபடம் 4](../../../../../translated_images/ta/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.jpg) ஒப்பிட முடியாதவற்றை ஒப்பிடுவது இன்னொரு சந்தேகத்திற்குரிய தந்திரமாகும். மெய்னில் விவாகரத்து விகிதம் மற்றும் மார்ஜரின் நுகர்வு போன்றவற்றை ஒப்பிடும் 'தவறான தொடர்புகள்' பற்றிய [அற்புதமான இணையதளம்](https://tylervigen.com/spurious-correlations) உள்ளது. ஒரு Reddit குழு கூட தரவின் [அழகற்ற பயன்பாடுகளை](https://www.reddit.com/r/dataisugly/top/?t=all) சேகரிக்கிறது. @@ -100,13 +100,13 @@ CO_OP_TRANSLATOR_METADATA: உங்கள் தரவு உரை மற்றும் விரிவானதாக இருந்தால், X அச்சில் உரையை சிறந்த வாசிப்புக்காக சாய்க்கலாம். [plot3D](https://cran.r-project.org/web/packages/plot3D/index.html) உங்கள் தரவை ஆதரித்தால், 3D வரைபடங்களை வழங்குகிறது. அதிநவீன தரவுக் காட்சிப்படுத்தல்களை இதைப் பயன்படுத்தி உருவாக்கலாம். -![3D வரைபடங்கள்](../../../../../translated_images/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.ta.png) +![3D வரைபடங்கள்](../../../../../translated_images/ta/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.png) ## அனிமேஷன் மற்றும் 3D வரைபட காட்சி இன்றைய சிறந்த தரவுக் காட்சிப்படுத்தல்களில் சில அனிமேஷன்களாக உள்ளன. D3 உடன் ஷிர்லி வூ உருவாக்கிய அற்புதமானவை உள்ளன, உதாரணமாக '[film flowers](http://bl.ocks.org/sxywu/raw/d612c6c653fb8b4d7ff3d422be164a5d/)', இதில் ஒவ்வொரு பூவும் ஒரு திரைப்படத்தின் காட்சிப்படுத்தலாக உள்ளது. 'தி கார்டியன்'க்காக மற்றொரு உதாரணம் 'bussed out', இது காட்சிப்படுத்தல்களுடன் கூடிய ஒரு இடைமுக அனுபவமாகும், இது கிரீன்சாக் மற்றும் D3 உடன் இணைந்து, நியூயார்க் நகரம் தனது வீடற்ற பிரச்சனையை எவ்வாறு கையாளுகிறது என்பதை காட்டுகிறது. -![busing](../../../../../translated_images/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.ta.png) +![busing](../../../../../translated_images/ta/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.png) > "Bussed Out: How America Moves its Homeless" [தி கார்டியன்](https://www.theguardian.com/us-news/ng-interactive/2017/dec/20/bussed-out-america-moves-homeless-people-country-study) மூலம். காட்சிப்படுத்தல்கள்: நதீஹ் பிரெமர் & ஷிர்லி வூ @@ -116,7 +116,7 @@ CO_OP_TRANSLATOR_METADATA: இந்த சமூக வலையமைப்பின் அனிமேஷன் காட்சியை காட்டும் ஒரு வலை பயன்பாட்டை நீங்கள் முடிக்க வேண்டும். இது Vue.js மற்றும் D3 ஐப் பயன்படுத்தி ஒரு [வலையமைப்பின் காட்சியை](https://github.com/emiliorizzo/vue-d3-network) உருவாக்க ஒரு நூலகத்தைப் பயன்படுத்தி உருவாக்கப்பட்டது. பயன்பாடு இயங்கும்போது, நீங்கள் திரையில் உள்ள நொடுகளை இழுத்து தரவுகளைச் சீரமைக்கலாம். -![liaisons](../../../../../translated_images/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.ta.png) +![liaisons](../../../../../translated_images/ta/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.png) ## திட்டம்: D3.js ஐப் பயன்படுத்தி ஒரு வலையமைப்பை காட்ட ஒரு வரைபடத்தை உருவாக்கவும் diff --git a/translations/ta/3-Data-Visualization/README.md b/translations/ta/3-Data-Visualization/README.md index 36c31af4..8d93f9fa 100644 --- a/translations/ta/3-Data-Visualization/README.md +++ b/translations/ta/3-Data-Visualization/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # காட்சிப்படுத்தல்கள் -![ஒரு லாவெண்டர் மலரின் மீது ஒரு தேனீ](../../../translated_images/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.ta.jpg) +![ஒரு லாவெண்டர் மலரின் மீது ஒரு தேனீ](../../../translated_images/ta/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.jpg) > புகைப்படம் ஜென்னா லீ மூலம் Unsplash இல் தரவை காட்சிப்படுத்துவது ஒரு தரவியல் விஞ்ஞானியின் மிக முக்கியமான பணிகளில் ஒன்றாகும். ஒரு படம் 1000 வார்த்தைகளுக்கு சமம், மேலும் ஒரு காட்சிப்படுத்தல் உங்கள் தரவின் பல்வேறு சுவாரஸ்யமான பகுதிகளை, உதாரணமாக உச்சங்கள், விலகல்கள், குழுக்கள், போக்குகள் மற்றும் பலவற்றை அடையாளம் காண உதவுகிறது, இது உங்கள் தரவு சொல்ல முயற்சிக்கும் கதையை நீங்கள் புரிந்துகொள்ள உதவும். diff --git a/translations/ta/4-Data-Science-Lifecycle/14-Introduction/README.md b/translations/ta/4-Data-Science-Lifecycle/14-Introduction/README.md index 4f90d631..fd89cece 100644 --- a/translations/ta/4-Data-Science-Lifecycle/14-Introduction/README.md +++ b/translations/ta/4-Data-Science-Lifecycle/14-Introduction/README.md @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: இந்த பாடம் வாழ்க்கைச் சுழற்சியின் 3 பகுதிகளை மையமாகக் கொண்டுள்ளது: தரவுகளைப் பெறுதல், செயலாக்கம் மற்றும் பராமரிப்பு. -![தரவியல் அறிவியல் வாழ்க்கைச் சுழற்சியின் வரைபடம்](../../../../translated_images/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.ta.jpg) +![தரவியல் அறிவியல் வாழ்க்கைச் சுழற்சியின் வரைபடம்](../../../../translated_images/ta/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.jpg) > [Berkeley School of Information](https://ischoolonline.berkeley.edu/data-science/what-is-data-science/) எடுத்த படம் ## தரவுகளைப் பெறுதல் @@ -98,7 +98,7 @@ Here’s some things that a team may do with security in mind: |Team Data Science Process (TDSP)|Cross-industry standard process for data mining (CRISP-DM)| |--|--| -|![Team Data Science Lifecycle](../../../../translated_images/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.ta.png) | ![Data Science Process Alliance Image](../../../../translated_images/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.ta.png) | +|![Team Data Science Lifecycle](../../../../translated_images/ta/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.png) | ![Data Science Process Alliance Image](../../../../translated_images/ta/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.png) | | Image by [Microsoft](https://docs.microsoft.comazure/architecture/data-science-process/lifecycle) | Image by [Data Science Process Alliance](https://www.datascience-pm.com/crisp-dm-2/) | ## [பாடத்திற்குப் பின் வினாடி வினா](https://ff-quizzes.netlify.app/en/ds/quiz/27) diff --git a/translations/ta/4-Data-Science-Lifecycle/README.md b/translations/ta/4-Data-Science-Lifecycle/README.md index f64108d0..3fd961dc 100644 --- a/translations/ta/4-Data-Science-Lifecycle/README.md +++ b/translations/ta/4-Data-Science-Lifecycle/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # தரவியல் அறிவியல் வாழ்க்கைச் சுழற்சி -![communication](../../../translated_images/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.ta.jpg) +![communication](../../../translated_images/ta/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.jpg) > புகைப்படம் Headway மூலம் Unsplash இல் இந்த பாடங்களில், தரவின் பகுப்பாய்வு மற்றும் தொடர்பு ஆகியவற்றை உள்ளடக்கிய தரவியல் அறிவியல் வாழ்க்கைச் சுழற்சியின் சில அம்சங்களை நீங்கள் ஆராய்வீர்கள். diff --git a/translations/ta/5-Data-Science-In-Cloud/18-Low-Code/README.md b/translations/ta/5-Data-Science-In-Cloud/18-Low-Code/README.md index 03a5b61e..f64be6e1 100644 --- a/translations/ta/5-Data-Science-In-Cloud/18-Low-Code/README.md +++ b/translations/ta/5-Data-Science-In-Cloud/18-Low-Code/README.md @@ -62,7 +62,7 @@ Azure ML தரவியல் விஞ்ஞானிகள் மற்று திறன்கள் மற்றும் அறிவை சோதிக்க திட்டங்களை உருவாக்குவது மற்றும் கட்டுவது சிறந்த வழி என்பதில் சந்தேகமில்லை. இந்த பாடத்தில், Azure ML Studio-வில் இதய செயலிழப்பு தாக்குதல்களை கணிக்க ஒரு தரவியல் அறிவியல் திட்டத்தை உருவாக்க இரண்டு விதமான வழிகளை ஆராயப்போகிறோம்: குறைந்த குறியீடு/குறியீடு இல்லாமல் மற்றும் Azure ML SDK மூலம், கீழே உள்ள வரைபடத்தில் காட்டப்பட்டுள்ளபடி: -![project-schema](../../../../translated_images/project-schema.736f6e403f321eb48d10242b3f4334dc6ccf0eabef8ff87daf52b89781389fcb.ta.png) +![project-schema](../../../../translated_images/ta/project-schema.736f6e403f321eb48d10242b3f4334dc6ccf0eabef8ff87daf52b89781389fcb.png) ஒவ்வொரு முறையும் அதன் சொந்த நன்மைகள் மற்றும் குறைகள் உள்ளன. குறைந்த குறியீடு/குறியீடு இல்லாமல் வழி தொடங்க எளிதானது, ஏனெனில் இது GUI (Graphical User Interface) உடன் தொடர்பு கொள்ளும், குறியீடு பற்றிய முன்னோட்ட அறிவு தேவையில்லை. இந்த முறை திட்டத்தின் செயல்திறனை விரைவாக சோதிக்க மற்றும் POC (Proof Of Concept) உருவாக்க அனுமதிக்கிறது. ஆனால், திட்டம் வளரும்போது மற்றும் விஷயங்கள் உற்பத்தி தயாராக இருக்க வேண்டும், GUI மூலம் வளங்களை உருவாக்குவது சாத்தியமில்லை. எல்லாவற்றையும், வளங்களை உருவாக்குதல் முதல் மாதிரியை பிரசுரம் செய்வது வரை, நிரலாக்க முறையில் தானியக்கமாக்க வேண்டும். இதுதான் Azure ML SDK-ஐ பயன்படுத்துவது எப்படி என்பதை அறிந்து கொள்ள முக்கியமாகிறது. @@ -115,15 +115,15 @@ Azure Machine Learning-ஐப் பயன்படுத்த, உங்கள 1. [Azure போர்ட்டலில்](https://ms.portal.azure.com/) Microsoft சான்றுகளைப் பயன்படுத்தி உள்நுழைக. 2. **＋ஒரு வளத்தை உருவாக்கவும்** தேர்ந்தெடுக்கவும் - ![workspace-1](../../../../translated_images/workspace-1.ac8694d60b073ed1ae8333d71244dc8a9b3e439d54593724f98f1beefdd27b08.ta.png) + ![workspace-1](../../../../translated_images/ta/workspace-1.ac8694d60b073ed1ae8333d71244dc8a9b3e439d54593724f98f1beefdd27b08.png) Machine Learning-ஐ தேடவும் மற்றும் Machine Learning டைலை தேர்ந்தெடுக்கவும் - ![workspace-2](../../../../translated_images/workspace-2.ae7c486db8796147075e4a56566aa819827dd6c4c8d18d64590317c3be625f17.ta.png) + ![workspace-2](../../../../translated_images/ta/workspace-2.ae7c486db8796147075e4a56566aa819827dd6c4c8d18d64590317c3be625f17.png) உருவாக்கு பொத்தானை அழுத்தவும் - ![workspace-3](../../../../translated_images/workspace-3.398ca4a5858132cce584db9df10c5a011cd9075eb182e647a77d5cac01771eea.ta.png) + ![workspace-3](../../../../translated_images/ta/workspace-3.398ca4a5858132cce584db9df10c5a011cd9075eb182e647a77d5cac01771eea.png) அமைப்புகளை பின்வருமாறு நிரப்பவும்: - Subscription: உங்கள் Azure சந்தா @@ -135,17 +135,17 @@ Azure Machine Learning-ஐப் பயன்படுத்த, உங்கள - Application insights: உங்கள் வேலைநிலைக்காக உருவாக்கப்படும் புதிய Application insights வளத்தை கவனிக்கவும் - Container registry: இல்லை (மாதிரியை ஒரு கன்டெய்னருக்கு பிரசுரம் செய்யும் முதல் முறையில் தானாகவே ஒன்று உருவாக்கப்படும்) - ![workspace-4](../../../../translated_images/workspace-4.bac87f6599c4df63e624fc2608990f965887bee551d9dedc71c687b43b986b6a.ta.png) + ![workspace-4](../../../../translated_images/ta/workspace-4.bac87f6599c4df63e624fc2608990f965887bee551d9dedc71c687b43b986b6a.png) - உருவாக்கு + மதிப்பீடு பொத்தானை அழுத்தி பின்னர் உருவாக்கு பொத்தானை அழுத்தவும் 3. உங்கள் வேலைநிலை உருவாக்கப்படும் வரை காத்திருக்கவும் (இது சில நிமிடங்கள் ஆகலாம்). பின்னர் போர்ட்டலில் அதைத் தேடவும். Machine Learning Azure சேவையின் மூலம் அதை நீங்கள் கண்டுபிடிக்கலாம். 4. உங்கள் வேலைநிலையின் Overview பக்கத்தில், Azure Machine Learning ஸ்டுடியோவை தொடங்கவும் (அல்லது புதிய உலாவி தாவலில் https://ml.azure.com-க்கு செல்லவும்), மற்றும் உங்கள் Microsoft கணக்கைப் பயன்படுத்தி Azure Machine Learning ஸ்டுடியோவில் உள்நுழைக. கேட்கப்பட்டால், உங்கள் Azure directory மற்றும் subscription, மற்றும் உங்கள் Azure Machine Learning வேலைநிலையைத் தேர்ந்தெடுக்கவும். -![workspace-5](../../../../translated_images/workspace-5.a6eb17e0a5e6420018b08bdaf3755ce977f96f1df3ea363d2476a9dce7e15adb.ta.png) +![workspace-5](../../../../translated_images/ta/workspace-5.a6eb17e0a5e6420018b08bdaf3755ce977f96f1df3ea363d2476a9dce7e15adb.png) 5. Azure Machine Learning ஸ்டுடியோவில், இடைமுகத்தில் உள்ள பல்வேறு பக்கங்களைப் பார்க்க மேல் இடது ☰ ஐகானை மாற்றவும். உங்கள் வேலைநிலையின் வளங்களை மேலாண்மை செய்ய இந்த பக்கங்களை நீங்கள் பயன்படுத்தலாம். -![workspace-6](../../../../translated_images/workspace-6.8dd81fe841797ee17f8f73916769576260b16c4e17e850d277a49db35fd74a15.ta.png) +![workspace-6](../../../../translated_images/ta/workspace-6.8dd81fe841797ee17f8f73916769576260b16c4e17e850d277a49db35fd74a15.png) Azure போர்ட்டல் மூலம் உங்கள் வேலைநிலையை மேலாண்மை செய்யலாம், ஆனால் தரவியல் விஞ்ஞானிகள் மற்றும் Machine Learning செயல்பாட்டு பொறியாளர்களுக்கு, Azure Machine Learning Studio வேலைநிலை வளங்களை மேலாண்மை செய்ய ஒரு கவனம் செலுத்தப்பட்ட பயனர் இடைமுகத்தை வழங்குகிறது. @@ -189,18 +189,18 @@ Low-priority instance என்பது அது இடையூறாக இ முந்தைய கட்டத்தில் நாம் உருவாக்கிய [Azure ML workspace](https://ml.azure.com/)க்கு செல்லவும், அங்கு கணினி என்ற பகுதியில் நீங்கள் முந்தைய விவாதத்தில் உள்ள கணினி வளங்களை (உதாரணமாக compute instances, compute clusters, inference clusters மற்றும் attached compute) காணலாம். இந்த திட்டத்திற்காக, மாதிரி பயிற்சிக்காக ஒரு கணினி க்ளஸ்டர் தேவைப்படும். Studio-வில், "Compute" மெனுவை கிளிக் செய்யவும், பின்னர் "Compute cluster" தாவலைத் தேர்ந்தெடுக்கவும் மற்றும் "+ New" பொத்தானை கிளிக் செய்து கணினி க்ளஸ்டரை உருவாக்கவும். -![22](../../../../translated_images/cluster-1.b78cb630bb543729b11f60c34d97110a263f8c27b516ba4dc47807b3cee5579f.ta.png) +![22](../../../../translated_images/ta/cluster-1.b78cb630bb543729b11f60c34d97110a263f8c27b516ba4dc47807b3cee5579f.png) 1. உங்கள் விருப்பங்களைத் தேர்ந்தெடுக்கவும்: Dedicated vs Low priority, CPU அல்லது GPU, VM அளவு மற்றும் கோர் எண்ணிக்கை (இந்த திட்டத்திற்கான இயல்புநிலை அமைப்புகளை வைத்திருக்கலாம்). 2. Next பொத்தானை கிளிக் செய்யவும். -![23](../../../../translated_images/cluster-2.ea30cdbc9f926bb9e05af3fdbc1f679811c796dc2a6847f935290aec15526e88.ta.png) +![23](../../../../translated_images/ta/cluster-2.ea30cdbc9f926bb9e05af3fdbc1f679811c796dc2a6847f935290aec15526e88.png) 3. க்ளஸ்டருக்கு ஒரு பெயரை கொடுக்கவும். 4. உங்கள் விருப்பங்களைத் தேர்ந்தெடுக்கவும்: குறைந்தபட்ச/அதிகபட்ச நொடிகள் எண்ணிக்கை, Idle seconds before scale down, SSH access. குறைந்தபட்ச நொடிகள் எண்ணிக்கை 0 என்றால், க்ளஸ்டர் செயலற்ற நிலையில் இருக்கும் போது பணத்தைச் சேமிக்கலாம். அதிகபட்ச நொடிகள் எண்ணிக்கை அதிகமாக இருந்தால், பயிற்சி குறுகிய நேரத்தில் முடியும். அதிகபட்சமாக 3 நொடிகள் பரிந்துரைக்கப்படுகிறது. 5. "Create" பொத்தானை கிளிக் செய்யவும். இந்த படி சில நிமிடங்கள் ஆகலாம். -![29](../../../../translated_images/cluster-3.8a334bc070ec173a329ce5abd2a9d727542e83eb2347676c9af20f2c8870b3e7.ta.png) +![29](../../../../translated_images/ta/cluster-3.8a334bc070ec173a329ce5abd2a9d727542e83eb2347676c9af20f2c8870b3e7.png) சிறந்தது! இப்போது நமக்கு ஒரு Compute cluster உள்ளது, நாம் தரவுகளை Azure ML Studio-க்கு ஏற்ற வேண்டும். @@ -208,15 +208,15 @@ Low-priority instance என்பது அது இடையூறாக இ 1. முந்தைய கட்டத்தில் நாம் உருவாக்கிய [Azure ML workspace](https://ml.azure.com/)க்கு சென்று, இடது மெனுவில் "Datasets" என்பதை கிளிக் செய்து "+ Create dataset" பொத்தானை கிளிக் செய்து ஒரு dataset உருவாக்கவும். "From local files" விருப்பத்தைத் தேர்ந்தெடுத்து, முந்தைய கட்டத்தில் பதிவிறக்கம் செய்த Kaggle dataset-ஐத் தேர்ந்தெடுக்கவும். - ![24](../../../../translated_images/dataset-1.e86ab4e10907a6e9c2a72577b51db35f13689cb33702337b8b7032f2ef76dac2.ta.png) + ![24](../../../../translated_images/ta/dataset-1.e86ab4e10907a6e9c2a72577b51db35f13689cb33702337b8b7032f2ef76dac2.png) 2. உங்கள் dataset-க்கு ஒரு பெயர், ஒரு வகை மற்றும் ஒரு விளக்கத்தை கொடுக்கவும். Next என்பதை கிளிக் செய்யவும். கோப்புகளில் இருந்து தரவுகளைப் பதிவேற்றவும். Next என்பதை கிளிக் செய்யவும். - ![25](../../../../translated_images/dataset-2.f58de1c435d5bf9ccb16ccc5f5d4380eb2b50affca85cfbf4f97562bdab99f77.ta.png) + ![25](../../../../translated_images/ta/dataset-2.f58de1c435d5bf9ccb16ccc5f5d4380eb2b50affca85cfbf4f97562bdab99f77.png) 3. Schema-வில், anaemia, diabetes, high blood pressure, sex, smoking, மற்றும் DEATH_EVENT ஆகிய அம்சங்களுக்கு Boolean தரவுத் வகையை மாற்றவும். Next என்பதை கிளிக் செய்து Create என்பதை கிளிக் செய்யவும். - ![26](../../../../translated_images/dataset-3.58db8c0eb783e89236a02bbce5bb4ba808d081a87d994d5284b1ae59928c95bf.ta.png) + ![26](../../../../translated_images/ta/dataset-3.58db8c0eb783e89236a02bbce5bb4ba808d081a87d994d5284b1ae59928c95bf.png) சிறந்தது! இப்போது dataset இடத்தில் உள்ளது மற்றும் கணினி க்ளஸ்டர் உருவாக்கப்பட்டுள்ளது, நாம் மாதிரியைப் பயிற்சி செய்யத் தொடங்கலாம்! @@ -227,19 +227,19 @@ Automated machine learning (AutoML) என்பது இயந்திர க 1. முந்தைய கட்டத்தில் நாம் உருவாக்கிய [Azure ML workspace](https://ml.azure.com/)க்கு சென்று, இடது மெனுவில் "Automated ML" என்பதை கிளிக் செய்து, நீங்கள் பதிவேற்றிய dataset-ஐத் தேர்ந்தெடுக்கவும். Next என்பதை கிளிக் செய்யவும். - ![27](../../../../translated_images/aml-1.67281a85d3a1e2f34eb367b2d0f74e1039d13396e510f363cd8766632106d1ec.ta.png) + ![27](../../../../translated_images/ta/aml-1.67281a85d3a1e2f34eb367b2d0f74e1039d13396e510f363cd8766632106d1ec.png) 2. புதிய முயற்சி பெயரை, இலக்கு நெடுவரிசை (DEATH_EVENT) மற்றும் நாம் உருவாக்கிய கணினி க்ளஸ்டரை உள்ளிடவும். Next என்பதை கிளிக் செய்யவும். - ![28](../../../../translated_images/aml-2.c9fb9cffb39ccbbe21ab9810ae937195d41a489744e15cff2b8477ed4dcae1ec.ta.png) + ![28](../../../../translated_images/ta/aml-2.c9fb9cffb39ccbbe21ab9810ae937195d41a489744e15cff2b8477ed4dcae1ec.png) 3. "Classification" என்பதைத் தேர்ந்தெடுத்து Finish என்பதை கிளிக் செய்யவும். இந்த படி 30 நிமிடங்கள் முதல் 1 மணி நேரம் வரை ஆகலாம், உங்கள் கணினி க்ளஸ்டர் அளவைப் பொறுத்து. - ![30](../../../../translated_images/aml-3.a7952e4295f38cc6cdb0c7ed6dc71ea756b7fb5697ec126bc1220f87c5fa9231.ta.png) + ![30](../../../../translated_images/ta/aml-3.a7952e4295f38cc6cdb0c7ed6dc71ea756b7fb5697ec126bc1220f87c5fa9231.png) 4. செயல்பாடு முடிந்தவுடன், "Automated ML" தாவலை கிளிக் செய்து, உங்கள் செயல்பாட்டைத் தேர்ந்தெடுத்து, "Best model summary" கார்டில் உள்ள Algorithm-ஐ கிளிக் செய்யவும். - ![31](../../../../translated_images/aml-4.7a627e09cb6f16d0aa246059d9faee3d1725cc4258d0c8df15e801f73afc7e2c.ta.png) + ![31](../../../../translated_images/ta/aml-4.7a627e09cb6f16d0aa246059d9faee3d1725cc4258d0c8df15e801f73afc7e2c.png) இங்கே AutoML உருவாக்கிய சிறந்த மாதிரியின் விரிவான விளக்கத்தை நீங்கள் காணலாம். மேலும், Models தாவலில் உருவாக்கப்பட்ட பிற மாதிரிகளை ஆராயலாம். சில நிமிடங்கள் எடுத்துக்கொண்டு மாதிரிகளை Explanations (preview button) பகுதியில் ஆராயவும். நீங்கள் பயன்படுத்த விரும்பும் மாதிரியைத் தேர்ந்தெடுத்த பிறகு (இங்கே நாம் AutoML தேர்ந்தெடுத்த சிறந்த மாதிரியைத் தேர்ந்தெடுப்போம்), அதை எவ்வாறு பிரசுரிக்கலாம் என்பதைப் பார்ப்போம். @@ -250,7 +250,7 @@ Automated machine learning இடைமுகம் சிறந்த மாத சிறந்த மாதிரி விளக்கத்தில், "Deploy" பொத்தானை கிளிக் செய்யவும். -![deploy-1](../../../../translated_images/deploy-1.ddad725acadc84e34553c3d09e727160faeb32527a9fb8b904c0f99235a34bb6.ta.png) +![deploy-1](../../../../translated_images/ta/deploy-1.ddad725acadc84e34553c3d09e727160faeb32527a9fb8b904c0f99235a34bb6.png) 15. அதற்கு ஒரு பெயர், விளக்கம், கணினி வகை (Azure Container Instance), அங்கீகாரத்தை இயக்கவும் மற்றும் Deploy என்பதை கிளிக் செய்யவும். diff --git a/translations/ta/5-Data-Science-In-Cloud/19-Azure/README.md b/translations/ta/5-Data-Science-In-Cloud/19-Azure/README.md index 8804c5b8..e5a11924 100644 --- a/translations/ta/5-Data-Science-In-Cloud/19-Azure/README.md +++ b/translations/ta/5-Data-Science-In-Cloud/19-Azure/README.md @@ -57,7 +57,7 @@ SDK-யின் முக்கிய பகுதிகள்: [முந்தைய பாடத்தில்](../18-Low-Code/README.md), குறைந்த குறியீடு/குறியீடு இல்லாத முறையில் மாதிரியை பயிற்சி செய்ய, பிரசாரம் செய்ய மற்றும் பயன்படுத்துவது எப்படி என்பதைப் பார்த்தோம். இதய செயலிழப்பு தரவுத்தொகுப்பைப் பயன்படுத்தி இதய செயலிழப்பு கணிப்பு மாதிரியை உருவாக்கினோம். இந்த பாடத்தில், அதே செயல்முறையை Azure Machine Learning SDK-ஐப் பயன்படுத்தி செய்வோம். -![திட்டம் வரைபடம்](../../../../translated_images/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.ta.png) +![திட்டம் வரைபடம்](../../../../translated_images/ta/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.png) ### 1.2 இதய செயலிழப்பு கணிப்பு திட்டம் மற்றும் தரவுத்தொகுப்பு அறிமுகம் @@ -75,7 +75,7 @@ SDK-யின் முக்கிய பகுதிகள்: நாம் முன்பு உருவாக்கிய [Azure ML வேலைப்பகுதியில்](https://ml.azure.com/), கணிப்பொறி மெனுவுக்கு சென்று கிடைக்கும் கணிப்பொறி வளங்களைப் பாருங்கள். -![கணிப்பொறி உதாரணம் 1](../../../../translated_images/compute-instance-1.dba347cb199ca4996b3e3d649295ed95626ba481479d3986557b9b98e76d8816.ta.png) +![கணிப்பொறி உதாரணம் 1](../../../../translated_images/ta/compute-instance-1.dba347cb199ca4996b3e3d649295ed95626ba481479d3986557b9b98e76d8816.png) Jupyter Notebook-ஐ வழங்க ஒரு கணிப்பொறி உதாரணத்தை உருவாக்குவோம். 1. + New பொத்தானை அழுத்தவும். @@ -99,10 +99,10 @@ Jupyter Notebook-ஐ வழங்க ஒரு கணிப்பொறி உ 1. Applications பிரிவில், Jupyter விருப்பத்தை கிளிக் செய்யவும். 2. "Yes, I understand" பெட்டியை அடையாளமிடி மற்றும் Continue பொத்தானை அழுத்தவும். -![நோட்புக் 1](../../../../translated_images/notebook-1.12998af7b02c83f536c11b3aeba561be16e0f05e94146600728ec64270ce1105.ta.png) +![நோட்புக் 1](../../../../translated_images/ta/notebook-1.12998af7b02c83f536c11b3aeba561be16e0f05e94146600728ec64270ce1105.png) 3. இது உங்கள் Jupyter Notebook உதாரணத்துடன் புதிய உலாவி தாவலைத் திறக்கும். "New" பொத்தானை அழுத்தி ஒரு நோட்புக் உருவாக்கவும். -![நோட்புக் 2](../../../../translated_images/notebook-2.9a657c037e34f1cf26c0212f5ee9e2da8545b3e107c7682c55114e494167a8aa.ta.png) +![நோட்புக் 2](../../../../translated_images/ta/notebook-2.9a657c037e34f1cf26c0212f5ee9e2da8545b3e107c7682c55114e494167a8aa.png) இப்போது, நமக்கு ஒரு நோட்புக் உள்ளது. Azure ML SDK-யுடன் மாதிரியை பயிற்சி செய்ய தொடங்கலாம். diff --git a/translations/ta/5-Data-Science-In-Cloud/README.md b/translations/ta/5-Data-Science-In-Cloud/README.md index 8dfceced..dbe629b1 100644 --- a/translations/ta/5-Data-Science-In-Cloud/README.md +++ b/translations/ta/5-Data-Science-In-Cloud/README.md @@ -9,13 +9,13 @@ CO_OP_TRANSLATOR_METADATA: --> # கிளவுடில் தரவியல் அறிவியல் -![cloud-picture](../../../translated_images/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.ta.jpg) +![cloud-picture](../../../translated_images/ta/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.jpg) > படம் [Jelleke Vanooteghem](https://unsplash.com/@ilumire) மூலம் [Unsplash](https://unsplash.com/s/photos/cloud?orientation=landscape) இல் இருந்து பெரிய தரவுகளுடன் தரவியல் அறிவியல் செய்யும்போது, கிளவுட் ஒரு முக்கிய மாற்றத்தை ஏற்படுத்த முடியும். அடுத்த மூன்று பாடங்களில், கிளவுட் என்ன மற்றும் அது ஏன் மிகவும் பயனுள்ளதாக இருக்க முடியும் என்பதை நாம் காணப்போகிறோம். மேலும், நாங்கள் ஒரு இதய செயலிழப்பு தரவுத்தொகுப்பை ஆராய்ந்து, ஒருவருக்கு இதய செயலிழப்பு ஏற்படும் சாத்தியத்தை மதிப்பீடு செய்ய உதவும் ஒரு மாதிரியை உருவாக்கப் போகிறோம். கிளவுட்டின் சக்தியை பயன்படுத்தி, இரண்டு விதங்களில் ஒரு மாதிரியை பயிற்சி, வெளியிட மற்றும் பயன்படுத்தப் போகிறோம். ஒன்று, "குறைந்த குறியீடு/குறியீடு இல்லாத" முறையில் பயனர் இடைமுகத்தை மட்டுமே பயன்படுத்துவது; மற்றொன்று, Azure Machine Learning Software Developer Kit (Azure ML SDK) பயன்படுத்துவது. -![project-schema](../../../translated_images/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.ta.png) +![project-schema](../../../translated_images/ta/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.png) ### தலைப்புகள் diff --git a/translations/ta/6-Data-Science-In-Wild/20-Real-World-Examples/README.md b/translations/ta/6-Data-Science-In-Wild/20-Real-World-Examples/README.md index 2bcfdc8e..bff03bcb 100644 --- a/translations/ta/6-Data-Science-In-Wild/20-Real-World-Examples/README.md +++ b/translations/ta/6-Data-Science-In-Wild/20-Real-World-Examples/README.md @@ -41,7 +41,7 @@ AI-யின் ஜனநாயகமயமாக்கலுக்கு நன * [சுகாதாரத்தில் தரவியல் அறிவியல்](https://data-flair.training/blogs/data-science-in-healthcare/) - மருத்துவ படங்கள் (எ.கா., MRI, X-Ray, CT-Scan), ஜீனோமிக்ஸ் (DNA வரிசைப்படுத்தல்), மருந்து மேம்பாடு (அபாய மதிப்பீடு, வெற்றியின் கணிப்பு), முன்கூட்டிய கணிப்புகள் (நோயாளி பராமரிப்பு & வழங்கல் தளவாடங்கள்), நோய் கண்காணிப்பு மற்றும் தடுப்பு போன்ற பயன்பாடுகளை விளக்குகிறது. -![உண்மையான உலகில் தரவியல் அறிவியல் பயன்பாடுகள்](../../../../translated_images/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.ta.png) படத்தின் உரிமை: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) +![உண்மையான உலகில் தரவியல் அறிவியல் பயன்பாடுகள்](../../../../translated_images/ta/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.png) படத்தின் உரிமை: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) இந்த படத்தில் தரவியல் அறிவியல் தொழில்நுட்பங்களைப் பயன்படுத்துவதற்கான பிற துறைகள் மற்றும் உதாரணங்கள் காட்டப்படுகின்றன. பிற பயன்பாடுகளை ஆராய விரும்புகிறீர்களா? கீழே உள்ள [மதிப்பீடு & சுய கற்றல்](../../../../6-Data-Science-In-Wild/20-Real-World-Examples) பிரிவைப் பாருங்கள். diff --git a/translations/ta/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md b/translations/ta/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md index f02cf658..725bbcdf 100644 --- a/translations/ta/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md +++ b/translations/ta/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md @@ -22,7 +22,7 @@ Explorer இடைமுகம் (கீழே உள்ள ஸ்கிரீ 2. [Catalog](https://planetarycomputer.microsoft.com/catalog) தரவுத்தொகுப்பை ஆராயவும் - ஒவ்வொன்றின் நோக்கத்தை அறியவும். 3. Explorer ஐப் பயன்படுத்தவும் - உங்களுக்கு விருப்பமான ஒரு தரவுத்தொகுப்பைத் தேர்ந்தெடுக்கவும், தொடர்புடைய கேள்வி மற்றும் காட்சிப்படுத்தல் விருப்பத்தைத் தேர்ந்தெடுக்கவும். -![The Planetary Computer Explorer](../../../../translated_images/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.ta.png) +![The Planetary Computer Explorer](../../../../translated_images/ta/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.png) `உங்கள் பணி:` இப்போது உலாவியில் உருவாக்கப்படும் காட்சிப்படுத்தலை ஆராய்ந்து, பின்வரும் கேள்விகளுக்கு பதிலளிக்கவும்: diff --git a/translations/ta/CONTRIBUTING.md b/translations/ta/CONTRIBUTING.md index 52fd06af..e4c47dd8 100644 --- a/translations/ta/CONTRIBUTING.md +++ b/translations/ta/CONTRIBUTING.md @@ -316,7 +316,7 @@ def calculate_mean(data): ``` ```` -- படங்களுக்கு மாற்று உரையைச் சேர்க்கவும்: `![Alt text](../../translated_images/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.ta.png)` +- படங்களுக்கு மாற்று உரையைச் சேர்க்கவும்: `![Alt text](../../translated_images/ta/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.png)` - வரி நீளங்களை நியாயமான அளவில் வைத்திருங்கள் (சுமார் 80-100 எழுத்துகள்) ### Python diff --git a/translations/ta/README.md b/translations/ta/README.md index 24c409d1..101865ab 100644 --- a/translations/ta/README.md +++ b/translations/ta/README.md @@ -33,7 +33,7 @@ Microsoft இல் உள்ள Azure Cloud Advocates தங்கள் 10-வ **🙏 சிறப்பு நன்றி 🙏 எங்கள் [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/) ஆசிரியர்கள், விமர்சகர்கள் மற்றும் உள்ளடக்க பங்களிப்பாளர்களுக்கு,** குறிப்பாக Aaryan Arora, [Aditya Garg](https://github.com/AdityaGarg00), [Alondra Sanchez](https://www.linkedin.com/in/alondra-sanchez-molina/), [Ankita Singh](https://www.linkedin.com/in/ankitasingh007), [Anupam Mishra](https://www.linkedin.com/in/anupam--mishra/), [Arpita Das](https://www.linkedin.com/in/arpitadas01/), ChhailBihari Dubey, [Dibri Nsofor](https://www.linkedin.com/in/dibrinsofor), [Dishita Bhasin](https://www.linkedin.com/in/dishita-bhasin-7065281bb), [Majd Safi](https://www.linkedin.com/in/majd-s/), [Max Blum](https://www.linkedin.com/in/max-blum-6036a1186/), [Miguel Correa](https://www.linkedin.com/in/miguelmque/), [Mohamma Iftekher (Iftu) Ebne Jalal](https://twitter.com/iftu119), [Nawrin Tabassum](https://www.linkedin.com/in/nawrin-tabassum), [Raymond Wangsa Putra](https://www.linkedin.com/in/raymond-wp/), [Rohit Yadav](https://www.linkedin.com/in/rty2423), Samridhi Sharma, [Sanya Sinha](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200), [Sheena Narula](https://www.linkedin.com/in/sheena-narua-n/), [Tauqeer Ahmad](https://www.linkedin.com/in/tauqeerahmad5201/), Yogendrasingh Pawar , [Vidushi Gupta](https://www.linkedin.com/in/vidushi-gupta07/), [Jasleen Sondhi](https://www.linkedin.com/in/jasleen-sondhi/) -|![ஸ்கெட்ச்நோட் எழுதியவர் @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.ta.png)| +|![ஸ்கெட்ச்நோட் எழுதியவர் @sketchthedocs https://sketchthedocs.dev](../../translated_images/ta/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.png)| |:---:| | ஆரம்பநிலை தரவு அறிவியல் - _ஸ்கெட்ச்நோட் எழுதியவர் [@nitya](https://twitter.com/nitya)_ | @@ -52,7 +52,7 @@ Microsoft இல் உள்ள Azure Cloud Advocates தங்கள் 10-வ 18 - 30 செப்டம்பர், 2025 இடையே நடைபெறும், AI உடன் கற்கும் தொடர் நிகழ்ச்சியில் நாங்கள் ஒரு தொடர்ச்சியான Discord செயற்பாட்டை நடத்துகிறோம், மேலும் அறிந்து எங்கள் [Learn with AI Series](https://aka.ms/learnwithai/discord) இல் சேருங்கள். நீங்கள் Data Science க்காக GitHub Copilot பயன்படுத்துவதற்கான குறிப்புகள் மற்றும் உத்திகளைக் பெறுவீர்கள். -![AI உடன் கற்கும் தொடர்](../../translated_images/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.ta.jpg) +![AI உடன் கற்கும் தொடர்](../../translated_images/ta/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.jpg) # நீங்களா ஒரு மாணவர்? @@ -132,7 +132,7 @@ Microsoft இல் உள்ள Azure Cloud Advocates தங்கள் 10-வ ## பாடங்கள் -|![ ஸ்கெட்ச்னோட் @sketchthedocs எழுதியவர் https://sketchthedocs.dev](../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.ta.png)| +|![ ஸ்கெட்ச்னோட் @sketchthedocs எழுதியவர் https://sketchthedocs.dev](../../translated_images/ta/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png)| |:---:| | தரவு அறிவியல் ஆரம்பத்திற்கான வரைபடம் - _ஸ்கெட்ச்னோட் by [@nitya](https://twitter.com/nitya)_ | diff --git a/translations/ta/sketchnotes/README.md b/translations/ta/sketchnotes/README.md index edb3f214..3772888f 100644 --- a/translations/ta/sketchnotes/README.md +++ b/translations/ta/sketchnotes/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: நித்யா நரசிம்மன், கலைஞர் -![ரோட்மேப் ஸ்கெட்ச் நோட்](../../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.ta.png) +![ரோட்மேப் ஸ்கெட்ச் நோட்](../../../translated_images/ta/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png) --- diff --git a/translations/te/1-Introduction/01-defining-data-science/README.md b/translations/te/1-Introduction/01-defining-data-science/README.md index b4278e2f..bb57e116 100644 --- a/translations/te/1-Introduction/01-defining-data-science/README.md +++ b/translations/te/1-Introduction/01-defining-data-science/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: --- -[![డేటా సైన్స్ నిర్వచనం వీడియో](../../../../translated_images/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.te.png)](https://youtu.be/beZ7Mb_oz9I) +[![డేటా సైన్స్ నిర్వచనం వీడియో](../../../../translated_images/te/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.png)](https://youtu.be/beZ7Mb_oz9I) ## [పూర్వ-లెక్చర్ క్విజ్](https://ff-quizzes.netlify.app/en/ds/quiz/0) @@ -153,7 +153,7 @@ CO_OP_TRANSLATOR_METADATA: ఈ సవాలలో, మేము డేటా సైన్స్ రంగానికి సంబంధించిన భావనలను పాఠ్యాలను పరిశీలించడం ద్వారా కనుగొనడానికి ప్రయత్నిస్తాము. మేము డేటా సైన్స్ పై వికీపీడియా వ్యాసాన్ని తీసుకుని, టెక్స్ట్‌ను డౌన్లోడ్ చేసి ప్రాసెస్ చేసి, ఈ విధమైన పద మేఘాన్ని నిర్మిస్తాము: -![Word Cloud for Data Science](../../../../translated_images/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.te.png) +![Word Cloud for Data Science](../../../../translated_images/te/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.png) కోడ్‌ను చదవడానికి [`notebook.ipynb`](../../../../1-Introduction/01-defining-data-science/notebook.ipynb ':ignore') ను సందర్శించండి. మీరు కోడ్‌ను కూడా నడిపించి, అన్ని డేటా మార్పిడి ప్రక్రియలను ప్రత్యక్షంగా చూడవచ్చు. diff --git a/translations/te/1-Introduction/04-stats-and-probability/README.md b/translations/te/1-Introduction/04-stats-and-probability/README.md index 75dd0fc0..67951283 100644 --- a/translations/te/1-Introduction/04-stats-and-probability/README.md +++ b/translations/te/1-Introduction/04-stats-and-probability/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: గణాంకాలు మరియు సంభావ్యత సిద్ధాంతం గణితశాస్త్రంలోని రెండు అత్యంత సంబంధిత విభాగాలు, ఇవి డేటా సైన్స్‌కు చాలా సంబంధం కలిగి ఉంటాయి. గణితంపై లోతైన జ్ఞానం లేకుండా కూడా డేటాతో పని చేయవచ్చు, కానీ కనీసం కొన్ని ప్రాథమిక భావనలను తెలుసుకోవడం మంచిది. ఇక్కడ మేము మీకు ప్రారంభం కావడానికి సహాయపడే సంక్షిప్త పరిచయాన్ని అందిస్తాము. -[![పరిచయ వీడియో](../../../../translated_images/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.te.png)](https://youtu.be/Z5Zy85g4Yjw) +[![పరిచయ వీడియో](../../../../translated_images/te/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.png)](https://youtu.be/Z5Zy85g4Yjw) ## [పాఠం ముందు క్విజ్](https://ff-quizzes.netlify.app/en/ds/quiz/6) @@ -40,7 +40,7 @@ CO_OP_TRANSLATOR_METADATA: మేము ఒక విలువ ఇచ్చిన పరిధిలో పడే సంభావ్యత గురించి మాత్రమే మాట్లాడవచ్చు, ఉదా. P(t1≤X<t2). ఈ సందర్భంలో, సంభావ్యత పంపిణీని **సంభావ్యత సాంద్రత ఫంక్షన్** p(x) ద్వారా వివరించవచ్చు, అలా -![P(t_1\le X +Box Plot Explanation ఇక్కడ మేము **ఇంటర్-క్వార్టైల్ పరిధి** IQR=Q3-Q1 ను లెక్కిస్తాము, మరియు **అత్యధిక విలువలు** (outliers) - [Q1-1.5*IQR,Q3+1.5*IQR] పరిధికి వెలుపల ఉన్న విలువలు. @@ -82,11 +82,11 @@ CO_OP_TRANSLATOR_METADATA: ఇది మా డేటా కోసం సగటు, మాధ్యమం మరియు క్వార్టైల్‌లను చూపించే బాక్స్ ప్లాట్: -![బరువు బాక్స్ ప్లాట్](../../../../translated_images/weight-boxplot.1dbab1c03af26f8a008fff4e17680082c8ab147d6df646cbac440bbf8f5b9c42.te.png) +![బరువు బాక్స్ ప్లాట్](../../../../translated_images/te/weight-boxplot.1dbab1c03af26f8a008fff4e17680082c8ab147d6df646cbac440bbf8f5b9c42.png) మా డేటాలో వేర్వేరు ఆటగాళ్ల **పాత్రలు** గురించి సమాచారం ఉన్నందున, పాత్రల వారీగా బాక్స్ ప్లాట్ కూడా చేయవచ్చు - ఇది పాత్రల మధ్య పరిమాణాల విలువలు ఎలా భిన్నమవుతాయో అర్థం చేసుకోవడానికి సహాయపడుతుంది. ఈసారి మేము ఎత్తును పరిగణిస్తాము: -![పాత్రల వారీగా బాక్స్ ప్లాట్](../../../../translated_images/boxplot_byrole.036b27a1c3f52d42f66fba2324ec5cde0a1bca6a01a619eeb0ce7cd054b2527b.te.png) +![పాత్రల వారీగా బాక్స్ ప్లాట్](../../../../translated_images/te/boxplot_byrole.036b27a1c3f52d42f66fba2324ec5cde0a1bca6a01a619eeb0ce7cd054b2527b.png) ఈ చిత్రణ సూచిస్తుంది, సగటున మొదటి బేస్‌మెన్ ఎత్తు రెండవ బేస్‌మెన్ కంటే ఎక్కువ. ఈ పాఠంలో తర్వాత మేము ఈ హైపోథిసిస్‌ను మరింత అధికారికంగా పరీక్షించడం మరియు మా డేటా గణాంకపరంగా ప్రామాణికమని చూపడం నేర్చుకుంటాము. @@ -94,7 +94,7 @@ CO_OP_TRANSLATOR_METADATA: మా డేటా పంపిణీని చూడటానికి, మేము **హిస్టోగ్రామ్** అనే గ్రాఫ్‌ను చిత్రించవచ్చు. X-అక్షంలో వివిధ బరువు పరిధులు (అంటే **బిన్లు**) ఉంటాయి, మరియు నిలువు అక్షం ఆ పరిధిలో మా యాదృచ్ఛిక చరము నమూనా ఉన్న సార్లు చూపిస్తుంది. -![వాస్తవ ప్రపంచ డేటా హిస్టోగ్రామ్](../../../../translated_images/weight-histogram.bfd00caf7fc30b145b21e862dba7def41c75635d5280de25d840dd7f0b00545e.te.png) +![వాస్తవ ప్రపంచ డేటా హిస్టోగ్రామ్](../../../../translated_images/te/weight-histogram.bfd00caf7fc30b145b21e862dba7def41c75635d5280de25d840dd7f0b00545e.png) ఈ హిస్టోగ్రామ్ నుండి మీరు చూడవచ్చు అన్ని విలువలు ఒక నిర్దిష్ట సగటు బరువు చుట్టూ కేంద్రీకృతమై ఉన్నాయి, మరియు ఆ బరువునుండి దూరంగా పోతే ఆ బరువుల సంఖ్య తక్కువగా ఉంటుంది. అంటే, బేస్‌బాల్ ఆటగాడి బరువు సగటు బరువుతో చాలా భిన్నంగా ఉండటం చాలా అసంభావ్యం. బరువుల వ్యత్యాసం సగటు నుండి బరువులు ఎంత భిన్నంగా ఉండవచ్చో చూపిస్తుంది. @@ -110,7 +110,7 @@ samples = np.random.normal(mean,std,1000) సృష్టించిన నమూనాల హిస్టోగ్రామ్‌ను చిత్రిస్తే, పై చూపిన చిత్రానికి చాలా సమీపంగా ఉంటుంది. నమూనాల సంఖ్య మరియు బిన్ల సంఖ్య పెంచితే, మేము సిద్దాంతానికి మరింత దగ్గరగా ఉన్న సాధారణ పంపిణీ చిత్రాన్ని సృష్టించవచ్చు: -![సగటు=0 మరియు ప్రమాణ విభిన్నత=1 ఉన్న సాధారణ పంపిణీ](../../../../translated_images/normal-histogram.dfae0d67c202137d552d0015fb87581eca263925e512404f3c12d8885315432e.te.png) +![సగటు=0 మరియు ప్రమాణ విభిన్నత=1 ఉన్న సాధారణ పంపిణీ](../../../../translated_images/te/normal-histogram.dfae0d67c202137d552d0015fb87581eca263925e512404f3c12d8885315432e.png) *సగటు=0 మరియు ప్రమాణ విభిన్నత=1 ఉన్న సాధారణ పంపిణీ* @@ -229,7 +229,7 @@ array([[1. , 0.52959196], మన సందర్భంలో, 0.53 విలువ వ్యక్తి బరువు మరియు ఎత్తు మధ్య కొంత సహసంబంధం ఉందని సూచిస్తుంది. మనం ఒక విలువను మరొకదానిపై స్కాటర్ ప్లాట్ కూడా చేయవచ్చు సంబంధాన్ని దృశ్యంగా చూడటానికి: -![బరువు మరియు ఎత్తు మధ్య సంబంధం](../../../../translated_images/weight-height-relationship.3f06bde4ca2aba9974182c4ef037ed602acd0fbbbbe2ca91cefd838a9e66bcf9.te.png) +![బరువు మరియు ఎత్తు మధ్య సంబంధం](../../../../translated_images/te/weight-height-relationship.3f06bde4ca2aba9974182c4ef037ed602acd0fbbbbe2ca91cefd838a9e66bcf9.png) > సహసంబంధం మరియు కోవేరియన్స్ పై మరిన్ని ఉదాహరణలు [సహాయక నోట్బుక్](notebook.ipynb) లో చూడవచ్చు. diff --git a/translations/te/1-Introduction/README.md b/translations/te/1-Introduction/README.md index dbe13719..9e6d2fc8 100644 --- a/translations/te/1-Introduction/README.md +++ b/translations/te/1-Introduction/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # డేటా సైన్స్ పరిచయం -![data in action](../../../translated_images/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.te.jpg) +![data in action](../../../translated_images/te/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.jpg) > ఫోటో స్టీఫెన్ డాసన్ ద్వారా అన్స్ప్లాష్లో ఈ పాఠాలలో, మీరు డేటా సైన్స్ ఎలా నిర్వచించబడిందో తెలుసుకుంటారు మరియు డేటా శాస్త్రవేత్తలు పరిగణించవలసిన నైతిక అంశాలను తెలుసుకుంటారు. మీరు డేటా ఎలా నిర్వచించబడిందో తెలుసుకుంటారు మరియు డేటా సైన్స్ యొక్క ప్రాథమిక అకాడమిక్ విభాగాలు అయిన గణాంకాలు మరియు సంభావ్యత గురించి కొంత తెలుసుకుంటారు. diff --git a/translations/te/2-Working-With-Data/06-non-relational/README.md b/translations/te/2-Working-With-Data/06-non-relational/README.md index 67b3cf84..103635db 100644 --- a/translations/te/2-Working-With-Data/06-non-relational/README.md +++ b/translations/te/2-Working-With-Data/06-non-relational/README.md @@ -21,7 +21,7 @@ CO_OP_TRANSLATOR_METADATA: స్ప్రెడ్షీట్స్ డేటాను నిల్వ చేయడానికి మరియు అన్వేషించడానికి ప్రాచుర్యం పొందిన మార్గం, ఎందుకంటే దీన్ని సెటప్ చేయడం మరియు ప్రారంభించడం తక్కువ పని అవసరం. ఈ పాఠంలో మీరు స్ప్రెడ్షీట్ యొక్క ప్రాథమిక భాగాలు, అలాగే ఫార్మూలాలు మరియు ఫంక్షన్లను నేర్చుకుంటారు. ఉదాహరణలు మైక్రోసాఫ్ట్ ఎక్సెల్‌తో చూపబడతాయి, కానీ చాలా భాగాలు మరియు విషయాలు ఇతర స్ప్రెడ్షీట్ సాఫ్ట్‌వేర్‌తో పోలిస్తే సమాన పేర్లు మరియు దశలను కలిగి ఉంటాయి. -![రెండు వర్క్‌షీట్లతో ఖాళీ మైక్రోసాఫ్ట్ ఎక్సెల్ వర్క్‌బుక్](../../../../translated_images/parts-of-spreadsheet.120711c82aa18a45c3e62a491a15bba0a31ab0e9db407ec022702fed8ffd89bf.te.png) +![రెండు వర్క్‌షీట్లతో ఖాళీ మైక్రోసాఫ్ట్ ఎక్సెల్ వర్క్‌బుక్](../../../../translated_images/te/parts-of-spreadsheet.120711c82aa18a45c3e62a491a15bba0a31ab0e9db407ec022702fed8ffd89bf.png) స్ప్రెడ్షీట్ ఒక ఫైల్ మరియు కంప్యూటర్, పరికరం లేదా క్లౌడ్ ఆధారిత ఫైల్ సిస్టమ్‌లో అందుబాటులో ఉంటుంది. సాఫ్ట్‌వేర్ బ్రౌజర్ ఆధారితంగా ఉండవచ్చు లేదా కంప్యూటర్‌లో ఇన్‌స్టాల్ చేయాల్సిన అనువర్తనం లేదా యాప్‌గా డౌన్లోడ్ చేయవలసినది కావచ్చు. ఎక్సెల్‌లో ఈ ఫైళ్లను **వర్క్‌బుక్స్** అని కూడా నిర్వచిస్తారు మరియు ఈ పదజాలం ఈ పాఠం మిగిలిన భాగంలో ఉపయోగించబడుతుంది. @@ -33,11 +33,11 @@ CO_OP_TRANSLATOR_METADATA: "InventoryExample" అనే స్ప్రెడ్షీట్ ఫైల్ ఒక ఫార్మాట్ చేయబడిన ఇన్వెంటరీలోని అంశాల స్ప్రెడ్షీట్, ఇది మూడు వర్క్‌షీట్లను కలిగి ఉంటుంది, టాబ్‌లు "Inventory List", "Inventory Pick List" మరియు "Bin Lookup" అని లేబుల్ చేయబడ్డాయి. Inventory List వర్క్‌షీట్‌లో నాల్గవ వరుస హెడర్, ఇది హెడర్ కాలమ్‌లోని ప్రతి సెల్ విలువను వివరిస్తుంది. -![మైక్రోసాఫ్ట్ ఎక్సెల్‌లో ఉదాహరణ ఇన్వెంటరీ జాబితా నుండి హైలైట్ చేయబడిన ఫార్ములా](../../../../translated_images/formula-excel.ad1068c220892f5ead570d12f2394897961d31a5043a1dd4e6fc5d7690c7a14e.te.png) +![మైక్రోసాఫ్ట్ ఎక్సెల్‌లో ఉదాహరణ ఇన్వెంటరీ జాబితా నుండి హైలైట్ చేయబడిన ఫార్ములా](../../../../translated_images/te/formula-excel.ad1068c220892f5ead570d12f2394897961d31a5043a1dd4e6fc5d7690c7a14e.png) కొన్ని సందర్భాల్లో ఒక సెల్ విలువను ఉత్పత్తి చేయడానికి ఇతర సెల్స్ విలువలపై ఆధారపడి ఉంటుంది. ఇన్వెంటరీ జాబితా స్ప్రెడ్షీట్ తన ఇన్వెంటరీలోని ప్రతి అంశం యొక్క ఖర్చును ట్రాక్ చేస్తుంది, కానీ ఇన్వెంటరీలోని మొత్తం విలువ తెలుసుకోవాలంటే? [**ఫార్మూలాలు**](https://support.microsoft.com/en-us/office/overview-of-formulas-34519a4e-1e8d-4f4b-84d4-d642c4f63263) సెల్ డేటాపై చర్యలు నిర్వహిస్తాయి మరియు ఈ ఉదాహరణలో ఇన్వెంటరీ ఖర్చును లెక్కించడానికి ఉపయోగిస్తారు. ఈ స్ప్రెడ్షీట్ ఇన్వెంటరీ విలువ కాలమ్‌లో ఫార్ములాను ఉపయోగించి ప్రతి అంశం విలువను లెక్కిస్తుంది, ఇది QTY హెడర్ కింద ఉన్న పరిమాణాన్ని COST హెడర్ కింద ఉన్న ఖర్చులతో గుణిస్తుంది. ఒక సెల్‌ను డబుల్ క్లిక్ చేయడం లేదా హైలైట్ చేయడం ద్వారా ఫార్ములా కనిపిస్తుంది. మీరు గమనిస్తారు ఫార్మూలాలు సమాన చిహ్నంతో ప్రారంభమవుతాయి, తరువాత లెక్కింపు లేదా ఆపరేషన్ ఉంటుంది. -![మైక్రోసాఫ్ట్ ఎక్సెల్‌లో ఉదాహరణ ఇన్వెంటరీ జాబితా నుండి హైలైట్ చేయబడిన ఫంక్షన్](../../../../translated_images/function-excel.be2ae4feddc10ca089f3d4363040d93b7fd046c8d4f83ba975ec46483ee99895.te.png) +![మైక్రోసాఫ్ట్ ఎక్సెల్‌లో ఉదాహరణ ఇన్వెంటరీ జాబితా నుండి హైలైట్ చేయబడిన ఫంక్షన్](../../../../translated_images/te/function-excel.be2ae4feddc10ca089f3d4363040d93b7fd046c8d4f83ba975ec46483ee99895.png) మేము మరో ఫార్ములాను ఉపయోగించి ఇన్వెంటరీ విలువలన్నింటినీ కలిపి మొత్తం విలువను పొందవచ్చు. ప్రతి సెల్‌ను జోడించడం ద్వారా మొత్తం లెక్కించవచ్చు, కానీ అది కష్టమైన పని. ఎక్సెల్ [**ఫంక్షన్లు**](https://support.microsoft.com/en-us/office/sum-function-043e1c7d-7726-4e80-8f32-07b23e057f89) కలిగి ఉంది, ఇవి సెల్ విలువలపై లెక్కింపులు నిర్వహించడానికి ముందుగా నిర్వచించబడిన ఫార్మూలాలు. ఫంక్షన్లు ఆర్గ్యుమెంట్లను అవసరం చేస్తాయి, ఇవి లెక్కింపులు నిర్వహించడానికి అవసరమైన విలువలు. ఫంక్షన్లు ఒక కంటే ఎక్కువ ఆర్గ్యుమెంట్లు అవసరం అయితే, అవి నిర్దిష్ట క్రమంలో జాబితా చేయబడాలి లేకపోతే ఫంక్షన్ సరైన విలువను లెక్కించకపోవచ్చు. ఈ ఉదాహరణ SUM ఫంక్షన్‌ను ఉపయోగిస్తుంది, ఇది ఇన్వెంటరీ విలువల విలువలను ఆర్గ్యుమెంట్‌గా ఉపయోగించి వరుస 3, కాలమ్ B (B3 అని కూడా పిలవబడుతుంది) కింద మొత్తం లెక్కిస్తుంది. @@ -45,17 +45,17 @@ CO_OP_TRANSLATOR_METADATA: నోస్క్యూఎల్ అనేది నాన్-రిలేషనల్ డేటాను నిల్వ చేయడానికి వివిధ మార్గాలకు umbrella పదం, దీన్ని "నాన్-ఎస్‌క్యూఎల్", "నాన్-రిలేషనల్" లేదా "నాట్ ఓన్లీ SQL" అని అర్థం చేసుకోవచ్చు. ఈ రకమైన డేటాబేస్ సిస్టమ్స్ 4 రకాలుగా వర్గీకరించబడతాయి. -![కీ-విలువ డేటా స్టోర్ యొక్క గ్రాఫికల్ ప్రాతినిధ్యం, 4 ప్రత్యేక సంఖ్యా కీలు 4 వివిధ విలువలతో అనుసంధానించబడ్డాయి](../../../../translated_images/kv-db.e8f2b75686bbdfcba0c827b9272c10ae0821611ea0fe98429b9d13194383afa6.te.png) +![కీ-విలువ డేటా స్టోర్ యొక్క గ్రాఫికల్ ప్రాతినిధ్యం, 4 ప్రత్యేక సంఖ్యా కీలు 4 వివిధ విలువలతో అనుసంధానించబడ్డాయి](../../../../translated_images/te/kv-db.e8f2b75686bbdfcba0c827b9272c10ae0821611ea0fe98429b9d13194383afa6.png) > మూలం [Michał Białecki Blog](https://www.michalbialecki.com/2018/03/18/azure-cosmos-db-key-value-database-cloud/) [కీ-విలువ](https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/non-relational-data#keyvalue-data-stores) డేటాబేస్‌లు ప్రత్యేక కీలు, అంటే విలువతో అనుసంధానించబడిన ప్రత్యేక గుర్తింపును జతచేస్తాయి. ఈ జంటలు సరైన హాషింగ్ ఫంక్షన్‌తో [హాష్ టేబుల్](https://www.hackerearth.com/practice/data-structures/hash-tables/basics-of-hash-tables/tutorial/) ఉపయోగించి నిల్వ చేయబడతాయి. -![గ్రాఫ్ డేటా స్టోర్ యొక్క గ్రాఫికల్ ప్రాతినిధ్యం, వ్యక్తులు, వారి ఆసక్తులు మరియు ప్రదేశాల మధ్య సంబంధాలను చూపిస్తుంది](../../../../translated_images/graph-db.d13629152f79a9dac895b20fa7d841d4d4d6f6008b1382227c3bbd200fd4cfa1.te.png) +![గ్రాఫ్ డేటా స్టోర్ యొక్క గ్రాఫికల్ ప్రాతినిధ్యం, వ్యక్తులు, వారి ఆసక్తులు మరియు ప్రదేశాల మధ్య సంబంధాలను చూపిస్తుంది](../../../../translated_images/te/graph-db.d13629152f79a9dac895b20fa7d841d4d4d6f6008b1382227c3bbd200fd4cfa1.png) > మూలం [Microsoft](https://docs.microsoft.com/en-us/azure/cosmos-db/graph/graph-introduction#graph-database-by-example) [గ్రాఫ్](https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/non-relational-data#graph-data-stores) డేటాబేస్‌లు డేటాలో సంబంధాలను వివరించడానికి ఉపయోగిస్తారు మరియు నోడ్స్ మరియు ఎడ్జెస్ సేకరణగా ప్రాతినిధ్యం వహిస్తాయి. ఒక నోడ్ ఒక ఎంటిటీని సూచిస్తుంది, ఇది వాస్తవ ప్రపంచంలో ఉన్న ఏదైనా, ఉదాహరణకు ఒక విద్యార్థి లేదా బ్యాంక్ స్టేట్‌మెంట్. ఎడ్జెస్ రెండు ఎంటిటీల మధ్య సంబంధాన్ని సూచిస్తాయి. ప్రతి నోడ్ మరియు ఎడ్జ్‌కు అదనపు సమాచారం అందించే లక్షణాలు ఉంటాయి. -![కస్టమర్ డేటాబేస్‌తో కాలమ్నార్ డేటా స్టోర్ యొక్క గ్రాఫికల్ ప్రాతినిధ్యం, రెండు కాలమ్ ఫ్యామిలీలు Identity మరియు Contact Info పేర్లతో](../../../../translated_images/columnar-db.ffcfe73c3e9063a8c8f93f8ace85e1200863584b1e324eb5159d8ca10f62ec04.te.png) +![కస్టమర్ డేటాబేస్‌తో కాలమ్నార్ డేటా స్టోర్ యొక్క గ్రాఫికల్ ప్రాతినిధ్యం, రెండు కాలమ్ ఫ్యామిలీలు Identity మరియు Contact Info పేర్లతో](../../../../translated_images/te/columnar-db.ffcfe73c3e9063a8c8f93f8ace85e1200863584b1e324eb5159d8ca10f62ec04.png) [కాలమ్నార్](https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/non-relational-data#columnar-data-stores) డేటా స్టోర్‌లు డేటాను కాలమ్స్ మరియు వరుసలుగా ఏర్పాటు చేస్తాయి, ఇది రిలేషనల్ డేటా నిర్మాణంలా ఉంటుంది, కానీ ప్రతి కాలమ్ కాలమ్ ఫ్యామిలీ అని పిలవబడే సమూహాలుగా విభజించబడుతుంది, ఒక కాలమ్ కింద ఉన్న అన్ని డేటా సంబంధితంగా ఉంటుంది మరియు ఒక యూనిట్‌గా పొందవచ్చు మరియు మార్చవచ్చు. @@ -88,11 +88,11 @@ Cosmos DB డేటాబేస్ "నాట్ ఓన్లీ SQL" నిర ఎమ్యులేటర్ బ్రౌజర్ విండోను ప్రారంభిస్తుంది, ఇక్కడ ఎక్స్‌ప్లోరర్ వీక్షణ డాక్యుమెంట్లను అన్వేషించడానికి అనుమతిస్తుంది. -![Cosmos DB ఎమ్యులేటర్ యొక్క ఎక్స్‌ప్లోరర్ వీక్షణ](../../../../translated_images/cosmosdb-emulator-explorer.a1c80b1347206fe2f30f88fc123821636587d04fc5a56a9eb350c7da6b31f361.te.png) +![Cosmos DB ఎమ్యులేటర్ యొక్క ఎక్స్‌ప్లోరర్ వీక్షణ](../../../../translated_images/te/cosmosdb-emulator-explorer.a1c80b1347206fe2f30f88fc123821636587d04fc5a56a9eb350c7da6b31f361.png) మీరు అనుసరిస్తున్నట్లయితే, "Start with Sample" క్లిక్ చేసి SampleDB అనే నమూనా డేటాబేస్‌ను సృష్టించండి. Sample DB ను ఎర్రో క్లిక్ చేసి విస్తరించండి, మీరు `Persons` అనే కంటైనర్‌ను కనుగొంటారు, కంటైనర్ ఒక అంశాల సేకరణను కలిగి ఉంటుంది, ఇవి కంటైనర్‌లోని డాక్యుమెంట్లు. మీరు `Items` కింద ఉన్న నాలుగు వ్యక్తిగత డాక్యుమెంట్లను అన్వేషించవచ్చు. -![Cosmos DB ఎమ్యులేటర్‌లో నమూనా డేటాను అన్వేషించడం](../../../../translated_images/cosmosdb-emulator-persons.bf640586a7077c8985dfd3071946465c8e074c722c7c202d6d714de99a93b90a.te.png) +![Cosmos DB ఎమ్యులేటర్‌లో నమూనా డేటాను అన్వేషించడం](../../../../translated_images/te/cosmosdb-emulator-persons.bf640586a7077c8985dfd3071946465c8e074c722c7c202d6d714de99a93b90a.png) #### Cosmos DB ఎమ్యులేటర్‌తో డాక్యుమెంట్ డేటాను క్వెరీ చేయడం @@ -102,7 +102,7 @@ Cosmos DB డేటాబేస్ "నాట్ ఓన్లీ SQL" నిర `SELECT * FROM c where c.age < 40` - ![Cosmos DB ఎమ్యులేటర్‌లో SELECT క్వెరీ నడుపుతూ, వయస్సు 40 కంటే తక్కువ ఉన్న డాక్యుమెంట్లను కనుగొనడం](../../../../translated_images/cosmosdb-emulator-persons-query.6905ebb497e3cd047cd96e55a0a03f69ce1b91b2b3d8c147e617b746b22b7e33.te.png) + ![Cosmos DB ఎమ్యులేటర్‌లో SELECT క్వెరీ నడుపుతూ, వయస్సు 40 కంటే తక్కువ ఉన్న డాక్యుమెంట్లను కనుగొనడం](../../../../translated_images/te/cosmosdb-emulator-persons-query.6905ebb497e3cd047cd96e55a0a03f69ce1b91b2b3d8c147e617b746b22b7e33.png) క్వెరీ రెండు డాక్యుమెంట్లను తిరిగి ఇస్తుంది, ప్రతి డాక్యుమెంట్ వయస్సు విలువ 40 కంటే తక్కువగా ఉంది. diff --git a/translations/te/2-Working-With-Data/07-python/README.md b/translations/te/2-Working-With-Data/07-python/README.md index ddadf6cf..d90d3ddd 100644 --- a/translations/te/2-Working-With-Data/07-python/README.md +++ b/translations/te/2-Working-With-Data/07-python/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: | :-------------------------------------------------------------------------------------------------------: | | Python తో పని చేయడం - _Sketchnote by [@nitya](https://twitter.com/nitya)_ | -[![Intro Video](../../../../translated_images/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.te.png)](https://youtu.be/dZjWOGbsN4Y) +[![Intro Video](../../../../translated_images/te/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.png)](https://youtu.be/dZjWOGbsN4Y) డేటాబేసులు డేటాను నిల్వ చేయడానికి మరియు క్వెరీ భాషలను ఉపయోగించి వాటిని క్వెరీ చేయడానికి చాలా సమర్థవంతమైన మార్గాలను అందించినప్పటికీ, డేటాను ప్రాసెస్ చేయడానికి అత్యంత అనుకూలమైన మార్గం మీ స్వంత ప్రోగ్రామ్ రాయడం. చాలా సందర్భాల్లో, డేటాబేస్ క్వెరీ చేయడం మరింత సమర్థవంతమైన మార్గం అవుతుంది. అయితే, కొన్ని సందర్భాల్లో, మరింత సంక్లిష్టమైన డేటా ప్రాసెసింగ్ అవసరం అయితే, అది SQL ఉపయోగించి సులభంగా చేయలేము. డేటా ప్రాసెసింగ్ ఏ ప్రోగ్రామింగ్ భాషలోనైనా ప్రోగ్రామ్ చేయవచ్చు, కానీ డేటాతో పని చేయడంలో కొంతమంది భాషలు ఉన్నత స్థాయిలో ఉంటాయి. డేటా సైంటిస్టులు సాధారణంగా క్రింది భాషలలో ఒకదాన్ని ఇష్టపడతారు: @@ -75,7 +75,7 @@ print(f"Length of index is {len(idx)}") items_sold = pd.Series(np.random.randint(25,50,size=len(idx)),index=idx) items_sold.plot() ``` -![Time Series Plot](../../../../translated_images/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.te.png) +![Time Series Plot](../../../../translated_images/te/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.png) ఇప్పుడు ప్రతీ వారం మేము స్నేహితుల కోసం పార్టీ నిర్వహిస్తున్నాము, మరియు పార్టీ కోసం అదనంగా 10 ఐస్-క్రీమ్ ప్యాకెట్లు తీసుకుంటాము అనుకోండి. మేము వారానికి ఇండెక్స్ చేయబడిన మరో సిరీస్ సృష్టించవచ్చు, దీన్ని చూపించడానికి: ```python @@ -86,7 +86,7 @@ additional_items = pd.Series(10,index=pd.date_range(start_date,end_date,freq="W" total_items = items_sold.add(additional_items,fill_value=0) total_items.plot() ``` -![Time Series Plot](../../../../translated_images/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.te.png) +![Time Series Plot](../../../../translated_images/te/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.png) > **గమనిక** మేము సాదారణ సింటాక్స్ `total_items+additional_items` ఉపయోగించట్లేదు. అలా చేస్తే, ఫలిత సిరీస్‌లో చాలా `NaN` (*Not a Number*) విలువలు వస్తాయి. ఇది ఎందుకంటే `additional_items` సిరీస్‌లో కొన్ని ఇండెక్స్ పాయింట్లకు విలువలు లేవు, మరియు `NaN` ను ఏదైనా విలువకు జోడిస్తే ఫలితం `NaN` అవుతుంది. అందువల్ల జోడింపు సమయంలో `fill_value` పారామీటర్‌ను నిర్దేశించాలి. @@ -95,7 +95,7 @@ total_items.plot() monthly = total_items.resample("1M").mean() ax = monthly.plot(kind='bar') ``` -![Monthly Time Series Averages](../../../../translated_images/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.te.png) +![Monthly Time Series Averages](../../../../translated_images/te/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.png) ### డేటాఫ్రేమ్ @@ -222,7 +222,7 @@ df = pd.read_csv('file.csv') మనం డేటాతో ఎలా వ్యవహరించాలో చూపించాలనుకుంటున్నందున, మీరు [`notebook-covidspread.ipynb`](notebook-covidspread.ipynb) ను తెరిచి పై నుండి క్రింద వరకు చదవమని ఆహ్వానిస్తున్నాము. మీరు సెల్స్‌ను కూడా నడిపించవచ్చు, మరియు చివరలో మేము మీ కోసం వదిలిన కొన్ని ఛాలెంజ్‌లను చేయవచ్చు. -![COVID Spread](../../../../translated_images/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.te.png) +![COVID Spread](../../../../translated_images/te/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.png) > మీరు Jupyter Notebook లో కోడ్ ఎలా నడుపాలో తెలియకపోతే, [ఈ వ్యాసం](https://soshnikov.com/education/how-to-execute-notebooks-from-github/)ను చూడండి. @@ -244,7 +244,7 @@ df = pd.read_csv('file.csv') [`notebook-papers.ipynb`](notebook-papers.ipynb) ను తెరిచి పై నుండి క్రింద వరకు చదవండి. మీరు సెల్స్‌ను కూడా నడిపించవచ్చు, మరియు చివరలో మేము మీ కోసం వదిలిన కొన్ని ఛాలెంజ్‌లను చేయవచ్చు. -![Covid Medical Treatment](../../../../translated_images/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.te.png) +![Covid Medical Treatment](../../../../translated_images/te/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.png) ## చిత్ర డేటా ప్రాసెసింగ్ diff --git a/translations/te/2-Working-With-Data/README.md b/translations/te/2-Working-With-Data/README.md index 7aa81726..4450dac4 100644 --- a/translations/te/2-Working-With-Data/README.md +++ b/translations/te/2-Working-With-Data/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # డేటాతో పని చేయడం -![data love](../../../translated_images/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.te.jpg) +![data love](../../../translated_images/te/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.jpg) > ఫోటో Alexander Sinn ద్వారా Unsplashలో ఈ పాఠాలలో, డేటాను ఎలా నిర్వహించవచ్చు, మార్చవచ్చు మరియు అనువర్తనాలలో ఉపయోగించవచ్చు అనే కొన్ని మార్గాలను మీరు నేర్చుకుంటారు. మీరు సంబంధిత మరియు అసంబంధిత డేటాబేస్‌ల గురించి మరియు వాటిలో డేటాను ఎలా నిల్వ చేయవచ్చో తెలుసుకుంటారు. డేటాను నిర్వహించడానికి Pythonతో పని చేసే ప్రాథమిక అంశాలను మీరు నేర్చుకుంటారు, మరియు Pythonతో డేటాను నిర్వహించడానికి మరియు తవ్వడానికి మీరు ఉపయోగించగల అనేక మార్గాలను మీరు కనుగొంటారు. diff --git a/translations/te/3-Data-Visualization/09-visualization-quantities/README.md b/translations/te/3-Data-Visualization/09-visualization-quantities/README.md index 1cf7958e..b146f7f9 100644 --- a/translations/te/3-Data-Visualization/09-visualization-quantities/README.md +++ b/translations/te/3-Data-Visualization/09-visualization-quantities/README.md @@ -61,7 +61,7 @@ birds.head() wingspan = birds['MaxWingspan'] wingspan.plot() ``` -![గరిష్ఠ రెక్కల వ్యాప్తి](../../../../translated_images/max-wingspan-02.e79fd847b2640b89e21e340a3a9f4c5d4b224c4fcd65f54385e84f1c9ed26d52.te.png) +![గరిష్ఠ రెక్కల వ్యాప్తి](../../../../translated_images/te/max-wingspan-02.e79fd847b2640b89e21e340a3a9f4c5d4b224c4fcd65f54385e84f1c9ed26d52.png) మీకు వెంటనే ఏమి కనిపిస్తుంది? కనీసం ఒక అవుట్లయర్ ఉన్నట్లు కనిపిస్తోంది - అది చాలా పెద్ద రెక్కల వ్యాప్తి! 2300 సెంటీమీటర్ల రెక్కల వ్యాప్తి అంటే 23 మీటర్లు - మినెసోటాలో ప్టెరోడాక్టిల్స్ తిరుగుతున్నారా? పరిశీలిద్దాం. @@ -81,7 +81,7 @@ plt.plot(x, y) plt.show() ``` -![లేబుల్స్ తో రెక్కల వ్యాప్తి](../../../../translated_images/max-wingspan-labels-02.aa90e826ca49a9d1dde78075e9755c1849ef56a4e9ec60f7e9f3806daf9283e2.te.png) +![లేబుల్స్ తో రెక్కల వ్యాప్తి](../../../../translated_images/te/max-wingspan-labels-02.aa90e826ca49a9d1dde78075e9755c1849ef56a4e9ec60f7e9f3806daf9283e2.png) లేబుల్స్ 45 డిగ్రీల కోణంలో తిప్పినా కూడా చదవడానికి చాలా ఉన్నాయి. వేరే వ్యూహం ప్రయత్నిద్దాం: అవుట్లయర్లకు మాత్రమే లేబుల్స్ ఇవ్వండి మరియు లేబుల్స్‌ను చార్ట్ లోపల ఉంచండి. లేబలింగ్ కోసం మరింత స్థలం కోసం స్కాటర్ చార్ట్ ఉపయోగించవచ్చు: @@ -103,7 +103,7 @@ plt.show() మీరు ఏమి కనుగొన్నారు? -![అవుట్లయర్లు](../../../../translated_images/labeled-wingspan-02.6110e2d2401cd5238ccc24dfb6d04a6c19436101f6cec151e3992e719f9f1e1f.te.png) +![అవుట్లయర్లు](../../../../translated_images/te/labeled-wingspan-02.6110e2d2401cd5238ccc24dfb6d04a6c19436101f6cec151e3992e719f9f1e1f.png) ## మీ డేటాను ఫిల్టర్ చేయండి బాల్డ్ ఈగిల్ మరియు ప్రేరి ఫాల్కన్, చాలా పెద్ద పక్షులు కావచ్చు, గరిష్ఠ రెక్కల వ్యాప్తికి అదనపు `0` తప్పుగా జోడించబడినట్లు కనిపిస్తున్నాయి. 25 మీటర్ల రెక్కల వ్యాప్తి ఉన్న బాల్డ్ ఈగిల్‌ను మీరు కలుసుకోవడం అసాధ్యమే, అయితే ఉంటే, దయచేసి మాకు తెలియజేయండి! ఆ రెండు అవుట్లయర్లను తీసేసి కొత్త డేటాఫ్రేమ్ సృష్టిద్దాం: @@ -123,7 +123,7 @@ plt.show() అవుట్లయర్లను ఫిల్టర్ చేయడం ద్వారా, మీ డేటా ఇప్పుడు మరింత సమగ్రంగా మరియు అర్థమయ్యేలా మారింది. -![రెక్కల వ్యాప్తుల స్కాటర్‌ప్లాట్](../../../../translated_images/scatterplot-wingspan-02.1c33790094ce36a75f5fb45b25ed2cf27f0356ea609e43c11e97a2cedd7011a4.te.png) +![రెక్కల వ్యాప్తుల స్కాటర్‌ప్లాట్](../../../../translated_images/te/scatterplot-wingspan-02.1c33790094ce36a75f5fb45b25ed2cf27f0356ea609e43c11e97a2cedd7011a4.png) ఇప్పుడు రెక్కల వ్యాప్తి పరంగా కనీసం శుభ్రమైన డేటాసెట్ ఉన్నందున, ఈ పక్షుల గురించి మరింత తెలుసుకుందాం. @@ -149,7 +149,7 @@ birds.plot(x='Category', title='Birds of Minnesota') ``` -![పూర్తి డేటా బార్ చార్ట్](../../../../translated_images/full-data-bar-02.aaa3fda71c63ed564b917841a1886c177dd9a26424142e510c0c0498fd6ca160.te.png) +![పూర్తి డేటా బార్ చార్ట్](../../../../translated_images/te/full-data-bar-02.aaa3fda71c63ed564b917841a1886c177dd9a26424142e510c0c0498fd6ca160.png) ఈ బార్ చార్ట్ చదవలేనిది ఎందుకంటే చాలా ఎక్కువ గుంపు కాని డేటా ఉంది. మీరు ప్లాట్ చేయదలచుకున్న డేటాను మాత్రమే ఎంచుకోవాలి, కాబట్టి పక్షుల పొడవును వారి వర్గం ఆధారంగా చూద్దాం. @@ -164,7 +164,7 @@ category_count = birds.value_counts(birds['Category'].values, sort=True) plt.rcParams['figure.figsize'] = [6, 12] category_count.plot.barh() ``` -![వర్గం మరియు సంఖ్య](../../../../translated_images/category-counts-02.0b9a0a4de42275ae5096d0f8da590d8bf520d9e7e40aad5cc4fc8d276480cc32.te.png) +![వర్గం మరియు సంఖ్య](../../../../translated_images/te/category-counts-02.0b9a0a4de42275ae5096d0f8da590d8bf520d9e7e40aad5cc4fc8d276480cc32.png) ఈ బార్ చార్ట్ ప్రతి వర్గంలో పక్షుల సంఖ్యను బాగా చూపిస్తుంది. ఒక చూపులోనే, ఈ ప్రాంతంలో అత్యధిక సంఖ్యలో పక్షులు డక్స్/గీస్స్/వాటర్‌ఫౌల్ వర్గంలో ఉన్నాయని మీరు చూడవచ్చు. మినెసోటా '10,000 సరస్సుల భూమి' కాబట్టి ఇది ఆశ్చర్యకరం కాదు! @@ -180,7 +180,7 @@ plt.barh(y=birds['Category'], width=maxlength) plt.rcParams['figure.figsize'] = [6, 12] plt.show() ``` -![డేటా పోలిక](../../../../translated_images/category-length-02.7304bf519375c9807d8165cc7ec60dd2a60f7b365b23098538e287d89adb7d76.te.png) +![డేటా పోలిక](../../../../translated_images/te/category-length-02.7304bf519375c9807d8165cc7ec60dd2a60f7b365b23098538e287d89adb7d76.png) ఇక్కడ ఏ ఆశ్చర్యం లేదు: హమ్మింగ్‌బర్డ్స్ పెలికాన్స్ లేదా గీస్స్ కంటే తక్కువ MaxLength కలిగి ఉంటాయి. డేటా తార్కికంగా అర్థం కావడం మంచిది! @@ -198,7 +198,7 @@ plt.show() ``` ఈ ప్లాట్‌లో, మీరు పక్షి వర్గం ప్రకారం కనిష్ఠ పొడవు మరియు గరిష్ఠ పొడవు పరిధిని చూడవచ్చు. ఈ డేటా ప్రకారం, పక్షి పెద్దదైతే, దాని పొడవు పరిధి కూడా పెద్దదని మీరు సురక్షితంగా చెప్పవచ్చు. ఆసక్తికరం! -![సూపర్ ఇంపోజ్ చేసిన విలువలు](../../../../translated_images/superimposed-02.f03058536baeb2ed7864f01102538464d4c2fd7ade881ddd7d5ba74dc5d2fdae.te.png) +![సూపర్ ఇంపోజ్ చేసిన విలువలు](../../../../translated_images/te/superimposed-02.f03058536baeb2ed7864f01102538464d4c2fd7ade881ddd7d5ba74dc5d2fdae.png) ## 🚀 సవాలు diff --git a/translations/te/3-Data-Visualization/10-visualization-distributions/README.md b/translations/te/3-Data-Visualization/10-visualization-distributions/README.md index 939df2ac..99a0622c 100644 --- a/translations/te/3-Data-Visualization/10-visualization-distributions/README.md +++ b/translations/te/3-Data-Visualization/10-visualization-distributions/README.md @@ -49,7 +49,7 @@ plt.xlabel('Max Length') plt.show() ``` -![max length per order](../../../../translated_images/scatter-wb.9d98b0ed7f0388af979441853361a11df5f518f5307938a503ca7913e986111b.te.png) +![max length per order](../../../../translated_images/te/scatter-wb.9d98b0ed7f0388af979441853361a11df5f518f5307938a503ca7913e986111b.png) ఇది పక్షుల ఆర్డర్ ప్రకారం శరీర పొడవు యొక్క సాధారణ పంపిణీకి అవలోకనం ఇస్తుంది, కానీ ఇది నిజమైన పంపిణీలను ప్రదర్శించడానికి ఉత్తమ మార్గం కాదు. ఆ పని సాధారణంగా హిస్టోగ్రామ్ సృష్టించడం ద్వారా నిర్వహించబడుతుంది. ## హిస్టోగ్రామ్‌లతో పని చేయడం @@ -60,7 +60,7 @@ Matplotlib డేటా పంపిణీని హిస్టోగ్రా birds['MaxBodyMass'].plot(kind = 'hist', bins = 10, figsize = (12,12)) plt.show() ``` -![distribution over the entire dataset](../../../../translated_images/dist1-wb.0d0cac82e2974fbbec635826fefead401af795f82e2279e2e2678bf2c117d827.te.png) +![distribution over the entire dataset](../../../../translated_images/te/dist1-wb.0d0cac82e2974fbbec635826fefead401af795f82e2279e2e2678bf2c117d827.png) మీరు చూడగలిగినట్లుగా, ఈ డేటాసెట్‌లో 400+ పక్షులలో ఎక్కువ భాగం వారి గరిష్ట శరీర ద్రవ్యరాశి 2000 కంటే తక్కువ పరిధిలో ఉంటాయి. `bins` పారామీటర్‌ను 30 లాంటి ఎక్కువ సంఖ్యకు మార్చి డేటా గురించి మరింత అవగాహన పొందండి: @@ -68,7 +68,7 @@ plt.show() birds['MaxBodyMass'].plot(kind = 'hist', bins = 30, figsize = (12,12)) plt.show() ``` -![distribution over the entire dataset with larger bins param](../../../../translated_images/dist2-wb.2c0a7a3499b2fbf561e9f93b69f265dfc538dc78f6de15088ba84a88152e26ba.te.png) +![distribution over the entire dataset with larger bins param](../../../../translated_images/te/dist2-wb.2c0a7a3499b2fbf561e9f93b69f265dfc538dc78f6de15088ba84a88152e26ba.png) ఈ చార్ట్ మరింత సూక్ష్మంగా పంపిణీని చూపిస్తుంది. ఎడమవైపు తక్కువ వంకరగా ఉన్న చార్ట్‌ను మీరు ఒక నిర్దిష్ట పరిధిలో మాత్రమే డేటాను ఎంచుకోవడం ద్వారా సృష్టించవచ్చు: @@ -79,7 +79,7 @@ filteredBirds = birds[(birds['MaxBodyMass'] > 1) & (birds['MaxBodyMass'] < 60)] filteredBirds['MaxBodyMass'].plot(kind = 'hist',bins = 40,figsize = (12,12)) plt.show() ``` -![filtered histogram](../../../../translated_images/dist3-wb.64b88db7f9780200bd486a2c2a3252548dd439672dbd3f778193db7f654b100c.te.png) +![filtered histogram](../../../../translated_images/te/dist3-wb.64b88db7f9780200bd486a2c2a3252548dd439672dbd3f778193db7f654b100c.png) ✅ మరిన్ని ఫిల్టర్లు మరియు డేటా పాయింట్లను ప్రయత్నించండి. డేటా యొక్క పూర్తి పంపిణీని చూడటానికి, `['MaxBodyMass']` ఫిల్టర్‌ను తీసివేయండి మరియు లేబుల్ చేసిన పంపిణీలను చూపించండి. @@ -96,7 +96,7 @@ hist = ax.hist2d(x, y) ``` ఈ రెండు అంశాల మధ్య ఒక అంచనా సంబంధం కనిపిస్తుంది, ఒక ప్రత్యేకంగా బలమైన సమీకరణ బిందువు తో: -![2D plot](../../../../translated_images/2D-wb.ae22fdd33936507a41e3af22e11e4903b04a9be973b23a4e05214efaccfd66c8.te.png) +![2D plot](../../../../translated_images/te/2D-wb.ae22fdd33936507a41e3af22e11e4903b04a9be973b23a4e05214efaccfd66c8.png) హిస్టోగ్రామ్‌లు సంఖ్యాత్మక డేటాకు డిఫాల్ట్‌గా బాగా పనిచేస్తాయి. మీరు టెక్స్ట్ డేటా ప్రకారం పంపిణీలను చూడాలనుకుంటే ఏమవుతుంది? ## టెక్స్ట్ డేటా ఉపయోగించి పంపిణీల కోసం డేటాసెట్‌ను అన్వేషించండి @@ -135,7 +135,7 @@ plt.gca().set(title='Conservation Status', ylabel='Min Wingspan') plt.legend(); ``` -![wingspan and conservation collation](../../../../translated_images/histogram-conservation-wb.3c40450eb072c14de7a1a3ec5c0fcba4995531024760741b392911b567fd8b70.te.png) +![wingspan and conservation collation](../../../../translated_images/te/histogram-conservation-wb.3c40450eb072c14de7a1a3ec5c0fcba4995531024760741b392911b567fd8b70.png) కనిష్ట రెక్కపట్టు మరియు సంరక్షణ స్థితి మధ్య మంచి సంబంధం కనిపించడం లేదు. ఈ పద్ధతిని ఉపయోగించి డేటాసెట్ యొక్క ఇతర అంశాలను పరీక్షించండి. మీరు ఏదైనా సంబంధం కనుగొంటారా? @@ -153,7 +153,7 @@ import matplotlib.pyplot as plt sns.kdeplot(filteredBirds['MinWingspan']) plt.show() ``` -![Density plot](../../../../translated_images/density1.8801043bd4af2567b0f706332b5853c7614e5e4b81b457acc27eb4e092a65cbd.te.png) +![Density plot](../../../../translated_images/te/density1.8801043bd4af2567b0f706332b5853c7614e5e4b81b457acc27eb4e092a65cbd.png) మీరు చూడవచ్చు, ఈ ప్లాట్ కనిష్ట రెక్కపట్టు డేటాకు ముందు ప్లాట్‌ను ప్రతిధ్వనిస్తుంది; ఇది కేవలం కొంచెం మృదువుగా ఉంటుంది. Seaborn డాక్యుమెంటేషన్ ప్రకారం, "హిస్టోగ్రామ్‌తో పోలిస్తే, KDE ఒక ప్లాట్‌ను తక్కువ గందరగోళంగా మరియు మరింత అర్థం చేసుకునేలా ఉత్పత్తి చేయగలదు, ముఖ్యంగా బహుళ పంపిణీలను డ్రా చేస్తున్నప్పుడు. కానీ ఇది ప్రాథమిక పంపిణీ పరిమితమైన లేదా మృదువుగా లేనప్పుడు వక్రీకరణలను పరిచయం చేసే అవకాశం ఉంది. హిస్టోగ్రామ్ లాగా, ప్రాతినిధ్యం నాణ్యత కూడా మంచి స్మూతింగ్ పారామీటర్ల ఎంపికపై ఆధారపడి ఉంటుంది." [మూలం](https://seaborn.pydata.org/generated/seaborn.kdeplot.html) అంటే, అవుట్లయర్లు ఎప్పుడూ మీ చార్ట్లను చెడగొడతాయి. @@ -163,7 +163,7 @@ plt.show() sns.kdeplot(filteredBirds['MaxBodyMass']) plt.show() ``` -![smooth bodymass line](../../../../translated_images/density2.8e7647257060ff544a1aaded57e8dd1887586bfe340139e9b77ac1e5287f7977.te.png) +![smooth bodymass line](../../../../translated_images/te/density2.8e7647257060ff544a1aaded57e8dd1887586bfe340139e9b77ac1e5287f7977.png) మీకు మృదువైనది కావాలి కానీ చాలా మృదువైనది కాదు అనుకుంటే, `bw_adjust` పారామీటర్‌ను సవరించండి: @@ -171,7 +171,7 @@ plt.show() sns.kdeplot(filteredBirds['MaxBodyMass'], bw_adjust=.2) plt.show() ``` -![less smooth bodymass line](../../../../translated_images/density3.84ae27da82f31e6b83ad977646f029a1d21186574d7581facd70123b3eb257ee.te.png) +![less smooth bodymass line](../../../../translated_images/te/density3.84ae27da82f31e6b83ad977646f029a1d21186574d7581facd70123b3eb257ee.png) ✅ ఈ రకమైన ప్లాట్ కోసం అందుబాటులో ఉన్న పారామీటర్ల గురించి చదవండి మరియు ప్రయోగాలు చేయండి! @@ -185,7 +185,7 @@ sns.kdeplot( ) ``` -![bodymass per order](../../../../translated_images/density4.e9d6c033f15c500fd33df94cb592b9f5cf1ed2a3d213c448a3f9e97ba39573ce.te.png) +![bodymass per order](../../../../translated_images/te/density4.e9d6c033f15c500fd33df94cb592b9f5cf1ed2a3d213c448a3f9e97ba39573ce.png) మీరు ఒకే చార్ట్‌లో అనేక వేరియబుల్స్ డెన్సిటీని కూడా మ్యాప్ చేయవచ్చు. పక్షుల గరిష్ట పొడవు మరియు కనిష్ట పొడవును వారి సంరక్షణ స్థితితో పోల్చండి: @@ -193,7 +193,7 @@ sns.kdeplot( sns.kdeplot(data=filteredBirds, x="MinLength", y="MaxLength", hue="ConservationStatus") ``` -![multiple densities, superimposed](../../../../translated_images/multi.56548caa9eae8d0fd9012a8586295538c7f4f426e2abc714ba070e2e4b1fc2c1.te.png) +![multiple densities, superimposed](../../../../translated_images/te/multi.56548caa9eae8d0fd9012a8586295538c7f4f426e2abc714ba070e2e4b1fc2c1.png) వозможно, 'Vulnerable' పక్షుల పొడవుల ప్రకారం క్లస్టర్ అర్థవంతమో లేదో పరిశోధించడం విలువైనది. diff --git a/translations/te/3-Data-Visualization/11-visualization-proportions/README.md b/translations/te/3-Data-Visualization/11-visualization-proportions/README.md index 9dee58ab..9bc8b5f9 100644 --- a/translations/te/3-Data-Visualization/11-visualization-proportions/README.md +++ b/translations/te/3-Data-Visualization/11-visualization-proportions/README.md @@ -93,7 +93,7 @@ plt.show() ``` ఇది, ఈ రెండు మష్రూమ్ తరగతుల ప్రకారం ఈ డేటా భాగాలను చూపించే పై చార్ట్. లేబుల్స్ క్రమం సరిగ్గా ఉండటం చాలా ముఖ్యం, కాబట్టి లేబుల్ అర్రే ఎలా నిర్మించబడిందో నిర్ధారించుకోండి! -![pie chart](../../../../translated_images/pie1-wb.e201f2fcc335413143ce37650fb7f5f0bb21358e7823a327ed8644dfb84be9db.te.png) +![pie chart](../../../../translated_images/te/pie1-wb.e201f2fcc335413143ce37650fb7f5f0bb21358e7823a327ed8644dfb84be9db.png) ## డోనట్స్! @@ -123,7 +123,7 @@ plt.title('Mushroom Habitats') plt.show() ``` -![donut chart](../../../../translated_images/donut-wb.be3c12a22712302b5d10c40014d5389d4a1ae4412fe1655b3cf4af57b64f799a.te.png) +![donut chart](../../../../translated_images/te/donut-wb.be3c12a22712302b5d10c40014d5389d4a1ae4412fe1655b3cf4af57b64f799a.png) ఈ కోడ్ ఒక చార్ట్ మరియు మధ్యలో ఒక వృత్తాన్ని గీయడం, ఆ మధ్య వృత్తాన్ని చార్ట్‌లో చేర్చడం చేస్తుంది. మధ్య వృత్తం వెడల్పును మార్చడానికి `0.40` ను మరొక విలువగా మార్చండి. @@ -171,7 +171,7 @@ fig = plt.figure( వాఫిల్ చార్ట్ ఉపయోగించి, మీరు ఈ మష్రూమ్ dataset క్యాప్ రంగుల భాగాలను స్పష్టంగా చూడవచ్చు. ఆసక్తికరంగా, చాలా గ్రీన్-క్యాప్ మష్రూమ్‌లు ఉన్నాయి! -![waffle chart](../../../../translated_images/waffle.5455dbae4ccf17d53bb40ff0a657ecef7b8aa967e27a19cc96325bd81598f65e.te.png) +![waffle chart](../../../../translated_images/te/waffle.5455dbae4ccf17d53bb40ff0a657ecef7b8aa967e27a19cc96325bd81598f65e.png) ✅ Pywaffle చార్ట్లలో [Font Awesome](https://fontawesome.com/)లో అందుబాటులో ఉన్న ఏ ఐకాన్ అయినా ఉపయోగించగలదు. చతురస్రాల స్థానంలో ఐకాన్లను ఉపయోగించి మరింత ఆసక్తికరమైన వాఫిల్ చార్ట్ సృష్టించడానికి ప్రయోగాలు చేయండి. diff --git a/translations/te/3-Data-Visualization/12-visualization-relationships/README.md b/translations/te/3-Data-Visualization/12-visualization-relationships/README.md index 85fae33f..0267b75d 100644 --- a/translations/te/3-Data-Visualization/12-visualization-relationships/README.md +++ b/translations/te/3-Data-Visualization/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ honey.head() ```python sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ``` -![scatterplot 1](../../../../translated_images/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.te.png) +![scatterplot 1](../../../../translated_images/te/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.png) ఇప్పుడు, తేనె రంగు ప్యాలెట్ ఉపయోగించి అదే డేటాను చూపించండి, సంవత్సరాల వారీగా ధర ఎలా మారిందో చూపించడానికి. మీరు 'hue' పారామీటర్ జోడించడం ద్వారా సంవత్సరాల మార్పును చూపవచ్చు: @@ -60,7 +60,7 @@ sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ```python sns.relplot(x="priceperlb", y="state", hue="year", palette="YlOrBr", data=honey, height=15, aspect=.5); ``` -![scatterplot 2](../../../../translated_images/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.te.png) +![scatterplot 2](../../../../translated_images/te/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.png) ఈ రంగు ప్యాలెట్ మార్పుతో, తేనె ధరలో సంవత్సరాల వారీగా స్పష్టమైన పెరుగుదల ఉందని మీరు చూడవచ్చు. నిజంగా, డేటాలో ఒక ఉదాహరణ రాష్ట్రం (ఉదాహరణకు అరిజోనా) తీసుకుని పరిశీలిస్తే, సంవత్సరాల వారీగా ధర పెరుగుదల ఒక నమూనా కనిపిస్తుంది, కొన్ని తప్పులతో: @@ -89,7 +89,7 @@ sns.relplot(x="priceperlb", y="state", size="year", data=honey, height=15, aspec ``` డాట్ల పరిమాణం క్రమంగా పెరుగుతున్నట్లు మీరు చూడవచ్చు. -![scatterplot 3](../../../../translated_images/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.te.png) +![scatterplot 3](../../../../translated_images/te/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.png) ఇది సరళమైన సరఫరా మరియు డిమాండ్ కేసా కాదా? వాతావరణ మార్పు మరియు కాలనీ కాలాప్స్ వంటి కారణాల వల్ల, సంవత్సరాల వారీగా తేనె కొరకు అందుబాటు తగ్గుతుందా, అందువల్ల ధర పెరుగుతుందా? @@ -104,7 +104,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); ``` జవాబు: అవును, 2003 సంవత్సరానికి చుట్టూ కొన్ని తప్పులతో: -![line chart 1](../../../../translated_images/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.te.png) +![line chart 1](../../../../translated_images/te/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.png) ✅ Seaborn ఒకే లైన్ చుట్టూ డేటాను సమీకరించడంతో, "ప్రతి x విలువ వద్ద బహుళ కొలతలను సగటు మరియు సగటు చుట్టూ 95% విశ్వాస అంతరాన్ని ప్లాట్ చేస్తుంది". [మూలం](https://seaborn.pydata.org/tutorial/relational.html). ఈ సమయం తీసుకునే ప్రవర్తనను `ci=None` జోడించడం ద్వారా నిలిపివేయవచ్చు. @@ -114,7 +114,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); sns.relplot(x="year", y="totalprod", kind="line", data=honey); ``` -![line chart 2](../../../../translated_images/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.te.png) +![line chart 2](../../../../translated_images/te/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.png) జవాబు: అంతగా కాదు. మొత్తం ఉత్పత్తిని చూస్తే, ఆ ప్రత్యేక సంవత్సరంలో పెరిగినట్లు కనిపిస్తుంది, అయితే సాధారణంగా ఈ సంవత్సరాలలో తేనె ఉత్పత్తి తగ్గుతోంది. @@ -139,7 +139,7 @@ sns.relplot( ``` ఈ దృశ్యీకరణలో, మీరు సంవత్సరాల వారీగా కాలనీకి ఉత్పత్తి మరియు కాలనీల సంఖ్యను పక్కపక్కనే 3 కాలమ్స్ లో ర్యాప్ సెట్ తో పోల్చవచ్చు: -![facet grid](../../../../translated_images/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.te.png) +![facet grid](../../../../translated_images/te/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.png) ఈ డేటాసెట్ కోసం, కాలనీల సంఖ్య మరియు వారి ఉత్పత్తి విషయంలో సంవత్సరాల వారీగా మరియు రాష్ట్రాల వారీగా ప్రత్యేకంగా ఏమీ కనిపించదు. ఈ రెండు వేరియబుల్స్ మధ్య సంబంధం కనుగొనడానికి వేరే దృశ్య పద్ధతి ఉందా? @@ -162,7 +162,7 @@ sns.despine(right=False) plt.ylabel('colony yield') ax.figure.legend(); ``` -![superimposed plots](../../../../translated_images/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.te.png) +![superimposed plots](../../../../translated_images/te/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.png) 2003 సంవత్సరానికి చుట్టూ ఏదీ ప్రత్యేకంగా కనిపించకపోయినా, ఈ పాఠాన్ని కొంత సంతోషకరమైన నోటుతో ముగించడానికి ఇది సహాయపడుతుంది: మొత్తం కాలనీల సంఖ్య తగ్గుతున్నప్పటికీ, వారి ఉత్పత్తి తగ్గుతున్నా కాలనీల సంఖ్య స్థిరపడుతోంది. diff --git a/translations/te/3-Data-Visualization/13-meaningful-visualizations/README.md b/translations/te/3-Data-Visualization/13-meaningful-visualizations/README.md index 063e27ad..e74a80af 100644 --- a/translations/te/3-Data-Visualization/13-meaningful-visualizations/README.md +++ b/translations/te/3-Data-Visualization/13-meaningful-visualizations/README.md @@ -47,25 +47,25 @@ CO_OP_TRANSLATOR_METADATA: డేటా సైంటిస్ట్ సరైన డేటాకు సరైన చార్ట్ ఎంచుకోవడంలో జాగ్రత్తగా ఉన్నా కూడా, డేటాను ఒక పాయింట్ నిరూపించడానికి ప్రదర్శించే అనేక మార్గాలు ఉన్నాయి, తరచుగా డేటాను తక్కువగా చూపించే ఖర్చుతో. మోసపూరిత చార్టులు మరియు ఇన్ఫోగ్రాఫిక్స్ అనేక ఉదాహరణలు ఉన్నాయి! -[![అల్బెర్టో కైరో ద్వారా "హౌ చార్ట్స్ లై"](../../../../translated_images/tornado.9f42168791208f970d6faefc11d1226d7ca89518013b14aa66b1c9edcd7678d2.te.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "How charts lie") +[![అల్బెర్టో కైరో ద్వారా "హౌ చార్ట్స్ లై"](../../../../translated_images/te/tornado.9f42168791208f970d6faefc11d1226d7ca89518013b14aa66b1c9edcd7678d2.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "How charts lie") > 🎥 మోసపూరిత చార్టుల గురించి కాన్ఫరెన్స్ టాక్ కోసం పై చిత్రం క్లిక్ చేయండి ఈ చార్ట్ X అక్షాన్ని తిరగబెడుతుంది, తేదీ ఆధారంగా నిజానికి వ్యతిరేకంగా చూపించడానికి: -![తప్పు చార్ట్ 1](../../../../translated_images/bad-chart-1.93130f495b748bedfb3423d91b1e754d9026e17f94ad967aecdc9ca7203373bf.te.png) +![తప్పు చార్ట్ 1](../../../../translated_images/te/bad-chart-1.93130f495b748bedfb3423d91b1e754d9026e17f94ad967aecdc9ca7203373bf.png) [ఈ చార్ట్](https://media.firstcoastnews.com/assets/WTLV/images/170ae16f-4643-438f-b689-50d66ca6a8d8/170ae16f-4643-438f-b689-50d66ca6a8d8_1140x641.jpg) మరింత మోసపూరితంగా ఉంది, కళ్ళు కుడి వైపు ఆకర్షించబడి, కాలక్రమేణా COVID కేసులు వివిధ కౌంటీలలో తగ్గాయని తేల్చుకుంటుంది. నిజానికి, తేదీలను జాగ్రత్తగా పరిశీలిస్తే, అవి మోసపూరిత దిగువ ధోరణిని ఇవ్వడానికి తిరగబడినట్లు కనుగొంటారు. -![తప్పు చార్ట్ 2](../../../../translated_images/bad-chart-2.c20e36dd4e6f617c0c325878dd421a563885bbf30a394884c147438827254e0e.te.jpg) +![తప్పు చార్ట్ 2](../../../../translated_images/te/bad-chart-2.c20e36dd4e6f617c0c325878dd421a563885bbf30a394884c147438827254e0e.jpg) ఈ ప్రసిద్ధ ఉదాహరణ రంగు మరియు తిరగబడిన Y అక్షాన్ని ఉపయోగించి మోసం చేస్తుంది: తుపాకీ మిత్ర చట్టం ఆమోదం తర్వాత తుపాకీ మరణాలు పెరిగాయని తేల్చుకోవడం కాకుండా, కళ్ళు వ్యతిరేకం నిజమని భావించడానికి మోసం చేస్తాయి: -![తప్పు చార్ట్ 3](../../../../translated_images/bad-chart-3.6865d0afac4108d737558d90a61547d23a8722896397ec792264ee51a1be4be5.te.jpg) +![తప్పు చార్ట్ 3](../../../../translated_images/te/bad-chart-3.6865d0afac4108d737558d90a61547d23a8722896397ec792264ee51a1be4be5.jpg) ఈ విచిత్ర చార్ట్ భాగస్వామ్యాన్ని ఎలా మోసం చేయవచ్చో హాస్యాస్పదంగా చూపిస్తుంది: -![తప్పు చార్ట్ 4](../../../../translated_images/bad-chart-4.68cfdf4011b454471053ee1231172747e1fbec2403b4443567f1dc678134f4f2.te.jpg) +![తప్పు చార్ట్ 4](../../../../translated_images/te/bad-chart-4.68cfdf4011b454471053ee1231172747e1fbec2403b4443567f1dc678134f4f2.jpg) అసమానమైన వాటిని పోల్చడం మరొక మోసపూరిత ట్రిక్. 'స్పూరియస్ కారెలేషన్స్' గురించి ఒక [అద్భుతమైన వెబ్ సైట్](https://tylervigen.com/spurious-correlations) ఉంది, ఇది మైన్లో విడాకుల రేటు మరియు మార్జరిన్ వినియోగం వంటి విషయాలను కలిపి 'వాస్తవాలు' చూపిస్తుంది. Reddit గ్రూప్ కూడా డేటా యొక్క [అందం లేని ఉపయోగాలను](https://www.reddit.com/r/dataisugly/top/?t=all) సేకరిస్తుంది. @@ -100,13 +100,13 @@ CO_OP_TRANSLATOR_METADATA: మీ డేటా X అక్షంపై వర్ణనాత్మకంగా ఉంటే, చదవడానికి మెరుగ్గా ఉండేందుకు టెక్స్ట్‌ను కోణంలో చూపించవచ్చు. [Matplotlib](https://matplotlib.org/stable/tutorials/toolkits/mplot3d.html) 3D ప్లాటింగ్ అందిస్తుంది, మీ డేటా మద్దతు ఇస్తే. సున్నితమైన డేటా విజువలైజేషన్లు `mpl_toolkits.mplot3d` ఉపయోగించి తయారు చేయవచ్చు. -![3D ప్లాట్లు](../../../../translated_images/3d.0cec12bcc60f0ce7284c63baed1411a843e24716f7d7425de878715ebad54a15.te.png) +![3D ప్లాట్లు](../../../../translated_images/te/3d.0cec12bcc60f0ce7284c63baed1411a843e24716f7d7425de878715ebad54a15.png) ## యానిమేషన్ మరియు 3D చార్ట్ ప్రదర్శన ఈ రోజుల్లో కొన్ని ఉత్తమ డేటా విజువలైజేషన్లు యానిమేటెడ్ ఉంటాయి. షిర్లీ వూ D3 తో అద్భుతమైనవి చేసింది, ఉదాహరణకు '[ఫిల్మ్ ఫ్లవర్స్](http://bl.ocks.org/sxywu/raw/d612c6c653fb8b4d7ff3d422be164a5d/)', ఇందులో ప్రతి పువ్వు ఒక సినిమా విజువలైజేషన్. గార్డియన్ కోసం మరో ఉదాహరణ 'బస్స్డ్ అవుట్', ఇది గ్రీన్సాక్ మరియు D3 తో విజువలైజేషన్లను కలిపి స్క్రోలిటెల్లింగ్ ఆర్టికల్ ఫార్మాట్‌లో ఇంటరాక్టివ్ అనుభవం, NYC తన హోంలెస్ సమస్యను నగరానికి బయటికి బస్సు ద్వారా ఎలా నిర్వహిస్తుందో చూపిస్తుంది. -![బస్సింగ్](../../../../translated_images/busing.7b9e3b41cd4b981c6d63922cd82004cc1cf18895155536c1d98fcc0999bdd23e.te.png) +![బస్సింగ్](../../../../translated_images/te/busing.7b9e3b41cd4b981c6d63922cd82004cc1cf18895155536c1d98fcc0999bdd23e.png) > "బస్స్డ్ అవుట్: హౌ అమెరికా మూవ్స్ ఇట్ హోంలెస్" [గార్డియన్](https://www.theguardian.com/us-news/ng-interactive/2017/dec/20/bussed-out-america-moves-homeless-people-country-study) నుండి. విజువలైజేషన్లు: నాడీహ్ బ్రెమర్ & షిర్లీ వూ @@ -116,7 +116,7 @@ CO_OP_TRANSLATOR_METADATA: మీరు ఈ సోషల్ నెట్‌వర్క్ యొక్క యానిమేటెడ్ వీక్షణను ప్రదర్శించే వెబ్ యాప్‌ను పూర్తి చేస్తారు. ఇది Vue.js మరియు D3 ఉపయోగించి [నెట్‌వర్క్ విజువల్](https://github.com/emiliorizzo/vue-d3-network) సృష్టించడానికి రూపొందించిన లైబ్రరీని ఉపయోగిస్తుంది. యాప్ నడుస్తున్నప్పుడు, మీరు స్క్రీన్‌పై నోడ్లను తీయవచ్చు, డేటాను తిరగరాయడానికి. -![లియాజన్స్](../../../../translated_images/liaisons.7b440b28f6d07ea430244fdf1fc4c64ff48f473f143b8e921846eda1c302aeba.te.png) +![లియాజన్స్](../../../../translated_images/te/liaisons.7b440b28f6d07ea430244fdf1fc4c64ff48f473f143b8e921846eda1c302aeba.png) ## ప్రాజెక్ట్: D3.js ఉపయోగించి నెట్‌వర్క్ చూపించే చార్ట్ నిర్మించండి diff --git a/translations/te/3-Data-Visualization/R/09-visualization-quantities/README.md b/translations/te/3-Data-Visualization/R/09-visualization-quantities/README.md index f8ca7617..79375e59 100644 --- a/translations/te/3-Data-Visualization/R/09-visualization-quantities/README.md +++ b/translations/te/3-Data-Visualization/R/09-visualization-quantities/README.md @@ -66,7 +66,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` ఇక్కడ, మీరు `ggplot2` ప్యాకేజీని ఇన్‌స్టాల్ చేసి, తరువాత `library("ggplot2")` కమాండ్ ఉపయోగించి వర్క్‌స్పేస్‌లో దిగుమతి చేసుకుంటారు. ggplot లో ఏదైనా ప్లాట్ చేయడానికి `ggplot()` ఫంక్షన్ ఉపయోగిస్తారు మరియు డేటాసెట్, x మరియు y వేరియబుల్స్‌ను లక్షణాలుగా పేర్కొంటారు. ఈ సందర్భంలో, లైన్ ప్లాట్ చేయాలనుకున్నందున `geom_line()` ఫంక్షన్ ఉపయోగిస్తారు. -![MaxWingspan-lineplot](../../../../../translated_images/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.te.png) +![MaxWingspan-lineplot](../../../../../translated_images/te/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.png) మీకు వెంటనే ఏమి కనిపిస్తుంది? కనీసం ఒక అవుట్లయర్ ఉన్నట్లు కనిపిస్తోంది - అది చాలా పెద్ద రెక్కల వ్యాప్తి! 2000+ సెంటీమీటర్ల రెక్కల వ్యాప్తి అంటే 20 మీటర్లకు పైగా - మినెసోటాలో ప్టెరోడాక్టిల్స్ తిరుగుతున్నారా? పరిశీలిద్దాం. @@ -84,7 +84,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` `theme` లో కోణాన్ని పేర్కొంటాము మరియు `xlab()` మరియు `ylab()` లో వరుసగా x మరియు y అక్ష లేబుల్స్‌ను పేర్కొంటాము. `ggtitle()` గ్రాఫ్/ప్లాట్‌కు పేరు ఇస్తుంది. -![MaxWingspan-lineplot-improved](../../../../../translated_images/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.te.png) +![MaxWingspan-lineplot-improved](../../../../../translated_images/te/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.png) లేబుల్స్ 45 డిగ్రీల కోణంలో తిరిగినా, చదవడానికి చాలా ఉన్నాయి. వేరే వ్యూహం ప్రయత్నిద్దాం: అవుట్లయర్లకు మాత్రమే లేబుల్స్ ఇవ్వండి మరియు లేబుల్స్‌ను చార్ట్ లోపల సెట్ చేయండి. లేబులింగ్‌కు మరింత స్థలం కోసం స్కాటర్ చార్ట్ ఉపయోగించవచ్చు: @@ -100,7 +100,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + మీరు ఏమి కనుగొంటారు? -![MaxWingspan-scatterplot](../../../../../translated_images/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.te.png) +![MaxWingspan-scatterplot](../../../../../translated_images/te/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.png) ## మీ డేటాను ఫిల్టర్ చేయండి @@ -119,7 +119,7 @@ ggplot(data=birds_filtered, aes(x=Name, y=MaxWingspan,group=1)) + ``` మేము కొత్త డేటాఫ్రేమ్ `birds_filtered` సృష్టించి స్కాటర్ ప్లాట్ చేశాము. అవుట్లయర్లను తీసివేసిన తర్వాత, మీ డేటా మరింత సమగ్రమైనది మరియు అర్థమయ్యేలా మారింది. -![MaxWingspan-scatterplot-improved](../../../../../translated_images/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.te.png) +![MaxWingspan-scatterplot-improved](../../../../../translated_images/te/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.png) ఇప్పుడు రెక్కల వ్యాప్తి పరంగా కనీసం శుభ్రమైన డేటాసెట్ ఉన్నందున, ఈ పక్షుల గురించి మరింత తెలుసుకుందాం. @@ -160,7 +160,7 @@ birds_filtered %>% group_by(Category) %>% ``` తదుపరి కోడ్‌లో, డేటాను మానిప్యులేట్ చేసి గ్రూప్ చేయడానికి సహాయపడే [dplyr](https://www.rdocumentation.org/packages/dplyr/versions/0.7.8) మరియు [lubridate](https://www.rdocumentation.org/packages/lubridate/versions/1.8.0) ప్యాకేజీలను ఇన్‌స్టాల్ చేస్తారు. మొదట, పక్షుల `Category` ప్రకారం డేటాను గ్రూప్ చేసి, `MinLength`, `MaxLength`, `MinBodyMass`, `MaxBodyMass`, `MinWingspan`, `MaxWingspan` కాలమ్స్‌ను సమ్మరీ చేస్తారు. తరువాత, `ggplot2` ప్యాకేజీ ఉపయోగించి బార్ చార్ట్ ప్లాట్ చేసి వేర్వేరు వర్గాల రంగులు మరియు లేబుల్స్‌ను పేర్కొంటారు. -![Stacked bar chart](../../../../../translated_images/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.te.png) +![Stacked bar chart](../../../../../translated_images/te/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.png) ఈ బార్ చార్ట్ చదవడానికి కష్టమైనది ఎందుకంటే చాలా డేటా గ్రూప్ చేయబడలేదు. మీరు ప్లాట్ చేయదలచుకున్న డేటాను మాత్రమే ఎంచుకోవాలి, కాబట్టి పక్షుల వర్గం ఆధారంగా వారి పొడవును చూద్దాం. @@ -175,7 +175,7 @@ ggplot(birds_count,aes(Category,n))+geom_bar(stat="identity")+coord_flip() ``` ముందుగా `Category` కాలమ్‌లోని ప్రత్యేక విలువలను లెక్కించి, వాటిని కొత్త డేటాఫ్రేమ్ `birds_count` లో సర్దుబాటు చేస్తారు. ఈ సర్దుబాటు చేసిన డేటాను అదే స్థాయిలో ఫ్యాక్టర్ చేసి, సర్దుబాటు చేసిన విధంగా ప్లాట్ చేయబడుతుంది. `ggplot2` ఉపయోగించి బార్ చార్ట్ ప్లాట్ చేస్తారు. `coord_flip()` హారిజాంటల్ బార్లను ప్లాట్ చేస్తుంది. -![category-length](../../../../../translated_images/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.te.png) +![category-length](../../../../../translated_images/te/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.png) ఈ బార్ చార్ట్ ప్రతి వర్గంలో పక్షుల సంఖ్యను బాగా చూపిస్తుంది. ఒక చూపులోనే, ఈ ప్రాంతంలో అత్యధిక సంఖ్యలో పక్షులు Ducks/Geese/Waterfowl వర్గంలో ఉన్నాయని మీరు చూడవచ్చు. మినెసోటా '10,000 సరస్సుల భూమి' కాబట్టి ఇది ఆశ్చర్యకరం కాదు! @@ -198,7 +198,7 @@ ggplot(birds_grouped,aes(Category,MaxLength))+geom_bar(stat="identity")+coord_fl ``` `birds_filtered` డేటాను `Category` ప్రకారం గ్రూప్ చేసి బార్ గ్రాఫ్ ప్లాట్ చేస్తారు. -![comparing data](../../../../../translated_images/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.te.png) +![comparing data](../../../../../translated_images/te/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.png) ఇక్కడ ఏ ఆశ్చర్యం లేదు: హమ్మింగ్‌బర్డ్స్‌కు పెలికాన్స్ లేదా గీస్లతో పోలిస్తే తక్కువ MaxLength ఉంటుంది. డేటా తార్కికంగా అర్థమయ్యేలా ఉండటం మంచిది! @@ -210,7 +210,7 @@ ggplot(data=birds_grouped, aes(x=Category)) + geom_bar(aes(y=MinLength), stat="identity", position="identity", fill='orange')+ coord_flip() ``` -![super-imposed values](../../../../../translated_images/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.te.png) +![super-imposed values](../../../../../translated_images/te/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.png) ## 🚀 సవాలు diff --git a/translations/te/3-Data-Visualization/R/10-visualization-distributions/README.md b/translations/te/3-Data-Visualization/R/10-visualization-distributions/README.md index 725f8f40..f0672c2a 100644 --- a/translations/te/3-Data-Visualization/R/10-visualization-distributions/README.md +++ b/translations/te/3-Data-Visualization/R/10-visualization-distributions/README.md @@ -45,7 +45,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + geom_point() + ggtitle("Max Length per order") + coord_flip() ``` -![max length per order](../../../../../translated_images/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.te.png) +![max length per order](../../../../../translated_images/te/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.png) ఇది పక్షుల ఆర్డర్ ప్రకారం శరీర పొడవు యొక్క సాధారణ పంపిణీకి అవలోకనం ఇస్తుంది, కానీ ఇది నిజమైన పంపిణీలను ప్రదర్శించడానికి ఉత్తమ మార్గం కాదు. ఆ పని సాధారణంగా హిస్టోగ్రామ్ సృష్టించడం ద్వారా నిర్వహించబడుతుంది. ## హిస్టోగ్రామ్లతో పని చేయడం @@ -56,7 +56,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=10)+ylab('Frequency') ``` -![distribution over entire dataset](../../../../../translated_images/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.te.png) +![distribution over entire dataset](../../../../../translated_images/te/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.png) మీరు చూడగలిగినట్లుగా, ఈ డేటాసెట్‌లో 400+ పక్షులలో ఎక్కువ భాగం వారి గరిష్ట శరీర ద్రవ్యరాశి 2000 కంటే తక్కువ పరిధిలో ఉంటాయి. `bins` పారామీటర్‌ను 30 లాంటి ఎక్కువ సంఖ్యకు మార్చి డేటా గురించి మరింత అవగాహన పొందండి: @@ -64,7 +64,7 @@ ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![distribution-30bins](../../../../../translated_images/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.te.png) +![distribution-30bins](../../../../../translated_images/te/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.png) ఈ చార్ట్ పంపిణీని మరింత సూక్ష్మంగా చూపిస్తుంది. ఎడమవైపు తక్కువ వంకరగా ఉన్న చార్ట్‌ను మీరు ఇచ్చిన పరిధిలో మాత్రమే డేటాను ఎంచుకోవడం ద్వారా సృష్టించవచ్చు: @@ -76,7 +76,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![filtered histogram](../../../../../translated_images/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.te.png) +![filtered histogram](../../../../../translated_images/te/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.png) ✅ మరిన్ని ఫిల్టర్లు మరియు డేటా పాయింట్లను ప్రయత్నించండి. డేటా యొక్క పూర్తి పంపిణీని చూడటానికి, లేబుల్ చేయబడిన పంపిణీలను చూపించడానికి `['MaxBodyMass']` ఫిల్టర్‌ను తీసివేయండి. @@ -90,7 +90,7 @@ ggplot(data=birds_filtered_1, aes(x=MaxBodyMass, y=MaxLength) ) + ``` ఈ రెండు అంశాల మధ్య అంచనా వేయదగిన సంబంధం కనిపిస్తుంది, ఒక ప్రత్యేకంగా బలమైన సమీకరణ బిందువు తో: -![2d plot](../../../../../translated_images/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.te.png) +![2d plot](../../../../../translated_images/te/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.png) హిస్టోగ్రామ్లు సంఖ్యాత్మక డేటాకు డిఫాల్ట్‌గా బాగా పనిచేస్తాయి. మీరు టెక్స్ట్ డేటా ప్రకారం పంపిణీలను చూడాలనుకుంటే ఏమవుతుంది? ## టెక్స్ట్ డేటా ఉపయోగించి పంపిణీలను అన్వేషించండి @@ -121,7 +121,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + scale_fill_manual(name="Conservation Status",values=c("red","green","blue","pink"),labels=c("Endangered","Near Threathened","Vulnerable","Least Concern")) ``` -![wingspan and conservation collation](../../../../../translated_images/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.te.png) +![wingspan and conservation collation](../../../../../translated_images/te/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.png) కనిష్ట రెక్కపట్టు మరియు సంరక్షణ స్థితి మధ్య మంచి సంబంధం కనిపించట్లేదు. ఈ పద్ధతిని ఉపయోగించి డేటాసెట్ యొక్క ఇతర అంశాలను పరీక్షించండి. మీరు ఏదైనా సంబంధం కనుగొంటారా? @@ -135,7 +135,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + geom_density() ``` -![density plot](../../../../../translated_images/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.te.png) +![density plot](../../../../../translated_images/te/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.png) మీరు చూడగలరు, ఈ ప్లాట్ కనిష్ట రెక్కపట్టు డేటాకు ముందు ప్లాట్‌ను ప్రతిధ్వనిస్తుంది; ఇది కేవలం కొంచెం మృదువుగా ఉంటుంది. మీరు రెండవ చార్ట్‌లో ఉన్న ఆ జాగ్గీడ్ MaxBodyMass లైన్‌ను మృదువుగా చేయాలనుకుంటే, ఈ పద్ధతిని ఉపయోగించి దాన్ని బాగా సృష్టించవచ్చు: @@ -143,7 +143,7 @@ ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density() ``` -![bodymass density](../../../../../translated_images/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.te.png) +![bodymass density](../../../../../translated_images/te/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.png) మీకు చాలా మృదువుగా కాకుండా కొంత మృదువుగా ఉండే లైన్ కావాలంటే, `adjust` పారామీటర్‌ను సవరించండి: @@ -151,7 +151,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density(adjust = 1/5) ``` -![less smooth bodymass](../../../../../translated_images/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.te.png) +![less smooth bodymass](../../../../../translated_images/te/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.png) ✅ ఈ రకమైన ప్లాట్ కోసం అందుబాటులో ఉన్న పారామీటర్ల గురించి చదవండి మరియు ప్రయోగాలు చేయండి! @@ -161,7 +161,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + ggplot(data=birds_filtered_1,aes(x = MaxBodyMass, fill = Order)) + geom_density(alpha=0.5) ``` -![bodymass per order](../../../../../translated_images/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.te.png) +![bodymass per order](../../../../../translated_images/te/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.png) ## 🚀 సవాలు diff --git a/translations/te/3-Data-Visualization/R/11-visualization-proportions/README.md b/translations/te/3-Data-Visualization/R/11-visualization-proportions/README.md index 2cfab9c9..1d90cc10 100644 --- a/translations/te/3-Data-Visualization/R/11-visualization-proportions/README.md +++ b/translations/te/3-Data-Visualization/R/11-visualization-proportions/README.md @@ -97,7 +97,7 @@ pie(grouped$count,grouped$class, main="Edible?") ``` ఇది, ఈ రెండు మష్రూమ్ తరగతుల ప్రకారం డేటా యొక్క నిష్పత్తులను చూపించే పై చార్ట్. లేబుల్స్ క్రమం సరిగ్గా ఉండటం చాలా ముఖ్యం, కాబట్టి లేబుల్ అర్రే ఎలా తయారవుతుందో నిర్ధారించుకోండి! -![pie chart](../../../../../translated_images/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.te.png) +![pie chart](../../../../../translated_images/te/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.png) ## డోనట్స్! @@ -132,7 +132,7 @@ library(webr) PieDonut(habitat, aes(habitat, count=count)) ``` -![donut chart](../../../../../translated_images/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.te.png) +![donut chart](../../../../../translated_images/te/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.png) ఈ కోడ్ రెండు లైబ్రరీలు - ggplot2 మరియు webr ఉపయోగిస్తుంది. webr లైబ్రరీలో PieDonut ఫంక్షన్ ఉపయోగించి, డోనట్ చార్ట్ సులభంగా సృష్టించవచ్చు! @@ -170,7 +170,7 @@ waffle((cap_color$count/10), rows = 7, title = "Waffle Chart")+scale_fill_manual వాఫిల్ చార్ట్ ఉపయోగించి, ఈ మష్రూమ్ dataset లో క్యాప్ రంగుల నిష్పత్తులను స్పష్టంగా చూడవచ్చు. ఆసక్తికరంగా, చాలా గ్రీన్ క్యాప్ మష్రూమ్స్ ఉన్నాయి! -![waffle chart](../../../../../translated_images/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.te.png) +![waffle chart](../../../../../translated_images/te/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.png) ఈ పాఠంలో, మీరు నిష్పత్తులను విజువలైజ్ చేయడానికి మూడు మార్గాలు నేర్చుకున్నారు. మొదట, డేటాను వర్గాలుగా వర్గీకరించి, ఆ తర్వాత డేటాను ప్రదర్శించడానికి ఉత్తమ మార్గం - పై, డోనట్ లేదా వాఫిల్ ఎంచుకోవాలి. ఇవన్నీ రుచికరమైనవి మరియు dataset యొక్క తక్షణ స్నాప్‌షాట్ తో వినియోగదారుని సంతృప్తి పరుస్తాయి. diff --git a/translations/te/3-Data-Visualization/R/12-visualization-relationships/README.md b/translations/te/3-Data-Visualization/R/12-visualization-relationships/README.md index 2d984933..d36f5bf1 100644 --- a/translations/te/3-Data-Visualization/R/12-visualization-relationships/README.md +++ b/translations/te/3-Data-Visualization/R/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ library(ggplot2) ggplot(honey, aes(x = priceperlb, y = state)) + geom_point(colour = "blue") ``` -![scatterplot 1](../../../../../translated_images/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.te.png) +![scatterplot 1](../../../../../translated_images/te/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.png) ఇప్పుడు, అదే డేటాను తేనె రంగు స్కీమ్‌తో చూపించి, సంవత్సరాల వారీగా ధర ఎలా మారిందో చూపించండి. మీరు 'scale_color_gradientn' పారామీటర్ జోడించడం ద్వారా సంవత్సరాల మార్పును చూపవచ్చు: @@ -61,7 +61,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ggplot(honey, aes(x = priceperlb, y = state, color=year)) + geom_point()+scale_color_gradientn(colours = colorspace::heat_hcl(7)) ``` -![scatterplot 2](../../../../../translated_images/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.te.png) +![scatterplot 2](../../../../../translated_images/te/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.png) ఈ రంగు స్కీమ్ మార్పుతో, తేనె ధరలో సంవత్సరాల వారీగా స్పష్టమైన పెరుగుదల ఉందని మీరు చూడవచ్చు. నిజంగా, డేటాలో ఒక నమూనా సెట్ (ఉదాహరణకు అరిజోనా రాష్ట్రం) పరిశీలిస్తే, కొన్నిసార్లు తప్పులు ఉన్నా, ధర సంవత్సరాల వారీగా పెరుగుతుందని కనిపిస్తుంది: @@ -92,7 +92,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ``` డాట్ల పరిమాణం క్రమంగా పెరుగుతున్నట్లు మీరు చూడవచ్చు. -![scatterplot 3](../../../../../translated_images/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.te.png) +![scatterplot 3](../../../../../translated_images/te/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.png) ఇది సరఫరా మరియు డిమాండ్ యొక్క సాదారణ ఉదాహరణనా? వాతావరణ మార్పులు మరియు కాలనీ కాలపనితీరు వంటి కారణాల వల్ల, సంవత్సరాల వారీగా తేనె కొంత తక్కువగా అందుబాటులో ఉండి, అందువల్ల ధర పెరుగుతుందా? @@ -107,7 +107,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab ``` జవాబు: అవును, 2003 సంవత్సరానికి చుట్టూ కొన్ని మినహాయింపులతో: -![line chart 1](../../../../../translated_images/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.te.png) +![line chart 1](../../../../../translated_images/te/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.png) ప్రశ్న: 2003లో తేనె సరఫరాలో కూడా పెరుగుదల కనిపిస్తుందా? సంవత్సరాల వారీగా మొత్తం ఉత్పత్తిని చూస్తే? @@ -115,7 +115,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab qplot(honey$year,honey$totalprod, geom='smooth', span =0.5, xlab = "year",ylab = "totalprod") ``` -![line chart 2](../../../../../translated_images/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.te.png) +![line chart 2](../../../../../translated_images/te/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.png) జవాబు: అంతగా కాదు. మొత్తం ఉత్పత్తిని చూస్తే, ఆ ప్రత్యేక సంవత్సరంలో పెరిగినట్లు కనిపిస్తుంది, అయితే సాధారణంగా ఈ సంవత్సరాలలో తేనె ఉత్పత్తి తగ్గుతోంది. @@ -135,7 +135,7 @@ ggplot(honey, aes(x=yieldpercol, y = numcol,group = 1)) + ``` ఈ దృశ్యీకరణలో, మీరు కాలనీకి ఉత్పత్తి మరియు కాలనీల సంఖ్యను సంవత్సరాల వారీగా, 3 కాలమ్స్‌తో wrap సెట్‌తో పక్కపక్కనే పోల్చవచ్చు: -![facet grid](../../../../../translated_images/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.te.png) +![facet grid](../../../../../translated_images/te/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.png) ఈ డేటాసెట్ కోసం, కాలనీల సంఖ్య మరియు వాటి ఉత్పత్తి విషయంలో సంవత్సరాల వారీగా మరియు రాష్ట్రాల వారీగా ప్రత్యేకంగా ఏమీ కనిపించదు. ఈ రెండు వేరియబుల్స్ మధ్య సంబంధం కనుగొనడానికి వేరే దృశ్య పద్ధతి ఉందా? @@ -152,7 +152,7 @@ plot(honey$year, honey$yieldpercol, pch = 17, col = 3, axis(side = 4, at = pretty(range(y2))) mtext("colony yield", side = 4, line = 3) ``` -![superimposed plots](../../../../../translated_images/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.te.png) +![superimposed plots](../../../../../translated_images/te/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.png) 2003 సంవత్సరానికి చుట్టూ ఏదీ స్పష్టంగా కనిపించకపోయినా, ఈ పాఠాన్ని కొంత సంతోషకరంగా ముగించడానికి ఇది సహాయపడుతుంది: మొత్తం కాలనీల సంఖ్య తగ్గుతున్నప్పటికీ, వాటి ఉత్పత్తి తగ్గుతున్నా, కాలనీల సంఖ్య స్థిరపడుతోంది. diff --git a/translations/te/3-Data-Visualization/R/13-meaningful-vizualizations/README.md b/translations/te/3-Data-Visualization/R/13-meaningful-vizualizations/README.md index eee1477a..0dc3b62d 100644 --- a/translations/te/3-Data-Visualization/R/13-meaningful-vizualizations/README.md +++ b/translations/te/3-Data-Visualization/R/13-meaningful-vizualizations/README.md @@ -47,25 +47,25 @@ CO_OP_TRANSLATOR_METADATA: డేటా సైంటిస్ట్ సరైన డేటాకు సరైన చార్ట్ ఎంచుకోవడానికి జాగ్రత్తగా ఉన్నా కూడా, డేటాను ఒక పాయింట్ నిరూపించడానికి ప్రదర్శించే అనేక మార్గాలు ఉన్నాయి, ఇది తరచుగా డేటాను తక్కువగా చూపించడంలో దోషం కలిగిస్తుంది. మోసపూరిత చార్టులు మరియు ఇన్ఫోగ్రాఫిక్స్ యొక్క అనేక ఉదాహరణలు ఉన్నాయి! -[![అల్బెర్టో కైరో ద్వారా How Charts Lie](../../../../../translated_images/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.te.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "How charts lie") +[![అల్బెర్టో కైరో ద్వారా How Charts Lie](../../../../../translated_images/te/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "How charts lie") > 🎥 మోసపూరిత చార్టుల గురించి కాన్ఫరెన్స్ టాక్ కోసం పై చిత్రాన్ని క్లిక్ చేయండి ఈ చార్ట్ X అక్షాన్ని తిరగబెడుతుంది, నిజానికి వ్యతిరేకంగా చూపించడానికి, తేదీ ఆధారంగా: -![తప్పు చార్ట్ 1](../../../../../translated_images/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.te.png) +![తప్పు చార్ట్ 1](../../../../../translated_images/te/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.png) [ఈ చార్ట్](https://media.firstcoastnews.com/assets/WTLV/images/170ae16f-4643-438f-b689-50d66ca6a8d8/170ae16f-4643-438f-b689-50d66ca6a8d8_1140x641.jpg) మరింత మోసపూరితంగా ఉంది, కళ్ళు కుడి వైపు ఆకర్షించబడి, కాలక్రమేణా వివిధ కౌంటీలలో COVID కేసులు తగ్గాయని తేల్చుకుంటుంది. నిజానికి, తేదీలను జాగ్రత్తగా పరిశీలిస్తే, అవి మోసపూరిత దిగువ ధోరణిని ఇవ్వడానికి తిరగబడినట్లు కనబడతాయి. -![తప్పు చార్ట్ 2](../../../../../translated_images/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.te.jpg) +![తప్పు చార్ట్ 2](../../../../../translated_images/te/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.jpg) ఈ ప్రసిద్ధ ఉదాహరణ రంగు మరియు తిరగబడిన Y అక్షాన్ని ఉపయోగించి మోసం చేస్తుంది: తుపాకీ మిత్ర చట్టం ఆమోదం తర్వాత తుపాకీ మరణాలు పెరిగాయని తేల్చుకోవడం కాకుండా, కళ్ళు వ్యతిరేకం నిజమని భావించడానికి మోసం చేస్తాయి: -![తప్పు చార్ట్ 3](../../../../../translated_images/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.te.jpg) +![తప్పు చార్ట్ 3](../../../../../translated_images/te/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.jpg) ఈ విచిత్ర చార్ట్ భాగస్వామ్యాన్ని ఎలా మోసం చేయవచ్చో హాస్యాస్పదంగా చూపిస్తుంది: -![తప్పు చార్ట్ 4](../../../../../translated_images/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.te.jpg) +![తప్పు చార్ట్ 4](../../../../../translated_images/te/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.jpg) సమానమైన వాటిని పోల్చడం మరొక అన్యాయమైన ట్రిక్. 'స్పూరియస్ కారెలేషన్స్' గురించి ఒక [అద్భుతమైన వెబ్ సైట్](https://tylervigen.com/spurious-correlations) ఉంది, ఇది మైన్లో విడాకుల రేటు మరియు మార్జరిన్ వినియోగం వంటి విషయాలను కలిపి 'వాస్తవాలు' చూపిస్తుంది. Reddit గ్రూప్ కూడా డేటా యొక్క [అందం లేని ఉపయోగాలను](https://www.reddit.com/r/dataisugly/top/?t=all) సేకరిస్తుంది. @@ -100,13 +100,13 @@ CO_OP_TRANSLATOR_METADATA: మీ డేటా X అక్షంపై వర్ణనాత్మకంగా ఉంటే, చదవడానికి మెరుగ్గా ఉండేందుకు టెక్స్ట్‌ను కోణంలో చూపించవచ్చు. [plot3D](https://cran.r-project.org/web/packages/plot3D/index.html) 3D ప్లాటింగ్ అందిస్తుంది, మీ డేటా దీనికి మద్దతు ఇస్తే. దీని ద్వారా సున్నితమైన డేటా విజువలైజేషన్లు తయారు చేయవచ్చు. -![3D ప్లాట్లు](../../../../../translated_images/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.te.png) +![3D ప్లాట్లు](../../../../../translated_images/te/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.png) ## యానిమేషన్ మరియు 3D చార్ట్ ప్రదర్శన ఈ రోజుల్లో కొన్ని ఉత్తమ డేటా విజువలైజేషన్లు యానిమేటెడ్ ఉంటాయి. షిర్లీ వూ D3తో అద్భుతమైనవి చేసింది, ఉదాహరణకు '[ఫిల్మ్ ఫ్లవర్స్](http://bl.ocks.org/sxywu/raw/d612c6c653fb8b4d7ff3d422be164a5d/)', ఇందులో ప్రతి పువ్వు ఒక సినిమా విజువలైజేషన్. గార్డియన్ కోసం మరో ఉదాహరణ 'బస్స్డ్ అవుట్', ఇది విజువలైజేషన్లను Greensock మరియు D3తో కలిపి NYC తన హోంలెస్ సమస్యను నగరానికి బయటికి బస్సు ద్వారా ఎలా నిర్వహిస్తుందో చూపించే స్క్రోలిటెల్లింగ్ ఆర్టికల్ ఫార్మాట్. -![బస్సింగ్](../../../../../translated_images/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.te.png) +![బస్సింగ్](../../../../../translated_images/te/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.png) > "Bussed Out: How America Moves its Homeless" [గార్డియన్](https://www.theguardian.com/us-news/ng-interactive/2017/dec/20/bussed-out-america-moves-homeless-people-country-study) నుండి. విజువలైజేషన్లు: నాడీహ్ బ్రెమర్ & షిర్లీ వూ @@ -116,7 +116,7 @@ CO_OP_TRANSLATOR_METADATA: మీరు ఈ సోషల్ నెట్‌వర్క్ యొక్క యానిమేటెడ్ వీక్షణను ప్రదర్శించే వెబ్ యాప్‌ను పూర్తి చేస్తారు. ఇది Vue.js మరియు D3 ఉపయోగించి [నెట్‌వర్క్ విజువలైజేషన్](https://github.com/emiliorizzo/vue-d3-network) సృష్టించడానికి రూపొందించిన లైబ్రరీని ఉపయోగిస్తుంది. యాప్ నడుస్తున్నప్పుడు, మీరు స్క్రీన్‌పై నోడ్లను తీయవచ్చు, డేటాను తిరగరాయడానికి. -![లియాజన్స్](../../../../../translated_images/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.te.png) +![లియాజన్స్](../../../../../translated_images/te/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.png) ## ప్రాజెక్ట్: D3.js ఉపయోగించి నెట్‌వర్క్ చూపించే చార్ట్ నిర్మించండి diff --git a/translations/te/3-Data-Visualization/README.md b/translations/te/3-Data-Visualization/README.md index 77531328..3bb6f573 100644 --- a/translations/te/3-Data-Visualization/README.md +++ b/translations/te/3-Data-Visualization/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # విజువలైజేషన్లు -![a bee on a lavender flower](../../../translated_images/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.te.jpg) +![a bee on a lavender flower](../../../translated_images/te/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.jpg) > ఫోటో జెన్నా లీ చేత అన్స్ప్లాష్ లో diff --git a/translations/te/4-Data-Science-Lifecycle/14-Introduction/README.md b/translations/te/4-Data-Science-Lifecycle/14-Introduction/README.md index 22a2e844..a8413d44 100644 --- a/translations/te/4-Data-Science-Lifecycle/14-Introduction/README.md +++ b/translations/te/4-Data-Science-Lifecycle/14-Introduction/README.md @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: ఈ పాఠం లైఫ్‌సైకిల్ యొక్క 3 భాగాలపై దృష్టి సారిస్తుంది: సేకరణ, ప్రాసెసింగ్ మరియు నిర్వహణ. -![డేటా సైన్స్ లైఫ్‌సైకిల్ యొక్క డయాగ్రామ్](../../../../translated_images/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.te.jpg) +![డేటా సైన్స్ లైఫ్‌సైకిల్ యొక్క డయాగ్రామ్](../../../../translated_images/te/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.jpg) > ఫోటో [బర్క్లీ స్కూల్ ఆఫ్ ఇన్ఫర్మేషన్](https://ischoolonline.berkeley.edu/data-science/what-is-data-science/) ద్వారా ## సేకరణ @@ -98,7 +98,7 @@ CO_OP_TRANSLATOR_METADATA: |టీమ్ డేటా సైన్స్ ప్రాసెస్ (TDSP)|క్రాస్-ఇండస్ట్రీ స్టాండర్డ్ ప్రాసెస్ ఫర్ డేటా మైనింగ్ (CRISP-DM)| |--|--| -|![టీమ్ డేటా సైన్స్ లైఫ్‌సైకిల్](../../../../translated_images/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.te.png) | ![డేటా సైన్స్ ప్రాసెస్ అలయన్స్ చిత్రం](../../../../translated_images/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.te.png) | +|![టీమ్ డేటా సైన్స్ లైఫ్‌సైకిల్](../../../../translated_images/te/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.png) | ![డేటా సైన్స్ ప్రాసెస్ అలయన్స్ చిత్రం](../../../../translated_images/te/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.png) | | చిత్రం [Microsoft](https://docs.microsoft.comazure/architecture/data-science-process/lifecycle) ద్వారా | చిత్రం [డేటా సైన్స్ ప్రాసెస్ అలయన్స్](https://www.datascience-pm.com/crisp-dm-2/) ద్వారా | ## [పోస్ట్-లెక్చర్ క్విజ్](https://ff-quizzes.netlify.app/en/ds/quiz/27) diff --git a/translations/te/4-Data-Science-Lifecycle/README.md b/translations/te/4-Data-Science-Lifecycle/README.md index 9eb6ede2..6bc8c80d 100644 --- a/translations/te/4-Data-Science-Lifecycle/README.md +++ b/translations/te/4-Data-Science-Lifecycle/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # డేటా సైన్స్ లైఫ్‌సైకిల్ -![communication](../../../translated_images/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.te.jpg) +![communication](../../../translated_images/te/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.jpg) > ఫోటో హెడ్‌వే ద్వారా అన్స్ప్లాష్లో ఈ పాఠాలలో, మీరు డేటా సైన్స్ లైఫ్‌సైకిల్ యొక్క కొన్ని అంశాలను, డేటా చుట్టూ విశ్లేషణ మరియు కమ్యూనికేషన్ సహా, అన్వేషించబోతున్నారు. diff --git a/translations/te/5-Data-Science-In-Cloud/18-Low-Code/README.md b/translations/te/5-Data-Science-In-Cloud/18-Low-Code/README.md index fc7323d5..2a09fd07 100644 --- a/translations/te/5-Data-Science-In-Cloud/18-Low-Code/README.md +++ b/translations/te/5-Data-Science-In-Cloud/18-Low-Code/README.md @@ -62,7 +62,7 @@ CO_OP_TRANSLATOR_METADATA: ప్రాజెక్టులు తయారు చేయడం మరియు నిర్మించడం మీ నైపుణ్యాలు మరియు జ్ఞానాన్ని పరీక్షించడానికి ఉత్తమ మార్గం అని సందేహం లేదు. ఈ పాఠంలో, అజ్యూర్ ML స్టూడియోలో హార్ట్ ఫెయిల్యూర్ దాడుల ప్రిడిక్షన్ కోసం డేటా సైన్స్ ప్రాజెక్ట్‌ను రెండు వేర్వేరు మార్గాల్లో నిర్మించడం గురించి తెలుసుకోబోతున్నాము, లో కోడ్/నో కోడ్ ద్వారా మరియు అజ్యూర్ ML SDK ద్వారా, క్రింది స్కీమా ప్రకారం: -![project-schema](../../../../translated_images/project-schema.736f6e403f321eb48d10242b3f4334dc6ccf0eabef8ff87daf52b89781389fcb.te.png) +![project-schema](../../../../translated_images/te/project-schema.736f6e403f321eb48d10242b3f4334dc6ccf0eabef8ff87daf52b89781389fcb.png) ప్రతి విధానానికి తన స్వంత లాభాలు మరియు నష్టాలు ఉన్నాయి. లో కోడ్/నో కోడ్ విధానం GUI (గ్రాఫికల్ యూజర్ ఇంటర్‌ఫేస్)తో ఇంటరాక్ట్ చేయడం వల్ల ప్రారంభించడానికి సులభం, కోడ్ గురించి ముందస్తు జ్ఞానం అవసరం లేదు. ఈ పద్ధతి ప్రాజెక్ట్ యొక్క సాధ్యతను త్వరగా పరీక్షించడానికి మరియు POC (ప్రూఫ్ ఆఫ్ కాన్సెప్ట్) సృష్టించడానికి అనుమతిస్తుంది. అయితే, ప్రాజెక్ట్ పెరిగినప్పుడు మరియు ప్రొడక్షన్ సిద్ధంగా ఉండాల్సినప్పుడు, GUI ద్వారా వనరులను సృష్టించడం సాధ్యం కాదు. వనరుల సృష్టి నుండి మోడల్ డిప్లాయ్‌మెంట్ వరకు ప్రతిదీ ప్రోగ్రామాటిక్‌గా ఆటోమేట్ చేయాలి. అప్పుడు అజ్యూర్ ML SDK ఉపయోగించడం చాలా ముఖ్యం అవుతుంది. @@ -115,15 +115,15 @@ Kaggle ఒక [Heart Failure dataset](https://www.kaggle.com/andrewmvd/heart-fai 1. మీ అజ్యూర్ సబ్‌స్క్రిప్షన్‌కు సంబంధించిన Microsoft క్రెడెన్షియల్స్ ఉపయోగించి [Azure పోర్టల్](https://ms.portal.azure.com/)లో సైన్ ఇన్ అవ్వండి. 2. **＋Create a resource** ఎంచుకోండి - ![workspace-1](../../../../translated_images/workspace-1.ac8694d60b073ed1ae8333d71244dc8a9b3e439d54593724f98f1beefdd27b08.te.png) + ![workspace-1](../../../../translated_images/te/workspace-1.ac8694d60b073ed1ae8333d71244dc8a9b3e439d54593724f98f1beefdd27b08.png) మెషీన్ లెర్నింగ్ కోసం శోధించి, మెషీన్ లెర్నింగ్ టైల్ ఎంచుకోండి - ![workspace-2](../../../../translated_images/workspace-2.ae7c486db8796147075e4a56566aa819827dd6c4c8d18d64590317c3be625f17.te.png) + ![workspace-2](../../../../translated_images/te/workspace-2.ae7c486db8796147075e4a56566aa819827dd6c4c8d18d64590317c3be625f17.png) క్రియేట్ బటన్ క్లిక్ చేయండి - ![workspace-3](../../../../translated_images/workspace-3.398ca4a5858132cce584db9df10c5a011cd9075eb182e647a77d5cac01771eea.te.png) + ![workspace-3](../../../../translated_images/te/workspace-3.398ca4a5858132cce584db9df10c5a011cd9075eb182e647a77d5cac01771eea.png) క్రింది విధంగా సెట్టింగ్స్ నింపండి: - సబ్‌స్క్రిప్షన్: మీ అజ్యూర్ సబ్‌స్క్రిప్షన్ @@ -135,17 +135,17 @@ Kaggle ఒక [Heart Failure dataset](https://www.kaggle.com/andrewmvd/heart-fai - అప్లికేషన్ ఇన్సైట్స్: మీ వర్క్‌స్పేస్ కోసం సృష్టించబడే డిఫాల్ట్ కొత్త అప్లికేషన్ ఇన్సైట్స్ వనరు గమనించండి - కంటైనర్ రిజిస్ట్రీ: లేదు (మొదటి సారి మోడల్‌ను కంటైనర్‌కు డిప్లాయ్ చేసినప్పుడు ఒకటి ఆటోమేటిక్‌గా సృష్టించబడుతుంది) - ![workspace-4](../../../../translated_images/workspace-4.bac87f6599c4df63e624fc2608990f965887bee551d9dedc71c687b43b986b6a.te.png) + ![workspace-4](../../../../translated_images/te/workspace-4.bac87f6599c4df63e624fc2608990f965887bee551d9dedc71c687b43b986b6a.png) - create + review క్లిక్ చేసి, తరువాత create బటన్ క్లిక్ చేయండి 3. మీ వర్క్‌స్పేస్ సృష్టించబడే వరకు వేచి ఉండండి (కొన్ని నిమిషాలు పట్టవచ్చు). తరువాత పోర్టల్‌లో దానికి వెళ్లండి. మీరు మెషీన్ లెర్నింగ్ అజ్యూర్ సర్వీస్ ద్వారా దాన్ని కనుగొనవచ్చు. 4. మీ వర్క్‌స్పేస్ యొక్క ఓవర్వ్యూ పేజీలో, అజ్యూర్ మెషీన్ లెర్నింగ్ స్టూడియో ప్రారంభించండి (లేదా కొత్త బ్రౌజర్ ట్యాబ్ తెరిచి https://ml.azure.com కి వెళ్లండి), మరియు మీ Microsoft ఖాతాతో సైన్ ఇన్ అవ్వండి. ప్రాంప్ట్ అయితే, మీ అజ్యూర్ డైరెక్టరీ, సబ్‌స్క్రిప్షన్ మరియు అజ్యూర్ మెషీన్ లెర్నింగ్ వర్క్‌స్పేస్ ఎంచుకోండి. -![workspace-5](../../../../translated_images/workspace-5.a6eb17e0a5e6420018b08bdaf3755ce977f96f1df3ea363d2476a9dce7e15adb.te.png) +![workspace-5](../../../../translated_images/te/workspace-5.a6eb17e0a5e6420018b08bdaf3755ce977f96f1df3ea363d2476a9dce7e15adb.png) 5. అజ్యూర్ మెషీన్ లెర్నింగ్ స్టూడియోలో, ఎడమ పైభాగంలో ☰ ఐకాన్‌ను టోగుల్ చేసి ఇంటర్‌ఫేస్‌లోని వివిధ పేజీలను చూడండి. మీరు ఈ పేజీలను ఉపయోగించి మీ వర్క్‌స్పేస్ వనరులను నిర్వహించవచ్చు. -![workspace-6](../../../../translated_images/workspace-6.8dd81fe841797ee17f8f73916769576260b16c4e17e850d277a49db35fd74a15.te.png) +![workspace-6](../../../../translated_images/te/workspace-6.8dd81fe841797ee17f8f73916769576260b16c4e17e850d277a49db35fd74a15.png) మీరు అజ్యూర్ పోర్టల్ ఉపయోగించి మీ వర్క్‌స్పేస్‌ను నిర్వహించవచ్చు, కానీ డేటా శాస్త్రవేత్తలు మరియు మెషీన్ లెర్నింగ్ ఆపరేషన్స్ ఇంజనీర్ల కోసం, అజ్యూర్ మెషీన్ లెర్నింగ్ స్టూడియో వర్క్‌స్పేస్ వనరులను నిర్వహించడానికి మరింత కేంద్రీకృత యూజర్ ఇంటర్‌ఫేస్‌ను అందిస్తుంది. @@ -193,18 +193,18 @@ Low-priority instance అంటే అది అంతరాయం కలిగ ముందుగా సృష్టించిన [Azure ML వర్క్‌స్పేస్](https://ml.azure.com/)లో, compute కి వెళ్లండి మరియు మనం చర్చించిన వివిధ compute వనరులను చూడగలుగుతారు (అంటే compute instances, compute clusters, inference clusters మరియు attached compute). ఈ ప్రాజెక్ట్ కోసం, మోడల్ శిక్షణకు compute cluster అవసరం. స్టూడియోలో, "Compute" మెనూకి క్లిక్ చేయండి, తరువాత "Compute cluster" ట్యాబ్ మరియు "+ New" బటన్ క్లిక్ చేసి compute cluster సృష్టించండి. -![22](../../../../translated_images/cluster-1.b78cb630bb543729b11f60c34d97110a263f8c27b516ba4dc47807b3cee5579f.te.png) +![22](../../../../translated_images/te/cluster-1.b78cb630bb543729b11f60c34d97110a263f8c27b516ba4dc47807b3cee5579f.png) 1. మీ ఎంపికలను ఎంచుకోండి: Dedicated vs Low priority, CPU లేదా GPU, VM పరిమాణం మరియు కోర్ సంఖ్య (ఈ ప్రాజెక్ట్ కోసం డిఫాల్ట్ సెట్టింగ్స్ ఉంచవచ్చు). 2. Next బటన్ క్లిక్ చేయండి. -![23](../../../../translated_images/cluster-2.ea30cdbc9f926bb9e05af3fdbc1f679811c796dc2a6847f935290aec15526e88.te.png) +![23](../../../../translated_images/te/cluster-2.ea30cdbc9f926bb9e05af3fdbc1f679811c796dc2a6847f935290aec15526e88.png) 3. క్లస్టర్‌కు compute పేరు ఇవ్వండి 4. మీ ఎంపికలను ఎంచుకోండి: కనీస/గరిష్ట నోడ్స్ సంఖ్య, scale down కు ముందు idle సెకన్లు, SSH యాక్సెస్. కనీస నోడ్స్ సంఖ్య 0 అయితే, క్లస్టర్ idle ఉన్నప్పుడు మీరు డబ్బు ఆదా చేస్తారు. గరిష్ట నోడ్స్ సంఖ్య ఎక్కువగా ఉంటే శిక్షణ తక్కువ సమయం తీసుకుంటుంది. గరిష్ట నోడ్స్ సంఖ్య 3 సిఫార్సు చేయబడింది. 5. "Create" బటన్ క్లిక్ చేయండి. ఈ దశ కొంత సమయం తీసుకోవచ్చు. -![29](../../../../translated_images/cluster-3.8a334bc070ec173a329ce5abd2a9d727542e83eb2347676c9af20f2c8870b3e7.te.png) +![29](../../../../translated_images/te/cluster-3.8a334bc070ec173a329ce5abd2a9d727542e83eb2347676c9af20f2c8870b3e7.png) అద్భుతం! ఇప్పుడు మనకు Compute cluster ఉంది, Azure ML Studioకి డేటాను లోడ్ చేయాలి. @@ -212,15 +212,15 @@ Low-priority instance అంటే అది అంతరాయం కలిగ 1. ముందుగా సృష్టించిన [Azure ML వర్క్‌స్పేస్](https://ml.azure.com/)లో, ఎడమ మెనూలో "Datasets" క్లిక్ చేసి "+ Create dataset" బటన్ క్లిక్ చేసి dataset సృష్టించండి. "From local files" ఎంపికను ఎంచుకుని ముందుగా డౌన్లోడ్ చేసిన Kaggle dataset ఎంచుకోండి. - ![24](../../../../translated_images/dataset-1.e86ab4e10907a6e9c2a72577b51db35f13689cb33702337b8b7032f2ef76dac2.te.png) + ![24](../../../../translated_images/te/dataset-1.e86ab4e10907a6e9c2a72577b51db35f13689cb33702337b8b7032f2ef76dac2.png) 2. మీ dataset కు పేరు, రకం మరియు వివరణ ఇవ్వండి. Next క్లిక్ చేయండి. ఫైళ్ల నుండి డేటాను అప్‌లోడ్ చేయండి. Next క్లిక్ చేయండి. - ![25](../../../../translated_images/dataset-2.f58de1c435d5bf9ccb16ccc5f5d4380eb2b50affca85cfbf4f97562bdab99f77.te.png) + ![25](../../../../translated_images/te/dataset-2.f58de1c435d5bf9ccb16ccc5f5d4380eb2b50affca85cfbf4f97562bdab99f77.png) 3. Schema లో, క్రింది ఫీచర్ల కోసం డేటా రకాన్ని Boolean గా మార్చండి: anaemia, diabetes, high blood pressure, sex, smoking, మరియు DEATH_EVENT. Next క్లిక్ చేసి Create క్లిక్ చేయండి. - ![26](../../../../translated_images/dataset-3.58db8c0eb783e89236a02bbce5bb4ba808d081a87d994d5284b1ae59928c95bf.te.png) + ![26](../../../../translated_images/te/dataset-3.58db8c0eb783e89236a02bbce5bb4ba808d081a87d994d5284b1ae59928c95bf.png) చాలా బాగుంది! ఇప్పుడు dataset సెట్ అయింది మరియు compute cluster సృష్టించబడింది, మోడల్ శిక్షణ ప్రారంభించవచ్చు! @@ -231,19 +231,19 @@ Automated machine learning (AutoML) అనేది మెషీన్ లెర 1. ముందుగా సృష్టించిన [Azure ML వర్క్‌స్పేస్](https://ml.azure.com/)లో, ఎడమ మెనూలో "Automated ML" క్లిక్ చేసి మీరు అప్‌లోడ్ చేసిన dataset ఎంచుకోండి. Next క్లిక్ చేయండి. - ![27](../../../../translated_images/aml-1.67281a85d3a1e2f34eb367b2d0f74e1039d13396e510f363cd8766632106d1ec.te.png) + ![27](../../../../translated_images/te/aml-1.67281a85d3a1e2f34eb367b2d0f74e1039d13396e510f363cd8766632106d1ec.png) 2. కొత్త ఎక్స్‌పెరిమెంట్ పేరు, లక్ష్య కాలమ్ (DEATH_EVENT) మరియు మనం సృష్టించిన compute cluster ఎంచుకోండి. Next క్లిక్ చేయండి. - ![28](../../../../translated_images/aml-2.c9fb9cffb39ccbbe21ab9810ae937195d41a489744e15cff2b8477ed4dcae1ec.te.png) + ![28](../../../../translated_images/te/aml-2.c9fb9cffb39ccbbe21ab9810ae937195d41a489744e15cff2b8477ed4dcae1ec.png) 3. "Classification" ఎంచుకుని Finish క్లిక్ చేయండి. ఈ దశ compute cluster పరిమాణం ఆధారంగా 30 నిమిషాల నుండి 1 గంట వరకు పడవచ్చు. - ![30](../../../../translated_images/aml-3.a7952e4295f38cc6cdb0c7ed6dc71ea756b7fb5697ec126bc1220f87c5fa9231.te.png) + ![30](../../../../translated_images/te/aml-3.a7952e4295f38cc6cdb0c7ed6dc71ea756b7fb5697ec126bc1220f87c5fa9231.png) 4. రన్ పూర్తయిన తర్వాత, "Automated ML" ట్యాబ్ క్లిక్ చేసి మీ రన్ ఎంచుకోండి, "Best model summary" కార్డులో Algorithm క్లిక్ చేయండి. - ![31](../../../../translated_images/aml-4.7a627e09cb6f16d0aa246059d9faee3d1725cc4258d0c8df15e801f73afc7e2c.te.png) + ![31](../../../../translated_images/te/aml-4.7a627e09cb6f16d0aa246059d9faee3d1725cc4258d0c8df15e801f73afc7e2c.png) ఇక్కడ మీరు AutoML రూపొందించిన ఉత్తమ మోడల్ యొక్క వివరమైన వివరణ చూడవచ్చు. మీరు Models ట్యాబ్‌లో ఇతర మోడల్స్‌ను కూడా అన్వేషించవచ్చు. Explanations (preview button) లో మోడల్స్‌ను కొంత సమయం తీసుకుని పరిశీలించండి. మీరు ఉపయోగించదలచుకున్న మోడల్ ఎంచుకున్న తర్వాత (ఇక్కడ మనం AutoML ఎంచుకున్న ఉత్తమ మోడల్ తీసుకుంటాం), దాన్ని ఎలా డిప్లాయ్ చేయాలో చూద్దాం. @@ -254,15 +254,15 @@ Automated machine learning (AutoML) అనేది మెషీన్ లెర ఉత్తమ మోడల్ వివరణలో, "Deploy" బటన్ క్లిక్ చేయండి. -![deploy-1](../../../../translated_images/deploy-1.ddad725acadc84e34553c3d09e727160faeb32527a9fb8b904c0f99235a34bb6.te.png) +![deploy-1](../../../../translated_images/te/deploy-1.ddad725acadc84e34553c3d09e727160faeb32527a9fb8b904c0f99235a34bb6.png) 15. దానికి పేరు, వివరణ, compute రకం (Azure Container Instance), authentication ఎనేబుల్ చేసి Deploy క్లిక్ చేయండి. ఈ దశ సుమారు 20 నిమిషాలు పడవచ్చు. డిప్లాయ్‌మెంట్ ప్రక్రియలో మోడల్ రిజిస్టర్ చేయడం, వనరులు సృష్టించడం మరియు వాటిని వెబ్ సర్వీస్ కోసం కాన్ఫిగర్ చేయడం ఉంటాయి. Deploy స్థితి క్రింద ఒక స్థితి సందేశం కనిపిస్తుంది. Deploy స్థితి "Healthy" అయినప్పుడు అది డిప్లాయ్ అయి నడుస్తోంది అని అర్థం. -![deploy-2](../../../../translated_images/deploy-2.94dbb13f239086473aa4bf814342fd40483d136849b080f02bafbb995383940e.te.png) +![deploy-2](../../../../translated_images/te/deploy-2.94dbb13f239086473aa4bf814342fd40483d136849b080f02bafbb995383940e.png) 16. డిప్లాయ్ అయిన తర్వాత, Endpoint ట్యాబ్ క్లిక్ చేసి మీరు డిప్లాయ్ చేసిన ఎండ్‌పాయింట్ ఎంచుకోండి. ఇక్కడ ఎండ్‌పాయింట్ గురించి అవసరమైన అన్ని వివరాలు ఉంటాయి. -![deploy-3](../../../../translated_images/deploy-3.fecefef070e8ef3b28e802326d107f61ac4e672d20bf82d05f78d025f9e6c611.te.png) +![deploy-3](../../../../translated_images/te/deploy-3.fecefef070e8ef3b28e802326d107f61ac4e672d20bf82d05f78d025f9e6c611.png) అద్భుతం! ఇప్పుడు మోడల్ డిప్లాయ్ అయింది, ఎండ్‌పాయింట్ వినియోగం ప్రారంభించవచ్చు. @@ -272,7 +272,7 @@ Automated machine learning (AutoML) అనేది మెషీన్ లెర ఈ స్క్రిప్ట్ మీ స్థానిక యంత్రం నుండి నేరుగా నడిపించవచ్చు మరియు మీ ఎండ్‌పాయింట్‌ను వినియోగిస్తుంది. -![35](../../../../translated_images/consumption-1.700abd196452842a020c7d745908637a6e4c5c50494ad1217be80e283e0de154.te.png) +![35](../../../../translated_images/te/consumption-1.700abd196452842a020c7d745908637a6e4c5c50494ad1217be80e283e0de154.png) ఈ 2 కోడ్ లైన్లను ఒకసారి పరిశీలించండి: diff --git a/translations/te/5-Data-Science-In-Cloud/19-Azure/README.md b/translations/te/5-Data-Science-In-Cloud/19-Azure/README.md index 359a87e1..df233671 100644 --- a/translations/te/5-Data-Science-In-Cloud/19-Azure/README.md +++ b/translations/te/5-Data-Science-In-Cloud/19-Azure/README.md @@ -57,7 +57,7 @@ SDK యొక్క ముఖ్యమైన ప్రాంతాలు: [మునుపటి పాఠంలో](../18-Low-Code/README.md), మేము ఎలా మోడల్‌ను శిక్షణ, డిప్లాయ్ మరియు వినియోగించాలో చూశాము, అది Low code/No code విధానంలో జరిగింది. మేము హార్ట్ ఫెయిల్యూర్ డేటాసెట్ ఉపయోగించి హార్ట్ ఫెయిల్యూర్ ప్రిడిక్షన్ మోడల్‌ను సృష్టించాము. ఈ పాఠంలో, అదే పని Azure Machine Learning SDK ఉపయోగించి చేయబోతున్నాము. -![project-schema](../../../../translated_images/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.te.png) +![project-schema](../../../../translated_images/te/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.png) ### 1.2 హార్ట్ ఫెయిల్యూర్ ప్రిడిక్షన్ ప్రాజెక్ట్ మరియు డేటాసెట్ పరిచయం @@ -74,7 +74,7 @@ SDK యొక్క ముఖ్యమైన ప్రాంతాలు: ముందుగా సృష్టించిన [Azure ML వర్క్‌స్పేస్](https://ml.azure.com/)లో, కంప్యూట్ మెనూకి వెళ్లండి, అక్కడ మీరు అందుబాటులో ఉన్న వివిధ కంప్యూట్ వనరులను చూడవచ్చు -![compute-instance-1](../../../../translated_images/compute-instance-1.dba347cb199ca4996b3e3d649295ed95626ba481479d3986557b9b98e76d8816.te.png) +![compute-instance-1](../../../../translated_images/te/compute-instance-1.dba347cb199ca4996b3e3d649295ed95626ba481479d3986557b9b98e76d8816.png) జూపిటర్ నోట్బుక్ ప్రావిజన్ చేయడానికి కంప్యూట్ ఇన్స్టాన్స్ సృష్టిద్దాం. 1. + New బటన్‌పై క్లిక్ చేయండి. @@ -97,10 +97,10 @@ SDK యొక్క ముఖ్యమైన ప్రాంతాలు: 1. Applications విభాగంలో, Jupyter ఎంపికపై క్లిక్ చేయండి. 2. "Yes, I understand" బాక్స్‌ను టిక్ చేసి Continue బటన్‌పై క్లిక్ చేయండి. -![notebook-1](../../../../translated_images/notebook-1.12998af7b02c83f536c11b3aeba561be16e0f05e94146600728ec64270ce1105.te.png) +![notebook-1](../../../../translated_images/te/notebook-1.12998af7b02c83f536c11b3aeba561be16e0f05e94146600728ec64270ce1105.png) 3. ఇది మీ జూపిటర్ నోట్బుక్ ఇన్స్టాన్స్‌తో కొత్త బ్రౌజర్ ట్యాబ్‌ను తెరుస్తుంది. నూతన నోట్బుక్ సృష్టించడానికి "New" బటన్‌పై క్లిక్ చేయండి. -![notebook-2](../../../../translated_images/notebook-2.9a657c037e34f1cf26c0212f5ee9e2da8545b3e107c7682c55114e494167a8aa.te.png) +![notebook-2](../../../../translated_images/te/notebook-2.9a657c037e34f1cf26c0212f5ee9e2da8545b3e107c7682c55114e494167a8aa.png) ఇప్పుడు మనకు నోట్బుక్ ఉన్నందున, Azure ML SDK తో మోడల్ శిక్షణ ప్రారంభించవచ్చు. diff --git a/translations/te/5-Data-Science-In-Cloud/README.md b/translations/te/5-Data-Science-In-Cloud/README.md index 92c72344..19d5eedd 100644 --- a/translations/te/5-Data-Science-In-Cloud/README.md +++ b/translations/te/5-Data-Science-In-Cloud/README.md @@ -9,13 +9,13 @@ CO_OP_TRANSLATOR_METADATA: --> # క్లౌడ్‌లో డేటా సైన్స్ -![cloud-picture](../../../translated_images/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.te.jpg) +![cloud-picture](../../../translated_images/te/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.jpg) > ఫోటో [Jelleke Vanooteghem](https://unsplash.com/@ilumire) నుండి [Unsplash](https://unsplash.com/s/photos/cloud?orientation=landscape) బిగ్ డేటాతో డేటా సైన్స్ చేయడంలో, క్లౌడ్ ఒక గేమ్ చేంజర్ కావచ్చు. తదుపరి మూడు పాఠాలలో, క్లౌడ్ అంటే ఏమిటి మరియు అది ఎందుకు చాలా సహాయకరమో మనం చూడబోతున్నాము. మనం హార్ట్ ఫెయిల్యూర్ డేటాసెట్‌ను కూడా పరిశీలించి, ఎవరికైనా హార్ట్ ఫెయిల్యూర్ ఉండే అవకాశాన్ని అంచనా వేయడానికి ఒక మోడల్‌ను నిర్మించబోతున్నాము. మోడల్‌ను శిక్షణ ఇవ్వడం, డిప్లాయ్ చేయడం మరియు రెండు వేర్వేరు మార్గాల్లో వినియోగించడం కోసం క్లౌడ్ శక్తిని ఉపయోగిస్తాము. ఒక మార్గం లో కోడ్/నో కోడ్ ఫ్యాషన్‌లో కేవలం యూజర్ ఇంటర్‌ఫేస్ ఉపయోగించడం, మరొక మార్గం Azure మెషీన్ లెర్నింగ్ సాఫ్ట్‌వేర్ డెవలపర్ కిట్ (Azure ML SDK) ఉపయోగించడం. -![project-schema](../../../translated_images/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.te.png) +![project-schema](../../../translated_images/te/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.png) ### విషయాలు diff --git a/translations/te/6-Data-Science-In-Wild/20-Real-World-Examples/README.md b/translations/te/6-Data-Science-In-Wild/20-Real-World-Examples/README.md index 70889800..571c0f59 100644 --- a/translations/te/6-Data-Science-In-Wild/20-Real-World-Examples/README.md +++ b/translations/te/6-Data-Science-In-Wild/20-Real-World-Examples/README.md @@ -40,7 +40,7 @@ AI ప్రజాస్వామ్యీకరణకు కృతజ్ఞత * [Data Science in Healthcare](https://data-flair.training/blogs/data-science-in-healthcare/) - వైద్య ఇమేజింగ్ (ఉదా: MRI, X-Ray, CT-స్కాన్), జెనోమిక్స్ (DNA సీక్వెన్సింగ్), ఔషధ అభివృద్ధి (ప్రమాద అంచనా, విజయ అంచనా), పూర్వానుమాన విశ్లేషణ (రోగి సంరక్షణ & సరఫరా లాజిస్టిక్స్), వ్యాధి ట్రాకింగ్ & నివారణ వంటి అనువర్తనాలను హైలైట్ చేస్తుంది. -![Data Science Applications in The Real World](../../../../translated_images/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.te.png) చిత్రం క్రెడిట్: [Data Flair: 6 అద్భుతమైన డేటా సైన్స్ అనువర్తనాలు](https://data-flair.training/blogs/data-science-applications/) +![Data Science Applications in The Real World](../../../../translated_images/te/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.png) చిత్రం క్రెడిట్: [Data Flair: 6 అద్భుతమైన డేటా సైన్స్ అనువర్తనాలు](https://data-flair.training/blogs/data-science-applications/) ఈ చిత్రం ఇతర డొమైన్‌లు మరియు డేటా సైన్స్ సాంకేతికతలను అన్వయించడానికి ఉదాహరణలను చూపిస్తుంది. ఇతర అనువర్తనాలను అన్వేషించాలనుకుంటున్నారా? క్రింద [సమీక్ష & స్వీయ అధ్యయనం](../../../../6-Data-Science-In-Wild/20-Real-World-Examples) విభాగాన్ని చూడండి. diff --git a/translations/te/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md b/translations/te/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md index ac4098ca..282666ec 100644 --- a/translations/te/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md +++ b/translations/te/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md @@ -22,7 +22,7 @@ Explorer ఇంటర్‌ఫేస్ (క్రింద స్క్రీ 2. డేటాసెట్ [క్యాటలాగ్](https://planetarycomputer.microsoft.com/catalog) అన్వేషించండి - ప్రతి డేటాసెట్ యొక్క ఉద్దేశ్యాన్ని తెలుసుకోండి. 3. Explorer ఉపయోగించండి - ఆసక్తికరమైన డేటాసెట్ ఎంచుకోండి, సంబంధిత క్వెరీ & రేండరింగ్ ఆప్షన్ ఎంచుకోండి. -![The Planetary Computer Explorer](../../../../translated_images/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.te.png) +![The Planetary Computer Explorer](../../../../translated_images/te/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.png) `మీ పని:` ఇప్పుడు బ్రౌజర్‌లో రేండర్ అయిన విజువలైజేషన్‌ను అధ్యయనం చేసి క్రింది ప్రశ్నలకు సమాధానం ఇవ్వండి: diff --git a/translations/te/CONTRIBUTING.md b/translations/te/CONTRIBUTING.md index 00be6ba3..b13f22bb 100644 --- a/translations/te/CONTRIBUTING.md +++ b/translations/te/CONTRIBUTING.md @@ -312,7 +312,7 @@ def calculate_mean(data): import pandas as pd ``` ```` -- చిత్రాలకు alt టెక్స్ట్ జోడించండి: `![Alt text](../../translated_images/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.te.png)` +- చిత్రాలకు alt టెక్స్ట్ జోడించండి: `![Alt text](../../translated_images/te/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.png)` - లైన్ పొడవులు సుమారు 80-100 అక్షరాలుగా ఉంచండి ### Python diff --git a/translations/te/README.md b/translations/te/README.md index 5f3fabf2..94d014af 100644 --- a/translations/te/README.md +++ b/translations/te/README.md @@ -28,7 +28,7 @@ Microsoftలోని Azure Cloud Advocates డేటా సైన్స్ గ **🙏 ప్రత్యేక ధన్యవాదాలు 🙏 మా [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/) రచయితలు, సమీక్షకులు మరియు కంటెంట్ సహకారులకు,** ముఖ్యంగా Aaryan Arora, [Aditya Garg](https://github.com/AdityaGarg00), [Alondra Sanchez](https://www.linkedin.com/in/alondra-sanchez-molina/), [Ankita Singh](https://www.linkedin.com/in/ankitasingh007), [Anupam Mishra](https://www.linkedin.com/in/anupam--mishra/), [Arpita Das](https://www.linkedin.com/in/arpitadas01/), ChhailBihari Dubey, [Dibri Nsofor](https://www.linkedin.com/in/dibrinsofor), [Dishita Bhasin](https://www.linkedin.com/in/dishita-bhasin-7065281bb), [Majd Safi](https://www.linkedin.com/in/majd-s/), [Max Blum](https://www.linkedin.com/in/max-blum-6036a1186/), [Miguel Correa](https://www.linkedin.com/in/miguelmque/), [Mohamma Iftekher (Iftu) Ebne Jalal](https://twitter.com/iftu119), [Nawrin Tabassum](https://www.linkedin.com/in/nawrin-tabassum), [Raymond Wangsa Putra](https://www.linkedin.com/in/raymond-wp/), [Rohit Yadav](https://www.linkedin.com/in/rty2423), Samridhi Sharma, [Sanya Sinha](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200), [Sheena Narula](https://www.linkedin.com/in/sheena-narua-n/), [Tauqeer Ahmad](https://www.linkedin.com/in/tauqeerahmad5201/), Yogendrasingh Pawar , [Vidushi Gupta](https://www.linkedin.com/in/vidushi-gupta07/), [Jasleen Sondhi](https://www.linkedin.com/in/jasleen-sondhi/) -|![స్కెచ్‌నోట్ ద్వారా @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.te.png)| +|![స్కెచ్‌నోట్ ద్వారా @sketchthedocs https://sketchthedocs.dev](../../translated_images/te/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.png)| |:---:| | ప్రాథమికుల కోసం డేటా సైన్స్ - _స్కెచ్‌నోట్ ద్వారా [@nitya](https://twitter.com/nitya)_ | @@ -47,7 +47,7 @@ Microsoftలోని Azure Cloud Advocates డేటా సైన్స్ గ మా వద్ద Discord లెర్న్ విత్ AI సిరీస్ కొనసాగుతోంది, మరింత తెలుసుకోండి మరియు 18 - 30 సెప్టెంబర్, 2025 కాలంలో మాతో చేరడానికి [Learn with AI Series](https://aka.ms/learnwithai/discord)లో జాయిన్ అవ్వండి. మీరు Data Science కోసం GitHub Copilot ఉపయోగించే టిప్స్ మరియు ట్రిక్స్ పొందుతారు. -![AIతో నేర్చుకునే సిరీస్](../../translated_images/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.te.jpg) +![AIతో నేర్చుకునే సిరీస్](../../translated_images/te/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.jpg) # మీరు విద్యార్థి? @@ -127,7 +127,7 @@ Microsoftలోని Azure Cloud Advocates డేటా సైన్స్ గ ## పాఠాలు -|![ స్కెచ్‌నోట్ రచన @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.te.png)| +|![ స్కెచ్‌నోట్ రచన @sketchthedocs https://sketchthedocs.dev](../../translated_images/te/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png)| |:---:| | ప్రారంభికుల కోసం డేటా సైన్స్: రోడ్‌మ్యాప్ - _స్కెచ్‌నోట్ రచన [@nitya](https://twitter.com/nitya)_ | diff --git a/translations/te/sketchnotes/README.md b/translations/te/sketchnotes/README.md index 265fd2f6..a8119974 100644 --- a/translations/te/sketchnotes/README.md +++ b/translations/te/sketchnotes/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: నిత్య నరసింహన్, కళాకారుడు -![roadmap sketchnote](../../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.te.png) +![roadmap sketchnote](../../../translated_images/te/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png) --- diff --git a/translations/th/1-Introduction/01-defining-data-science/README.md b/translations/th/1-Introduction/01-defining-data-science/README.md index c8093d59..be39bab6 100644 --- a/translations/th/1-Introduction/01-defining-data-science/README.md +++ b/translations/th/1-Introduction/01-defining-data-science/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: --- -[![วิดีโอการนิยามวิทยาศาสตร์ข้อมูล](../../../../translated_images/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.th.png)](https://youtu.be/beZ7Mb_oz9I) +[![วิดีโอการนิยามวิทยาศาสตร์ข้อมูล](../../../../translated_images/th/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.png)](https://youtu.be/beZ7Mb_oz9I) ## [แบบทดสอบก่อนการบรรยาย](https://ff-quizzes.netlify.app/en/ds/quiz/0) @@ -154,7 +154,7 @@ CO_OP_TRANSLATOR_METADATA: ในความท้าทายนี้ เราจะพยายามค้นหาแนวคิดที่เกี่ยวข้องกับสาขาวิทยาศาสตร์ข้อมูลโดยการดูจากข้อความ เราจะนำบทความ Wikipedia เกี่ยวกับวิทยาศาสตร์ข้อมูลมาดาวน์โหลดและประมวลผลข้อความ จากนั้นสร้าง Word Cloud แบบนี้: -![Word Cloud for Data Science](../../../../translated_images/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.th.png) +![Word Cloud for Data Science](../../../../translated_images/th/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.png) เยี่ยมชม [`notebook.ipynb`](../../../../1-Introduction/01-defining-data-science/notebook.ipynb ':ignore') เพื่ออ่านโค้ด คุณยังสามารถรันโค้ดและดูว่ามันทำการแปลงข้อมูลแบบเรียลไทม์อย่างไร diff --git a/translations/th/1-Introduction/04-stats-and-probability/README.md b/translations/th/1-Introduction/04-stats-and-probability/README.md index 58a3b284..065f4bc9 100644 --- a/translations/th/1-Introduction/04-stats-and-probability/README.md +++ b/translations/th/1-Introduction/04-stats-and-probability/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: ทฤษฎีสถิติและความน่าจะเป็นเป็นสองสาขาที่เกี่ยวข้องกันอย่างมากในคณิตศาสตร์ และมีความสำคัญอย่างยิ่งต่อวิทยาศาสตร์ข้อมูล แม้ว่าเราจะสามารถทำงานกับข้อมูลได้โดยไม่ต้องมีความรู้ทางคณิตศาสตร์ลึกซึ้ง แต่การมีความเข้าใจพื้นฐานบางอย่างก็ยังดีกว่า ที่นี่เราจะนำเสนอการแนะนำสั้น ๆ ที่จะช่วยให้คุณเริ่มต้นได้ -[![วิดีโอแนะนำ](../../../../translated_images/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.th.png)](https://youtu.be/Z5Zy85g4Yjw) +[![วิดีโอแนะนำ](../../../../translated_images/th/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.png)](https://youtu.be/Z5Zy85g4Yjw) ## [แบบทดสอบก่อนเรียน](https://ff-quizzes.netlify.app/en/ds/quiz/6) @@ -39,7 +39,7 @@ CO_OP_TRANSLATOR_METADATA: เราสามารถพูดถึงความน่าจะเป็นของตัวแปรที่อยู่ในช่วงค่าที่กำหนด เช่น P(t1≤X2) ในกรณีนี้ การแจกแจงความน่าจะเป็นจะถูกอธิบายโดย **ฟังก์ชันความหนาแน่นของความน่าจะเป็น** p(x) โดยที่ -![P(t_1\le X ตัวอย่างเพิ่มเติมเกี่ยวกับความสัมพันธ์และความแปรปรวนร่วมสามารถดูได้ใน [notebook ที่แนบมา](notebook.ipynb) diff --git a/translations/th/1-Introduction/README.md b/translations/th/1-Introduction/README.md index a15385ad..996556d0 100644 --- a/translations/th/1-Introduction/README.md +++ b/translations/th/1-Introduction/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # บทนำสู่วิทยาศาสตร์ข้อมูล -![ข้อมูลในแอคชั่น](../../../translated_images/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.th.jpg) +![ข้อมูลในแอคชั่น](../../../translated_images/th/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.jpg) > ภาพถ่ายโดย Stephen Dawson บน Unsplash ในบทเรียนนี้ คุณจะได้เรียนรู้ว่าวิทยาศาสตร์ข้อมูลถูกนิยามอย่างไร และพิจารณาประเด็นด้านจริยธรรมที่นักวิทยาศาสตร์ข้อมูลต้องคำนึงถึง นอกจากนี้ คุณจะได้เรียนรู้ว่าข้อมูลถูกนิยามอย่างไร รวมถึงพื้นฐานของสถิติและความน่าจะเป็น ซึ่งเป็นแกนหลักของวิชาการในวิทยาศาสตร์ข้อมูล diff --git a/translations/th/2-Working-With-Data/07-python/README.md b/translations/th/2-Working-With-Data/07-python/README.md index 412e2154..0dbb95ff 100644 --- a/translations/th/2-Working-With-Data/07-python/README.md +++ b/translations/th/2-Working-With-Data/07-python/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: | :-------------------------------------------------------------------------------------------------------: | | การทำงานกับ Python - _Sketchnote โดย [@nitya](https://twitter.com/nitya)_ | -[![วิดีโอแนะนำ](../../../../translated_images/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.th.png)](https://youtu.be/dZjWOGbsN4Y) +[![วิดีโอแนะนำ](../../../../translated_images/th/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.png)](https://youtu.be/dZjWOGbsN4Y) แม้ว่าฐานข้อมูลจะเป็นวิธีที่มีประสิทธิภาพในการจัดเก็บข้อมูลและเรียกใช้ข้อมูลด้วยภาษาคำสั่ง แต่การเขียนโปรแกรมเพื่อจัดการข้อมูลด้วยตัวเองถือเป็นวิธีที่ยืดหยุ่นที่สุด ในหลายกรณี การใช้คำสั่งฐานข้อมูลอาจมีประสิทธิภาพมากกว่า แต่ในบางกรณีที่ต้องการการประมวลผลข้อมูลที่ซับซ้อนมากขึ้น SQL อาจไม่สามารถทำได้ง่าย ๆ การประมวลผลข้อมูลสามารถเขียนได้ในทุกภาษาโปรแกรม แต่มีบางภาษาที่เหมาะสมกับการทำงานกับข้อมูลมากกว่า นักวิทยาศาสตร์ข้อมูลมักนิยมใช้ภาษาต่อไปนี้: @@ -72,7 +72,7 @@ print(f"Length of index is {len(idx)}") items_sold = pd.Series(np.random.randint(25,50,size=len(idx)),index=idx) items_sold.plot() ``` -![Time Series Plot](../../../../translated_images/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.th.png) +![Time Series Plot](../../../../translated_images/th/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.png) สมมติว่าในแต่ละสัปดาห์เราจัดงานเลี้ยงสำหรับเพื่อน ๆ และนำไอศกรีมเพิ่มอีก 10 แพ็คสำหรับงานเลี้ยง เราสามารถสร้าง series อีกตัวที่มี index เป็นสัปดาห์เพื่อแสดงสิ่งนี้: ```python @@ -83,7 +83,7 @@ additional_items = pd.Series(10,index=pd.date_range(start_date,end_date,freq="W" total_items = items_sold.add(additional_items,fill_value=0) total_items.plot() ``` -![Time Series Plot](../../../../translated_images/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.th.png) +![Time Series Plot](../../../../translated_images/th/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.png) > **หมายเหตุ** เราไม่ได้ใช้ syntax ง่าย ๆ `total_items+additional_items` หากเราใช้ syntax นี้ เราจะได้รับค่ามากมายที่เป็น `NaN` (*Not a Number*) ใน series ที่ได้ เนื่องจากมีค่าที่หายไปสำหรับบาง index point ใน series `additional_items` และการบวก `NaN` กับค่าใด ๆ จะให้ผลลัพธ์เป็น `NaN` ดังนั้นเราจำเป็นต้องระบุพารามิเตอร์ `fill_value` ระหว่างการบวก @@ -92,7 +92,7 @@ total_items.plot() monthly = total_items.resample("1M").mean() ax = monthly.plot(kind='bar') ``` -![Monthly Time Series Averages](../../../../translated_images/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.th.png) +![Monthly Time Series Averages](../../../../translated_images/th/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.png) ### DataFrame @@ -218,7 +218,7 @@ Data Scientist มักต้องสำรวจข้อมูล ดัง เนื่องจากเราต้องการแสดงวิธีการจัดการกับข้อมูล เราขอเชิญคุณเปิด [`notebook-covidspread.ipynb`](notebook-covidspread.ipynb) และอ่านตั้งแต่ต้นจนจบ คุณยังสามารถรันเซลล์ และทำบาง Challenge ที่เราได้ทิ้งไว้ให้คุณในตอนท้าย -![COVID Spread](../../../../translated_images/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.th.png) +![COVID Spread](../../../../translated_images/th/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.png) > หากคุณไม่ทราบวิธีการรันโค้ดใน Jupyter Notebook ลองดู [บทความนี้](https://soshnikov.com/education/how-to-execute-notebooks-from-github/) @@ -240,7 +240,7 @@ Data Scientist มักต้องสำรวจข้อมูล ดัง เปิด [`notebook-papers.ipynb`](notebook-papers.ipynb) และอ่านตั้งแต่ต้นจนจบ คุณยังสามารถรันเซลล์ และทำบาง Challenge ที่เราได้ทิ้งไว้ให้คุณในตอนท้าย -![Covid Medical Treatment](../../../../translated_images/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.th.png) +![Covid Medical Treatment](../../../../translated_images/th/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.png) ## การประมวลผลข้อมูลภาพ diff --git a/translations/th/2-Working-With-Data/README.md b/translations/th/2-Working-With-Data/README.md index 77a3f42e..599ff97e 100644 --- a/translations/th/2-Working-With-Data/README.md +++ b/translations/th/2-Working-With-Data/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # การทำงานกับข้อมูล -![data love](../../../translated_images/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.th.jpg) +![data love](../../../translated_images/th/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.jpg) > ภาพถ่ายโดย Alexander Sinn บน Unsplash ในบทเรียนนี้ คุณจะได้เรียนรู้วิธีการต่าง ๆ ในการจัดการ ปรับเปลี่ยน และใช้งานข้อมูลในแอปพลิเคชัน คุณจะได้เรียนรู้เกี่ยวกับฐานข้อมูลเชิงสัมพันธ์และไม่เชิงสัมพันธ์ รวมถึงวิธีการจัดเก็บข้อมูลในฐานข้อมูลเหล่านี้ นอกจากนี้ คุณจะได้เรียนรู้พื้นฐานของการใช้ Python เพื่อจัดการข้อมูล และค้นพบวิธีการหลากหลายที่สามารถใช้ Python ในการจัดการและวิเคราะห์ข้อมูล diff --git a/translations/th/3-Data-Visualization/12-visualization-relationships/README.md b/translations/th/3-Data-Visualization/12-visualization-relationships/README.md index d8f120ea..379048e4 100644 --- a/translations/th/3-Data-Visualization/12-visualization-relationships/README.md +++ b/translations/th/3-Data-Visualization/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ honey.head() ```python sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ``` -![scatterplot 1](../../../../translated_images/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.th.png) +![scatterplot 1](../../../../translated_images/th/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.png) ตอนนี้ แสดงข้อมูลเดียวกันด้วยโทนสีของน้ำผึ้งเพื่อแสดงว่าราคาเปลี่ยนแปลงไปอย่างไรในแต่ละปี คุณสามารถทำได้โดยเพิ่มพารามิเตอร์ 'hue' เพื่อแสดงการเปลี่ยนแปลงในแต่ละปี: @@ -60,7 +60,7 @@ sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ```python sns.relplot(x="priceperlb", y="state", hue="year", palette="YlOrBr", data=honey, height=15, aspect=.5); ``` -![scatterplot 2](../../../../translated_images/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.th.png) +![scatterplot 2](../../../../translated_images/th/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.png) ด้วยการเปลี่ยนชุดสีนี้ คุณจะเห็นได้ชัดเจนว่ามีการเปลี่ยนแปลงอย่างต่อเนื่องในราคาน้ำผึ้งต่อปอนด์ในแต่ละปี หากคุณดูชุดข้อมูลตัวอย่างเพื่อยืนยัน (เลือกดูรัฐหนึ่ง เช่น รัฐแอริโซนา) คุณจะเห็นรูปแบบของการเพิ่มขึ้นของราคาปีต่อปี โดยมีข้อยกเว้นเล็กน้อย: @@ -89,7 +89,7 @@ sns.relplot(x="priceperlb", y="state", size="year", data=honey, height=15, aspec ``` คุณจะเห็นขนาดของจุดเพิ่มขึ้นเรื่อย ๆ -![scatterplot 3](../../../../translated_images/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.th.png) +![scatterplot 3](../../../../translated_images/th/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.png) นี่เป็นกรณีง่าย ๆ ของอุปสงค์และอุปทานหรือไม่? เนื่องจากปัจจัยต่าง ๆ เช่น การเปลี่ยนแปลงสภาพภูมิอากาศและการล่มสลายของรังผึ้ง มีน้ำผึ้งน้อยลงสำหรับการซื้อในแต่ละปี และทำให้ราคาสูงขึ้น? @@ -104,7 +104,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); ``` คำตอบ: ใช่ โดยมีข้อยกเว้นบางประการในปี 2003: -![line chart 1](../../../../translated_images/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.th.png) +![line chart 1](../../../../translated_images/th/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.png) ✅ เนื่องจาก Seaborn กำลังรวบรวมข้อมูลรอบเส้นเดียว มันจะแสดง "การวัดหลายครั้งที่แต่ละค่าของ x โดยการวางค่าเฉลี่ยและช่วงความเชื่อมั่น 95% รอบค่าเฉลี่ย" [แหล่งที่มา](https://seaborn.pydata.org/tutorial/relational.html) พฤติกรรมที่ใช้เวลานานนี้สามารถปิดได้โดยเพิ่ม `ci=None` @@ -114,7 +114,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); sns.relplot(x="year", y="totalprod", kind="line", data=honey); ``` -![line chart 2](../../../../translated_images/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.th.png) +![line chart 2](../../../../translated_images/th/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.png) คำตอบ: ไม่จริง หากคุณดูการผลิตรวม ดูเหมือนว่ามันจะเพิ่มขึ้นในปีนั้น แม้ว่าปริมาณน้ำผึ้งที่ผลิตโดยทั่วไปจะลดลงในช่วงปีเหล่านี้ @@ -139,7 +139,7 @@ sns.relplot( ``` ในภาพนี้ คุณสามารถเปรียบเทียบผลผลิตต่อรังและจำนวนรังในแต่ละปีแบบเคียงข้างกัน โดยตั้งค่าการจัดวาง wrap ที่ 3 สำหรับคอลัมน์: -![facet grid](../../../../translated_images/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.th.png) +![facet grid](../../../../translated_images/th/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.png) สำหรับชุดข้อมูลนี้ ไม่มีสิ่งใดที่โดดเด่นเกี่ยวกับจำนวนรังและผลผลิตของมันในแต่ละปีและแต่ละรัฐ มีวิธีอื่นในการค้นหาความสัมพันธ์ระหว่างตัวแปรสองตัวนี้หรือไม่? @@ -162,7 +162,7 @@ sns.despine(right=False) plt.ylabel('colony yield') ax.figure.legend(); ``` -![superimposed plots](../../../../translated_images/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.th.png) +![superimposed plots](../../../../translated_images/th/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.png) แม้ว่าไม่มีสิ่งใดที่โดดเด่นในปี 2003 แต่ก็ช่วยให้เราจบบทเรียนนี้ด้วยโน้ตที่มีความสุขเล็กน้อย: แม้ว่าจำนวนรังจะลดลงโดยรวม แต่จำนวนรังก็เริ่มคงที่ แม้ว่าผลผลิตต่อรังจะลดลง diff --git a/translations/th/3-Data-Visualization/R/09-visualization-quantities/README.md b/translations/th/3-Data-Visualization/R/09-visualization-quantities/README.md index 8e9c0c28..64d7c6c2 100644 --- a/translations/th/3-Data-Visualization/R/09-visualization-quantities/README.md +++ b/translations/th/3-Data-Visualization/R/09-visualization-quantities/README.md @@ -67,7 +67,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` ในที่นี้ คุณติดตั้งแพ็กเกจ `ggplot2` และนำเข้ามาใน workspace โดยใช้คำสั่ง `library("ggplot2")` เพื่อสร้างกราฟใน ggplot ใช้ฟังก์ชัน `ggplot()` และระบุชุดข้อมูล ตัวแปร x และ y เป็น attribute ในกรณีนี้ เราใช้ฟังก์ชัน `geom_line()` เนื่องจากเราต้องการสร้างกราฟเส้น -![MaxWingspan-lineplot](../../../../../translated_images/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.th.png) +![MaxWingspan-lineplot](../../../../../translated_images/th/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.png) คุณสังเกตเห็นอะไรทันที? ดูเหมือนว่าจะมีค่าผิดปกติอย่างน้อยหนึ่งค่า - นั่นคือความกว้างปีกที่ใหญ่มาก! ความกว้างปีกกว่า 2000 เซนติเมตรเท่ากับมากกว่า 20 เมตร - มี Pterodactyls อยู่ในมินนิโซตาหรือเปล่า? มาสำรวจเพิ่มเติมกันเถอะ @@ -85,7 +85,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` เราระบุมุมใน `theme` และระบุป้ายกำกับแกน x และ y ใน `xlab()` และ `ylab()` ตามลำดับ ส่วน `ggtitle()` ให้ชื่อกราฟ/แผนภูมิ -![MaxWingspan-lineplot-improved](../../../../../translated_images/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.th.png) +![MaxWingspan-lineplot-improved](../../../../../translated_images/th/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.png) แม้จะหมุนป้ายกำกับเป็น 45 องศา แต่ก็ยังอ่านยากเกินไป ลองใช้กลยุทธ์อื่น: แสดงป้ายกำกับเฉพาะค่าผิดปกติและตั้งป้ายกำกับภายในกราฟ คุณสามารถใช้กราฟกระจายเพื่อเพิ่มพื้นที่สำหรับการติดป้ายกำกับ: @@ -101,7 +101,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + คุณค้นพบอะไร? -![MaxWingspan-scatterplot](../../../../../translated_images/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.th.png) +![MaxWingspan-scatterplot](../../../../../translated_images/th/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.png) ## กรองข้อมูลของคุณ @@ -120,7 +120,7 @@ ggplot(data=birds_filtered, aes(x=Name, y=MaxWingspan,group=1)) + ``` เราสร้าง dataframe ใหม่ชื่อ `birds_filtered` และสร้างกราฟกระจายอีกครั้ง โดยการกรองค่าผิดปกติออก ข้อมูลของคุณจะดูสอดคล้องและเข้าใจง่ายขึ้น -![MaxWingspan-scatterplot-improved](../../../../../translated_images/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.th.png) +![MaxWingspan-scatterplot-improved](../../../../../translated_images/th/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.png) ตอนนี้เรามีชุดข้อมูลที่สะอาดขึ้นในแง่ของความกว้างปีก มาค้นพบข้อมูลเพิ่มเติมเกี่ยวกับนกเหล่านี้กัน @@ -163,7 +163,7 @@ birds_filtered %>% group_by(Category) %>% ``` ในตัวอย่างนี้ เราติดตั้งแพ็กเกจ [dplyr](https://www.rdocumentation.org/packages/dplyr/versions/0.7.8) และ [lubridate](https://www.rdocumentation.org/packages/lubridate/versions/1.8.0) เพื่อช่วยจัดการและจัดกลุ่มข้อมูลเพื่อสร้างกราฟแท่งแบบซ้อนกัน ก่อนอื่น คุณจัดกลุ่มข้อมูลตาม `Category` ของนกและสรุปคอลัมน์ `MinLength`, `MaxLength`, `MinBodyMass`, `MaxBodyMass`, `MinWingspan`, `MaxWingspan` จากนั้นสร้างกราฟแท่งโดยใช้แพ็กเกจ `ggplot2` และระบุสีสำหรับหมวดหมู่ต่างๆ และป้ายกำกับ -![Stacked bar chart](../../../../../translated_images/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.th.png) +![Stacked bar chart](../../../../../translated_images/th/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.png) อย่างไรก็ตาม กราฟแท่งนี้อ่านยากเนื่องจากมีข้อมูลที่ไม่ได้จัดกลุ่มมากเกินไป คุณต้องเลือกเฉพาะข้อมูลที่คุณต้องการแสดงผล ดังนั้นมาดูความยาวของนกตามหมวดหมู่กัน @@ -178,7 +178,7 @@ ggplot(birds_count,aes(Category,n))+geom_bar(stat="identity")+coord_flip() ``` คุณนับค่าที่ไม่ซ้ำกันในคอลัมน์ `Category` และจัดเรียงข้อมูลลงใน dataframe ใหม่ชื่อ `birds_count` ข้อมูลที่จัดเรียงนี้จะถูกจัดระดับในลำดับเดียวกันเพื่อให้แสดงผลในลำดับที่จัดเรียงไว้ จากนั้นใช้ `ggplot2` เพื่อสร้างกราฟแท่ง โดย `coord_flip()` จะสร้างกราฟแท่งแนวนอน -![category-length](../../../../../translated_images/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.th.png) +![category-length](../../../../../translated_images/th/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.png) กราฟแท่งนี้แสดงภาพที่ดีเกี่ยวกับจำนวนของนกในแต่ละหมวดหมู่ เพียงแค่ดู คุณจะเห็นว่าจำนวนมากที่สุดของนกในภูมิภาคนี้อยู่ในหมวด Ducks/Geese/Waterfowl มินนิโซตาเป็น 'ดินแดนแห่งทะเลสาบ 10,000 แห่ง' ดังนั้นจึงไม่น่าแปลกใจ! @@ -201,7 +201,7 @@ ggplot(birds_grouped,aes(Category,MaxLength))+geom_bar(stat="identity")+coord_fl ``` เราจัดกลุ่มข้อมูล `birds_filtered` ตาม `Category` และสร้างกราฟแท่ง -![comparing data](../../../../../translated_images/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.th.png) +![comparing data](../../../../../translated_images/th/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.png) ไม่มีอะไรน่าแปลกใจที่นี่: นกฮัมมิงเบิร์ดมีค่าความยาวสูงสุดน้อยที่สุดเมื่อเทียบกับนกกระทุงหรือนกห่าน ข้อมูลที่มีเหตุผลเป็นสิ่งที่ดี! @@ -213,7 +213,7 @@ ggplot(data=birds_grouped, aes(x=Category)) + geom_bar(aes(y=MinLength), stat="identity", position="identity", fill='orange')+ coord_flip() ``` -![super-imposed values](../../../../../translated_images/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.th.png) +![super-imposed values](../../../../../translated_images/th/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.png) ## 🚀 ความท้าทาย diff --git a/translations/th/3-Data-Visualization/R/10-visualization-distributions/README.md b/translations/th/3-Data-Visualization/R/10-visualization-distributions/README.md index 5bb1a070..3fd55db1 100644 --- a/translations/th/3-Data-Visualization/R/10-visualization-distributions/README.md +++ b/translations/th/3-Data-Visualization/R/10-visualization-distributions/README.md @@ -45,7 +45,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + geom_point() + ggtitle("Max Length per order") + coord_flip() ``` -![ความยาวสูงสุดต่ออันดับ](../../../../../translated_images/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.th.png) +![ความยาวสูงสุดต่ออันดับ](../../../../../translated_images/th/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.png) นี่เป็นภาพรวมของการกระจายตัวทั่วไปของความยาวร่างกายต่ออันดับของนก แต่ไม่ใช่วิธีที่เหมาะสมที่สุดในการแสดงการกระจายตัวที่แท้จริง งานนี้มักจะทำโดยการสร้างฮิสโตแกรม (Histogram) @@ -57,7 +57,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=10)+ylab('Frequency') ``` -![การกระจายตัวในชุดข้อมูลทั้งหมด](../../../../../translated_images/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.th.png) +![การกระจายตัวในชุดข้อมูลทั้งหมด](../../../../../translated_images/th/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.png) ดังที่คุณเห็น นกส่วนใหญ่ในชุดข้อมูลกว่า 400 ตัวนี้มีมวลร่างกายสูงสุดต่ำกว่า 2000 ลองเปลี่ยนพารามิเตอร์ `bins` เป็นค่าที่สูงขึ้น เช่น 30 เพื่อดูข้อมูลเพิ่มเติม: @@ -65,7 +65,7 @@ ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![การกระจายตัว-30bins](../../../../../translated_images/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.th.png) +![การกระจายตัว-30bins](../../../../../translated_images/th/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.png) แผนภูมินี้แสดงการกระจายตัวในลักษณะที่ละเอียดขึ้นเล็กน้อย คุณสามารถสร้างแผนภูมิที่ไม่เอนเอียงไปทางซ้ายมากเกินไปได้โดยการเลือกข้อมูลเฉพาะในช่วงที่กำหนด: @@ -77,7 +77,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![ฮิสโตแกรมที่กรองแล้ว](../../../../../translated_images/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.th.png) +![ฮิสโตแกรมที่กรองแล้ว](../../../../../translated_images/th/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.png) ✅ ลองใช้ตัวกรองและจุดข้อมูลอื่น ๆ เพื่อดูการกระจายตัวของข้อมูลทั้งหมด ลบตัวกรอง `['MaxBodyMass']` เพื่อแสดงการกระจายตัวที่มีป้ายกำกับ @@ -91,7 +91,7 @@ ggplot(data=birds_filtered_1, aes(x=MaxBodyMass, y=MaxLength) ) + ``` ดูเหมือนว่าจะมีความสัมพันธ์ที่คาดหวังระหว่างสององค์ประกอบนี้ตามแกนที่คาดไว้ โดยมีจุดบรรจบที่แข็งแกร่งจุดหนึ่ง: -![แผนภูมิ 2 มิติ](../../../../../translated_images/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.th.png) +![แผนภูมิ 2 มิติ](../../../../../translated_images/th/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.png) ฮิสโตแกรมทำงานได้ดีโดยค่าเริ่มต้นสำหรับข้อมูลเชิงตัวเลข แล้วถ้าคุณต้องการดูการกระจายตัวตามข้อมูลข้อความล่ะ? ## สำรวจชุดข้อมูลเพื่อดูการกระจายตัวโดยใช้ข้อมูลข้อความ @@ -122,7 +122,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + scale_fill_manual(name="Conservation Status",values=c("red","green","blue","pink"),labels=c("Endangered","Near Threathened","Vulnerable","Least Concern")) ``` -![การเปรียบเทียบความกว้างปีกและสถานะการอนุรักษ์](../../../../../translated_images/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.th.png) +![การเปรียบเทียบความกว้างปีกและสถานะการอนุรักษ์](../../../../../translated_images/th/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.png) ดูเหมือนว่าจะไม่มีความสัมพันธ์ที่ดีระหว่างความกว้างปีกต่ำสุดและสถานะการอนุรักษ์ ลองทดสอบองค์ประกอบอื่น ๆ ในชุดข้อมูลโดยใช้วิธีนี้ คุณสามารถลองใช้ตัวกรองที่แตกต่างกันได้ คุณพบความสัมพันธ์ใดหรือไม่? @@ -136,7 +136,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + geom_density() ``` -![แผนภูมิความหนาแน่น](../../../../../translated_images/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.th.png) +![แผนภูมิความหนาแน่น](../../../../../translated_images/th/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.png) คุณจะเห็นว่าแผนภูมินี้สะท้อนแผนภูมิก่อนหน้านี้สำหรับข้อมูลความกว้างปีกต่ำสุด เพียงแต่ดูราบรื่นขึ้นเล็กน้อย หากคุณต้องการแก้ไขเส้นที่ไม่ราบรื่นของ MaxBodyMass ในแผนภูมิที่สองที่คุณสร้าง คุณสามารถทำให้มันราบรื่นได้ดีมากโดยสร้างใหม่ด้วยวิธีนี้: @@ -144,7 +144,7 @@ ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density() ``` -![ความหนาแน่นของมวลร่างกาย](../../../../../translated_images/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.th.png) +![ความหนาแน่นของมวลร่างกาย](../../../../../translated_images/th/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.png) หากคุณต้องการเส้นที่ราบรื่น แต่ไม่ราบรื่นเกินไป ให้แก้ไขพารามิเตอร์ `adjust`: @@ -152,7 +152,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density(adjust = 1/5) ``` -![มวลร่างกายที่ราบรื่นน้อยลง](../../../../../translated_images/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.th.png) +![มวลร่างกายที่ราบรื่นน้อยลง](../../../../../translated_images/th/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.png) ✅ อ่านเกี่ยวกับพารามิเตอร์ที่มีให้สำหรับแผนภูมิประเภทนี้และทดลองใช้! @@ -162,7 +162,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + ggplot(data=birds_filtered_1,aes(x = MaxBodyMass, fill = Order)) + geom_density(alpha=0.5) ``` -![มวลร่างกายต่ออันดับ](../../../../../translated_images/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.th.png) +![มวลร่างกายต่ออันดับ](../../../../../translated_images/th/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.png) ## 🚀 ความท้าทาย diff --git a/translations/th/3-Data-Visualization/R/11-visualization-proportions/README.md b/translations/th/3-Data-Visualization/R/11-visualization-proportions/README.md index 9759ec42..81d9768a 100644 --- a/translations/th/3-Data-Visualization/R/11-visualization-proportions/README.md +++ b/translations/th/3-Data-Visualization/R/11-visualization-proportions/README.md @@ -97,7 +97,7 @@ pie(grouped$count,grouped$class, main="Edible?") ``` และนี่คือแผนภูมิวงกลมที่แสดงสัดส่วนข้อมูลตามสองคลาสของเห็ดนี้ สิ่งสำคัญคือการจัดลำดับป้ายให้ถูกต้อง โดยเฉพาะในกรณีนี้ ดังนั้นอย่าลืมตรวจสอบลำดับที่สร้างอาร์เรย์ป้าย! -![pie chart](../../../../../translated_images/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.th.png) +![pie chart](../../../../../translated_images/th/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.png) ## โดนัท! @@ -132,7 +132,7 @@ library(webr) PieDonut(habitat, aes(habitat, count=count)) ``` -![donut chart](../../../../../translated_images/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.th.png) +![donut chart](../../../../../translated_images/th/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.png) โค้ดนี้ใช้สองไลบรารี - ggplot2 และ webr โดยใช้ฟังก์ชัน PieDonut ของไลบรารี webr เราสามารถสร้างแผนภูมิโดนัทได้อย่างง่ายดาย! @@ -169,7 +169,7 @@ waffle((cap_color$count/10), rows = 7, title = "Waffle Chart")+scale_fill_manual ด้วยแผนภูมิแบบวาฟเฟิล คุณสามารถเห็นสัดส่วนของสีหมวกเห็ดในชุดข้อมูลนี้ได้อย่างชัดเจน น่าสนใจที่มีเห็ดที่มีหมวกสีเขียวจำนวนมาก! -![waffle chart](../../../../../translated_images/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.th.png) +![waffle chart](../../../../../translated_images/th/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.png) ในบทเรียนนี้ คุณได้เรียนรู้สามวิธีในการแสดงสัดส่วนข้อมูล ก่อนอื่น คุณต้องจัดกลุ่มข้อมูลของคุณเป็นหมวดหมู่ จากนั้นตัดสินใจว่าวิธีใดดีที่สุดในการแสดงข้อมูล - วงกลม โดนัท หรือวาฟเฟิล ทั้งหมดนี้น่าสนใจและให้ภาพรวมของชุดข้อมูลแก่ผู้ใช้ได้ทันที diff --git a/translations/th/3-Data-Visualization/R/12-visualization-relationships/README.md b/translations/th/3-Data-Visualization/R/12-visualization-relationships/README.md index bbd243d8..8c5f6a23 100644 --- a/translations/th/3-Data-Visualization/R/12-visualization-relationships/README.md +++ b/translations/th/3-Data-Visualization/R/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ library(ggplot2) ggplot(honey, aes(x = priceperlb, y = state)) + geom_point(colour = "blue") ``` -![scatterplot 1](../../../../../translated_images/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.th.png) +![scatterplot 1](../../../../../translated_images/th/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.png) ตอนนี้ แสดงข้อมูลเดียวกันด้วยโทนสีของน้ำผึ้งเพื่อแสดงให้เห็นว่าราคาเปลี่ยนแปลงไปอย่างไรในแต่ละปี คุณสามารถทำได้โดยเพิ่มพารามิเตอร์ 'scale_color_gradientn' เพื่อแสดงการเปลี่ยนแปลงในแต่ละปี: @@ -61,7 +61,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ggplot(honey, aes(x = priceperlb, y = state, color=year)) + geom_point()+scale_color_gradientn(colours = colorspace::heat_hcl(7)) ``` -![scatterplot 2](../../../../../translated_images/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.th.png) +![scatterplot 2](../../../../../translated_images/th/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.png) ด้วยการเปลี่ยนโทนสีนี้ คุณจะเห็นได้ชัดเจนว่ามีการเปลี่ยนแปลงอย่างต่อเนื่องในราคาน้ำผึ้งต่อปอนด์ในแต่ละปี หากคุณดูตัวอย่างข้อมูลในชุดข้อมูลเพื่อยืนยัน (เลือกดูรัฐหนึ่ง เช่น รัฐแอริโซนา) คุณจะเห็นรูปแบบการเพิ่มขึ้นของราคาปีต่อปี โดยมีข้อยกเว้นบางประการ: @@ -92,7 +92,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ``` คุณจะเห็นว่าขนาดของจุดเพิ่มขึ้นเรื่อย ๆ -![scatterplot 3](../../../../../translated_images/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.th.png) +![scatterplot 3](../../../../../translated_images/th/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.png) นี่เป็นกรณีง่าย ๆ ของอุปสงค์และอุปทานหรือไม่? เนื่องจากปัจจัยต่าง ๆ เช่น การเปลี่ยนแปลงสภาพภูมิอากาศและการล่มสลายของรังผึ้ง มีน้ำผึ้งน้อยลงสำหรับการซื้อในแต่ละปี และทำให้ราคาสูงขึ้น? @@ -107,7 +107,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab ``` คำตอบ: ใช่ โดยมีข้อยกเว้นบางประการในปี 2003: -![line chart 1](../../../../../translated_images/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.th.png) +![line chart 1](../../../../../translated_images/th/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.png) คำถาม: ในปี 2003 เราเห็นการเพิ่มขึ้นของปริมาณน้ำผึ้งหรือไม่? หากคุณดูการผลิตรวมในแต่ละปีล่ะ? @@ -115,7 +115,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab qplot(honey$year,honey$totalprod, geom='smooth', span =0.5, xlab = "year",ylab = "totalprod") ``` -![line chart 2](../../../../../translated_images/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.th.png) +![line chart 2](../../../../../translated_images/th/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.png) คำตอบ: ไม่จริง หากคุณดูการผลิตรวม ดูเหมือนว่าจะเพิ่มขึ้นในปีนั้น แต่โดยทั่วไปแล้วปริมาณน้ำผึ้งที่ผลิตลดลงในช่วงปีเหล่านี้ @@ -135,7 +135,7 @@ ggplot(honey, aes(x=yieldpercol, y = numcol,group = 1)) + ``` ในภาพนี้ คุณสามารถเปรียบเทียบผลผลิตต่อรังและจำนวนรังในแต่ละปี โดยจัดเรียง wrap เป็น 3 คอลัมน์: -![facet grid](../../../../../translated_images/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.th.png) +![facet grid](../../../../../translated_images/th/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.png) สำหรับชุดข้อมูลนี้ ไม่มีสิ่งใดที่โดดเด่นเกี่ยวกับจำนวนรังและผลผลิตของรังในแต่ละปีและแต่ละรัฐ มีวิธีอื่นในการค้นหาความสัมพันธ์ระหว่างตัวแปรสองตัวนี้หรือไม่? @@ -152,7 +152,7 @@ plot(honey$year, honey$yieldpercol, pch = 17, col = 3, axis(side = 4, at = pretty(range(y2))) mtext("colony yield", side = 4, line = 3) ``` -![superimposed plots](../../../../../translated_images/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.th.png) +![superimposed plots](../../../../../translated_images/th/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.png) แม้ว่าไม่มีสิ่งใดที่โดดเด่นในปี 2003 แต่ก็ช่วยให้เราจบบทเรียนนี้ด้วยข่าวดีเล็กน้อย: แม้ว่าจำนวนรังจะลดลงโดยรวม แต่จำนวนรังก็เริ่มคงที่ แม้ว่าผลผลิตต่อรังจะลดลง diff --git a/translations/th/3-Data-Visualization/R/13-meaningful-vizualizations/README.md b/translations/th/3-Data-Visualization/R/13-meaningful-vizualizations/README.md index 558c0012..775866e0 100644 --- a/translations/th/3-Data-Visualization/R/13-meaningful-vizualizations/README.md +++ b/translations/th/3-Data-Visualization/R/13-meaningful-vizualizations/README.md @@ -47,25 +47,25 @@ CO_OP_TRANSLATOR_METADATA: แม้ว่านักวิทยาศาสตร์ข้อมูลจะระมัดระวังในการเลือกกราฟที่เหมาะสมกับข้อมูล แต่ก็ยังมีวิธีมากมายที่ข้อมูลสามารถถูกแสดงออกมาเพื่อสนับสนุนจุดยืนบางอย่าง ซึ่งมักจะทำให้ข้อมูลเสียความน่าเชื่อถือ มีตัวอย่างมากมายของกราฟและอินโฟกราฟิกที่หลอกลวง! -[![How Charts Lie โดย Alberto Cairo](../../../../../translated_images/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.th.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "How charts lie") +[![How Charts Lie โดย Alberto Cairo](../../../../../translated_images/th/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "How charts lie") > 🎥 คลิกที่ภาพด้านบนเพื่อดูการบรรยายเกี่ยวกับกราฟที่หลอกลวง กราฟนี้กลับด้านแกน X เพื่อแสดงสิ่งที่ตรงข้ามกับความจริงตามวันที่: -![bad chart 1](../../../../../translated_images/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.th.png) +![bad chart 1](../../../../../translated_images/th/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.png) [กราฟนี้](https://media.firstcoastnews.com/assets/WTLV/images/170ae16f-4643-438f-b689-50d66ca6a8d8/170ae16f-4643-438f-b689-50d66ca6a8d8_1140x641.jpg) ยิ่งหลอกลวงมากขึ้น เพราะสายตาจะถูกดึงไปทางขวาเพื่อสรุปว่าเมื่อเวลาผ่านไป จำนวนผู้ติดเชื้อ COVID ในแต่ละเขตลดลง แต่ถ้าคุณดูวันที่อย่างละเอียด คุณจะพบว่ามันถูกจัดเรียงใหม่เพื่อสร้างแนวโน้มที่ลดลงอย่างหลอกลวง -![bad chart 2](../../../../../translated_images/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.th.jpg) +![bad chart 2](../../../../../translated_images/th/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.jpg) ตัวอย่างที่มีชื่อเสียงนี้ใช้สีและกลับด้านแกน Y เพื่อหลอกลวง: แทนที่จะสรุปว่าการเสียชีวิตจากอาวุธปืนเพิ่มขึ้นหลังจากการผ่านกฎหมายที่สนับสนุนอาวุธปืน สายตากลับถูกหลอกให้คิดว่าตรงกันข้าม: -![bad chart 3](../../../../../translated_images/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.th.jpg) +![bad chart 3](../../../../../translated_images/th/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.jpg) กราฟแปลก ๆ นี้แสดงให้เห็นว่าสัดส่วนสามารถถูกบิดเบือนได้อย่างน่าขบขัน: -![bad chart 4](../../../../../translated_images/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.th.jpg) +![bad chart 4](../../../../../translated_images/th/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.jpg) การเปรียบเทียบสิ่งที่ไม่สามารถเปรียบเทียบได้เป็นอีกหนึ่งกลวิธีที่ไม่ซื่อสัตย์ มี [เว็บไซต์ที่ยอดเยี่ยม](https://tylervigen.com/spurious-correlations) ที่รวบรวม 'ความสัมพันธ์ที่ผิดพลาด' แสดง 'ข้อเท็จจริง' ที่เชื่อมโยงสิ่งต่าง ๆ เช่น อัตราการหย่าร้างใน Maine กับการบริโภคมาการีน กลุ่ม Reddit ยังรวบรวม [การใช้ข้อมูลที่น่าเกลียด](https://www.reddit.com/r/dataisugly/top/?t=all) อีกด้วย @@ -100,13 +100,13 @@ CO_OP_TRANSLATOR_METADATA: หากข้อมูลของคุณเป็นข้อความและยาวบนแกน X คุณสามารถปรับมุมข้อความเพื่อให้อ่านง่ายขึ้น [plot3D](https://cran.r-project.org/web/packages/plot3D/index.html) เสนอการสร้างกราฟแบบ 3 มิติ หากข้อมูลของคุณรองรับ กราฟข้อมูลที่ซับซ้อนสามารถสร้างได้โดยใช้เครื่องมือนี้ -![3d plots](../../../../../translated_images/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.th.png) +![3d plots](../../../../../translated_images/th/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.png) ## การแสดงกราฟแบบเคลื่อนไหวและ 3 มิติ ภาพข้อมูลที่ดีที่สุดในปัจจุบันบางส่วนเป็นแบบเคลื่อนไหว Shirley Wu มีตัวอย่างที่น่าทึ่งที่สร้างด้วย D3 เช่น '[film flowers](http://bl.ocks.org/sxywu/raw/d612c6c653fb8b4d7ff3d422be164a5d/)' ซึ่งแต่ละดอกไม้เป็นภาพข้อมูลของภาพยนตร์ อีกตัวอย่างหนึ่งสำหรับ Guardian คือ 'bussed out' ซึ่งเป็นประสบการณ์แบบโต้ตอบที่รวมภาพข้อมูลกับ Greensock และ D3 พร้อมบทความแบบเลื่อนเพื่อแสดงวิธีที่ NYC จัดการกับปัญหาคนไร้บ้านโดยการส่งพวกเขาออกจากเมือง -![busing](../../../../../translated_images/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.th.png) +![busing](../../../../../translated_images/th/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.png) > "Bussed Out: How America Moves its Homeless" จาก [the Guardian](https://www.theguardian.com/us-news/ng-interactive/2017/dec/20/bussed-out-america-moves-homeless-people-country-study). ภาพข้อมูลโดย Nadieh Bremer & Shirley Wu @@ -116,7 +116,7 @@ CO_OP_TRANSLATOR_METADATA: คุณจะสร้างแอปเว็บที่จะแสดงมุมมองแบบเคลื่อนไหวของเครือข่ายสังคมนี้ โดยใช้ไลบรารีที่สร้างขึ้นเพื่อสร้าง [ภาพเครือข่าย](https://github.com/emiliorizzo/vue-d3-network) โดยใช้ Vue.js และ D3 เมื่อแอปทำงาน คุณสามารถลากโหนดไปรอบ ๆ บนหน้าจอเพื่อจัดเรียงข้อมูลใหม่ได้ -![liaisons](../../../../../translated_images/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.th.png) +![liaisons](../../../../../translated_images/th/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.png) ## โครงการ: สร้างกราฟเพื่อแสดงเครือข่ายโดยใช้ D3.js diff --git a/translations/th/3-Data-Visualization/README.md b/translations/th/3-Data-Visualization/README.md index 5cc0c0d7..ab5fab9f 100644 --- a/translations/th/3-Data-Visualization/README.md +++ b/translations/th/3-Data-Visualization/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # การสร้างภาพข้อมูล -![ผึ้งบนดอกลาเวนเดอร์](../../../translated_images/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.th.jpg) +![ผึ้งบนดอกลาเวนเดอร์](../../../translated_images/th/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.jpg) > ภาพถ่ายโดย Jenna Lee บน Unsplash การสร้างภาพข้อมูลเป็นหนึ่งในงานที่สำคัญที่สุดของนักวิทยาศาสตร์ข้อมูล ภาพหนึ่งภาพมีค่ามากกว่าคำพูดนับพันคำ และการสร้างภาพข้อมูลสามารถช่วยให้คุณระบุส่วนที่น่าสนใจในข้อมูลของคุณ เช่น จุดพุ่งสูงสุด ค่าผิดปกติ การจัดกลุ่ม แนวโน้ม และอื่นๆ ซึ่งช่วยให้คุณเข้าใจเรื่องราวที่ข้อมูลของคุณพยายามจะบอก diff --git a/translations/th/4-Data-Science-Lifecycle/14-Introduction/README.md b/translations/th/4-Data-Science-Lifecycle/14-Introduction/README.md index 28c31d25..02073930 100644 --- a/translations/th/4-Data-Science-Lifecycle/14-Introduction/README.md +++ b/translations/th/4-Data-Science-Lifecycle/14-Introduction/README.md @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: บทเรียนนี้จะเน้นที่ 3 ส่วนของวงจรชีวิต: การเก็บข้อมูล การประมวลผล และการบำรุงรักษา -![แผนภาพของวงจรชีวิตวิทยาศาสตร์ข้อมูล](../../../../translated_images/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.th.jpg) +![แผนภาพของวงจรชีวิตวิทยาศาสตร์ข้อมูล](../../../../translated_images/th/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.jpg) > ภาพโดย [Berkeley School of Information](https://ischoolonline.berkeley.edu/data-science/what-is-data-science/) ## การเก็บข้อมูล @@ -101,7 +101,7 @@ CO_OP_TRANSLATOR_METADATA: |Team Data Science Process (TDSP)|Cross-industry standard process for data mining (CRISP-DM)| |--|--| -|![วงจรชีวิต Team Data Science](../../../../translated_images/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.th.png) | ![ภาพจาก Data Science Process Alliance](../../../../translated_images/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.th.png) | +|![วงจรชีวิต Team Data Science](../../../../translated_images/th/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.png) | ![ภาพจาก Data Science Process Alliance](../../../../translated_images/th/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.png) | | ภาพโดย [Microsoft](https://docs.microsoft.comazure/architecture/data-science-process/lifecycle) | ภาพโดย [Data Science Process Alliance](https://www.datascience-pm.com/crisp-dm-2/) | ## [แบบทดสอบหลังเรียน](https://ff-quizzes.netlify.app/en/ds/quiz/27) diff --git a/translations/th/4-Data-Science-Lifecycle/README.md b/translations/th/4-Data-Science-Lifecycle/README.md index 1d53ed32..334d68fa 100644 --- a/translations/th/4-Data-Science-Lifecycle/README.md +++ b/translations/th/4-Data-Science-Lifecycle/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # วงจรชีวิตของวิทยาศาสตร์ข้อมูล -![communication](../../../translated_images/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.th.jpg) +![communication](../../../translated_images/th/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.jpg) > ภาพถ่ายโดย Headway บน Unsplash ในบทเรียนนี้ คุณจะได้สำรวจบางแง่มุมของวงจรชีวิตวิทยาศาสตร์ข้อมูล รวมถึงการวิเคราะห์และการสื่อสารเกี่ยวกับข้อมูล diff --git a/translations/th/5-Data-Science-In-Cloud/README.md b/translations/th/5-Data-Science-In-Cloud/README.md index a8e5b2a7..ca19e598 100644 --- a/translations/th/5-Data-Science-In-Cloud/README.md +++ b/translations/th/5-Data-Science-In-Cloud/README.md @@ -9,13 +9,13 @@ CO_OP_TRANSLATOR_METADATA: --> # วิทยาศาสตร์ข้อมูลบนคลาวด์ -![cloud-picture](../../../translated_images/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.th.jpg) +![cloud-picture](../../../translated_images/th/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.jpg) > ภาพโดย [Jelleke Vanooteghem](https://unsplash.com/@ilumire) จาก [Unsplash](https://unsplash.com/s/photos/cloud?orientation=landscape) เมื่อพูดถึงการทำวิทยาศาสตร์ข้อมูลกับข้อมูลขนาดใหญ่ คลาวด์สามารถเปลี่ยนเกมได้อย่างมาก ในสามบทเรียนถัดไป เราจะมาดูกันว่าคลาวด์คืออะไรและทำไมมันถึงมีประโยชน์มาก นอกจากนี้ เรายังจะสำรวจชุดข้อมูลเกี่ยวกับภาวะหัวใจล้มเหลวและสร้างโมเดลเพื่อช่วยประเมินความน่าจะเป็นที่ใครบางคนจะมีภาวะหัวใจล้มเหลว เราจะใช้พลังของคลาวด์ในการฝึกอบรม, ปรับใช้ และใช้งานโมเดลในสองวิธีที่แตกต่างกัน วิธีแรกใช้เฉพาะอินเทอร์เฟซผู้ใช้ในรูปแบบ Low code/No code และอีกวิธีใช้ Azure Machine Learning Software Developer Kit (Azure ML SDK) -![project-schema](../../../translated_images/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.th.png) +![project-schema](../../../translated_images/th/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.png) ### หัวข้อ diff --git a/translations/th/6-Data-Science-In-Wild/20-Real-World-Examples/README.md b/translations/th/6-Data-Science-In-Wild/20-Real-World-Examples/README.md index ec99ba84..bda243b8 100644 --- a/translations/th/6-Data-Science-In-Wild/20-Real-World-Examples/README.md +++ b/translations/th/6-Data-Science-In-Wild/20-Real-World-Examples/README.md @@ -41,7 +41,7 @@ CO_OP_TRANSLATOR_METADATA: * [วิทยาศาสตร์ข้อมูลในด้านสุขภาพ](https://data-flair.training/blogs/data-science-in-healthcare/) - เน้นการประยุกต์ใช้ เช่น การถ่ายภาพทางการแพทย์ (MRI, X-Ray, CT-Scan) จีโนมิกส์ (การถอดรหัส DNA) การพัฒนายา (การประเมินความเสี่ยง การคาดการณ์ความสำเร็จ) การวิเคราะห์เชิงพยากรณ์ (การดูแลผู้ป่วยและโลจิสติกส์) การติดตามและป้องกันโรค ฯลฯ -![การประยุกต์ใช้วิทยาศาสตร์ข้อมูลในโลกแห่งความจริง](../../../../translated_images/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.th.png) เครดิตภาพ: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) +![การประยุกต์ใช้วิทยาศาสตร์ข้อมูลในโลกแห่งความจริง](../../../../translated_images/th/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.png) เครดิตภาพ: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) ภาพนี้แสดงโดเมนและตัวอย่างอื่น ๆ ของการประยุกต์ใช้เทคนิควิทยาศาสตร์ข้อมูล ต้องการสำรวจการประยุกต์ใช้อื่น ๆ หรือไม่? ดูที่ส่วน [Review & Self Study](../../../../6-Data-Science-In-Wild/20-Real-World-Examples) ด้านล่าง diff --git a/translations/th/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md b/translations/th/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md index 05db9d3a..9cec8e2f 100644 --- a/translations/th/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md +++ b/translations/th/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md @@ -22,7 +22,7 @@ CO_OP_TRANSLATOR_METADATA: 2. สำรวจ [แคตตาล็อกชุดข้อมูล](https://planetarycomputer.microsoft.com/catalog) - เพื่อเรียนรู้วัตถุประสงค์ของแต่ละชุดข้อมูล 3. ใช้ Explorer - เลือกชุดข้อมูลที่คุณสนใจ เลือกคำสั่งค้นหาที่เกี่ยวข้อง และตัวเลือกการแสดงผล -![The Planetary Computer Explorer](../../../../translated_images/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.th.png) +![The Planetary Computer Explorer](../../../../translated_images/th/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.png) `งานของคุณ:` ศึกษาการแสดงผลที่ปรากฏในเบราว์เซอร์และตอบคำถามต่อไปนี้: diff --git a/translations/th/CONTRIBUTING.md b/translations/th/CONTRIBUTING.md index c03f6cd2..c28f517c 100644 --- a/translations/th/CONTRIBUTING.md +++ b/translations/th/CONTRIBUTING.md @@ -312,7 +312,7 @@ def calculate_mean(data): import pandas as pd ``` ```` -- เพิ่มข้อความ alt ให้กับภาพ: `![Alt text](../../translated_images/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.th.png)` +- เพิ่มข้อความ alt ให้กับภาพ: `![Alt text](../../translated_images/th/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.png)` - รักษาความยาวบรรทัดให้อยู่ในช่วงที่เหมาะสม (ประมาณ 80-100 ตัวอักษร) ### Python diff --git a/translations/th/README.md b/translations/th/README.md index c6795045..4e69781a 100644 --- a/translations/th/README.md +++ b/translations/th/README.md @@ -33,7 +33,7 @@ Azure Cloud Advocates ที่ Microsoft ยินดีเสนอหลั **🙏 ขอบคุณเป็นพิเศษ 🙏 ต่อผู้เขียน ทบทวน และผู้มีส่วนร่วมเนื้อหาจาก [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/),** โดยเฉพาะ Aaryan Arora, [Aditya Garg](https://github.com/AdityaGarg00), [Alondra Sanchez](https://www.linkedin.com/in/alondra-sanchez-molina/), [Ankita Singh](https://www.linkedin.com/in/ankitasingh007), [Anupam Mishra](https://www.linkedin.com/in/anupam--mishra/), [Arpita Das](https://www.linkedin.com/in/arpitadas01/), ChhailBihari Dubey, [Dibri Nsofor](https://www.linkedin.com/in/dibrinsofor), [Dishita Bhasin](https://www.linkedin.com/in/dishita-bhasin-7065281bb), [Majd Safi](https://www.linkedin.com/in/majd-s/), [Max Blum](https://www.linkedin.com/in/max-blum-6036a1186/), [Miguel Correa](https://www.linkedin.com/in/miguelmque/), [Mohamma Iftekher (Iftu) Ebne Jalal](https://twitter.com/iftu119), [Nawrin Tabassum](https://www.linkedin.com/in/nawrin-tabassum), [Raymond Wangsa Putra](https://www.linkedin.com/in/raymond-wp/), [Rohit Yadav](https://www.linkedin.com/in/rty2423), Samridhi Sharma, [Sanya Sinha](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200), [Sheena Narula](https://www.linkedin.com/in/sheena-narua-n/), [Tauqeer Ahmad](https://www.linkedin.com/in/tauqeerahmad5201/), Yogendrasingh Pawar , [Vidushi Gupta](https://www.linkedin.com/in/vidushi-gupta07/), [Jasleen Sondhi](https://www.linkedin.com/in/jasleen-sondhi/) -|![ภาพสเก็ตช์โดย @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.th.png)| +|![ภาพสเก็ตช์โดย @sketchthedocs https://sketchthedocs.dev](../../translated_images/th/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.png)| |:---:| | วิทยาศาสตร์ข้อมูลสำหรับผู้เริ่มต้น - _สเก็ตช์โน้ตโดย [@nitya](https://twitter.com/nitya)_ | @@ -52,7 +52,7 @@ Azure Cloud Advocates ที่ Microsoft ยินดีเสนอหลั เรามีซีรีส์ Discord เรียนรู้กับ AI กำลังจัดอยู่ เรียนรู้เพิ่มเติมและเข้าร่วมกับเราได้ที่ [Learn with AI Series](https://aka.ms/learnwithai/discord) ตั้งแต่วันที่ 18 - 30 กันยายน 2025 คุณจะได้รับเคล็ดลับและเทคนิคการใช้ GitHub Copilot สำหรับวิทยาศาสตร์ข้อมูล -![ซีรีส์ Learn with AI](../../translated_images/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.th.jpg) +![ซีรีส์ Learn with AI](../../translated_images/th/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.jpg) # คุณเป็นนักเรียนหรือไม่? @@ -132,7 +132,7 @@ Azure Cloud Advocates ที่ Microsoft ยินดีเสนอหลั ## บทเรียน -|![ สเก็ตช์โน้ตโดย @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.th.png)| +|![ สเก็ตช์โน้ตโดย @sketchthedocs https://sketchthedocs.dev](../../translated_images/th/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png)| |:---:| | Data Science For Beginners: Roadmap - _สเก็ตช์โน้ตโดย [@nitya](https://twitter.com/nitya)_ | diff --git a/translations/th/sketchnotes/README.md b/translations/th/sketchnotes/README.md index aa64e3f0..0f2fd119 100644 --- a/translations/th/sketchnotes/README.md +++ b/translations/th/sketchnotes/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: Nitya Narasimhan, ศิลปิน -![สเก็ตโน้ตแผนที่](../../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.th.png) +![สเก็ตโน้ตแผนที่](../../../translated_images/th/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png) --- diff --git a/translations/tl/1-Introduction/01-defining-data-science/README.md b/translations/tl/1-Introduction/01-defining-data-science/README.md index a59cf968..fc93e54b 100644 --- a/translations/tl/1-Introduction/01-defining-data-science/README.md +++ b/translations/tl/1-Introduction/01-defining-data-science/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: --- -[![Video ng Pagpapakilala sa Data Science](../../../../translated_images/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.tl.png)](https://youtu.be/beZ7Mb_oz9I) +[![Video ng Pagpapakilala sa Data Science](../../../../translated_images/tl/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.png)](https://youtu.be/beZ7Mb_oz9I) ## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ds/quiz/0) @@ -153,7 +153,7 @@ Kung nais nating gawing mas komplikado, maaari nating i-plot ang oras na ginugol Sa hamong ito, susubukan nating tukuyin ang mga konseptong may kaugnayan sa larangan ng Data Science sa pamamagitan ng pagsusuri sa mga teksto. Kukuha tayo ng isang artikulo mula sa Wikipedia tungkol sa Data Science, ida-download at ipoproseso ang teksto, at gagawa ng isang word cloud na ganito: -![Word Cloud para sa Data Science](../../../../translated_images/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.tl.png) +![Word Cloud para sa Data Science](../../../../translated_images/tl/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.png) Bisitahin ang [`notebook.ipynb`](../../../../1-Introduction/01-defining-data-science/notebook.ipynb ':ignore') upang basahin ang code. Maaari mo ring patakbuhin ang code, at makita kung paano nito isinasagawa ang lahat ng data transformations sa real time. diff --git a/translations/tl/1-Introduction/04-stats-and-probability/README.md b/translations/tl/1-Introduction/04-stats-and-probability/README.md index f45c914c..904e4b33 100644 --- a/translations/tl/1-Introduction/04-stats-and-probability/README.md +++ b/translations/tl/1-Introduction/04-stats-and-probability/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: Ang Estadistika at Teorya ng Probabilidad ay dalawang malapit na kaugnay na larangan ng Matematika na mahalaga sa Data Science. Posibleng magtrabaho gamit ang datos nang hindi masyadong malalim ang kaalaman sa matematika, ngunit mas mainam pa rin na malaman ang ilang pangunahing konsepto. Dito, magbibigay kami ng maikling panimula na makakatulong sa iyong magsimula. -[![Intro Video](../../../../translated_images/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.tl.png)](https://youtu.be/Z5Zy85g4Yjw) +[![Intro Video](../../../../translated_images/tl/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.png)](https://youtu.be/Z5Zy85g4Yjw) ## [Pre-lecture quiz](https://ff-quizzes.netlify.app/en/ds/quiz/6) @@ -39,7 +39,7 @@ Mas mahirap ilarawan ang probability distribution ng isang continuous variable, Maaari lamang nating pag-usapan ang probabilidad ng isang variable na nasa loob ng isang ibinigay na interval ng mga halaga, halimbawa P(t1≤X2). Sa kasong ito, ang probability distribution ay inilalarawan ng isang **probability density function** p(x), kung saan: -![P(t_1\le X Mas maraming halimbawa ng correlation at covariance ang matatagpuan sa [kasamang notebook](notebook.ipynb). diff --git a/translations/tl/1-Introduction/README.md b/translations/tl/1-Introduction/README.md index e1be3218..e7e76330 100644 --- a/translations/tl/1-Introduction/README.md +++ b/translations/tl/1-Introduction/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Panimula sa Data Science -![data in action](../../../translated_images/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.tl.jpg) +![data in action](../../../translated_images/tl/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.jpg) > Larawan ni Stephen Dawson sa Unsplash Sa mga araling ito, matutuklasan mo kung paano tinutukoy ang Data Science at matututo tungkol sa mga etikal na konsiderasyon na dapat isaalang-alang ng isang data scientist. Malalaman mo rin kung paano tinutukoy ang data at magkakaroon ng kaunting kaalaman tungkol sa estadistika at probabilidad, ang mga pangunahing akademikong larangan ng Data Science. diff --git a/translations/tl/2-Working-With-Data/07-python/README.md b/translations/tl/2-Working-With-Data/07-python/README.md index f1f457af..a4ffedb7 100644 --- a/translations/tl/2-Working-With-Data/07-python/README.md +++ b/translations/tl/2-Working-With-Data/07-python/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: | :-------------------------------------------------------------------------------------------------------: | | Paggamit ng Python - _Sketchnote ni [@nitya](https://twitter.com/nitya)_ | -[![Intro Video](../../../../translated_images/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.tl.png)](https://youtu.be/dZjWOGbsN4Y) +[![Intro Video](../../../../translated_images/tl/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.png)](https://youtu.be/dZjWOGbsN4Y) Bagama't ang mga database ay nagbibigay ng napaka-epektibong paraan upang mag-imbak ng data at mag-query gamit ang mga query language, ang pinaka-flexible na paraan ng pagproseso ng data ay ang pagsusulat ng sarili mong programa upang manipulahin ang data. Sa maraming pagkakataon, mas epektibo ang paggamit ng database query. Gayunpaman, sa ilang mga kaso kung saan mas kumplikadong pagproseso ng data ang kinakailangan, hindi ito madaling magawa gamit ang SQL. @@ -74,7 +74,7 @@ print(f"Length of index is {len(idx)}") items_sold = pd.Series(np.random.randint(25,50,size=len(idx)),index=idx) items_sold.plot() ``` -![Time Series Plot](../../../../translated_images/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.tl.png) +![Time Series Plot](../../../../translated_images/tl/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.png) Ngayon ipagpalagay na bawat linggo ay nag-oorganisa tayo ng party para sa mga kaibigan, at kumukuha ng karagdagang 10 pack ng ice-cream para sa party. Maaari tayong gumawa ng isa pang series, na naka-index sa linggo, upang ipakita iyon: ```python @@ -85,7 +85,7 @@ Kapag pinagsama natin ang dalawang series, makakakuha tayo ng kabuuang bilang: total_items = items_sold.add(additional_items,fill_value=0) total_items.plot() ``` -![Time Series Plot](../../../../translated_images/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.tl.png) +![Time Series Plot](../../../../translated_images/tl/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.png) > **Note** na hindi natin ginagamit ang simpleng syntax `total_items+additional_items`. Kung ginawa natin, makakakuha tayo ng maraming `NaN` (*Not a Number*) na halaga sa resulting series. Ito ay dahil may mga nawawalang halaga para sa ilang mga index point sa `additional_items` series, at ang pagdaragdag ng `NaN` sa anumang bagay ay nagreresulta sa `NaN`. Kaya kailangan nating tukuyin ang `fill_value` parameter sa panahon ng addition. @@ -94,7 +94,7 @@ Sa time series, maaari rin tayong mag-**resample** ng series gamit ang iba't iba monthly = total_items.resample("1M").mean() ax = monthly.plot(kind='bar') ``` -![Monthly Time Series Averages](../../../../translated_images/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.tl.png) +![Monthly Time Series Averages](../../../../translated_images/tl/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.png) ### DataFrame @@ -220,7 +220,7 @@ Ang unang problemang tututukan natin ay ang pagmomodelo ng pagkalat ng epidemya Dahil nais naming ipakita kung paano magtrabaho sa data, inaanyayahan ka naming buksan ang [`notebook-covidspread.ipynb`](notebook-covidspread.ipynb) at basahin ito mula sa simula hanggang sa dulo. Maaari mo ring i-execute ang mga cells, at gawin ang ilang mga hamon na iniwan namin para sa iyo sa dulo. -![COVID Spread](../../../../translated_images/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.tl.png) +![COVID Spread](../../../../translated_images/tl/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.png) > Kung hindi mo alam kung paano magpatakbo ng code sa Jupyter Notebook, tingnan ang [artikulong ito](https://soshnikov.com/education/how-to-execute-notebooks-from-github/). @@ -242,7 +242,7 @@ Ang isang buong halimbawa ng pagsusuri sa dataset na ito gamit ang [Text Analyti Buksan ang [`notebook-papers.ipynb`](notebook-papers.ipynb) at basahin ito mula sa simula hanggang sa dulo. Maaari mo ring i-execute ang mga cells, at gawin ang ilang mga hamon na iniwan namin para sa iyo sa dulo. -![Covid Medical Treatment](../../../../translated_images/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.tl.png) +![Covid Medical Treatment](../../../../translated_images/tl/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.png) ## Pagproseso ng Data ng Larawan diff --git a/translations/tl/2-Working-With-Data/README.md b/translations/tl/2-Working-With-Data/README.md index 208a76d2..dffa2745 100644 --- a/translations/tl/2-Working-With-Data/README.md +++ b/translations/tl/2-Working-With-Data/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Paggamit ng Data -![data love](../../../translated_images/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.tl.jpg) +![data love](../../../translated_images/tl/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.jpg) > Larawan ni Alexander Sinn sa Unsplash Sa mga araling ito, matututuhan mo ang iba't ibang paraan kung paano maaaring pamahalaan, manipulahin, at gamitin ang data sa mga aplikasyon. Malalaman mo ang tungkol sa relational at non-relational na mga database at kung paano naiimbak ang data sa mga ito. Matututuhan mo ang mga pangunahing kaalaman sa paggamit ng Python para pamahalaan ang data, at madidiskubre mo ang maraming paraan kung paano maaaring gamitin ang Python upang pamahalaan at tuklasin ang data. diff --git a/translations/tl/3-Data-Visualization/12-visualization-relationships/README.md b/translations/tl/3-Data-Visualization/12-visualization-relationships/README.md index 69e9ea9d..4eaa57c8 100644 --- a/translations/tl/3-Data-Visualization/12-visualization-relationships/README.md +++ b/translations/tl/3-Data-Visualization/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ Gumawa ng basic scatterplot upang ipakita ang relasyon sa pagitan ng presyo bawa ```python sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ``` -![scatterplot 1](../../../../translated_images/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.tl.png) +![scatterplot 1](../../../../translated_images/tl/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.png) Ngayon, ipakita ang parehong data gamit ang isang honey color scheme upang ipakita kung paano nagbabago ang presyo sa paglipas ng mga taon. Magagawa mo ito sa pamamagitan ng pagdaragdag ng 'hue' parameter upang ipakita ang pagbabago, taon-taon: @@ -60,7 +60,7 @@ Ngayon, ipakita ang parehong data gamit ang isang honey color scheme upang ipaki ```python sns.relplot(x="priceperlb", y="state", hue="year", palette="YlOrBr", data=honey, height=15, aspect=.5); ``` -![scatterplot 2](../../../../translated_images/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.tl.png) +![scatterplot 2](../../../../translated_images/tl/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.png) Sa pagbabago ng color scheme na ito, makikita mo na malinaw na may malakas na pagtaas sa paglipas ng mga taon sa presyo ng pulot bawat libra. Sa katunayan, kung titingnan mo ang isang sample set sa data upang i-verify (pumili ng isang partikular na estado, Arizona halimbawa) makikita mo ang pattern ng pagtaas ng presyo taon-taon, na may ilang mga eksepsyon: @@ -89,7 +89,7 @@ sns.relplot(x="priceperlb", y="state", size="year", data=honey, height=15, aspec ``` Makikita mo ang unti-unting pagtaas ng laki ng mga tuldok. -![scatterplot 3](../../../../translated_images/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.tl.png) +![scatterplot 3](../../../../translated_images/tl/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.png) Ito ba ay simpleng kaso ng supply at demand? Dahil sa mga salik tulad ng pagbabago ng klima at colony collapse, mas kaunti ba ang pulot na magagamit para bilhin taon-taon, kaya't tumataas ang presyo? @@ -104,7 +104,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); ``` Sagot: Oo, na may ilang mga eksepsyon sa paligid ng taong 2003: -![line chart 1](../../../../translated_images/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.tl.png) +![line chart 1](../../../../translated_images/tl/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.png) ✅ Dahil ang Seaborn ay nag-a-aggregate ng data sa isang linya, ipinapakita nito "ang maramihang sukat sa bawat x value sa pamamagitan ng pag-plot ng mean at ang 95% confidence interval sa paligid ng mean". [Source](https://seaborn.pydata.org/tutorial/relational.html). Ang time-consuming na behavior na ito ay maaaring i-disable sa pamamagitan ng pagdaragdag ng `ci=None`. @@ -114,7 +114,7 @@ Tanong: Sa 2003, makikita rin ba natin ang pagtaas sa supply ng pulot? Paano kun sns.relplot(x="year", y="totalprod", kind="line", data=honey); ``` -![line chart 2](../../../../translated_images/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.tl.png) +![line chart 2](../../../../translated_images/tl/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.png) Sagot: Hindi talaga. Kung titingnan mo ang kabuuang produksyon, tila ito ay tumaas sa partikular na taon, kahit na sa pangkalahatan ang dami ng pulot na ginagawa ay bumababa sa mga taong ito. @@ -139,7 +139,7 @@ sns.relplot( ``` Sa visualization na ito, maaari mong ihambing ang ani bawat kolonya at bilang ng mga kolonya taon-taon, magkatabi na may wrap na nakatakda sa 3 para sa mga column: -![facet grid](../../../../translated_images/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.tl.png) +![facet grid](../../../../translated_images/tl/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.png) Para sa dataset na ito, walang partikular na kapansin-pansin tungkol sa bilang ng mga kolonya at kanilang ani, taon-taon at estado sa estado. Mayroon bang ibang paraan upang tingnan ang paghahanap ng ugnayan sa pagitan ng dalawang variable na ito? @@ -162,7 +162,7 @@ sns.despine(right=False) plt.ylabel('colony yield') ax.figure.legend(); ``` -![superimposed plots](../../../../translated_images/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.tl.png) +![superimposed plots](../../../../translated_images/tl/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.png) Habang walang kapansin-pansin sa mata sa paligid ng taong 2003, pinapayagan tayo nitong tapusin ang araling ito sa isang mas masayang tala: habang may pangkalahatang pagbaba sa bilang ng mga kolonya, ang bilang ng mga kolonya ay nagiging matatag kahit na ang kanilang ani bawat kolonya ay bumababa. diff --git a/translations/tl/3-Data-Visualization/R/09-visualization-quantities/README.md b/translations/tl/3-Data-Visualization/R/09-visualization-quantities/README.md index 827ce18d..eb8d09b0 100644 --- a/translations/tl/3-Data-Visualization/R/09-visualization-quantities/README.md +++ b/translations/tl/3-Data-Visualization/R/09-visualization-quantities/README.md @@ -69,7 +69,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` Dito, ini-install mo ang `ggplot2` package at pagkatapos ay ini-import ito sa workspace gamit ang `library("ggplot2")` command. Para mag-plot ng anumang plot sa ggplot, ginagamit ang `ggplot()` function at tinutukoy mo ang dataset, x at y variables bilang attributes. Sa kasong ito, ginagamit natin ang `geom_line()` function dahil layunin nating mag-plot ng line plot. -![MaxWingspan-lineplot](../../../../../translated_images/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.tl.png) +![MaxWingspan-lineplot](../../../../../translated_images/tl/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.png) Ano ang napansin mo agad? Mukhang mayroong hindi bababa sa isang outlier - napakalaki ng wingspan! Ang wingspan na higit sa 2000 sentimetro ay katumbas ng higit sa 20 metro - may mga Pterodactyl ba sa Minnesota? Suriin natin. @@ -87,7 +87,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` Tinutukoy natin ang anggulo sa `theme` at tinutukoy ang mga label ng x at y axis sa `xlab()` at `ylab()` ayon sa pagkakabanggit. Ang `ggtitle()` ay nagbibigay ng pangalan sa graph/plot. -![MaxWingspan-lineplot-improved](../../../../../translated_images/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.tl.png) +![MaxWingspan-lineplot-improved](../../../../../translated_images/tl/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.png) Kahit na may rotation ng mga label na nakatakda sa 45 degrees, masyadong marami ang mga ito para basahin. Subukan natin ang ibang estratehiya: lagyan lamang ng label ang mga outlier at itakda ang mga label sa loob ng chart. Maaari kang gumamit ng scatter chart upang magkaroon ng mas maraming espasyo para sa paglalagay ng label: @@ -103,7 +103,7 @@ Ano ang nangyayari dito? Ginamit mo ang `geom_point()` function upang mag-plot n Ano ang natuklasan mo? -![MaxWingspan-scatterplot](../../../../../translated_images/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.tl.png) +![MaxWingspan-scatterplot](../../../../../translated_images/tl/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.png) ## I-filter ang iyong data @@ -122,7 +122,7 @@ ggplot(data=birds_filtered, aes(x=Name, y=MaxWingspan,group=1)) + ``` Gumawa tayo ng bagong dataframe `birds_filtered` at pagkatapos ay nag-plot ng scatter plot. Sa pamamagitan ng pag-filter ng mga outlier, ang iyong data ay mas cohesive at mas madaling maunawaan. -![MaxWingspan-scatterplot-improved](../../../../../translated_images/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.tl.png) +![MaxWingspan-scatterplot-improved](../../../../../translated_images/tl/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.png) Ngayon na mayroon tayong mas malinis na dataset, hindi bababa sa mga tuntunin ng wingspan, tuklasin natin ang higit pa tungkol sa mga ibon na ito. @@ -164,7 +164,7 @@ birds_filtered %>% group_by(Category) %>% ``` Sa sumusunod na snippet, ini-install natin ang [dplyr](https://www.rdocumentation.org/packages/dplyr/versions/0.7.8) at [lubridate](https://www.rdocumentation.org/packages/lubridate/versions/1.8.0) packages upang makatulong sa pag-manipula at pag-group ng data upang mag-plot ng stacked bar chart. Una, i-group mo ang data batay sa `Category` ng ibon at pagkatapos ay i-summarize ang `MinLength`, `MaxLength`, `MinBodyMass`, `MaxBodyMass`, `MinWingspan`, `MaxWingspan` columns. Pagkatapos, i-plot ang bar chart gamit ang `ggplot2` package at tukuyin ang mga kulay para sa iba't ibang kategorya at mga label. -![Stacked bar chart](../../../../../translated_images/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.tl.png) +![Stacked bar chart](../../../../../translated_images/tl/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.png) Gayunpaman, ang bar chart na ito ay hindi mababasa dahil masyadong maraming hindi naka-group na data. Kailangan mong piliin lamang ang data na nais mong i-plot, kaya't tingnan natin ang haba ng mga ibon batay sa kanilang kategorya. @@ -179,7 +179,7 @@ ggplot(birds_count,aes(Category,n))+geom_bar(stat="identity")+coord_flip() ``` Una mong binibilang ang mga unique na halaga sa `Category` column at pagkatapos ay inaayos ang mga ito sa isang bagong dataframe `birds_count`. Ang sorted data na ito ay pagkatapos ay factored sa parehong level upang ito ay ma-plot sa sorted na paraan. Gamit ang `ggplot2` ay i-plot mo ang data sa isang bar chart. Ang `coord_flip()` ay nag-plot ng horizontal bars. -![category-length](../../../../../translated_images/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.tl.png) +![category-length](../../../../../translated_images/tl/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.png) Ang bar chart na ito ay nagpapakita ng magandang view ng bilang ng mga ibon sa bawat kategorya. Sa isang sulyap, makikita mo na ang pinakamalaking bilang ng mga ibon sa rehiyong ito ay nasa kategoryang Ducks/Geese/Waterfowl. Ang Minnesota ay ang 'land of 10,000 lakes' kaya't hindi ito nakakagulat! @@ -202,7 +202,7 @@ ggplot(birds_grouped,aes(Category,MaxLength))+geom_bar(stat="identity")+coord_fl ``` I-group natin ang `birds_filtered` data batay sa `Category` at pagkatapos ay mag-plot ng bar graph. -![comparing data](../../../../../translated_images/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.tl.png) +![comparing data](../../../../../translated_images/tl/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.png) Walang nakakagulat dito: ang mga hummingbird ay may pinakamaliit na MaxLength kumpara sa Pelicans o Geese. Maganda kapag ang data ay may lohikal na kahulugan! @@ -214,7 +214,7 @@ ggplot(data=birds_grouped, aes(x=Category)) + geom_bar(aes(y=MinLength), stat="identity", position="identity", fill='orange')+ coord_flip() ``` -![super-imposed values](../../../../../translated_images/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.tl.png) +![super-imposed values](../../../../../translated_images/tl/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.png) ## 🚀 Hamon diff --git a/translations/tl/3-Data-Visualization/R/10-visualization-distributions/README.md b/translations/tl/3-Data-Visualization/R/10-visualization-distributions/README.md index 5c8bff70..9f9ffab2 100644 --- a/translations/tl/3-Data-Visualization/R/10-visualization-distributions/README.md +++ b/translations/tl/3-Data-Visualization/R/10-visualization-distributions/README.md @@ -45,7 +45,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + geom_point() + ggtitle("Max Length per order") + coord_flip() ``` -![max length per order](../../../../../translated_images/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.tl.png) +![max length per order](../../../../../translated_images/tl/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.png) Ipinapakita nito ang pangkalahatang pamamahagi ng haba ng katawan bawat Order ng ibon, ngunit hindi ito ang pinakamainam na paraan upang ipakita ang tunay na pamamahagi. Ang gawaing ito ay karaniwang ginagawa sa pamamagitan ng paglikha ng Histogram. @@ -57,7 +57,7 @@ Nag-aalok ang `ggplot2` ng mahusay na paraan upang ipakita ang pamamahagi ng dat ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=10)+ylab('Frequency') ``` -![distribution over entire dataset](../../../../../translated_images/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.tl.png) +![distribution over entire dataset](../../../../../translated_images/tl/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.png) Makikita mo na karamihan sa 400+ na ibon sa dataset na ito ay nasa saklaw na mas mababa sa 2000 para sa kanilang Max Body Mass. Makakuha ng mas maraming insight sa datos sa pamamagitan ng pagbabago ng `bins` parameter sa mas mataas na numero, tulad ng 30: @@ -65,7 +65,7 @@ Makikita mo na karamihan sa 400+ na ibon sa dataset na ito ay nasa saklaw na mas ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![distribution-30bins](../../../../../translated_images/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.tl.png) +![distribution-30bins](../../../../../translated_images/tl/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.png) Ipinapakita ng tsart na ito ang pamamahagi sa mas detalyadong paraan. Ang isang tsart na hindi masyadong skewed sa kaliwa ay maaaring malikha sa pamamagitan ng pagtiyak na pipiliin mo lamang ang datos sa loob ng isang ibinigay na saklaw: @@ -77,7 +77,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![filtered histogram](../../../../../translated_images/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.tl.png) +![filtered histogram](../../../../../translated_images/tl/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.png) ✅ Subukan ang iba pang mga filter at puntos ng datos. Upang makita ang buong pamamahagi ng datos, alisin ang `['MaxBodyMass']` filter upang ipakita ang mga labeled distributions. @@ -91,7 +91,7 @@ ggplot(data=birds_filtered_1, aes(x=MaxBodyMass, y=MaxLength) ) + ``` Mukhang may inaasahang ugnayan sa pagitan ng dalawang elementong ito sa isang inaasahang axis, na may isang partikular na malakas na punto ng convergence: -![2d plot](../../../../../translated_images/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.tl.png) +![2d plot](../../../../../translated_images/tl/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.png) Ang histograms ay mahusay na gumagana bilang default para sa numeric na datos. Paano kung kailangan mong makita ang pamamahagi ayon sa text na datos? @@ -123,7 +123,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + scale_fill_manual(name="Conservation Status",values=c("red","green","blue","pink"),labels=c("Endangered","Near Threathened","Vulnerable","Least Concern")) ``` -![wingspan and conservation collation](../../../../../translated_images/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.tl.png) +![wingspan and conservation collation](../../../../../translated_images/tl/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.png) Mukhang walang magandang ugnayan sa pagitan ng minimum wingspan at kalagayan ng konserbasyon. Subukan ang iba pang mga elemento ng dataset gamit ang pamamaraang ito. Maaari kang mag-eksperimento sa iba't ibang filter. May nakikita ka bang ugnayan? @@ -137,7 +137,7 @@ Subukan natin ang density plot ngayon! ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + geom_density() ``` -![density plot](../../../../../translated_images/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.tl.png) +![density plot](../../../../../translated_images/tl/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.png) Makikita mo kung paano ginagaya ng plot ang naunang isa para sa Minimum Wingspan na datos; medyo mas maayos lang ito. Kung nais mong balikan ang jagged MaxBodyMass line sa pangalawang tsart na ginawa mo, maaari mo itong gawing mas maayos sa pamamagitan ng muling paggawa nito gamit ang pamamaraang ito: @@ -145,7 +145,7 @@ Makikita mo kung paano ginagaya ng plot ang naunang isa para sa Minimum Wingspan ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density() ``` -![bodymass density](../../../../../translated_images/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.tl.png) +![bodymass density](../../../../../translated_images/tl/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.png) Kung nais mo ng maayos, ngunit hindi masyadong maayos na linya, i-edit ang `adjust` parameter: @@ -153,7 +153,7 @@ Kung nais mo ng maayos, ngunit hindi masyadong maayos na linya, i-edit ang `adju ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density(adjust = 1/5) ``` -![less smooth bodymass](../../../../../translated_images/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.tl.png) +![less smooth bodymass](../../../../../translated_images/tl/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.png) ✅ Basahin ang tungkol sa mga parameter na magagamit para sa ganitong uri ng plot at mag-eksperimento! @@ -163,7 +163,7 @@ Ang ganitong uri ng tsart ay nag-aalok ng magagandang paliwanag na visualization ggplot(data=birds_filtered_1,aes(x = MaxBodyMass, fill = Order)) + geom_density(alpha=0.5) ``` -![bodymass per order](../../../../../translated_images/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.tl.png) +![bodymass per order](../../../../../translated_images/tl/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.png) ## 🚀 Hamon diff --git a/translations/tl/3-Data-Visualization/R/11-visualization-proportions/README.md b/translations/tl/3-Data-Visualization/R/11-visualization-proportions/README.md index e002fb8d..0dede4c4 100644 --- a/translations/tl/3-Data-Visualization/R/11-visualization-proportions/README.md +++ b/translations/tl/3-Data-Visualization/R/11-visualization-proportions/README.md @@ -92,7 +92,7 @@ pie(grouped$count,grouped$class, main="Edible?") ``` Voila, isang pie chart na nagpapakita ng proporsyon ng datos ayon sa dalawang klase ng kabute. Napakahalaga na makuha ang tamang pagkakasunod ng labels, lalo na dito, kaya siguraduhing i-verify ang pagkakasunod ng label array! -![pie chart](../../../../../translated_images/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.tl.png) +![pie chart](../../../../../translated_images/tl/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.png) ## Donuts! @@ -126,7 +126,7 @@ library(webr) PieDonut(habitat, aes(habitat, count=count)) ``` -![donut chart](../../../../../translated_images/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.tl.png) +![donut chart](../../../../../translated_images/tl/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.png) Ang code na ito ay gumagamit ng dalawang library - ggplot2 at webr. Gamit ang PieDonut function ng webr library, madali tayong makakagawa ng donut chart! @@ -164,7 +164,7 @@ waffle((cap_color$count/10), rows = 7, title = "Waffle Chart")+scale_fill_manual Gamit ang waffle chart, makikita mo nang malinaw ang proporsyon ng mga cap colors sa dataset ng kabute. Nakakatuwa, maraming green-capped mushrooms! -![waffle chart](../../../../../translated_images/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.tl.png) +![waffle chart](../../../../../translated_images/tl/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.png) Sa araling ito, natutunan mo ang tatlong paraan upang ipakita ang proporsyon. Una, kailangan mong i-group ang iyong datos sa mga kategorya at pagkatapos ay magdesisyon kung alin ang pinakamahusay na paraan upang ipakita ang datos - pie, donut, o waffle. Lahat ay masarap at nagbibigay ng instant snapshot ng dataset sa user. diff --git a/translations/tl/3-Data-Visualization/R/12-visualization-relationships/README.md b/translations/tl/3-Data-Visualization/R/12-visualization-relationships/README.md index 9e97b47b..3fd61100 100644 --- a/translations/tl/3-Data-Visualization/R/12-visualization-relationships/README.md +++ b/translations/tl/3-Data-Visualization/R/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ library(ggplot2) ggplot(honey, aes(x = priceperlb, y = state)) + geom_point(colour = "blue") ``` -![scatterplot 1](../../../../../translated_images/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.tl.png) +![scatterplot 1](../../../../../translated_images/tl/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.png) Ngayon, ipakita ang parehong data gamit ang isang kulay ng pulot upang ipakita kung paano nagbago ang presyo sa paglipas ng mga taon. Magagawa mo ito sa pamamagitan ng pagdaragdag ng parameter na 'scale_color_gradientn' upang ipakita ang pagbabago, taon-taon: @@ -61,7 +61,7 @@ Ngayon, ipakita ang parehong data gamit ang isang kulay ng pulot upang ipakita k ggplot(honey, aes(x = priceperlb, y = state, color=year)) + geom_point()+scale_color_gradientn(colours = colorspace::heat_hcl(7)) ``` -![scatterplot 2](../../../../../translated_images/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.tl.png) +![scatterplot 2](../../../../../translated_images/tl/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.png) Sa pagbabago ng color scheme na ito, makikita mo na malinaw na may malakas na pagtaas sa paglipas ng mga taon sa presyo ng pulot bawat libra. Sa katunayan, kung titingnan mo ang isang sample set sa data upang i-verify (pumili ng isang partikular na estado, Arizona halimbawa) makikita mo ang pattern ng pagtaas ng presyo taon-taon, na may ilang mga pagbubukod: @@ -92,7 +92,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ``` Makikita mo ang unti-unting pagtaas ng laki ng mga tuldok. -![scatterplot 3](../../../../../translated_images/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.tl.png) +![scatterplot 3](../../../../../translated_images/tl/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.png) Ito ba ay simpleng kaso ng supply at demand? Dahil sa mga salik tulad ng pagbabago ng klima at colony collapse, mas kaunti ba ang pulot na magagamit para bilhin taon-taon, kaya't tumataas ang presyo? @@ -107,7 +107,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab ``` Sagot: Oo, na may ilang mga pagbubukod sa paligid ng taong 2003: -![line chart 1](../../../../../translated_images/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.tl.png) +![line chart 1](../../../../../translated_images/tl/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.png) Tanong: Sa 2003, makikita rin ba natin ang pagtaas sa supply ng pulot? Paano kung tingnan mo ang kabuuang produksyon taon-taon? @@ -115,7 +115,7 @@ Tanong: Sa 2003, makikita rin ba natin ang pagtaas sa supply ng pulot? Paano kun qplot(honey$year,honey$totalprod, geom='smooth', span =0.5, xlab = "year",ylab = "totalprod") ``` -![line chart 2](../../../../../translated_images/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.tl.png) +![line chart 2](../../../../../translated_images/tl/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.png) Sagot: Hindi talaga. Kung titingnan mo ang kabuuang produksyon, tila ito ay tumaas sa partikular na taon na iyon, kahit na sa pangkalahatan ang dami ng pulot na ginagawa ay bumababa sa mga taong ito. @@ -135,7 +135,7 @@ ggplot(honey, aes(x=yieldpercol, y = numcol,group = 1)) + ``` Sa visualization na ito, maaari mong ihambing ang ani bawat kolonya at bilang ng mga kolonya taon-taon, magkatabi na may wrap na nakatakda sa 3 para sa mga column: -![facet grid](../../../../../translated_images/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.tl.png) +![facet grid](../../../../../translated_images/tl/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.png) Para sa dataset na ito, walang partikular na kapansin-pansin tungkol sa bilang ng mga kolonya at kanilang ani, taon-taon at estado sa estado. Mayroon bang ibang paraan upang tingnan ang paghahanap ng ugnayan sa pagitan ng dalawang variable na ito? @@ -152,7 +152,7 @@ plot(honey$year, honey$yieldpercol, pch = 17, col = 3, axis(side = 4, at = pretty(range(y2))) mtext("colony yield", side = 4, line = 3) ``` -![superimposed plots](../../../../../translated_images/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.tl.png) +![superimposed plots](../../../../../translated_images/tl/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.png) Habang walang kapansin-pansin sa mata sa paligid ng taong 2003, pinapayagan tayo nitong tapusin ang araling ito sa mas masayang tala: habang may pangkalahatang pagbaba sa bilang ng mga kolonya, ang bilang ng mga kolonya ay nagiging matatag kahit na ang kanilang ani bawat kolonya ay bumababa. diff --git a/translations/tl/3-Data-Visualization/R/13-meaningful-vizualizations/README.md b/translations/tl/3-Data-Visualization/R/13-meaningful-vizualizations/README.md index 6a3f1d48..ea237dea 100644 --- a/translations/tl/3-Data-Visualization/R/13-meaningful-vizualizations/README.md +++ b/translations/tl/3-Data-Visualization/R/13-meaningful-vizualizations/README.md @@ -47,25 +47,25 @@ Sa mga nakaraang aralin, sinubukan mong gumawa ng iba't ibang uri ng visualisasy Kahit na maingat ang isang data scientist sa pagpili ng tamang tsart para sa tamang datos, maraming paraan upang maipakita ang datos sa paraang nagpapakita ng isang punto, madalas sa kapinsalaan ng datos mismo. Maraming halimbawa ng mapanlinlang na tsart at infographics! -[![Paano Nagsisinungaling ang mga Tsart ni Alberto Cairo](../../../../../translated_images/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.tl.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "Paano Nagsisinungaling ang mga Tsart") +[![Paano Nagsisinungaling ang mga Tsart ni Alberto Cairo](../../../../../translated_images/tl/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "Paano Nagsisinungaling ang mga Tsart") > 🎥 I-click ang larawan sa itaas para sa isang talakayan tungkol sa mapanlinlang na mga tsart Ang tsart na ito ay binabaliktad ang X axis upang ipakita ang kabaligtaran ng katotohanan, batay sa petsa: -![masamang tsart 1](../../../../../translated_images/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.tl.png) +![masamang tsart 1](../../../../../translated_images/tl/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.png) [Ang tsart na ito](https://media.firstcoastnews.com/assets/WTLV/images/170ae16f-4643-438f-b689-50d66ca6a8d8/170ae16f-4643-438f-b689-50d66ca6a8d8_1140x641.jpg) ay mas mapanlinlang pa, dahil ang mata ay naaakit sa kanan upang isipin na, sa paglipas ng panahon, ang mga kaso ng COVID ay bumaba sa iba't ibang mga county. Sa katunayan, kung titingnan mong mabuti ang mga petsa, makikita mong inayos ang mga ito upang magmukhang pababa ang trend. -![masamang tsart 2](../../../../../translated_images/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.tl.jpg) +![masamang tsart 2](../../../../../translated_images/tl/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.jpg) Ang kilalang halimbawa na ito ay gumagamit ng kulay AT baliktad na Y axis upang manlinlang: sa halip na isipin na tumaas ang mga pagkamatay dahil sa baril pagkatapos ng pagpasa ng batas na pabor sa baril, ang mata ay nalilinlang upang isipin ang kabaligtaran: -![masamang tsart 3](../../../../../translated_images/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.tl.jpg) +![masamang tsart 3](../../../../../translated_images/tl/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.jpg) Ang kakaibang tsart na ito ay nagpapakita kung paano maaaring manipulahin ang proporsyon, na nagdudulot ng nakakatawang epekto: -![masamang tsart 4](../../../../../translated_images/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.tl.jpg) +![masamang tsart 4](../../../../../translated_images/tl/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.jpg) Ang paghahambing ng mga bagay na hindi maihahambing ay isa pang mapanlinlang na taktika. Mayroong [kahanga-hangang website](https://tylervigen.com/spurious-correlations) na nagpapakita ng 'spurious correlations' na nagtatampok ng 'mga katotohanan' na nag-uugnay sa mga bagay tulad ng rate ng diborsyo sa Maine at ang pagkonsumo ng margarina. Ang isang Reddit group ay nangongolekta rin ng [pangit na paggamit](https://www.reddit.com/r/dataisugly/top/?t=all) ng datos. @@ -100,13 +100,13 @@ Lagyan ng label ang iyong mga axis, magbigay ng legend kung kinakailangan, at ma Kung ang iyong datos ay tekstwal at mahaba sa X axis, maaari mong i-anggulo ang teksto para sa mas madaling basahin. Ang [plot3D](https://cran.r-project.org/web/packages/plot3D/index.html) ay nag-aalok ng 3D plotting, kung sinusuportahan ito ng iyong datos. Ang mga sopistikadong visualisasyon ng datos ay maaaring gawin gamit ito. -![3d plots](../../../../../translated_images/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.tl.png) +![3d plots](../../../../../translated_images/tl/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.png) ## Animation at 3D na pagpapakita ng tsart Ang ilan sa mga pinakamahusay na visualisasyon ng datos ngayon ay animated. Si Shirley Wu ay may mga kamangha-manghang gawa gamit ang D3, tulad ng '[film flowers](http://bl.ocks.org/sxywu/raw/d612c6c653fb8b4d7ff3d422be164a5d/)', kung saan ang bawat bulaklak ay isang visualisasyon ng isang pelikula. Isa pang halimbawa para sa Guardian ay 'bussed out', isang interactive na karanasan na pinagsasama ang mga visualisasyon gamit ang Greensock at D3 kasama ang isang scrollytelling na format ng artikulo upang ipakita kung paano hinahawakan ng NYC ang problema nito sa mga walang tirahan sa pamamagitan ng pagdadala ng mga tao palabas ng lungsod. -![busing](../../../../../translated_images/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.tl.png) +![busing](../../../../../translated_images/tl/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.png) > "Bussed Out: Paano Inililipat ng Amerika ang mga Walang Tirahan" mula sa [the Guardian](https://www.theguardian.com/us-news/ng-interactive/2017/dec/20/bussed-out-america-moves-homeless-people-country-study). Mga visualisasyon nina Nadieh Bremer & Shirley Wu @@ -116,7 +116,7 @@ Bagama't hindi sapat ang araling ito upang talakayin nang malalim ang mga makapa Kukumpletuhin mo ang isang web app na magpapakita ng isang animated na view ng social network na ito. Gumagamit ito ng isang library na ginawa upang lumikha ng isang [visual ng isang network](https://github.com/emiliorizzo/vue-d3-network) gamit ang Vue.js at D3. Kapag tumatakbo na ang app, maaari mong hilahin ang mga node sa screen upang i-shuffle ang datos. -![liaisons](../../../../../translated_images/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.tl.png) +![liaisons](../../../../../translated_images/tl/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.png) ## Proyekto: Gumawa ng tsart upang ipakita ang isang network gamit ang D3.js diff --git a/translations/tl/3-Data-Visualization/README.md b/translations/tl/3-Data-Visualization/README.md index 46db21cb..a773c9dd 100644 --- a/translations/tl/3-Data-Visualization/README.md +++ b/translations/tl/3-Data-Visualization/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Mga Biswal na Presentasyon -![isang bubuyog sa bulaklak ng lavender](../../../translated_images/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.tl.jpg) +![isang bubuyog sa bulaklak ng lavender](../../../translated_images/tl/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.jpg) > Larawan ni Jenna Lee sa Unsplash Ang pagbibiswal ng datos ay isa sa pinakamahalagang gawain ng isang data scientist. Ang mga larawan ay nagkakahalaga ng 1000 salita, at ang isang biswal na presentasyon ay makakatulong sa iyong matukoy ang iba't ibang kawili-wiling bahagi ng iyong datos tulad ng biglaang pagtaas, mga outlier, mga pangkat, mga uso, at marami pang iba, na makakatulong sa iyong maunawaan ang kwento na nais ipahayag ng iyong datos. diff --git a/translations/tl/4-Data-Science-Lifecycle/14-Introduction/README.md b/translations/tl/4-Data-Science-Lifecycle/14-Introduction/README.md index 8864b54d..01ab4865 100644 --- a/translations/tl/4-Data-Science-Lifecycle/14-Introduction/README.md +++ b/translations/tl/4-Data-Science-Lifecycle/14-Introduction/README.md @@ -25,7 +25,7 @@ Sa puntong ito, malamang napagtanto mo na ang data science ay isang proseso. Ang Ang araling ito ay nakatuon sa 3 bahagi ng lifecycle: pagkuha, pagproseso, at pagpapanatili. -![Diagram ng lifecycle ng data science](../../../../translated_images/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.tl.jpg) +![Diagram ng lifecycle ng data science](../../../../translated_images/tl/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.jpg) > Larawan mula sa [Berkeley School of Information](https://ischoolonline.berkeley.edu/data-science/what-is-data-science/) ## Pagkuha @@ -101,7 +101,7 @@ Suriin ang [Team Data Science Process lifecycle](https://docs.microsoft.com/en-u |Team Data Science Process (TDSP)|Cross-industry standard process for data mining (CRISP-DM)| |--|--| -|![Team Data Science Lifecycle](../../../../translated_images/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.tl.png) | ![Data Science Process Alliance Image](../../../../translated_images/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.tl.png) | +|![Team Data Science Lifecycle](../../../../translated_images/tl/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.png) | ![Data Science Process Alliance Image](../../../../translated_images/tl/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.png) | | Larawan mula sa [Microsoft](https://docs.microsoft.comazure/architecture/data-science-process/lifecycle) | Larawan mula sa [Data Science Process Alliance](https://www.datascience-pm.com/crisp-dm-2/) | ## [Post-lecture quiz](https://ff-quizzes.netlify.app/en/ds/quiz/27) diff --git a/translations/tl/4-Data-Science-Lifecycle/README.md b/translations/tl/4-Data-Science-Lifecycle/README.md index 061c2fb2..e5f151ef 100644 --- a/translations/tl/4-Data-Science-Lifecycle/README.md +++ b/translations/tl/4-Data-Science-Lifecycle/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Ang Lifecycle ng Data Science -![communication](../../../translated_images/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.tl.jpg) +![communication](../../../translated_images/tl/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.jpg) > Larawan ni Headway sa Unsplash Sa mga araling ito, tatalakayin mo ang ilang aspeto ng lifecycle ng Data Science, kabilang ang pagsusuri at komunikasyon tungkol sa datos. diff --git a/translations/tl/5-Data-Science-In-Cloud/README.md b/translations/tl/5-Data-Science-In-Cloud/README.md index a8dd1083..81a73989 100644 --- a/translations/tl/5-Data-Science-In-Cloud/README.md +++ b/translations/tl/5-Data-Science-In-Cloud/README.md @@ -9,13 +9,13 @@ CO_OP_TRANSLATOR_METADATA: --> # Data Science sa Cloud -![cloud-picture](../../../translated_images/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.tl.jpg) +![cloud-picture](../../../translated_images/tl/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.jpg) > Larawan ni [Jelleke Vanooteghem](https://unsplash.com/@ilumire) mula sa [Unsplash](https://unsplash.com/s/photos/cloud?orientation=landscape) Kapag pinag-uusapan ang paggawa ng data science gamit ang malalaking datos, ang cloud ay maaaring maging isang malaking tulong. Sa susunod na tatlong aralin, tatalakayin natin kung ano ang cloud at kung bakit ito napaka-kapaki-pakinabang. Susuriin din natin ang isang dataset tungkol sa heart failure at gagawa ng modelo upang makatulong sa pagsusuri ng posibilidad na magkaroon ng heart failure ang isang tao. Gagamitin natin ang kapangyarihan ng cloud upang sanayin, i-deploy, at gamitin ang modelo sa dalawang magkaibang paraan. Ang isang paraan ay gamit lamang ang user interface sa isang Low code/No code na paraan, at ang isa pang paraan ay gamit ang Azure Machine Learning Software Developer Kit (Azure ML SDK). -![project-schema](../../../translated_images/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.tl.png) +![project-schema](../../../translated_images/tl/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.png) ### Mga Paksa diff --git a/translations/tl/6-Data-Science-In-Wild/20-Real-World-Examples/README.md b/translations/tl/6-Data-Science-In-Wild/20-Real-World-Examples/README.md index da1cfc64..ac3ad498 100644 --- a/translations/tl/6-Data-Science-In-Wild/20-Real-World-Examples/README.md +++ b/translations/tl/6-Data-Science-In-Wild/20-Real-World-Examples/README.md @@ -41,7 +41,7 @@ Dahil sa democratization ng AI, mas madali na ngayon para sa mga developer na ma * [Data Science sa Healthcare](https://data-flair.training/blogs/data-science-in-healthcare/) - binibigyang-diin ang mga aplikasyon tulad ng medical imaging (hal., MRI, X-Ray, CT-Scan), genomics (DNA sequencing), pagbuo ng gamot (pagtatasa ng panganib, prediksyon ng tagumpay), predictive analytics (pangangalaga sa pasyente at logistics ng suplay), pagsubaybay at pag-iwas sa sakit, atbp. -![Mga Aplikasyon ng Data Science sa Tunay na Mundo](../../../../translated_images/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.tl.png) Kredito ng Imahe: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) +![Mga Aplikasyon ng Data Science sa Tunay na Mundo](../../../../translated_images/tl/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.png) Kredito ng Imahe: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) Ipinapakita ng larawan ang iba pang mga domain at halimbawa ng paggamit ng mga teknik sa data science. Gusto mo bang tuklasin ang iba pang aplikasyon? Tingnan ang seksyong [Review & Self Study](../../../../6-Data-Science-In-Wild/20-Real-World-Examples) sa ibaba. diff --git a/translations/tl/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md b/translations/tl/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md index 28e96a3d..f8c1e2d2 100644 --- a/translations/tl/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md +++ b/translations/tl/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md @@ -22,7 +22,7 @@ Ang interface ng Explorer (makikita sa screenshot sa ibaba) ay nagbibigay-daan s 2. I-explore ang dataset [Catalog](https://planetarycomputer.microsoft.com/catalog) - alamin ang layunin ng bawat isa. 3. Gamitin ang Explorer - pumili ng dataset na interesado ka, piliin ang kaugnay na query at rendering option. -![Ang Planetary Computer Explorer](../../../../translated_images/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.tl.png) +![Ang Planetary Computer Explorer](../../../../translated_images/tl/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.png) `Ang Iyong Gawain:` Pag-aralan ang visualization na na-render sa browser at sagutin ang mga sumusunod: diff --git a/translations/tl/CONTRIBUTING.md b/translations/tl/CONTRIBUTING.md index 9a58016c..c3598096 100644 --- a/translations/tl/CONTRIBUTING.md +++ b/translations/tl/CONTRIBUTING.md @@ -312,7 +312,7 @@ Isama sa iyong deskripsyon ng PR: import pandas as pd ``` ```` -- Magdagdag ng alt text sa mga larawan: `![Alt text](../../translated_images/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.tl.png)` +- Magdagdag ng alt text sa mga larawan: `![Alt text](../../translated_images/tl/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.png)` - Panatilihin ang makatwirang haba ng linya (mga 80-100 karakter) ### Python diff --git a/translations/tl/README.md b/translations/tl/README.md index 9000c4ab..b8f6fd54 100644 --- a/translations/tl/README.md +++ b/translations/tl/README.md @@ -28,7 +28,7 @@ Ang Azure Cloud Advocates sa Microsoft ay nasisiyahang mag-alok ng 10-linggong, **🙏 Espesyal na pasasalamat 🙏 sa aming mga may-akda, tagasuri at mga kontribyutor ng nilalaman ng [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/),** lalo na sina Aaryan Arora, [Aditya Garg](https://github.com/AdityaGarg00), [Alondra Sanchez](https://www.linkedin.com/in/alondra-sanchez-molina/), [Ankita Singh](https://www.linkedin.com/in/ankitasingh007), [Anupam Mishra](https://www.linkedin.com/in/anupam--mishra/), [Arpita Das](https://www.linkedin.com/in/arpitadas01/), ChhailBihari Dubey, [Dibri Nsofor](https://www.linkedin.com/in/dibrinsofor), [Dishita Bhasin](https://www.linkedin.com/in/dishita-bhasin-7065281bb), [Majd Safi](https://www.linkedin.com/in/majd-s/), [Max Blum](https://www.linkedin.com/in/max-blum-6036a1186/), [Miguel Correa](https://www.linkedin.com/in/miguelmque/), [Mohamma Iftekher (Iftu) Ebne Jalal](https://twitter.com/iftu119), [Nawrin Tabassum](https://www.linkedin.com/in/nawrin-tabassum), [Raymond Wangsa Putra](https://www.linkedin.com/in/raymond-wp/), [Rohit Yadav](https://www.linkedin.com/in/rty2423), Samridhi Sharma, [Sanya Sinha](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200), [Sheena Narula](https://www.linkedin.com/in/sheena-narua-n/), [Tauqeer Ahmad](https://www.linkedin.com/in/tauqeerahmad5201/), Yogendrasingh Pawar , [Vidushi Gupta](https://www.linkedin.com/in/vidushi-gupta07/), [Jasleen Sondhi](https://www.linkedin.com/in/jasleen-sondhi/) -|![Sketchnote ni @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.tl.png)| +|![Sketchnote ni @sketchthedocs https://sketchthedocs.dev](../../translated_images/tl/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.png)| |:---:| | Agham ng Datos para sa mga Nagsisimula - _Sketchnote ni [@nitya](https://twitter.com/nitya)_ | @@ -47,7 +47,7 @@ Ang Azure Cloud Advocates sa Microsoft ay nasisiyahang mag-alok ng 10-linggong, Mayroon kaming serye ng Discord na "learn with AI" na kasalukuyang nagaganap, alamin pa at sumali sa amin sa [Learn with AI Series](https://aka.ms/learnwithai/discord) mula 18 - 30 September, 2025. Makakakuha ka ng mga tip at trick sa paggamit ng GitHub Copilot para sa Agham ng Datos. -![Serye na Learn with AI](../../translated_images/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.tl.jpg) +![Serye na Learn with AI](../../translated_images/tl/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.jpg) # Ikaw ba ay isang estudyante? @@ -127,7 +127,7 @@ Kasama sa bawat halimbawa ang detalyadong mga komento na nagpapaliwanag sa bawat ## Mga Aralin -|![ Sketchnote ni @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.tl.png)| +|![ Sketchnote ni @sketchthedocs https://sketchthedocs.dev](../../translated_images/tl/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png)| |:---:| | Data Science For Beginners: Roadmap - _Sketchnote ni [@nitya](https://twitter.com/nitya)_ | diff --git a/translations/tl/sketchnotes/README.md b/translations/tl/sketchnotes/README.md index eaa84c12..7d3eb491 100644 --- a/translations/tl/sketchnotes/README.md +++ b/translations/tl/sketchnotes/README.md @@ -13,7 +13,7 @@ Hanapin ang lahat ng sketchnotes dito! Nitya Narasimhan, artista -![roadmap sketchnote](../../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.tl.png) +![roadmap sketchnote](../../../translated_images/tl/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png) --- diff --git a/translations/tr/1-Introduction/01-defining-data-science/README.md b/translations/tr/1-Introduction/01-defining-data-science/README.md index 0f868556..e85dc454 100644 --- a/translations/tr/1-Introduction/01-defining-data-science/README.md +++ b/translations/tr/1-Introduction/01-defining-data-science/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: --- -[![Veri Biliminin Tanımı Videosu](../../../../translated_images/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.tr.png)](https://youtu.be/beZ7Mb_oz9I) +[![Veri Biliminin Tanımı Videosu](../../../../translated_images/tr/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.png)](https://youtu.be/beZ7Mb_oz9I) ## [Ders Öncesi Testi](https://ff-quizzes.netlify.app/en/ds/quiz/0) @@ -153,7 +153,7 @@ Daha karmaşık bir analiz yapmak istersek, her modül için harcanan zamanı ö Bu zorlukta, metinlere bakarak Veri Bilimi alanıyla ilgili kavramları bulmaya çalışacağız. Veri Bilimi ile ilgili bir Wikipedia makalesi alacak, metni indirecek ve işleyeceğiz, ardından aşağıdaki gibi bir kelime bulutu oluşturacağız: -![Veri Bilimi için Kelime Bulutu](../../../../translated_images/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.tr.png) +![Veri Bilimi için Kelime Bulutu](../../../../translated_images/tr/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.png) Kodları incelemek için [`notebook.ipynb`](../../../../1-Introduction/01-defining-data-science/notebook.ipynb ':ignore') dosyasını ziyaret edin. Ayrıca kodu çalıştırabilir ve tüm veri dönüşümlerinin gerçek zamanlı olarak nasıl gerçekleştirildiğini görebilirsiniz. diff --git a/translations/tr/1-Introduction/04-stats-and-probability/README.md b/translations/tr/1-Introduction/04-stats-and-probability/README.md index e2a2ce0c..dab4afb7 100644 --- a/translations/tr/1-Introduction/04-stats-and-probability/README.md +++ b/translations/tr/1-Introduction/04-stats-and-probability/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: İstatistik ve Olasılık Teorisi, Matematiğin birbiriyle yakından ilişkili ve Veri Bilimi açısından oldukça önemli iki alanıdır. Matematik hakkında derin bir bilgiye sahip olmadan veriyle çalışmak mümkün olsa da, en azından bazı temel kavramları bilmek her zaman daha iyidir. Burada, başlangıç yapmanıza yardımcı olacak kısa bir giriş sunacağız. -[![Tanıtım Videosu](../../../../translated_images/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.tr.png)](https://youtu.be/Z5Zy85g4Yjw) +[![Tanıtım Videosu](../../../../translated_images/tr/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.png)](https://youtu.be/Z5Zy85g4Yjw) ## [Ders Öncesi Testi](https://ff-quizzes.netlify.app/en/ds/quiz/6) @@ -39,7 +39,7 @@ Sürekli bir değişkenin olasılık dağılımını tanımlamak daha zordur; bu Bir değişkenin belirli bir değer aralığına düşme olasılığından bahsedebiliriz, örneğin P(t1≤X2). Bu durumda, olasılık dağılımı **olasılık yoğunluk fonksiyonu** p(x) ile tanımlanır, öyle ki: -![P(t_1\le X Korelasyon ve kovaryans ile ilgili daha fazla örnek [eşlik eden defterde](notebook.ipynb) bulunabilir. diff --git a/translations/tr/1-Introduction/README.md b/translations/tr/1-Introduction/README.md index e3a99cb9..84399ec0 100644 --- a/translations/tr/1-Introduction/README.md +++ b/translations/tr/1-Introduction/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Veri Bilimine Giriş -![veri iş başında](../../../translated_images/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.tr.jpg) +![veri iş başında](../../../translated_images/tr/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.jpg) > Fotoğraf: Stephen Dawson tarafından Unsplash üzerinde Bu derslerde, Veri Bilimi'nin nasıl tanımlandığını keşfedecek ve bir veri bilimcinin dikkate alması gereken etik konuları öğreneceksiniz. Ayrıca verinin nasıl tanımlandığını öğrenecek ve Veri Bilimi'nin temel akademik alanları olan istatistik ve olasılık hakkında biraz bilgi edineceksiniz. diff --git a/translations/tr/2-Working-With-Data/07-python/README.md b/translations/tr/2-Working-With-Data/07-python/README.md index c47e1c7c..a66e0d25 100644 --- a/translations/tr/2-Working-With-Data/07-python/README.md +++ b/translations/tr/2-Working-With-Data/07-python/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: | :-------------------------------------------------------------------------------------------------------: | | Python ile Çalışmak - _Sketchnote by [@nitya](https://twitter.com/nitya)_ | -[![Tanıtım Videosu](../../../../translated_images/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.tr.png)](https://youtu.be/dZjWOGbsN4Y) +[![Tanıtım Videosu](../../../../translated_images/tr/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.png)](https://youtu.be/dZjWOGbsN4Y) Veritabanları, verileri depolamak ve sorgulamak için oldukça verimli yollar sunarken, veri işleme konusunda en esnek yöntem, kendi programınızı yazarak veriyi manipüle etmektir. Çoğu durumda, bir veritabanı sorgusu yapmak daha etkili bir yol olabilir. Ancak, daha karmaşık veri işleme gerektiğinde, SQL kullanarak bunu kolayca yapmak mümkün olmayabilir. Veri işleme herhangi bir programlama diliyle yapılabilir, ancak bazı diller veriyle çalışmak açısından daha üst seviyedir. Veri bilimciler genellikle aşağıdaki dillerden birini tercih eder: @@ -73,7 +73,7 @@ print(f"Length of index is {len(idx)}") items_sold = pd.Series(np.random.randint(25,50,size=len(idx)),index=idx) items_sold.plot() ``` -![Zaman Serisi Grafiği](../../../../translated_images/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.tr.png) +![Zaman Serisi Grafiği](../../../../translated_images/tr/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.png) Şimdi, her hafta arkadaşlarımız için bir parti düzenlediğimizi ve parti için ek olarak 10 paket dondurma aldığımızı varsayalım. Bunu göstermek için haftalık olarak indekslenmiş başka bir seri oluşturabiliriz: ```python @@ -84,7 +84,7 @@ additional_items = pd.Series(10,index=pd.date_range(start_date,end_date,freq="W" total_items = items_sold.add(additional_items,fill_value=0) total_items.plot() ``` -![Zaman Serisi Grafiği](../../../../translated_images/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.tr.png) +![Zaman Serisi Grafiği](../../../../translated_images/tr/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.png) > **Not**: Basit `total_items+additional_items` sözdizimini kullanmıyoruz. Eğer kullansaydık, sonuç serisinde birçok `NaN` (*Not a Number*) değeri alırdık. Bunun nedeni, `additional_items` serisindeki bazı indeks noktaları için eksik değerler olmasıdır ve `NaN` ile herhangi bir şeyi toplamak `NaN` sonucunu verir. Bu nedenle toplama sırasında `fill_value` parametresini belirtmemiz gerekir. @@ -93,7 +93,7 @@ Zaman serileriyle, farklı zaman aralıklarıyla seriyi yeniden örnekleyebiliri monthly = total_items.resample("1M").mean() ax = monthly.plot(kind='bar') ``` -![Aylık Zaman Serisi Ortalamaları](../../../../translated_images/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.tr.png) +![Aylık Zaman Serisi Ortalamaları](../../../../translated_images/tr/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.png) ### DataFrame @@ -219,7 +219,7 @@ Odaklanacağımız ilk problem, COVID-19'un salgın yayılımını modellemektir Verilerle nasıl başa çıkılacağını göstermek istediğimiz için, [`notebook-covidspread.ipynb`](notebook-covidspread.ipynb) dosyasını açmanızı ve baştan sona okumanızı öneriyoruz. Hücreleri çalıştırabilir ve sonunda sizin için bıraktığımız bazı zorlukları deneyebilirsiniz. -![COVID Yayılımı](../../../../translated_images/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.tr.png) +![COVID Yayılımı](../../../../translated_images/tr/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.png) > Jupyter Notebook'ta kod nasıl çalıştırılır bilmiyorsanız, [bu makaleye](https://soshnikov.com/education/how-to-execute-notebooks-from-github/) göz atabilirsiniz. @@ -241,7 +241,7 @@ Bu veri setini [Text Analytics for Health](https://docs.microsoft.com/azure/cogn [`notebook-papers.ipynb`](notebook-papers.ipynb) dosyasını açın ve baştan sona okuyun. Hücreleri çalıştırabilir ve sonunda sizin için bıraktığımız bazı zorlukları deneyebilirsiniz. -![Covid Tıbbi Tedavi](../../../../translated_images/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.tr.png) +![Covid Tıbbi Tedavi](../../../../translated_images/tr/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.png) ## Görüntü Verilerini İşleme diff --git a/translations/tr/2-Working-With-Data/README.md b/translations/tr/2-Working-With-Data/README.md index 5eac602e..de267ba8 100644 --- a/translations/tr/2-Working-With-Data/README.md +++ b/translations/tr/2-Working-With-Data/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Verilerle Çalışmak -![veri aşkı](../../../translated_images/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.tr.jpg) +![veri aşkı](../../../translated_images/tr/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.jpg) > Fotoğraf: Alexander Sinn, Unsplash üzerinden Bu derslerde, verilerin nasıl yönetilebileceği, işlenebileceği ve uygulamalarda kullanılabileceği yolları öğreneceksiniz. İlişkisel ve ilişkisel olmayan veritabanlarını ve verilerin bu veritabanlarında nasıl saklanabileceğini keşfedeceksiniz. Verileri yönetmek için Python ile çalışmanın temellerini öğrenecek ve Python ile verileri yönetmek ve analiz etmek için kullanabileceğiniz birçok yöntemi keşfedeceksiniz. diff --git a/translations/tr/3-Data-Visualization/12-visualization-relationships/README.md b/translations/tr/3-Data-Visualization/12-visualization-relationships/README.md index f1fdabe7..e8e358d6 100644 --- a/translations/tr/3-Data-Visualization/12-visualization-relationships/README.md +++ b/translations/tr/3-Data-Visualization/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ Balın pound başına fiyatı ile ABD'deki üretim eyaletleri arasındaki ilişk ```python sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ``` -![scatterplot 1](../../../../translated_images/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.tr.png) +![scatterplot 1](../../../../translated_images/tr/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.png) Şimdi, aynı verileri yıllar içinde fiyatın nasıl değiştiğini göstermek için bal renk şemasıyla gösterin. Bunu, yıllar içinde değişimi göstermek için bir 'hue' parametresi ekleyerek yapabilirsiniz: @@ -60,7 +60,7 @@ sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ```python sns.relplot(x="priceperlb", y="state", hue="year", palette="YlOrBr", data=honey, height=15, aspect=.5); ``` -![scatterplot 2](../../../../translated_images/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.tr.png) +![scatterplot 2](../../../../translated_images/tr/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.png) Bu renk şeması değişikliğiyle, yıllar içinde pound başına bal fiyatında güçlü bir ilerleme olduğunu açıkça görebilirsiniz. Gerçekten de, verilerde bir örnek seti inceleyerek (örneğin Arizona'yı seçerek) yıllar içinde fiyat artışlarının bir desenini, birkaç istisna dışında görebilirsiniz: @@ -89,7 +89,7 @@ sns.relplot(x="priceperlb", y="state", size="year", data=honey, height=15, aspec ``` Noktaların boyutlarının kademeli olarak arttığını görebilirsiniz. -![scatterplot 3](../../../../translated_images/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.tr.png) +![scatterplot 3](../../../../translated_images/tr/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.png) Bu basit bir arz ve talep meselesi mi? İklim değişikliği ve koloni çöküşü gibi faktörler nedeniyle, yıllar içinde satın alınabilecek daha az bal mı var ve bu nedenle fiyat mı artıyor? @@ -104,7 +104,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); ``` Cevap: Evet, 2003 yılı civarındaki bazı istisnalar dışında: -![line chart 1](../../../../translated_images/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.tr.png) +![line chart 1](../../../../translated_images/tr/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.png) ✅ Seaborn, verileri tek bir çizgi etrafında topladığı için "her x değerindeki birden fazla ölçümü ortalamayı ve ortalama etrafındaki %95 güven aralığını çizerek" gösterir. [Kaynak](https://seaborn.pydata.org/tutorial/relational.html). Bu zaman alıcı davranış, `ci=None` eklenerek devre dışı bırakılabilir. @@ -114,7 +114,7 @@ Soru: Peki, 2003 yılında bal arzında bir artış da görebiliyor muyuz? Yıll sns.relplot(x="year", y="totalprod", kind="line", data=honey); ``` -![line chart 2](../../../../translated_images/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.tr.png) +![line chart 2](../../../../translated_images/tr/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.png) Cevap: Pek değil. Toplam üretime bakarsanız, aslında o yıl üretimin arttığını görürsünüz, ancak genel olarak bu yıllar boyunca üretilen bal miktarının azaldığı görülmektedir. @@ -139,7 +139,7 @@ sns.relplot( ``` Bu görselleştirmede, koloni başına verim ve koloni sayısını yıllar içinde yan yana, sütunlar için wrap 3 olarak ayarlanmış şekilde karşılaştırabilirsiniz: -![facet grid](../../../../translated_images/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.tr.png) +![facet grid](../../../../translated_images/tr/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.png) Bu veri seti için, eyaletler ve yıllar arasında koloni sayısı ve verim açısından belirgin bir şey öne çıkmıyor. Bu iki değişken arasında bir korelasyon bulmanın farklı bir yolu var mı? @@ -162,7 +162,7 @@ sns.despine(right=False) plt.ylabel('colony yield') ax.figure.legend(); ``` -![superimposed plots](../../../../translated_images/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.tr.png) +![superimposed plots](../../../../translated_images/tr/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.png) 2003 yılı civarında göze çarpan bir şey olmasa da, bu dersi biraz daha mutlu bir notla bitirmemize olanak tanıyor: kolonilerin sayısı genel olarak azalıyor olsa da, koloni sayısı sabitleniyor, ancak koloni başına verim azalıyor. diff --git a/translations/tr/3-Data-Visualization/R/09-visualization-quantities/README.md b/translations/tr/3-Data-Visualization/R/09-visualization-quantities/README.md index e899f6ca..5d085e35 100644 --- a/translations/tr/3-Data-Visualization/R/09-visualization-quantities/README.md +++ b/translations/tr/3-Data-Visualization/R/09-visualization-quantities/README.md @@ -66,7 +66,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` Burada `ggplot2` paketini yüklüyor ve ardından `library("ggplot2")` komutuyla çalışma alanına dahil ediyorsunuz. ggplot'ta herhangi bir grafik çizmek için `ggplot()` fonksiyonu kullanılır ve veri seti, x ve y değişkenleri öznitelik olarak belirtilir. Bu durumda, bir çizgi grafiği çizmeyi hedeflediğimiz için `geom_line()` fonksiyonunu kullanıyoruz. -![MaxWingspan-lineplot](../../../../../translated_images/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.tr.png) +![MaxWingspan-lineplot](../../../../../translated_images/tr/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.png) Hemen ne fark ediyorsunuz? En az bir aykırı değer var gibi görünüyor - bu oldukça büyük bir kanat açıklığı! 2000+ santimetrelik bir kanat açıklığı 20 metreden fazladır - Minnesota'da Pterodaktiller mi dolaşıyor? Hadi bunu araştıralım. @@ -84,7 +84,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` `theme` içinde açıyı belirtiyor ve `xlab()` ve `ylab()` ile x ve y ekseni etiketlerini belirtiyorsunuz. `ggtitle()` grafiğe bir ad verir. -![MaxWingspan-lineplot-improved](../../../../../translated_images/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.tr.png) +![MaxWingspan-lineplot-improved](../../../../../translated_images/tr/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.png) Etiketlerin 45 derece döndürülmesine rağmen, okunamayacak kadar çok etiket var. Farklı bir strateji deneyelim: yalnızca aykırı değerleri etiketleyin ve etiketleri grafik içinde ayarlayın. Daha fazla alan yaratmak için bir dağılım grafiği kullanabilirsiniz: @@ -100,7 +100,7 @@ Burada ne oluyor? `geom_point()` fonksiyonunu kullanarak dağılım noktaları Ne keşfediyorsunuz? -![MaxWingspan-scatterplot](../../../../../translated_images/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.tr.png) +![MaxWingspan-scatterplot](../../../../../translated_images/tr/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.png) ## Verilerinizi filtreleyin @@ -119,7 +119,7 @@ ggplot(data=birds_filtered, aes(x=Name, y=MaxWingspan,group=1)) + ``` Yeni bir `birds_filtered` veri çerçevesi oluşturduk ve ardından bir dağılım grafiği çizdik. Aykırı değerleri filtreleyerek, verileriniz artık daha tutarlı ve anlaşılır hale geldi. -![MaxWingspan-scatterplot-improved](../../../../../translated_images/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.tr.png) +![MaxWingspan-scatterplot-improved](../../../../../translated_images/tr/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.png) Kanat açıklığı açısından daha temiz bir veri setine sahip olduğumuza göre, bu kuşlar hakkında daha fazla şey keşfedelim. @@ -160,7 +160,7 @@ birds_filtered %>% group_by(Category) %>% ``` Aşağıdaki kod parçasında, verileri manipüle etmek ve gruplamak için [dplyr](https://www.rdocumentation.org/packages/dplyr/versions/0.7.8) ve [lubridate](https://www.rdocumentation.org/packages/lubridate/versions/1.8.0) paketlerini yüklüyoruz. Daha sonra `ggplot2` paketini kullanarak bir çubuk grafik çiziyoruz ve farklı kategoriler için renkleri ve etiketleri belirtiyoruz. -![Stacked bar chart](../../../../../translated_images/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.tr.png) +![Stacked bar chart](../../../../../translated_images/tr/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.png) Ancak bu çubuk grafiği okunamaz çünkü çok fazla gruplandırılmamış veri var. Çizmek istediğiniz verileri seçmeniz gerekiyor, bu yüzden kuşların kategorilerine göre uzunluklarına bakalım. @@ -175,7 +175,7 @@ ggplot(birds_count,aes(Category,n))+geom_bar(stat="identity")+coord_flip() ``` Önce `Category` sütunundaki benzersiz değerleri sayar ve ardından bunları yeni bir `birds_count` veri çerçevesine sıralarsınız. Bu sıralanmış veriler aynı seviyede faktörlenir, böylece sıralı bir şekilde çizilir. `ggplot2` kullanarak verileri bir çubuk grafikte çizersiniz. `coord_flip()` yatay çubuklar çizer. -![category-length](../../../../../translated_images/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.tr.png) +![category-length](../../../../../translated_images/tr/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.png) Bu çubuk grafiği, her kategorideki kuş sayısının iyi bir görünümünü sunar. Bir bakışta, bu bölgedeki en büyük kuş sayısının Ördekler/Kazlar/Su Kuşları kategorisinde olduğunu görüyorsunuz. Minnesota '10.000 gölün ülkesi' olduğu için bu şaşırtıcı değil! @@ -198,7 +198,7 @@ ggplot(birds_grouped,aes(Category,MaxLength))+geom_bar(stat="identity")+coord_fl ``` `birds_filtered` verilerini `Category`'ye göre gruplar ve ardından bir çubuk grafik çizersiniz. -![comparing data](../../../../../translated_images/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.tr.png) +![comparing data](../../../../../translated_images/tr/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.png) Burada şaşırtıcı bir şey yok: sinek kuşlarının Maksimum Uzunluğu, Pelikanlar veya Kazlarla karşılaştırıldığında en azdır. Verilerin mantıklı olması güzel bir şey! @@ -210,7 +210,7 @@ ggplot(data=birds_grouped, aes(x=Category)) + geom_bar(aes(y=MinLength), stat="identity", position="identity", fill='orange')+ coord_flip() ``` -![super-imposed values](../../../../../translated_images/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.tr.png) +![super-imposed values](../../../../../translated_images/tr/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.png) ## 🚀 Meydan Okuma diff --git a/translations/tr/3-Data-Visualization/R/10-visualization-distributions/README.md b/translations/tr/3-Data-Visualization/R/10-visualization-distributions/README.md index 17dfe6af..b65e875f 100644 --- a/translations/tr/3-Data-Visualization/R/10-visualization-distributions/README.md +++ b/translations/tr/3-Data-Visualization/R/10-visualization-distributions/README.md @@ -45,7 +45,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + geom_point() + ggtitle("Max Length per order") + coord_flip() ``` -![her takıma göre maksimum uzunluk](../../../../../translated_images/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.tr.png) +![her takıma göre maksimum uzunluk](../../../../../translated_images/tr/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.png) Bu, her kuş takımına göre vücut uzunluğunun genel dağılımını gösterir, ancak gerçek dağılımları göstermek için en uygun yol değildir. Bu görev genellikle bir Histogram oluşturarak gerçekleştirilir. @@ -57,7 +57,7 @@ Bu, her kuş takımına göre vücut uzunluğunun genel dağılımını gösteri ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=10)+ylab('Frequency') ``` -![tüm veri kümesi üzerindeki dağılım](../../../../../translated_images/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.tr.png) +![tüm veri kümesi üzerindeki dağılım](../../../../../translated_images/tr/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.png) Gördüğünüz gibi, bu veri kümesindeki 400'den fazla kuşun çoğu, Max Body Mass değerinin 2000'in altında olduğu aralığa düşmektedir. `bins` parametresini daha yüksek bir sayıya, örneğin 30'a değiştirerek veriler hakkında daha fazla bilgi edinin: @@ -65,7 +65,7 @@ Gördüğünüz gibi, bu veri kümesindeki 400'den fazla kuşun çoğu, Max Body ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![30 bölmeli dağılım](../../../../../translated_images/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.tr.png) +![30 bölmeli dağılım](../../../../../translated_images/tr/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.png) Bu grafik, dağılımı biraz daha ayrıntılı bir şekilde gösterir. Daha az sola eğimli bir grafik, yalnızca belirli bir aralıktaki verileri seçerek oluşturulabilir: @@ -77,7 +77,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![filtrelenmiş histogram](../../../../../translated_images/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.tr.png) +![filtrelenmiş histogram](../../../../../translated_images/tr/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.png) ✅ Diğer filtreleri ve veri noktalarını deneyin. Verilerin tam dağılımını görmek için, etiketli dağılımları göstermek için `['MaxBodyMass']` filtresini kaldırın. @@ -91,7 +91,7 @@ ggplot(data=birds_filtered_1, aes(x=MaxBodyMass, y=MaxLength) ) + ``` Bu iki öğe arasında beklenen bir eksen boyunca bir korelasyon olduğu ve bir noktada özellikle güçlü bir yakınsama olduğu görülüyor: -![2d grafik](../../../../../translated_images/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.tr.png) +![2d grafik](../../../../../translated_images/tr/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.png) Histogramlar, varsayılan olarak sayısal verilerle iyi çalışır. Peki ya metin verilerine göre dağılımları görmek isterseniz? ## Metin verilerini kullanarak veri kümesindeki dağılımları keşfedin @@ -122,7 +122,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + scale_fill_manual(name="Conservation Status",values=c("red","green","blue","pink"),labels=c("Endangered","Near Threathened","Vulnerable","Least Concern")) ``` -![kanat açıklığı ve koruma durumu](../../../../../translated_images/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.tr.png) +![kanat açıklığı ve koruma durumu](../../../../../translated_images/tr/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.png) Minimum kanat açıklığı ile koruma durumu arasında iyi bir korelasyon görünmüyor. Bu yöntemi kullanarak veri kümesinin diğer öğelerini test edin. Farklı filtreler de deneyebilirsiniz. Herhangi bir korelasyon buluyor musunuz? @@ -136,7 +136,7 @@ Hadi yoğunluk grafikleriyle çalışalım! ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + geom_density() ``` -![yoğunluk grafiği](../../../../../translated_images/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.tr.png) +![yoğunluk grafiği](../../../../../translated_images/tr/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.png) Grafiğin, Minimum Kanat Açıklığı verileri için önceki grafiği nasıl yansıttığını görebilirsiniz; sadece biraz daha düzgün. İkinci grafikte oluşturduğunuz o keskin MaxBodyMass çizgisini yeniden oluşturup bu yöntemle çok iyi bir şekilde düzeltebilirsiniz: @@ -144,7 +144,7 @@ Grafiğin, Minimum Kanat Açıklığı verileri için önceki grafiği nasıl ya ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density() ``` -![vücut kütlesi yoğunluğu](../../../../../translated_images/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.tr.png) +![vücut kütlesi yoğunluğu](../../../../../translated_images/tr/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.png) Çok düzgün ama aşırı düzgün olmayan bir çizgi istiyorsanız, `adjust` parametresini düzenleyin: @@ -152,7 +152,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density(adjust = 1/5) ``` -![daha az düzgün vücut kütlesi](../../../../../translated_images/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.tr.png) +![daha az düzgün vücut kütlesi](../../../../../translated_images/tr/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.png) ✅ Bu tür grafik için mevcut parametreler hakkında okuyun ve deneyin! @@ -162,7 +162,7 @@ Bu tür grafikler, açıklayıcı görselleştirmeler sunar. Örneğin, birkaç ggplot(data=birds_filtered_1,aes(x = MaxBodyMass, fill = Order)) + geom_density(alpha=0.5) ``` -![her takıma göre vücut kütlesi](../../../../../translated_images/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.tr.png) +![her takıma göre vücut kütlesi](../../../../../translated_images/tr/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.png) ## 🚀 Meydan Okuma diff --git a/translations/tr/3-Data-Visualization/R/11-visualization-proportions/README.md b/translations/tr/3-Data-Visualization/R/11-visualization-proportions/README.md index f97fab28..22b09b6b 100644 --- a/translations/tr/3-Data-Visualization/R/11-visualization-proportions/README.md +++ b/translations/tr/3-Data-Visualization/R/11-visualization-proportions/README.md @@ -97,7 +97,7 @@ pie(grouped$count,grouped$class, main="Edible?") ``` İşte, bu iki mantar sınıfına göre verilerin oranlarını gösteren bir pasta grafiği. Etiketlerin sırasını doğru almak oldukça önemlidir, özellikle burada, bu yüzden etiket dizisinin oluşturulma sırasını doğruladığınızdan emin olun! -![pasta grafiği](../../../../../translated_images/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.tr.png) +![pasta grafiği](../../../../../translated_images/tr/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.png) ## Donutlar! @@ -132,7 +132,7 @@ library(webr) PieDonut(habitat, aes(habitat, count=count)) ``` -![donut grafiği](../../../../../translated_images/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.tr.png) +![donut grafiği](../../../../../translated_images/tr/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.png) Bu kod iki kütüphaneyi kullanır - ggplot2 ve webr. webr kütüphanesinin PieDonut fonksiyonunu kullanarak kolayca bir donut grafiği oluşturabilirsiniz! @@ -169,7 +169,7 @@ waffle((cap_color$count/10), rows = 7, title = "Waffle Chart")+scale_fill_manual Bir waffle grafiği kullanarak, bu mantar veri setindeki şapka renklerinin oranlarını açıkça görebilirsiniz. İlginç bir şekilde, birçok yeşil şapkalı mantar var! -![waffle grafiği](../../../../../translated_images/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.tr.png) +![waffle grafiği](../../../../../translated_images/tr/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.png) Bu derste, oranları görselleştirmenin üç yolunu öğrendiniz. Öncelikle, verilerinizi kategorilere ayırmanız ve ardından verileri göstermek için en iyi yolu seçmeniz gerekiyor - pasta, donut veya waffle. Hepsi lezzetli ve kullanıcıya bir veri setinin anlık görüntüsünü sunar. diff --git a/translations/tr/3-Data-Visualization/R/12-visualization-relationships/README.md b/translations/tr/3-Data-Visualization/R/12-visualization-relationships/README.md index e59d8503..1902ef00 100644 --- a/translations/tr/3-Data-Visualization/R/12-visualization-relationships/README.md +++ b/translations/tr/3-Data-Visualization/R/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ library(ggplot2) ggplot(honey, aes(x = priceperlb, y = state)) + geom_point(colour = "blue") ``` -![scatterplot 1](../../../../../translated_images/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.tr.png) +![scatterplot 1](../../../../../translated_images/tr/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.png) Şimdi, aynı verileri yıllar içinde fiyatın nasıl değiştiğini göstermek için bal renk şemasıyla gösterin. Bunu, yıllar içinde değişimi göstermek için 'scale_color_gradientn' parametresini ekleyerek yapabilirsiniz: @@ -61,7 +61,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ggplot(honey, aes(x = priceperlb, y = state, color=year)) + geom_point()+scale_color_gradientn(colours = colorspace::heat_hcl(7)) ``` -![scatterplot 2](../../../../../translated_images/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.tr.png) +![scatterplot 2](../../../../../translated_images/tr/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.png) Bu renk şeması değişikliğiyle, pound başına bal fiyatında yıllar içinde açık bir ilerleme olduğunu görebilirsiniz. Gerçekten de, verilerde bir örnek seti doğrulamak için (örneğin Arizona'yı seçin) yıllar içinde fiyat artışlarının bir modelini birkaç istisna dışında görebilirsiniz: @@ -92,7 +92,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ``` Noktaların boyutlarının kademeli olarak arttığını görebilirsiniz. -![scatterplot 3](../../../../../translated_images/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.tr.png) +![scatterplot 3](../../../../../translated_images/tr/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.png) Bu basit bir arz ve talep meselesi mi? İklim değişikliği ve koloni çöküşü gibi faktörler nedeniyle, yıllar içinde satın alınabilecek daha az bal mı var ve bu nedenle fiyat mı artıyor? @@ -107,7 +107,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab ``` Cevap: Evet, 2003 yılı civarındaki bazı istisnalar dışında: -![line chart 1](../../../../../translated_images/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.tr.png) +![line chart 1](../../../../../translated_images/tr/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.png) Soru: Peki, 2003 yılında bal arzında bir artış görebiliyor muyuz? Yıllar içinde toplam üretime bakarsanız ne görürsünüz? @@ -115,7 +115,7 @@ Soru: Peki, 2003 yılında bal arzında bir artış görebiliyor muyuz? Yıllar qplot(honey$year,honey$totalprod, geom='smooth', span =0.5, xlab = "year",ylab = "totalprod") ``` -![line chart 2](../../../../../translated_images/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.tr.png) +![line chart 2](../../../../../translated_images/tr/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.png) Cevap: Pek değil. Toplam üretime bakarsanız, aslında o yıl artmış gibi görünüyor, ancak genel olarak bu yıllarda üretilen bal miktarı düşüşte. @@ -135,7 +135,7 @@ ggplot(honey, aes(x=yieldpercol, y = numcol,group = 1)) + ``` Bu görselleştirmede, koloni başına verim ve koloni sayısını yıllar içinde yan yana, sütunlar için 3 olarak ayarlanmış bir wrap ile karşılaştırabilirsiniz: -![facet grid](../../../../../translated_images/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.tr.png) +![facet grid](../../../../../translated_images/tr/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.png) Bu veri seti için, eyaletler ve yıllar arasında koloni sayısı ve verim açısından dikkat çeken bir şey yok. Bu iki değişken arasında bir korelasyon bulmanın farklı bir yolu var mı? @@ -152,7 +152,7 @@ plot(honey$year, honey$yieldpercol, pch = 17, col = 3, axis(side = 4, at = pretty(range(y2))) mtext("colony yield", side = 4, line = 3) ``` -![superimposed plots](../../../../../translated_images/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.tr.png) +![superimposed plots](../../../../../translated_images/tr/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.png) 2003 yılı civarında göze çarpan bir şey olmasa da, bu dersi biraz daha mutlu bir notla bitirmemize olanak tanıyor: genel olarak azalan koloni sayısına rağmen, koloni sayısı sabitleniyor, ancak koloni başına verim azalıyor. diff --git a/translations/tr/3-Data-Visualization/R/13-meaningful-vizualizations/README.md b/translations/tr/3-Data-Visualization/R/13-meaningful-vizualizations/README.md index 8d6e059f..11715f3b 100644 --- a/translations/tr/3-Data-Visualization/R/13-meaningful-vizualizations/README.md +++ b/translations/tr/3-Data-Visualization/R/13-meaningful-vizualizations/README.md @@ -47,25 +47,25 @@ Bu derste şunları gözden geçireceksiniz: Bir veri bilimcisi doğru veri için doğru grafiği seçmekte dikkatli olsa bile, veriler genellikle bir noktayı kanıtlamak için, çoğu zaman verilerin kendisini baltalama pahasına, yanıltıcı bir şekilde sunulabilir. Yanıltıcı grafikler ve infografikler için birçok örnek vardır! -[![Alberto Cairo'dan How Charts Lie](../../../../../translated_images/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.tr.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "How charts lie") +[![Alberto Cairo'dan How Charts Lie](../../../../../translated_images/tr/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "How charts lie") > 🎥 Yanıltıcı grafikler hakkında bir konferans konuşması için yukarıdaki görsele tıklayın Bu grafik, X eksenini ters çevirerek tarihe dayalı olarak gerçeğin tam tersini gösteriyor: -![kötü grafik 1](../../../../../translated_images/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.tr.png) +![kötü grafik 1](../../../../../translated_images/tr/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.png) [Bu grafik](https://media.firstcoastnews.com/assets/WTLV/images/170ae16f-4643-438f-b689-50d66ca6a8d8/170ae16f-4643-438f-b689-50d66ca6a8d8_1140x641.jpg) daha da yanıltıcıdır, çünkü göz sağa çekilerek COVID vakalarının zamanla azaldığı sonucuna varır. Ancak, tarihlere dikkatlice bakarsanız, bu yanıltıcı düşüş eğilimini vermek için yeniden düzenlendiklerini görürsünüz. -![kötü grafik 2](../../../../../translated_images/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.tr.jpg) +![kötü grafik 2](../../../../../translated_images/tr/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.jpg) Bu kötü şöhretli örnek, yanıltmak için renk ve ters çevrilmiş bir Y ekseni kullanır: Silah dostu yasaların geçmesinden sonra silahlı ölümlerin arttığı sonucuna varmak yerine, göz tam tersinin doğru olduğunu düşünmek için kandırılır: -![kötü grafik 3](../../../../../translated_images/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.tr.jpg) +![kötü grafik 3](../../../../../translated_images/tr/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.jpg) Bu garip grafik, oranın nasıl manipüle edilebileceğini komik bir şekilde gösteriyor: -![kötü grafik 4](../../../../../translated_images/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.tr.jpg) +![kötü grafik 4](../../../../../translated_images/tr/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.jpg) Karşılaştırılamaz olanı karşılaştırmak, başka bir gölgeli numaradır. [Harika bir web sitesi](https://tylervigen.com/spurious-correlations), Maine'deki boşanma oranı ile margarin tüketimi gibi şeyleri ilişkilendiren 'uydurma korelasyonlar' sergiliyor. Bir Reddit grubu da verilerin [çirkin kullanımlarını](https://www.reddit.com/r/dataisugly/top/?t=all) topluyor. @@ -100,13 +100,13 @@ Eksenlerinizi etiketleyin, gerekirse bir açıklama ekleyin ve verilerin daha iy Verileriniz X ekseninde metinsel ve ayrıntılıysa, daha iyi okunabilirlik için metni açılı hale getirebilirsiniz. [plot3D](https://cran.r-project.org/web/packages/plot3D/index.html), verileriniz destekliyorsa 3D grafikler sunar. Bununla sofistike veri görselleştirmeleri üretilebilir. -![3d grafikler](../../../../../translated_images/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.tr.png) +![3d grafikler](../../../../../translated_images/tr/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.png) ## Animasyon ve 3D grafik gösterimi Bugün en iyi veri görselleştirmelerinden bazıları animasyonludur. Shirley Wu, '[film flowers](http://bl.ocks.org/sxywu/raw/d612c6c653fb8b4d7ff3d422be164a5d/)' gibi D3 ile yapılmış harika örnekler sunar; burada her çiçek bir filmin görselleştirmesidir. Guardian için başka bir örnek ise 'bussed out', NYC'nin evsiz sorununu insanları şehirden otobüsle çıkararak nasıl ele aldığını göstermek için görselleştirmeleri Greensock ve D3 ile birleştiren bir interaktif deneyimdir. -![otobüsle taşınma](../../../../../translated_images/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.tr.png) +![otobüsle taşınma](../../../../../translated_images/tr/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.png) > "Bussed Out: How America Moves its Homeless" [the Guardian](https://www.theguardian.com/us-news/ng-interactive/2017/dec/20/bussed-out-america-moves-homeless-people-country-study). Görselleştirmeler Nadieh Bremer & Shirley Wu tarafından. @@ -116,7 +116,7 @@ Bu ders, bu güçlü görselleştirme kütüphanelerini öğretmek için yeterli Bir sosyal ağın animasyonlu bir görünümünü gösterecek bir web uygulamasını tamamlayacaksınız. Bu, Vue.js ve D3 kullanarak bir [ağ görselleştirmesi](https://github.com/emiliorizzo/vue-d3-network) oluşturmak için yapılmış bir kütüphane kullanır. Uygulama çalışırken, düğümleri ekranda sürükleyerek verileri karıştırabilirsiniz. -![ilişkiler](../../../../../translated_images/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.tr.png) +![ilişkiler](../../../../../translated_images/tr/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.png) ## Proje: D3.js kullanarak bir ağ göstermek için bir grafik oluşturun diff --git a/translations/tr/3-Data-Visualization/README.md b/translations/tr/3-Data-Visualization/README.md index 6870e839..268af206 100644 --- a/translations/tr/3-Data-Visualization/README.md +++ b/translations/tr/3-Data-Visualization/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Görselleştirmeler -![lavanta çiçeği üzerinde bir arı](../../../translated_images/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.tr.jpg) +![lavanta çiçeği üzerinde bir arı](../../../translated_images/tr/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.jpg) > Fotoğraf: Jenna Lee, Unsplash diff --git a/translations/tr/4-Data-Science-Lifecycle/14-Introduction/README.md b/translations/tr/4-Data-Science-Lifecycle/14-Introduction/README.md index f48cc210..2fabfa5f 100644 --- a/translations/tr/4-Data-Science-Lifecycle/14-Introduction/README.md +++ b/translations/tr/4-Data-Science-Lifecycle/14-Introduction/README.md @@ -25,7 +25,7 @@ Bu noktada, veri biliminin bir süreç olduğunu muhtemelen fark etmişsinizdir. Bu ders, yaşam döngüsünün 3 kısmına odaklanıyor: veri toplama, işleme ve bakım. -![Veri bilimi yaşam döngüsü diyagramı](../../../../translated_images/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.tr.jpg) +![Veri bilimi yaşam döngüsü diyagramı](../../../../translated_images/tr/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.jpg) > Fotoğraf: [Berkeley School of Information](https://ischoolonline.berkeley.edu/data-science/what-is-data-science/) ## Veri Toplama @@ -101,7 +101,7 @@ Veri Bilimi Yaşam Döngüsünün birçok versiyonu vardır; her adım farklı i |Team Data Science Process (TDSP)|Endüstriler arası veri madenciliği için standart süreç (CRISP-DM)| |--|--| -|![Team Data Science Lifecycle](../../../../translated_images/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.tr.png) | ![Data Science Process Alliance Image](../../../../translated_images/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.tr.png) | +|![Team Data Science Lifecycle](../../../../translated_images/tr/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.png) | ![Data Science Process Alliance Image](../../../../translated_images/tr/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.png) | | Görsel: [Microsoft](https://docs.microsoft.comazure/architecture/data-science-process/lifecycle) | Görsel: [Data Science Process Alliance](https://www.datascience-pm.com/crisp-dm-2/) | ## [Ders Sonrası Quiz](https://ff-quizzes.netlify.app/en/ds/quiz/27) diff --git a/translations/tr/4-Data-Science-Lifecycle/README.md b/translations/tr/4-Data-Science-Lifecycle/README.md index 90949413..c7d0f212 100644 --- a/translations/tr/4-Data-Science-Lifecycle/README.md +++ b/translations/tr/4-Data-Science-Lifecycle/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Veri Bilimi Yaşam Döngüsü -![communication](../../../translated_images/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.tr.jpg) +![communication](../../../translated_images/tr/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.jpg) > Fotoğraf: Headway, Unsplash üzerinden Bu derslerde, Veri Bilimi yaşam döngüsünün bazı yönlerini, veri analizi ve iletişim dahil olmak üzere keşfedeceksiniz. diff --git a/translations/tr/5-Data-Science-In-Cloud/README.md b/translations/tr/5-Data-Science-In-Cloud/README.md index 5409ac40..f13a0fef 100644 --- a/translations/tr/5-Data-Science-In-Cloud/README.md +++ b/translations/tr/5-Data-Science-In-Cloud/README.md @@ -9,13 +9,13 @@ CO_OP_TRANSLATOR_METADATA: --> # Bulutta Veri Bilimi -![cloud-picture](../../../translated_images/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.tr.jpg) +![cloud-picture](../../../translated_images/tr/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.jpg) > Fotoğraf: [Jelleke Vanooteghem](https://unsplash.com/@ilumire) tarafından [Unsplash](https://unsplash.com/s/photos/cloud?orientation=landscape) üzerinden sağlanmıştır. Büyük veriyle veri bilimi yaparken, bulut gerçek bir oyun değiştirici olabilir. Önümüzdeki üç derste, bulutun ne olduğunu ve neden çok faydalı olabileceğini göreceğiz. Ayrıca bir kalp yetmezliği veri setini inceleyecek ve bir kişinin kalp yetmezliği yaşama olasılığını değerlendirmeye yardımcı olacak bir model oluşturacağız. Bulutun gücünü kullanarak bir modeli iki farklı şekilde eğitecek, dağıtacak ve tüketeceğiz. Birinci yöntem, yalnızca kullanıcı arayüzünü kullanarak Düşük Kod/Kodsuz bir yaklaşımla; ikinci yöntem ise Azure Machine Learning Yazılım Geliştirme Kiti'ni (Azure ML SDK) kullanarak olacak. -![project-schema](../../../translated_images/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.tr.png) +![project-schema](../../../translated_images/tr/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.png) ### Konular diff --git a/translations/tr/6-Data-Science-In-Wild/20-Real-World-Examples/README.md b/translations/tr/6-Data-Science-In-Wild/20-Real-World-Examples/README.md index 007395de..3684e82f 100644 --- a/translations/tr/6-Data-Science-In-Wild/20-Real-World-Examples/README.md +++ b/translations/tr/6-Data-Science-In-Wild/20-Real-World-Examples/README.md @@ -41,7 +41,7 @@ Yapay zekanın demokratikleşmesi sayesinde, geliştiriciler artık yapay zeka d * [Sağlıkta Veri Bilimi](https://data-flair.training/blogs/data-science-in-healthcare/) - tıbbi görüntüleme (örneğin, MRI, X-Ray, CT-Scan), genomik (DNA dizilimi), ilaç geliştirme (risk değerlendirmesi, başarı tahmini), öngörücü analitik (hasta bakımı ve tedarik lojistiği), hastalık takibi ve önleme gibi uygulamaları vurgular. -![Gerçek Dünyada Veri Bilimi Uygulamaları](../../../../translated_images/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.tr.png) Görsel Kaynağı: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) +![Gerçek Dünyada Veri Bilimi Uygulamaları](../../../../translated_images/tr/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.png) Görsel Kaynağı: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) Şekil, veri bilimi tekniklerinin uygulanabileceği diğer alanları ve örnekleri göstermektedir. Diğer uygulamaları keşfetmek ister misiniz? Aşağıdaki [İnceleme ve Kendi Kendine Çalışma](../../../../6-Data-Science-In-Wild/20-Real-World-Examples) bölümüne göz atın. diff --git a/translations/tr/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md b/translations/tr/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md index 3ab8f310..683222f5 100644 --- a/translations/tr/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md +++ b/translations/tr/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md @@ -22,7 +22,7 @@ Explorer arayüzü (aşağıdaki ekran görüntüsünde gösterilmiştir), bir v 2. Veri seti [Kataloğunu](https://planetarycomputer.microsoft.com/catalog) keşfedin - her birinin amacını öğrenin. 3. Explorer'ı kullanın - ilginizi çeken bir veri seti seçin, ilgili bir sorgu ve görselleştirme seçeneği belirleyin. -![Gezegen Bilgisayarı Explorer](../../../../translated_images/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.tr.png) +![Gezegen Bilgisayarı Explorer](../../../../translated_images/tr/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.png) `Göreviniz:` Şimdi tarayıcıda oluşturulan görselleştirmeyi inceleyin ve aşağıdaki soruları yanıtlayın: diff --git a/translations/tr/CONTRIBUTING.md b/translations/tr/CONTRIBUTING.md index bc339720..10686709 100644 --- a/translations/tr/CONTRIBUTING.md +++ b/translations/tr/CONTRIBUTING.md @@ -310,7 +310,7 @@ PR açıklamanıza şunları ekleyin: import pandas as pd ``` ```` -- Görseller için alt metin ekleyin: `![Alt metin](../../translated_images/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.tr.png)` +- Görseller için alt metin ekleyin: `![Alt metin](../../translated_images/tr/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.png)` - Satır uzunluklarını makul tutun (yaklaşık 80-100 karakter) ### Python diff --git a/translations/tr/README.md b/translations/tr/README.md index f5dc9917..5c3e6ccd 100644 --- a/translations/tr/README.md +++ b/translations/tr/README.md @@ -33,7 +33,7 @@ Microsoft'taki Azure Cloud Advocates, Veri Bilimi hakkında 10 haftalık, 20 der **🙏 Özel teşekkürler 🙏 [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/) yazarlarımıza, inceleyicilerimize ve içerik katkıcılarımıza,** özellikle Aaryan Arora, [Aditya Garg](https://github.com/AdityaGarg00), [Alondra Sanchez](https://www.linkedin.com/in/alondra-sanchez-molina/), [Ankita Singh](https://www.linkedin.com/in/ankitasingh007), [Anupam Mishra](https://www.linkedin.com/in/anupam--mishra/), [Arpita Das](https://www.linkedin.com/in/arpitadas01/), ChhailBihari Dubey, [Dibri Nsofor](https://www.linkedin.com/in/dibrinsofor), [Dishita Bhasin](https://www.linkedin.com/in/dishita-bhasin-7065281bb), [Majd Safi](https://www.linkedin.com/in/majd-s/), [Max Blum](https://www.linkedin.com/in/max-blum-6036a1186/), [Miguel Correa](https://www.linkedin.com/in/miguelmque/), [Mohamma Iftekher (Iftu) Ebne Jalal](https://twitter.com/iftu119), [Nawrin Tabassum](https://www.linkedin.com/in/nawrin-tabassum), [Raymond Wangsa Putra](https://www.linkedin.com/in/raymond-wp/), [Rohit Yadav](https://www.linkedin.com/in/rty2423), Samridhi Sharma, [Sanya Sinha](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200), [Sheena Narula](https://www.linkedin.com/in/sheena-narua-n/), [Tauqeer Ahmad](https://www.linkedin.com/in/tauqeerahmad5201/), Yogendrasingh Pawar , [Vidushi Gupta](https://www.linkedin.com/in/vidushi-gupta07/), [Jasleen Sondhi](https://www.linkedin.com/in/jasleen-sondhi/) -|![Sketchnote @sketchthedocs tarafından https://sketchthedocs.dev](../../translated_images/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.tr.png)| +|![Sketchnote @sketchthedocs tarafından https://sketchthedocs.dev](../../translated_images/tr/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.png)| |:---:| | Yeni Başlayanlar için Veri Bilimi - _Sketchnote by [@nitya](https://twitter.com/nitya)_ | @@ -52,7 +52,7 @@ Microsoft'taki Azure Cloud Advocates, Veri Bilimi hakkında 10 haftalık, 20 der Yapay Zeka ile öğrenme serimiz Discord'da devam ediyor, daha fazla bilgi almak ve bize katılmak için 18 - 30 Eylül 2025 tarihleri arasında [Learn with AI Series](https://aka.ms/learnwithai/discord) sayfasını ziyaret edin. GitHub Copilot'ı Veri Bilimi için kullanmaya dair ipuçları ve püf noktaları edineceksiniz. -![Yapay Zeka ile Öğrenme serisi](../../translated_images/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.tr.jpg) +![Yapay Zeka ile Öğrenme serisi](../../translated_images/tr/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.jpg) # Öğrenci misiniz? @@ -132,7 +132,7 @@ Her örnek, her adımı açıklayan ayrıntılı yorumlar içerir, bu da onları ## Dersler -|![ Sketchnote @sketchthedocs tarafından https://sketchthedocs.dev](../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.tr.png)| +|![ Sketchnote @sketchthedocs tarafından https://sketchthedocs.dev](../../translated_images/tr/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png)| |:---:| | Veri Bilimine Yeni Başlayanlar: Yol Haritası - _Sketchnote [@nitya](https://twitter.com/nitya) tarafından_ | diff --git a/translations/tr/sketchnotes/README.md b/translations/tr/sketchnotes/README.md index c9709199..176ec419 100644 --- a/translations/tr/sketchnotes/README.md +++ b/translations/tr/sketchnotes/README.md @@ -13,7 +13,7 @@ Tüm sketchnotelere buradan ulaşabilirsiniz! Nitya Narasimhan, sanatçı -![yol haritası sketchnote](../../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.tr.png) +![yol haritası sketchnote](../../../translated_images/tr/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png) --- diff --git a/translations/tw/1-Introduction/01-defining-data-science/README.md b/translations/tw/1-Introduction/01-defining-data-science/README.md index b85b3c68..fc996387 100644 --- a/translations/tw/1-Introduction/01-defining-data-science/README.md +++ b/translations/tw/1-Introduction/01-defining-data-science/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: --- -[![資料科學定義影片](../../../../translated_images/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.tw.png)](https://youtu.be/beZ7Mb_oz9I) +[![資料科學定義影片](../../../../translated_images/tw/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.png)](https://youtu.be/beZ7Mb_oz9I) ## [課前測驗](https://ff-quizzes.netlify.app/en/ds/quiz/0) @@ -153,7 +153,7 @@ CO_OP_TRANSLATOR_METADATA: 在這次挑戰中，我們將嘗試通過分析文本來找出與資料科學領域相關的概念。我們將選取一篇關於資料科學的維基百科文章，下載並處理文本，然後建立一個像這樣的文字雲： -![資料科學文字雲](../../../../translated_images/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.tw.png) +![資料科學文字雲](../../../../translated_images/tw/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.png) 請訪問 [`notebook.ipynb`](../../../../1-Introduction/01-defining-data-science/notebook.ipynb ':ignore') 閱讀程式碼。您也可以執行程式碼，並即時查看它如何進行所有的資料轉換。 diff --git a/translations/tw/1-Introduction/04-stats-and-probability/README.md b/translations/tw/1-Introduction/04-stats-and-probability/README.md index 7c9b65bc..65ed2a69 100644 --- a/translations/tw/1-Introduction/04-stats-and-probability/README.md +++ b/translations/tw/1-Introduction/04-stats-and-probability/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: 統計學與機率論是數學中高度相關的兩個領域，對於數據科學來說尤為重要。即使在缺乏深厚數學知識的情況下也可以操作數據，但了解一些基本概念仍然是有益的。在這裡，我們將提供一個簡短的介紹，幫助您入門。 -[![介紹影片](../../../../translated_images/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.tw.png)](https://youtu.be/Z5Zy85g4Yjw) +[![介紹影片](../../../../translated_images/tw/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.png)](https://youtu.be/Z5Zy85g4Yjw) ## [課前測驗](https://ff-quizzes.netlify.app/en/ds/quiz/6) @@ -39,7 +39,7 @@ CO_OP_TRANSLATOR_METADATA: 我們只能討論變數落在某個值區間內的機率，例如 P(t1≤X2)。在這種情況下，機率分佈由 **機率密度函數** p(x) 描述，其滿足以下公式： -![P(t_1\le X 更多關於相關性和協方差的示例可以在 [附帶的筆記本](notebook.ipynb) 中找到。 diff --git a/translations/tw/1-Introduction/README.md b/translations/tw/1-Introduction/README.md index 14b8ca03..63c5315e 100644 --- a/translations/tw/1-Introduction/README.md +++ b/translations/tw/1-Introduction/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 資料科學入門 -![數據運作](../../../translated_images/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.tw.jpg) +![數據運作](../../../translated_images/tw/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.jpg) > 照片由 Stephen Dawson 提供，來自 Unsplash 在這些課程中，您將了解資料科學的定義，並學習作為資料科學家必須考慮的倫理問題。您還將學習資料的定義，並簡單了解統計與機率，這些是資料科學的核心學術領域。 diff --git a/translations/tw/2-Working-With-Data/07-python/README.md b/translations/tw/2-Working-With-Data/07-python/README.md index 133991fb..029750d2 100644 --- a/translations/tw/2-Working-With-Data/07-python/README.md +++ b/translations/tw/2-Working-With-Data/07-python/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: | :-------------------------------------------------------------------------------------------------------: | | 使用 Python - _由 [@nitya](https://twitter.com/nitya) 繪製的速記圖_ | -[![介紹影片](../../../../translated_images/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.tw.png)](https://youtu.be/dZjWOGbsN4Y) +[![介紹影片](../../../../translated_images/tw/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.png)](https://youtu.be/dZjWOGbsN4Y) 雖然資料庫提供了非常高效的方式來存儲數據並使用查詢語言進行查詢，但最靈活的數據處理方式是編寫自己的程式來操作數據。在許多情況下，使用資料庫查詢可能更有效。然而，在某些需要更複雜數據處理的情況下，使用 SQL 可能不容易完成。 @@ -73,7 +73,7 @@ print(f"Length of index is {len(idx)}") items_sold = pd.Series(np.random.randint(25,50,size=len(idx)),index=idx) items_sold.plot() ``` -![時間序列圖](../../../../translated_images/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.tw.png) +![時間序列圖](../../../../translated_images/tw/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.png) 假設每週我們都會為朋友舉辦派對，並額外拿出 10 盒冰淇淋。我們可以創建另一個以週為索引的 Series 來展示這一點： ```python @@ -84,7 +84,7 @@ additional_items = pd.Series(10,index=pd.date_range(start_date,end_date,freq="W" total_items = items_sold.add(additional_items,fill_value=0) total_items.plot() ``` -![時間序列圖](../../../../translated_images/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.tw.png) +![時間序列圖](../../../../translated_images/tw/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.png) > **注意**：我們並未使用簡單語法 `total_items+additional_items`。如果使用該語法，我們會在結果 Series 中得到許多 `NaN`（*非數值*）值。這是因為在 `additional_items` Series 的某些索引點缺少值，而將 `NaN` 與任何值相加會得到 `NaN`。因此，我們需要在相加時指定 `fill_value` 參數。 @@ -93,7 +93,7 @@ total_items.plot() monthly = total_items.resample("1M").mean() ax = monthly.plot(kind='bar') ``` -![每月時間序列平均值](../../../../translated_images/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.tw.png) +![每月時間序列平均值](../../../../translated_images/tw/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.png) ### DataFrame @@ -219,7 +219,7 @@ df = pd.read_csv('file.csv') 由於我們想展示如何處理數據，我們邀請你打開 [`notebook-covidspread.ipynb`](notebook-covidspread.ipynb) 並從頭到尾閱讀它。你還可以執行單元格，並完成我們在最後為你留下的一些挑戰。 -![COVID 傳播](../../../../translated_images/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.tw.png) +![COVID 傳播](../../../../translated_images/tw/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.png) > 如果你不知道如何在 Jupyter Notebook 中運行代碼，請查看 [這篇文章](https://soshnikov.com/education/how-to-execute-notebooks-from-github/)。 @@ -241,7 +241,7 @@ df = pd.read_csv('file.csv') 打開 [`notebook-papers.ipynb`](notebook-papers.ipynb) 並從頭到尾閱讀它。你還可以執行單元格，並完成我們在最後為你留下的一些挑戰。 -![COVID 醫療處理](../../../../translated_images/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.tw.png) +![COVID 醫療處理](../../../../translated_images/tw/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.png) ## 處理圖像數據 diff --git a/translations/tw/2-Working-With-Data/README.md b/translations/tw/2-Working-With-Data/README.md index 6a11d721..29f20550 100644 --- a/translations/tw/2-Working-With-Data/README.md +++ b/translations/tw/2-Working-With-Data/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 使用數據 -![data love](../../../translated_images/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.tw.jpg) +![data love](../../../translated_images/tw/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.jpg) > 圖片由 Alexander Sinn 提供，來自 Unsplash 在這些課程中，您將學習一些管理、操作和應用數據的方法。您將了解關聯式和非關聯式數據庫，以及數據如何存儲在其中。您還將學習使用 Python 管理數據的基礎知識，並探索使用 Python 管理和挖掘數據的多種方式。 diff --git a/translations/tw/3-Data-Visualization/12-visualization-relationships/README.md b/translations/tw/3-Data-Visualization/12-visualization-relationships/README.md index ef338209..0ae172b0 100644 --- a/translations/tw/3-Data-Visualization/12-visualization-relationships/README.md +++ b/translations/tw/3-Data-Visualization/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ honey.head() ```python sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ``` -![散點圖 1](../../../../translated_images/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.tw.png) +![散點圖 1](../../../../translated_images/tw/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.png) 接下來，使用蜂蜜色調展示價格如何隨年份演變。您可以通過添加 'hue' 參數來顯示年份的變化： @@ -60,7 +60,7 @@ sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ```python sns.relplot(x="priceperlb", y="state", hue="year", palette="YlOrBr", data=honey, height=15, aspect=.5); ``` -![散點圖 2](../../../../translated_images/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.tw.png) +![散點圖 2](../../../../translated_images/tw/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.png) 通過這種色彩方案的改變，您可以清楚地看到蜂蜜每磅價格在多年來的明顯增長趨勢。事實上，如果您查看數據中的樣本集（例如選擇亞利桑那州），您可以看到價格逐年上漲的模式，僅有少數例外： @@ -89,7 +89,7 @@ sns.relplot(x="priceperlb", y="state", size="year", data=honey, height=15, aspec ``` 您可以看到點的大小逐漸增大。 -![散點圖 3](../../../../translated_images/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.tw.png) +![散點圖 3](../../../../translated_images/tw/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.png) 這是否是一個簡單的供需問題？由於氣候變化和蜂群崩潰等因素，是否每年可供購買的蜂蜜減少，因此價格上漲？ @@ -104,7 +104,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); ``` 答案：是的，但在 2003 年左右有一些例外： -![折線圖 1](../../../../translated_images/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.tw.png) +![折線圖 1](../../../../translated_images/tw/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.png) ✅ 由於 Seaborn 將數據聚合到一條線上，它通過繪製均值和均值周圍的 95% 置信區間來顯示「每個 x 值的多個測量值」。[來源](https://seaborn.pydata.org/tutorial/relational.html)。這種耗時的行為可以通過添加 `ci=None` 禁用。 @@ -114,7 +114,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); sns.relplot(x="year", y="totalprod", kind="line", data=honey); ``` -![折線圖 2](../../../../translated_images/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.tw.png) +![折線圖 2](../../../../translated_images/tw/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.png) 答案：並不完全。如果您查看總產量，實際上在那一年似乎有所增加，儘管總體而言蜂蜜的生產量在這些年中呈下降趨勢。 @@ -139,7 +139,7 @@ sns.relplot( ``` 在此視覺化中，您可以比較逐年每群蜂的產量和蜂群數量，並將列的包裹設置為 3： -![Facet Grid](../../../../translated_images/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.tw.png) +![Facet Grid](../../../../translated_images/tw/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.png) 對於這個數據集，逐年和逐州的蜂群數量及其產量並沒有特別突出的地方。是否有其他方式來尋找這兩個變數之間的相關性？ @@ -162,7 +162,7 @@ sns.despine(right=False) plt.ylabel('colony yield') ax.figure.legend(); ``` -![疊加折線圖](../../../../translated_images/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.tw.png) +![疊加折線圖](../../../../translated_images/tw/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.png) 雖然在 2003 年左右沒有明顯的異常，但這讓我們可以以一個稍微樂觀的結論結束本課：儘管蜂群數量總體上在下降，但蜂群數量正在穩定，即使每群蜂的產量在減少。 diff --git a/translations/tw/3-Data-Visualization/R/09-visualization-quantities/README.md b/translations/tw/3-Data-Visualization/R/09-visualization-quantities/README.md index bb944f16..743279e5 100644 --- a/translations/tw/3-Data-Visualization/R/09-visualization-quantities/README.md +++ b/translations/tw/3-Data-Visualization/R/09-visualization-quantities/README.md @@ -67,7 +67,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` 在這裡，您安裝了 `ggplot2` 套件，然後使用 `library("ggplot2")` 命令將其導入工作空間。要在 ggplot 中繪製任何圖表，使用 `ggplot()` 函數並指定數據集、x 和 y 變量作為屬性。在此情況下，我們使用 `geom_line()` 函數，因為我們的目標是繪製折線圖。 -![MaxWingspan-lineplot](../../../../../translated_images/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.tw.png) +![MaxWingspan-lineplot](../../../../../translated_images/tw/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.png) 您立即注意到什麼？似乎至少有一個異常值——那是一個相當大的翼展！2000+ 公分的翼展超過 20 公尺——明尼蘇達州有翼龍在漫遊嗎？讓我們調查一下。 @@ -85,7 +85,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` 我們在 `theme` 中指定角度，並分別在 `xlab()` 和 `ylab()` 中指定 x 和 y 軸標籤。`ggtitle()` 為圖表/圖形命名。 -![MaxWingspan-lineplot-improved](../../../../../translated_images/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.tw.png) +![MaxWingspan-lineplot-improved](../../../../../translated_images/tw/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.png) 即使將標籤旋轉設置為 45 度，仍然有太多標籤無法閱讀。讓我們嘗試另一種策略：僅標記那些異常值並在圖表內設置標籤。您可以使用散點圖來為標籤留出更多空間： @@ -101,7 +101,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + 您發現了什麼？ -![MaxWingspan-scatterplot](../../../../../translated_images/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.tw.png) +![MaxWingspan-scatterplot](../../../../../translated_images/tw/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.png) ## 篩選數據 @@ -120,7 +120,7 @@ ggplot(data=birds_filtered, aes(x=Name, y=MaxWingspan,group=1)) + ``` 我們創建了一個新的數據框 `birds_filtered`，然後繪製了一個散點圖。通過篩選掉異常值，您的數據現在更加一致且易於理解。 -![MaxWingspan-scatterplot-improved](../../../../../translated_images/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.tw.png) +![MaxWingspan-scatterplot-improved](../../../../../translated_images/tw/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.png) 現在我們至少在翼展方面有了一個更乾淨的數據集，讓我們進一步了解這些鳥類。 @@ -163,7 +163,7 @@ birds_filtered %>% group_by(Category) %>% ``` 在以下代碼片段中，我們安裝了 [dplyr](https://www.rdocumentation.org/packages/dplyr/versions/0.7.8) 和 [lubridate](https://www.rdocumentation.org/packages/lubridate/versions/1.8.0) 套件，以幫助操作和分組數據以繪製堆疊條形圖。首先，您按鳥類的 `Category` 分組數據，然後總結 `MinLength`、`MaxLength`、`MinBodyMass`、`MaxBodyMass`、`MinWingspan`、`MaxWingspan` 列。接著，使用 `ggplot2` 套件繪製條形圖並指定不同類別的顏色和標籤。 -![Stacked bar chart](../../../../../translated_images/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.tw.png) +![Stacked bar chart](../../../../../translated_images/tw/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.png) 然而，這個條形圖因為有太多未分組的數據而難以閱讀。您需要選擇要繪製的數據，因此讓我們看看基於鳥類類別的鳥類長度。 @@ -178,7 +178,7 @@ ggplot(birds_count,aes(Category,n))+geom_bar(stat="identity")+coord_flip() ``` 您首先計算 `Category` 列中的唯一值，然後將它們排序到新的數據框 `birds_count` 中。這些排序後的數據在相同層次中進行分級，以便按排序方式繪製。使用 `ggplot2`，您接著繪製條形圖。`coord_flip()` 繪製水平條形圖。 -![category-length](../../../../../translated_images/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.tw.png) +![category-length](../../../../../translated_images/tw/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.png) 此條形圖清楚地顯示了每個類別中鳥類的數量。一眼就能看出，在這個地區，鴨/鵝/水禽類別的鳥類數量最多。明尼蘇達州是“萬湖之地”，所以這並不令人驚訝！ @@ -201,7 +201,7 @@ ggplot(birds_grouped,aes(Category,MaxLength))+geom_bar(stat="identity")+coord_fl ``` 我們按 `Category` 分組 `birds_filtered` 數據，然後繪製條形圖。 -![comparing data](../../../../../translated_images/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.tw.png) +![comparing data](../../../../../translated_images/tw/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.png) 這裡沒有什麼令人驚訝的：蜂鳥的最大長度比鵜鶘或鵝要小得多。當數據符合邏輯時，這是件好事！ @@ -213,7 +213,7 @@ ggplot(data=birds_grouped, aes(x=Category)) + geom_bar(aes(y=MinLength), stat="identity", position="identity", fill='orange')+ coord_flip() ``` -![super-imposed values](../../../../../translated_images/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.tw.png) +![super-imposed values](../../../../../translated_images/tw/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.png) ## 🚀 挑戰 diff --git a/translations/tw/3-Data-Visualization/R/10-visualization-distributions/README.md b/translations/tw/3-Data-Visualization/R/10-visualization-distributions/README.md index 23a8ae94..db080055 100644 --- a/translations/tw/3-Data-Visualization/R/10-visualization-distributions/README.md +++ b/translations/tw/3-Data-Visualization/R/10-visualization-distributions/README.md @@ -45,7 +45,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + geom_point() + ggtitle("Max Length per order") + coord_flip() ``` -![每目最大長度](../../../../../translated_images/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.tw.png) +![每目最大長度](../../../../../translated_images/tw/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.png) 這提供了每個鳥類目身體長度的一般分佈概覽，但這並不是顯示真實分佈的最佳方式。這項任務通常通過創建直方圖來完成。 ## 使用直方圖 @@ -56,7 +56,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=10)+ylab('Frequency') ``` -![整個數據集的分佈](../../../../../translated_images/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.tw.png) +![整個數據集的分佈](../../../../../translated_images/tw/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.png) 如你所見，這個數據集中大多數的 400 多種鳥類的最大體重都在 2000 以下。通過將 `bins` 參數更改為更高的數字，例如 30，可以獲得更多的數據洞察： @@ -64,7 +64,7 @@ ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![30 個區間的分佈](../../../../../translated_images/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.tw.png) +![30 個區間的分佈](../../../../../translated_images/tw/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.png) 此圖表以更細緻的方式顯示了分佈。通過確保僅選擇特定範圍內的數據，可以創建一個不那麼偏向左側的圖表： @@ -76,7 +76,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![篩選後的直方圖](../../../../../translated_images/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.tw.png) +![篩選後的直方圖](../../../../../translated_images/tw/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.png) ✅ 嘗試其他篩選條件和數據點。要查看數據的完整分佈，移除 `['MaxBodyMass']` 篩選條件以顯示帶標籤的分佈。 @@ -90,7 +90,7 @@ ggplot(data=birds_filtered_1, aes(x=MaxBodyMass, y=MaxLength) ) + ``` 看起來這兩個元素沿著預期的軸有一個預期的相關性，其中有一個特別強的匯聚點： -![2D 圖](../../../../../translated_images/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.tw.png) +![2D 圖](../../../../../translated_images/tw/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.png) 直方圖對於數值數據默認效果很好。如果你需要根據文本數據查看分佈該怎麼辦？ ## 使用文本數據探索數據集的分佈 @@ -121,7 +121,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + scale_fill_manual(name="Conservation Status",values=c("red","green","blue","pink"),labels=c("Endangered","Near Threathened","Vulnerable","Least Concern")) ``` -![翼展與保育狀況的對比](../../../../../translated_images/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.tw.png) +![翼展與保育狀況的對比](../../../../../translated_images/tw/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.png) 最小翼展與保育狀況之間似乎沒有明顯的相關性。使用此方法測試數據集的其他元素。你也可以嘗試不同的篩選條件。你發現了任何相關性嗎？ @@ -135,7 +135,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + geom_density() ``` -![密度圖](../../../../../translated_images/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.tw.png) +![密度圖](../../../../../translated_images/tw/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.png) 你可以看到，這個圖表反映了之前的最小翼展數據，只是稍微平滑了一些。如果你想重新訪問第二個圖表中那條鋸齒狀的 MaxBodyMass 線，可以通過這種方法非常好地將其平滑化： @@ -143,7 +143,7 @@ ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density() ``` -![體重密度](../../../../../translated_images/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.tw.png) +![體重密度](../../../../../translated_images/tw/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.png) 如果你想要一條平滑但不過於平滑的線，可以編輯 `adjust` 參數： @@ -151,7 +151,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density(adjust = 1/5) ``` -![較不平滑的體重](../../../../../translated_images/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.tw.png) +![較不平滑的體重](../../../../../translated_images/tw/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.png) ✅ 閱讀此類圖表可用的參數並進行實驗！ @@ -161,7 +161,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + ggplot(data=birds_filtered_1,aes(x = MaxBodyMass, fill = Order)) + geom_density(alpha=0.5) ``` -![每目體重](../../../../../translated_images/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.tw.png) +![每目體重](../../../../../translated_images/tw/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.png) ## 🚀 挑戰 diff --git a/translations/tw/3-Data-Visualization/R/11-visualization-proportions/README.md b/translations/tw/3-Data-Visualization/R/11-visualization-proportions/README.md index 4eb78039..cfc7dddd 100644 --- a/translations/tw/3-Data-Visualization/R/11-visualization-proportions/README.md +++ b/translations/tw/3-Data-Visualization/R/11-visualization-proportions/README.md @@ -93,7 +93,7 @@ pie(grouped$count,grouped$class, main="Edible?") ``` 瞧，一個圓餅圖展示了根據這兩類蘑菇的比例數據。在這裡，正確的標籤順序非常重要，因此請務必確認標籤數組的構建順序！ -![圓餅圖](../../../../../translated_images/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.tw.png) +![圓餅圖](../../../../../translated_images/tw/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.png) ## 甜甜圈圖！ @@ -128,7 +128,7 @@ library(webr) PieDonut(habitat, aes(habitat, count=count)) ``` -![甜甜圈圖](../../../../../translated_images/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.tw.png) +![甜甜圈圖](../../../../../translated_images/tw/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.png) 此代碼使用了兩個庫 - ggplot2 和 webr。使用 webr 庫的 PieDonut 函數，我們可以輕鬆創建甜甜圈圖！ @@ -166,7 +166,7 @@ waffle((cap_color$count/10), rows = 7, title = "Waffle Chart")+scale_fill_manual 使用華夫圖，你可以清楚地看到此蘑菇數據集中菌蓋顏色的比例。有趣的是，有許多綠色菌蓋的蘑菇！ -![華夫圖](../../../../../translated_images/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.tw.png) +![華夫圖](../../../../../translated_images/tw/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.png) 在本課程中，你學到了三種視覺化比例的方法。首先，你需要將數據分組到分類中，然後決定哪種方式最適合顯示數據 - 圓餅圖、甜甜圈圖或華夫圖。這些方法都很有趣，並能讓用戶快速了解數據集。 diff --git a/translations/tw/3-Data-Visualization/R/12-visualization-relationships/README.md b/translations/tw/3-Data-Visualization/R/12-visualization-relationships/README.md index fd6dca13..71f79f03 100644 --- a/translations/tw/3-Data-Visualization/R/12-visualization-relationships/README.md +++ b/translations/tw/3-Data-Visualization/R/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ library(ggplot2) ggplot(honey, aes(x = priceperlb, y = state)) + geom_point(colour = "blue") ``` -![散點圖 1](../../../../../translated_images/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.tw.png) +![散點圖 1](../../../../../translated_images/tw/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.png) 接下來，使用蜂蜜色彩方案展示價格如何隨年份演變。您可以通過添加 `scale_color_gradientn` 參數來展示年份的變化： @@ -61,7 +61,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ggplot(honey, aes(x = priceperlb, y = state, color=year)) + geom_point()+scale_color_gradientn(colours = colorspace::heat_hcl(7)) ``` -![散點圖 2](../../../../../translated_images/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.tw.png) +![散點圖 2](../../../../../translated_images/tw/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.png) 使用這種色彩方案，您可以清楚地看到蜂蜜每磅價格在多年來的明顯增長趨勢。事實上，如果您查看數據中的樣本集（例如選擇亞利桑那州），您可以看到價格逐年上漲的模式，只有少數例外： @@ -92,7 +92,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ``` 您可以看到點的大小逐漸增大。 -![散點圖 3](../../../../../translated_images/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.tw.png) +![散點圖 3](../../../../../translated_images/tw/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.png) 這是否是一個簡單的供需問題？由於氣候變化和蜂群崩壞等因素，是否每年可供購買的蜂蜜減少，導致價格上漲？ @@ -107,7 +107,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab ``` 答案：是的，但在2003年左右有一些例外： -![折線圖 1](../../../../../translated_images/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.tw.png) +![折線圖 1](../../../../../translated_images/tw/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.png) 問題：那麼在2003年，我們是否也能看到蜂蜜供應的激增？如果您查看每年的總產量呢？ @@ -115,7 +115,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab qplot(honey$year,honey$totalprod, geom='smooth', span =0.5, xlab = "year",ylab = "totalprod") ``` -![折線圖 2](../../../../../translated_images/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.tw.png) +![折線圖 2](../../../../../translated_images/tw/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.png) 答案：並不完全。如果您查看總產量，實際上在那一年似乎有所增加，儘管總的來說蜂蜜的生產量在這些年中呈下降趨勢。 @@ -135,7 +135,7 @@ ggplot(honey, aes(x=yieldpercol, y = numcol,group = 1)) + ``` 在這個視覺化中，您可以比較每年的每群產量和蜂群數量，並將列的分面設置為3： -![分面網格](../../../../../translated_images/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.tw.png) +![分面網格](../../../../../translated_images/tw/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.png) 對於這個數據集，關於蜂群數量和每群產量，年份與州之間並沒有特別突出的地方。是否有其他方式可以找到這兩個變數之間的相關性？ @@ -152,7 +152,7 @@ plot(honey$year, honey$yieldpercol, pch = 17, col = 3, axis(side = 4, at = pretty(range(y2))) mtext("colony yield", side = 4, line = 3) ``` -![疊加折線圖](../../../../../translated_images/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.tw.png) +![疊加折線圖](../../../../../translated_images/tw/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.png) 雖然在2003年沒有明顯的異常，但這讓我們可以以一個稍微樂觀的結論結束這節課：儘管蜂群數量總體上在下降，但蜂群數量正在穩定，即使每群產量在減少。 diff --git a/translations/tw/3-Data-Visualization/R/13-meaningful-vizualizations/README.md b/translations/tw/3-Data-Visualization/R/13-meaningful-vizualizations/README.md index f12b75cd..169fdfdf 100644 --- a/translations/tw/3-Data-Visualization/R/13-meaningful-vizualizations/README.md +++ b/translations/tw/3-Data-Visualization/R/13-meaningful-vizualizations/README.md @@ -47,25 +47,25 @@ CO_OP_TRANSLATOR_METADATA: 即使數據科學家謹慎地為數據選擇了正確的圖表類型，數據仍然可能以某種方式被展示來證明某個觀點，往往以犧牲數據本身為代價。有許多關於誤導性圖表和信息圖的例子！ -[![Alberto Cairo 的《圖表如何說謊》](../../../../../translated_images/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.tw.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "圖表如何說謊") +[![Alberto Cairo 的《圖表如何說謊》](../../../../../translated_images/tw/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "圖表如何說謊") > 🎥 點擊上方圖片觀看關於誤導性圖表的會議演講 這張圖表反轉了 X 軸，根據日期顯示了與事實相反的內容： -![壞圖表 1](../../../../../translated_images/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.tw.png) +![壞圖表 1](../../../../../translated_images/tw/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.png) [這張圖表](https://media.firstcoastnews.com/assets/WTLV/images/170ae16f-4643-438f-b689-50d66ca6a8d8/170ae16f-4643-438f-b689-50d66ca6a8d8_1140x641.jpg) 更加誤導，因為視線被吸引到右側，讓人得出結論：隨著時間推移，各縣的 COVID 病例數量下降了。事實上，如果仔細查看日期，你會發現它們被重新排列以製造這種誤導性的下降趨勢。 -![壞圖表 2](../../../../../translated_images/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.tw.jpg) +![壞圖表 2](../../../../../translated_images/tw/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.jpg) 這個臭名昭著的例子使用了顏色和反轉的 Y 軸來誤導：與其得出槍支友好立法通過後槍支死亡人數激增的結論，事實上視線被誤導以為情況正好相反： -![壞圖表 3](../../../../../translated_images/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.tw.jpg) +![壞圖表 3](../../../../../translated_images/tw/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.jpg) 這張奇怪的圖表展示了比例如何被操縱，效果令人啼笑皆非： -![壞圖表 4](../../../../../translated_images/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.tw.jpg) +![壞圖表 4](../../../../../translated_images/tw/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.jpg) 比較無法比較的事物是另一種陰險的手段。有一個[精彩的網站](https://tylervigen.com/spurious-correlations)專門展示「虛假的相關性」，顯示像緬因州離婚率與人造黃油消耗量這樣的「事實」。Reddit 上也有一個群組收集了[數據的醜陋用法](https://www.reddit.com/r/dataisugly/top/?t=all)。 @@ -100,13 +100,13 @@ CO_OP_TRANSLATOR_METADATA: 如果你的數據在 X 軸上是文本且冗長，可以將文本傾斜以提高可讀性。[plot3D](https://cran.r-project.org/web/packages/plot3D/index.html) 提供了 3D 繪圖功能，如果你的數據支持的話，可以使用它來製作更高級的數據視覺化。 -![3D 圖表](../../../../../translated_images/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.tw.png) +![3D 圖表](../../../../../translated_images/tw/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.png) ## 動畫和 3D 圖表展示 當今一些最好的數據視覺化是動畫化的。Shirley Wu 使用 D3 創作了許多令人驚嘆的作品，例如「[電影之花](http://bl.ocks.org/sxywu/raw/d612c6c653fb8b4d7ff3d422be164a5d/)」，每朵花都是一部電影的視覺化。另一個例子是《衛報》的「Bussed Out」，這是一個結合了 Greensock 和 D3 的互動體驗，並採用滾動敘事的文章格式，展示了紐約市如何通過將無家可歸者送出城市來處理這一問題。 -![Bussed Out](../../../../../translated_images/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.tw.png) +![Bussed Out](../../../../../translated_images/tw/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.png) > 「Bussed Out: How America Moves its Homeless」來自[衛報](https://www.theguardian.com/us-news/ng-interactive/2017/dec/20/bussed-out-america-moves-homeless-people-country-study)。視覺化由 Nadieh Bremer 和 Shirley Wu 創作。 @@ -116,7 +116,7 @@ CO_OP_TRANSLATOR_METADATA: 你將完成一個網頁應用，展示這個社交網絡的動畫化視圖。它使用了一個基於 Vue.js 和 D3 的庫來創建[網絡視覺化](https://github.com/emiliorizzo/vue-d3-network)。應用運行時，你可以在屏幕上拖動節點來重新排列數據。 -![危險關係](../../../../../translated_images/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.tw.png) +![危險關係](../../../../../translated_images/tw/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.png) ## 專案：使用 D3.js 構建一個展示網絡的圖表 diff --git a/translations/tw/3-Data-Visualization/README.md b/translations/tw/3-Data-Visualization/README.md index 55e27549..a145811d 100644 --- a/translations/tw/3-Data-Visualization/README.md +++ b/translations/tw/3-Data-Visualization/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 視覺化 -![一隻蜜蜂停在薰衣草花上](../../../translated_images/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.tw.jpg) +![一隻蜜蜂停在薰衣草花上](../../../translated_images/tw/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.jpg) > 照片由 Jenna Lee 提供，來自 Unsplash 視覺化數據是數據科學家最重要的任務之一。一張圖片勝過千言萬語，視覺化可以幫助你識別數據中的各種有趣部分，例如尖峰、異常值、分組、趨勢等等，這些都能幫助你理解數據背後的故事。 diff --git a/translations/tw/4-Data-Science-Lifecycle/14-Introduction/README.md b/translations/tw/4-Data-Science-Lifecycle/14-Introduction/README.md index d08398ec..8544fb2d 100644 --- a/translations/tw/4-Data-Science-Lifecycle/14-Introduction/README.md +++ b/translations/tw/4-Data-Science-Lifecycle/14-Introduction/README.md @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 本課程將重點放在生命週期的三個部分：資料捕捉、資料處理和資料維護。 -![資料科學生命週期圖示](../../../../translated_images/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.tw.jpg) +![資料科學生命週期圖示](../../../../translated_images/tw/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.jpg) > 圖片來源：[Berkeley School of Information](https://ischoolonline.berkeley.edu/data-science/what-is-data-science/) ## 資料捕捉 @@ -98,7 +98,7 @@ CO_OP_TRANSLATOR_METADATA: |團隊資料科學過程 (TDSP)|跨行業標準資料挖掘過程 (CRISP-DM)| |--|--| -|![團隊資料科學生命週期](../../../../translated_images/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.tw.png) | ![資料科學過程聯盟圖示](../../../../translated_images/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.tw.png) | +|![團隊資料科學生命週期](../../../../translated_images/tw/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.png) | ![資料科學過程聯盟圖示](../../../../translated_images/tw/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.png) | | 圖片來源：[Microsoft](https://docs.microsoft.comazure/architecture/data-science-process/lifecycle) | 圖片來源：[Data Science Process Alliance](https://www.datascience-pm.com/crisp-dm-2/) | ## [課後測驗](https://ff-quizzes.netlify.app/en/ds/quiz/27) diff --git a/translations/tw/4-Data-Science-Lifecycle/README.md b/translations/tw/4-Data-Science-Lifecycle/README.md index 49ac04a6..5bdd1c25 100644 --- a/translations/tw/4-Data-Science-Lifecycle/README.md +++ b/translations/tw/4-Data-Science-Lifecycle/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 數據科學生命週期 -![communication](../../../translated_images/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.tw.jpg) +![communication](../../../translated_images/tw/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.jpg) > 圖片由 Headway 提供，來自 Unsplash 在這些課程中，您將探索數據科學生命週期的一些方面，包括數據的分析和溝通。 diff --git a/translations/tw/5-Data-Science-In-Cloud/README.md b/translations/tw/5-Data-Science-In-Cloud/README.md index e8f93ebc..91270b72 100644 --- a/translations/tw/5-Data-Science-In-Cloud/README.md +++ b/translations/tw/5-Data-Science-In-Cloud/README.md @@ -9,13 +9,13 @@ CO_OP_TRANSLATOR_METADATA: --> # 雲端中的數據科學 -![cloud-picture](../../../translated_images/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.tw.jpg) +![cloud-picture](../../../translated_images/tw/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.jpg) > 照片由 [Jelleke Vanooteghem](https://unsplash.com/@ilumire) 提供，來自 [Unsplash](https://unsplash.com/s/photos/cloud?orientation=landscape) 在處理大數據的數據科學時，雲端可以帶來革命性的改變。在接下來的三節課中，我們將了解什麼是雲端以及它為什麼如此有用。我們還將探索一個心臟衰竭數據集，並建立一個模型來幫助評估某人患心臟衰竭的可能性。我們將利用雲端的強大功能來訓練、部署並以兩種不同的方式使用模型。一種方式是僅使用用戶界面，以低代碼/無代碼的方式進行；另一種方式是使用 Azure 機器學習軟件開發工具包 (Azure ML SDK)。 -![project-schema](../../../translated_images/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.tw.png) +![project-schema](../../../translated_images/tw/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.png) ### 主題 diff --git a/translations/tw/6-Data-Science-In-Wild/20-Real-World-Examples/README.md b/translations/tw/6-Data-Science-In-Wild/20-Real-World-Examples/README.md index 956cf2ee..95df6996 100644 --- a/translations/tw/6-Data-Science-In-Wild/20-Real-World-Examples/README.md +++ b/translations/tw/6-Data-Science-In-Wild/20-Real-World-Examples/README.md @@ -41,7 +41,7 @@ CO_OP_TRANSLATOR_METADATA: * [醫療保健中的數據科學](https://data-flair.training/blogs/data-science-in-healthcare/) - 強調應用如醫學影像（例如 MRI、X光、CT掃描）、基因組學（DNA測序）、藥物開發（風險評估、成功預測）、預測分析（患者護理和供應物流）、疾病追蹤和預防等。 -![數據科學在現實世界中的應用](../../../../translated_images/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.tw.png) 圖片來源：[Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) +![數據科學在現實世界中的應用](../../../../translated_images/tw/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.png) 圖片來源：[Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) 該圖展示了其他領域和應用數據科學技術的例子。想探索其他應用嗎？查看下面的[回顧與自學](../../../../6-Data-Science-In-Wild/20-Real-World-Examples)部分。 diff --git a/translations/tw/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md b/translations/tw/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md index a0a74926..8f99bc17 100644 --- a/translations/tw/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md +++ b/translations/tw/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md @@ -22,7 +22,7 @@ Explorer界面（如下圖所示）允許您選擇數據集（從提供的選項 2. 探索數據集[目錄](https://planetarycomputer.microsoft.com/catalog)——了解每個數據集的用途。 3. 使用Explorer——選擇一個感興趣的數據集，選擇相關的查詢和渲染選項。 -![行星電腦Explorer](../../../../translated_images/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.tw.png) +![行星電腦Explorer](../../../../translated_images/tw/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.png) `您的任務：` 現在研究瀏覽器中渲染的可視化，並回答以下問題： diff --git a/translations/tw/CONTRIBUTING.md b/translations/tw/CONTRIBUTING.md index 8a1c887f..89e653a0 100644 --- a/translations/tw/CONTRIBUTING.md +++ b/translations/tw/CONTRIBUTING.md @@ -311,7 +311,7 @@ def calculate_mean(data): import pandas as pd ``` ```` -- 為圖片添加替代文字：`![替代文字](../../translated_images/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.tw.png)` +- 為圖片添加替代文字：`![替代文字](../../translated_images/tw/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.png)` - 保持合理的行長度（約 80-100 字元） ### Python diff --git a/translations/tw/README.md b/translations/tw/README.md index e52691fc..34536190 100644 --- a/translations/tw/README.md +++ b/translations/tw/README.md @@ -28,7 +28,7 @@ Azure Cloud Advocates 在 Microsoft 很高興提供一個為期 10 週、共 20 **🙏 特別感謝 🙏 我們的 [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/) 作者、審閱者與內容貢獻者，** 尤其是 Aaryan Arora, [Aditya Garg](https://github.com/AdityaGarg00), [Alondra Sanchez](https://www.linkedin.com/in/alondra-sanchez-molina/), [Ankita Singh](https://www.linkedin.com/in/ankitasingh007), [Anupam Mishra](https://www.linkedin.com/in/anupam--mishra/), [Arpita Das](https://www.linkedin.com/in/arpitadas01/), ChhailBihari Dubey, [Dibri Nsofor](https://www.linkedin.com/in/dibrinsofor), [Dishita Bhasin](https://www.linkedin.com/in/dishita-bhasin-7065281bb), [Majd Safi](https://www.linkedin.com/in/majd-s/), [Max Blum](https://www.linkedin.com/in/max-blum-6036a1186/), [Miguel Correa](https://www.linkedin.com/in/miguelmque/), [Mohamma Iftekher (Iftu) Ebne Jalal](https://twitter.com/iftu119), [Nawrin Tabassum](https://www.linkedin.com/in/nawrin-tabassum), [Raymond Wangsa Putra](https://www.linkedin.com/in/raymond-wp/), [Rohit Yadav](https://www.linkedin.com/in/rty2423), Samridhi Sharma, [Sanya Sinha](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200), [Sheena Narula](https://www.linkedin.com/in/sheena-narua-n/), [Tauqeer Ahmad](https://www.linkedin.com/in/tauqeerahmad5201/), Yogendrasingh Pawar , [Vidushi Gupta](https://www.linkedin.com/in/vidushi-gupta07/), [Jasleen Sondhi](https://www.linkedin.com/in/jasleen-sondhi/) -|![速寫圖作者 @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.tw.png)| +|![速寫圖作者 @sketchthedocs https://sketchthedocs.dev](../../translated_images/tw/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.png)| |:---:| | 資料科學入門 - _速寫圖作者 [@nitya](https://twitter.com/nitya)_ | @@ -47,7 +47,7 @@ Azure Cloud Advocates 在 Microsoft 很高興提供一個為期 10 週、共 20 我們在 Discord 上有一個「與 AI 一起學習」系列，更多資訊及加入請見 [與 AI 一起學習系列](https://aka.ms/learnwithai/discord)，活動期間為 2025 年 9 月 18 日至 30 日。您將獲得使用 GitHub Copilot 做資料科學的提示與技巧。 -![與 AI 一起學習系列](../../translated_images/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.tw.jpg) +![與 AI 一起學習系列](../../translated_images/tw/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.jpg) # 你是學生嗎？ @@ -127,7 +127,7 @@ Azure Cloud Advocates 在 Microsoft 很高興提供一個為期 10 週、共 20 ## 課程 -|![ 手繪速記 由 @sketchthedocs 提供 https://sketchthedocs.dev](../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.tw.png)| +|![ 手繪速記 由 @sketchthedocs 提供 https://sketchthedocs.dev](../../translated_images/tw/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png)| |:---:| | 資料科學初學者：路線圖 - _手繪速記作者 [@nitya](https://twitter.com/nitya)_ | diff --git a/translations/tw/sketchnotes/README.md b/translations/tw/sketchnotes/README.md index caabdb4d..59dd21fe 100644 --- a/translations/tw/sketchnotes/README.md +++ b/translations/tw/sketchnotes/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: Nitya Narasimhan，藝術家 -![路線圖手繪筆記](../../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.tw.png) +![路線圖手繪筆記](../../../translated_images/tw/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png) **免責聲明**： 本文件使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。雖然我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。原始文件的母語版本應被視為權威來源。對於關鍵資訊，建議使用專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解釋不承擔責任。 \ No newline at end of file diff --git a/translations/uk/1-Introduction/01-defining-data-science/README.md b/translations/uk/1-Introduction/01-defining-data-science/README.md index e48959f2..5e0d5afe 100644 --- a/translations/uk/1-Introduction/01-defining-data-science/README.md +++ b/translations/uk/1-Introduction/01-defining-data-science/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: --- -[![Відео про визначення науки про дані](../../../../translated_images/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.uk.png)](https://youtu.be/beZ7Mb_oz9I) +[![Відео про визначення науки про дані](../../../../translated_images/uk/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.png)](https://youtu.be/beZ7Mb_oz9I) ## [Тест перед лекцією](https://ff-quizzes.netlify.app/en/ds/quiz/0) @@ -153,7 +153,7 @@ CO_OP_TRANSLATOR_METADATA: У цьому завданні ми спробуємо знайти концепції, пов'язані з галуззю Data Science, аналізуючи тексти. Ми візьмемо статтю з Вікіпедії про Data Science, завантажимо та обробимо текст, а потім створимо хмару слів, схожу на цю: -![Хмара слів для Data Science](../../../../translated_images/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.uk.png) +![Хмара слів для Data Science](../../../../translated_images/uk/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.png) Відвідайте [`notebook.ipynb`](../../../../1-Introduction/01-defining-data-science/notebook.ipynb ':ignore'), щоб ознайомитися з кодом. Ви також можете запустити код і побачити, як він виконує всі перетворення даних у реальному часі. diff --git a/translations/uk/1-Introduction/04-stats-and-probability/README.md b/translations/uk/1-Introduction/04-stats-and-probability/README.md index 0502f6ed..56dcf9f5 100644 --- a/translations/uk/1-Introduction/04-stats-and-probability/README.md +++ b/translations/uk/1-Introduction/04-stats-and-probability/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: Статистика та теорія ймовірностей — це дві тісно пов’язані галузі математики, які мають велике значення для науки про дані. Можна працювати з даними без глибоких знань математики, але все ж краще знати хоча б основні концепції. Тут ми представимо короткий вступ, який допоможе вам розпочати. -[![Вступне відео](../../../../translated_images/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.uk.png)](https://youtu.be/Z5Zy85g4Yjw) +[![Вступне відео](../../../../translated_images/uk/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.png)](https://youtu.be/Z5Zy85g4Yjw) ## [Тест перед лекцією](https://ff-quizzes.netlify.app/en/ds/quiz/6) @@ -39,7 +39,7 @@ CO_OP_TRANSLATOR_METADATA: Ми можемо говорити лише про ймовірність того, що змінна потрапить у заданий інтервал значень, наприклад P(t1≤X2). У цьому випадку розподіл ймовірностей описується **функцією щільності ймовірностей** p(x), такою, що -![P(t_1\le X Більше прикладів кореляції та коваріації можна знайти в [супровідному блокноті](notebook.ipynb). diff --git a/translations/uk/1-Introduction/README.md b/translations/uk/1-Introduction/README.md index 11e372dd..35621b46 100644 --- a/translations/uk/1-Introduction/README.md +++ b/translations/uk/1-Introduction/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Вступ до науки про дані -![дані в дії](../../../translated_images/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.uk.jpg) +![дані в дії](../../../translated_images/uk/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.jpg) > Фото зроблено Стівеном Доусоном на Unsplash У цих уроках ви дізнаєтеся, як визначається наука про дані, а також ознайомитеся з етичними аспектами, які має враховувати спеціаліст з даних. Ви також дізнаєтеся, як визначаються дані, і трохи ознайомитеся зі статистикою та ймовірністю — основними академічними напрямами науки про дані. diff --git a/translations/uk/2-Working-With-Data/07-python/README.md b/translations/uk/2-Working-With-Data/07-python/README.md index 590463ee..ecc99b44 100644 --- a/translations/uk/2-Working-With-Data/07-python/README.md +++ b/translations/uk/2-Working-With-Data/07-python/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: | :-------------------------------------------------------------------------------------------------------: | | Робота з Python - _Скетчноут від [@nitya](https://twitter.com/nitya)_ | -[![Вступне відео](../../../../translated_images/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.uk.png)](https://youtu.be/dZjWOGbsN4Y) +[![Вступне відео](../../../../translated_images/uk/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.png)](https://youtu.be/dZjWOGbsN4Y) Хоча бази даних пропонують дуже ефективні способи зберігання даних і запитів до них за допомогою мов запитів, найгнучкішим способом обробки даних є написання власної програми для їх маніпулювання. У багатьох випадках виконання запиту до бази даних буде більш ефективним. Однак у деяких випадках, коли потрібна більш складна обробка даних, це не можна легко зробити за допомогою SQL. Обробка даних може бути запрограмована на будь-якій мові програмування, але є певні мови, які є більш високорівневими для роботи з даними. Зазвичай, фахівці з даних віддають перевагу одній із наступних мов: @@ -73,7 +73,7 @@ print(f"Length of index is {len(idx)}") items_sold = pd.Series(np.random.randint(25,50,size=len(idx)),index=idx) items_sold.plot() ``` -![Графік часових рядів](../../../../translated_images/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.uk.png) +![Графік часових рядів](../../../../translated_images/uk/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.png) Тепер припустимо, що щотижня ми організовуємо вечірку для друзів і беремо додатково 10 упаковок морозива для вечірки. Ми можемо створити ще один Series, індексований за тижнями, щоб продемонструвати це: ```python @@ -84,7 +84,7 @@ additional_items = pd.Series(10,index=pd.date_range(start_date,end_date,freq="W" total_items = items_sold.add(additional_items,fill_value=0) total_items.plot() ``` -![Графік часових рядів](../../../../translated_images/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.uk.png) +![Графік часових рядів](../../../../translated_images/uk/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.png) > **Примітка**: Ми не використовуємо простий синтаксис `total_items+additional_items`. Якби ми це зробили, ми отримали б багато значень `NaN` (*Not a Number*) у результаті. Це тому, що для деяких точок індексу в Series `additional_items` відсутні значення, і додавання `NaN` до чого-небудь призводить до `NaN`. Тому нам потрібно вказати параметр `fill_value` під час додавання. @@ -93,7 +93,7 @@ total_items.plot() monthly = total_items.resample("1M").mean() ax = monthly.plot(kind='bar') ``` -![Середні значення часових рядів за місяць](../../../../translated_images/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.uk.png) +![Середні значення часових рядів за місяць](../../../../translated_images/uk/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.png) ### DataFrame @@ -219,7 +219,7 @@ df = pd.read_csv('file.csv') Оскільки ми хочемо продемонструвати, як працювати з даними, ми запрошуємо вас відкрити [`notebook-covidspread.ipynb`](notebook-covidspread.ipynb) і прочитати його від початку до кінця. Ви також можете виконувати комірки та виконувати завдання, які ми залишили для вас наприкінці. -![COVID Spread](../../../../translated_images/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.uk.png) +![COVID Spread](../../../../translated_images/uk/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.png) > Якщо ви не знаєте, як запускати код у Jupyter Notebook, перегляньте [цю статтю](https://soshnikov.com/education/how-to-execute-notebooks-from-github/). @@ -241,7 +241,7 @@ df = pd.read_csv('file.csv') Відкрийте [`notebook-papers.ipynb`](notebook-papers.ipynb) і прочитайте його від початку до кінця. Ви також можете виконувати комірки та виконувати завдання, які ми залишили для вас наприкінці. -![Covid Medical Treatment](../../../../translated_images/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.uk.png) +![Covid Medical Treatment](../../../../translated_images/uk/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.png) ## Обробка даних зображень diff --git a/translations/uk/2-Working-With-Data/README.md b/translations/uk/2-Working-With-Data/README.md index d3aa7c7f..fa821d4c 100644 --- a/translations/uk/2-Working-With-Data/README.md +++ b/translations/uk/2-Working-With-Data/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Робота з даними -![data love](../../../translated_images/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.uk.jpg) +![data love](../../../translated_images/uk/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.jpg) > Фото від Alexander Sinn на Unsplash У цих уроках ви дізнаєтеся про деякі способи управління, обробки та використання даних у додатках. Ви ознайомитеся з реляційними та нереляційними базами даних і тим, як у них можна зберігати дані. Ви вивчите основи роботи з Python для управління даними та відкриєте для себе багато способів використання Python для обробки та аналізу даних. diff --git a/translations/uk/3-Data-Visualization/12-visualization-relationships/README.md b/translations/uk/3-Data-Visualization/12-visualization-relationships/README.md index f56c5d83..e149a122 100644 --- a/translations/uk/3-Data-Visualization/12-visualization-relationships/README.md +++ b/translations/uk/3-Data-Visualization/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ honey.head() ```python sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ``` -![scatterplot 1](../../../../translated_images/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.uk.png) +![scatterplot 1](../../../../translated_images/uk/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.png) Тепер покажіть ті ж дані з кольоровою схемою меду, щоб продемонструвати, як ціна змінюється рік за роком. Ви можете зробити це, додавши параметр 'hue', щоб показати зміни за роками: @@ -60,7 +60,7 @@ sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ```python sns.relplot(x="priceperlb", y="state", hue="year", palette="YlOrBr", data=honey, height=15, aspect=.5); ``` -![scatterplot 2](../../../../translated_images/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.uk.png) +![scatterplot 2](../../../../translated_images/uk/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.png) З цією зміною кольорової схеми ви можете побачити очевидну тенденцію до зростання ціни за фунт меду протягом років. Дійсно, якщо ви перевірите вибірку даних (наприклад, виберіть штат Аризона), ви побачите закономірність зростання цін рік за роком, з деякими винятками: @@ -89,7 +89,7 @@ sns.relplot(x="priceperlb", y="state", size="year", data=honey, height=15, aspec ``` Ви можете побачити, як розмір точок поступово збільшується. -![scatterplot 3](../../../../translated_images/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.uk.png) +![scatterplot 3](../../../../translated_images/uk/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.png) Чи це простий випадок попиту та пропозиції? Через такі фактори, як зміна клімату та колапс колоній, чи стає меду менше для покупки рік за роком, і тому ціна зростає? @@ -104,7 +104,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); ``` Відповідь: Так, з деякими винятками близько 2003 року: -![line chart 1](../../../../translated_images/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.uk.png) +![line chart 1](../../../../translated_images/uk/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.png) ✅ Оскільки Seaborn агрегує дані навколо однієї лінії, він відображає "кілька вимірювань для кожного значення x, показуючи середнє значення та 95% довірчий інтервал навколо середнього". [Джерело](https://seaborn.pydata.org/tutorial/relational.html). Цю поведінку, яка займає багато часу, можна вимкнути, додавши `ci=None`. @@ -114,7 +114,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); sns.relplot(x="year", y="totalprod", kind="line", data=honey); ``` -![line chart 2](../../../../translated_images/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.uk.png) +![line chart 2](../../../../translated_images/uk/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.png) Відповідь: Не зовсім. Якщо подивитися на загальне виробництво, здається, що воно фактично збільшилося в цьому конкретному році, хоча загалом кількість виробленого меду зменшується протягом цих років. @@ -139,7 +139,7 @@ sns.relplot( ``` У цій візуалізації ви можете порівняти урожайність на колонію та кількість колоній рік за роком, поруч із wrap, встановленим на 3 для колонок: -![facet grid](../../../../translated_images/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.uk.png) +![facet grid](../../../../translated_images/uk/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.png) Для цього набору даних нічого особливого не виділяється щодо кількості колоній та їх урожайності рік за роком і штат за штатом. Чи є інший спосіб знайти кореляцію між цими двома змінними? @@ -162,7 +162,7 @@ sns.despine(right=False) plt.ylabel('colony yield') ax.figure.legend(); ``` -![superimposed plots](../../../../translated_images/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.uk.png) +![superimposed plots](../../../../translated_images/uk/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.png) Хоча нічого особливого не виділяється близько 2003 року, це дозволяє нам завершити цей урок на трохи щасливішій ноті: хоча загалом кількість колоній зменшується, їх кількість стабілізується, навіть якщо їх урожайність на колонію знижується. diff --git a/translations/uk/3-Data-Visualization/R/09-visualization-quantities/README.md b/translations/uk/3-Data-Visualization/R/09-visualization-quantities/README.md index ff8dd50a..3a43dadb 100644 --- a/translations/uk/3-Data-Visualization/R/09-visualization-quantities/README.md +++ b/translations/uk/3-Data-Visualization/R/09-visualization-quantities/README.md @@ -66,7 +66,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` Тут ви встановлюєте пакет `ggplot2`, а потім імпортуєте його в робоче середовище за допомогою команди `library("ggplot2")`. Для побудови будь-якого графіка в ggplot використовується функція `ggplot()`, і ви вказуєте набір даних, змінні x та y як атрибути. У цьому випадку ми використовуємо функцію `geom_line()`, оскільки хочемо побудувати лінійний графік. -![MaxWingspan-lineplot](../../../../../translated_images/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.uk.png) +![MaxWingspan-lineplot](../../../../../translated_images/uk/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.png) Що ви помічаєте одразу? Здається, є принаймні один викид — це досить великий розмах крил! Розмах крил понад 2000 сантиметрів дорівнює більше ніж 20 метрам — чи не літають у Міннесоті птеродактилі? Давайте дослідимо. @@ -84,7 +84,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` Ми вказуємо кут у `theme` і задаємо підписи осей x та y у `xlab()` та `ylab()` відповідно. `ggtitle()` задає назву графіку. -![MaxWingspan-lineplot-improved](../../../../../translated_images/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.uk.png) +![MaxWingspan-lineplot-improved](../../../../../translated_images/uk/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.png) Навіть із поворотом підписів на 45 градусів їх занадто багато, щоб прочитати. Спробуємо іншу стратегію: підписати лише ті викиди та розмістити підписи всередині графіка. Ви можете використати точкову діаграму, щоб звільнити більше місця для підписів: @@ -100,7 +100,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + Що ви виявляєте? -![MaxWingspan-scatterplot](../../../../../translated_images/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.uk.png) +![MaxWingspan-scatterplot](../../../../../translated_images/uk/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.png) ## Фільтрація даних @@ -119,7 +119,7 @@ ggplot(data=birds_filtered, aes(x=Name, y=MaxWingspan,group=1)) + ``` Ми створили новий датафрейм `birds_filtered`, а потім побудували точкову діаграму. Відфільтрувавши викиди, ваші дані тепер більш узгоджені та зрозумілі. -![MaxWingspan-scatterplot-improved](../../../../../translated_images/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.uk.png) +![MaxWingspan-scatterplot-improved](../../../../../translated_images/uk/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.png) Тепер, коли ми маємо очищений набір даних принаймні щодо розмаху крил, давайте дізнаємося більше про цих птахів. @@ -161,7 +161,7 @@ birds_filtered %>% group_by(Category) %>% ``` У наступному фрагменті ми встановлюємо пакети [dplyr](https://www.rdocumentation.org/packages/dplyr/versions/0.7.8) та [lubridate](https://www.rdocumentation.org/packages/lubridate/versions/1.8.0), щоб допомогти маніпулювати та групувати дані для побудови складеної стовпчастої діаграми. Спочатку ви групуєте дані за `Category` птахів, а потім підсумовуєте стовпці `MinLength`, `MaxLength`, `MinBodyMass`, `MaxBodyMass`, `MinWingspan`, `MaxWingspan`. Потім будуєте стовпчасту діаграму за допомогою пакета `ggplot2`, вказуючи кольори для різних категорій і підписи. -![Stacked bar chart](../../../../../translated_images/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.uk.png) +![Stacked bar chart](../../../../../translated_images/uk/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.png) Ця стовпчаста діаграма, однак, є нечитаємою через надто багато негрупованих даних. Вам потрібно вибрати лише ті дані, які ви хочете відобразити, тому давайте подивимося на довжину птахів залежно від їхньої категорії. @@ -176,7 +176,7 @@ ggplot(birds_count,aes(Category,n))+geom_bar(stat="identity")+coord_flip() ``` Спочатку ви рахуєте унікальні значення в стовпці `Category`, а потім сортуєте їх у новий датафрейм `birds_count`. Ці відсортовані дані потім враховуються на тому ж рівні, щоб вони були відображені у відсортованому вигляді. Використовуючи `ggplot2`, ви потім будуєте дані у вигляді стовпчастої діаграми. `coord_flip()` відображає горизонтальні стовпці. -![category-length](../../../../../translated_images/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.uk.png) +![category-length](../../../../../translated_images/uk/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.png) Ця стовпчаста діаграма дає гарний огляд кількості птахів у кожній категорії. Одним поглядом ви бачите, що найбільша кількість птахів у цьому регіоні належить до категорії Качки/Гуси/Водоплавні. Міннесота — це "країна 10 000 озер", тому це не дивно! @@ -199,7 +199,7 @@ ggplot(birds_grouped,aes(Category,MaxLength))+geom_bar(stat="identity")+coord_fl ``` Ми групуємо дані `birds_filtered` за `Category`, а потім будуємо стовпчасту діаграму. -![comparing data](../../../../../translated_images/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.uk.png) +![comparing data](../../../../../translated_images/uk/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.png) Тут нічого дивного: колібрі мають найменший MaxLength у порівнянні з пеліканами чи гусьми. Добре, коли дані мають логічний сенс! @@ -211,7 +211,7 @@ ggplot(data=birds_grouped, aes(x=Category)) + geom_bar(aes(y=MinLength), stat="identity", position="identity", fill='orange')+ coord_flip() ``` -![super-imposed values](../../../../../translated_images/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.uk.png) +![super-imposed values](../../../../../translated_images/uk/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.png) ## 🚀 Виклик diff --git a/translations/uk/3-Data-Visualization/R/10-visualization-distributions/README.md b/translations/uk/3-Data-Visualization/R/10-visualization-distributions/README.md index 318531cf..5b148bf5 100644 --- a/translations/uk/3-Data-Visualization/R/10-visualization-distributions/README.md +++ b/translations/uk/3-Data-Visualization/R/10-visualization-distributions/README.md @@ -45,7 +45,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + geom_point() + ggtitle("Max Length per order") + coord_flip() ``` -![максимальна довжина за рядом](../../../../../translated_images/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.uk.png) +![максимальна довжина за рядом](../../../../../translated_images/uk/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.png) Це дає загальний огляд розподілу довжини тіла за рядом птахів, але це не найкращий спосіб відображення справжніх розподілів. Для цього зазвичай створюють гістограму. ## Робота з гістограмами @@ -56,7 +56,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=10)+ylab('Frequency') ``` -![розподіл по всьому набору даних](../../../../../translated_images/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.uk.png) +![розподіл по всьому набору даних](../../../../../translated_images/uk/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.png) Як видно, більшість із 400+ птахів у цьому наборі даних мають масу тіла менше 2000. Отримайте більше інформації про дані, змінивши параметр `bins` на більше значення, наприклад, 30: @@ -64,7 +64,7 @@ ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![розподіл-30bins](../../../../../translated_images/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.uk.png) +![розподіл-30bins](../../../../../translated_images/uk/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.png) Цей графік показує розподіл у трохи більш деталізованому вигляді. Менш зміщений графік можна створити, вибравши дані лише в заданому діапазоні: @@ -76,7 +76,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![відфільтрована гістограма](../../../../../translated_images/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.uk.png) +![відфільтрована гістограма](../../../../../translated_images/uk/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.png) ✅ Спробуйте інші фільтри та точки даних. Щоб побачити повний розподіл даних, видаліть фільтр `['MaxBodyMass']`, щоб показати розподіли з мітками. @@ -90,7 +90,7 @@ ggplot(data=birds_filtered_1, aes(x=MaxBodyMass, y=MaxLength) ) + ``` Здається, є очікуваний кореляційний зв’язок між цими двома елементами вздовж передбачуваної осі, з одним особливо сильним точковим збігом: -![2d графік](../../../../../translated_images/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.uk.png) +![2d графік](../../../../../translated_images/uk/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.png) Гістограми добре працюють за замовчуванням для числових даних. А що, якщо потрібно побачити розподіли за текстовими даними? ## Дослідження набору даних для розподілів за текстовими даними @@ -121,7 +121,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + scale_fill_manual(name="Conservation Status",values=c("red","green","blue","pink"),labels=c("Endangered","Near Threathened","Vulnerable","Least Concern")) ``` -![розмах крил і статус збереження](../../../../../translated_images/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.uk.png) +![розмах крил і статус збереження](../../../../../translated_images/uk/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.png) Здається, немає чіткої кореляції між мінімальним розмахом крил і статусом збереження. Перевірте інші елементи набору даних, використовуючи цей метод. Ви можете спробувати різні фільтри. Чи знаходите ви якусь кореляцію? @@ -135,7 +135,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + geom_density() ``` -![графік щільності](../../../../../translated_images/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.uk.png) +![графік щільності](../../../../../translated_images/uk/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.png) Ви можете побачити, як графік повторює попередній для даних про мінімальний розмах крил; він просто трохи плавніший. Якщо ви хотіли б повернутися до того нерівного графіка MaxBodyMass у другому побудованому вами графіку, ви могли б дуже добре згладити його, відтворивши цей метод: @@ -143,7 +143,7 @@ ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density() ``` -![щільність маси тіла](../../../../../translated_images/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.uk.png) +![щільність маси тіла](../../../../../translated_images/uk/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.png) Якщо ви хочете отримати плавну, але не надто плавну лінію, змініть параметр `adjust`: @@ -151,7 +151,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density(adjust = 1/5) ``` -![менш плавна маса тіла](../../../../../translated_images/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.uk.png) +![менш плавна маса тіла](../../../../../translated_images/uk/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.png) ✅ Ознайомтеся з параметрами, доступними для цього типу графіка, і експериментуйте! @@ -161,7 +161,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + ggplot(data=birds_filtered_1,aes(x = MaxBodyMass, fill = Order)) + geom_density(alpha=0.5) ``` -![маса тіла за рядом](../../../../../translated_images/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.uk.png) +![маса тіла за рядом](../../../../../translated_images/uk/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.png) ## 🚀 Виклик diff --git a/translations/uk/3-Data-Visualization/R/11-visualization-proportions/README.md b/translations/uk/3-Data-Visualization/R/11-visualization-proportions/README.md index f54d86f7..f233d8fc 100644 --- a/translations/uk/3-Data-Visualization/R/11-visualization-proportions/README.md +++ b/translations/uk/3-Data-Visualization/R/11-visualization-proportions/README.md @@ -97,7 +97,7 @@ pie(grouped$count,grouped$class, main="Edible?") ``` Вуаля, кругова діаграма, яка показує пропорції цих даних відповідно до двох класів грибів. Дуже важливо правильно впорядкувати мітки, особливо тут, тому обов'язково перевірте порядок, у якому створюється масив міток! -![кругова діаграма](../../../../../translated_images/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.uk.png) +![кругова діаграма](../../../../../translated_images/uk/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.png) ## Кільцева діаграма! @@ -132,7 +132,7 @@ library(webr) PieDonut(habitat, aes(habitat, count=count)) ``` -![кільцева діаграма](../../../../../translated_images/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.uk.png) +![кільцева діаграма](../../../../../translated_images/uk/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.png) Цей код використовує дві бібліотеки - ggplot2 і webr. Використовуючи функцію PieDonut з бібліотеки webr, ми можемо легко створити кільцеву діаграму! @@ -169,7 +169,7 @@ waffle((cap_color$count/10), rows = 7, title = "Waffle Chart")+scale_fill_manual Використовуючи вафельну діаграму, ви можете чітко побачити пропорції кольорів капелюшків у цьому наборі даних про гриби. Цікаво, що є багато грибів із зеленими капелюшками! -![вафельна діаграма](../../../../../translated_images/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.uk.png) +![вафельна діаграма](../../../../../translated_images/uk/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.png) У цьому уроці ви дізналися три способи візуалізації пропорцій. Спочатку потрібно згрупувати свої дані в категорії, а потім вирішити, який спосіб відображення даних - кругова, кільцева чи вафельна діаграма - найкраще підходить. Усі вони "смачні" та надають користувачеві миттєвий знімок набору даних. diff --git a/translations/uk/3-Data-Visualization/R/12-visualization-relationships/README.md b/translations/uk/3-Data-Visualization/R/12-visualization-relationships/README.md index fe3ab450..2a915eea 100644 --- a/translations/uk/3-Data-Visualization/R/12-visualization-relationships/README.md +++ b/translations/uk/3-Data-Visualization/R/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ library(ggplot2) ggplot(honey, aes(x = priceperlb, y = state)) + geom_point(colour = "blue") ``` -![scatterplot 1](../../../../../translated_images/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.uk.png) +![scatterplot 1](../../../../../translated_images/uk/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.png) Тепер покажіть ті ж дані з кольоровою схемою меду, щоб продемонструвати, як ціна змінюється з роками. Ви можете зробити це, додавши параметр 'scale_color_gradientn', щоб показати зміни рік за роком: @@ -61,7 +61,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ggplot(honey, aes(x = priceperlb, y = state, color=year)) + geom_point()+scale_color_gradientn(colours = colorspace::heat_hcl(7)) ``` -![scatterplot 2](../../../../../translated_images/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.uk.png) +![scatterplot 2](../../../../../translated_images/uk/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.png) З цією зміною кольорової схеми ви можете побачити очевидну тенденцію до зростання ціни за фунт меду з роками. Дійсно, якщо ви перевірите вибірку даних (наприклад, штат Аризона), ви побачите закономірність підвищення цін рік за роком, з деякими винятками: @@ -92,7 +92,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ``` Ви можете побачити, як розмір точок поступово збільшується. -![scatterplot 3](../../../../../translated_images/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.uk.png) +![scatterplot 3](../../../../../translated_images/uk/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.png) Чи це простий випадок попиту та пропозиції? Через такі фактори, як зміна клімату та колапс колоній, чи стає мед менш доступним для покупки рік за роком, і тому ціна зростає? @@ -107,7 +107,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab ``` Відповідь: Так, з деякими винятками близько 2003 року: -![line chart 1](../../../../../translated_images/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.uk.png) +![line chart 1](../../../../../translated_images/uk/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.png) Запитання: Ну, а чи можемо ми також побачити сплеск у постачанні меду в 2003 році? Що, якщо подивитися на загальне виробництво рік за роком? @@ -115,7 +115,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab qplot(honey$year,honey$totalprod, geom='smooth', span =0.5, xlab = "year",ylab = "totalprod") ``` -![line chart 2](../../../../../translated_images/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.uk.png) +![line chart 2](../../../../../translated_images/uk/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.png) Відповідь: Не зовсім. Якщо подивитися на загальне виробництво, здається, що воно фактично збільшилося в цьому конкретному році, хоча загалом кількість виробленого меду зменшується протягом цих років. @@ -135,7 +135,7 @@ ggplot(honey, aes(x=yieldpercol, y = numcol,group = 1)) + ``` У цій візуалізації ви можете порівняти урожайність на колонію та кількість колоній рік за роком, поруч із wrap, встановленим на 3 для колонок: -![facet grid](../../../../../translated_images/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.uk.png) +![facet grid](../../../../../translated_images/uk/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.png) Для цього набору даних нічого особливого не виділяється щодо кількості колоній та їх урожайності рік за роком і штат за штатом. Чи є інший спосіб знайти кореляцію між цими двома змінними? @@ -152,7 +152,7 @@ plot(honey$year, honey$yieldpercol, pch = 17, col = 3, axis(side = 4, at = pretty(range(y2))) mtext("colony yield", side = 4, line = 3) ``` -![superimposed plots](../../../../../translated_images/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.uk.png) +![superimposed plots](../../../../../translated_images/uk/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.png) Хоча нічого особливого не виділяється близько 2003 року, це дозволяє нам завершити урок на трохи позитивнішій ноті: хоча загалом кількість колоній зменшується, їх кількість стабілізується, навіть якщо їх урожайність на колонію знижується. diff --git a/translations/uk/3-Data-Visualization/R/13-meaningful-vizualizations/README.md b/translations/uk/3-Data-Visualization/R/13-meaningful-vizualizations/README.md index 39cf0d5f..9236b4ef 100644 --- a/translations/uk/3-Data-Visualization/R/13-meaningful-vizualizations/README.md +++ b/translations/uk/3-Data-Visualization/R/13-meaningful-vizualizations/README.md @@ -47,25 +47,25 @@ CO_OP_TRANSLATOR_METADATA: Навіть якщо дата-сайєнтист ретельно обирає правильну діаграму для правильних даних, існує багато способів представити дані так, щоб довести певну точку зору, часто за рахунок спотворення самих даних. Є безліч прикладів оманливих діаграм та інфографік! -[![Як діаграми обманюють, Альберто Кайро](../../../../../translated_images/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.uk.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "Як діаграми обманюють") +[![Як діаграми обманюють, Альберто Кайро](../../../../../translated_images/uk/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "Як діаграми обманюють") > 🎥 Натисніть на зображення вище, щоб переглянути конференційну доповідь про оманливі діаграми Ця діаграма перевертає вісь X, щоб показати протилежне до правди, базуючись на датах: -![погана діаграма 1](../../../../../translated_images/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.uk.png) +![погана діаграма 1](../../../../../translated_images/uk/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.png) [Ця діаграма](https://media.firstcoastnews.com/assets/WTLV/images/170ae16f-4643-438f-b689-50d66ca6a8d8/170ae16f-4643-438f-b689-50d66ca6a8d8_1140x641.jpg) ще більш оманлива, оскільки погляд спрямовується вправо, щоб зробити висновок, що з часом випадки COVID зменшилися в різних округах. Насправді, якщо уважно подивитися на дати, ви побачите, що їх було переставлено, щоб створити цей оманливий спад. -![погана діаграма 2](../../../../../translated_images/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.uk.jpg) +![погана діаграма 2](../../../../../translated_images/uk/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.jpg) Цей сумнозвісний приклад використовує колір І перевернуту вісь Y, щоб обманути: замість висновку, що кількість смертей від зброї зросла після прийняття законодавства, сприятливого для зброї, погляд обманюється, думаючи, що сталося протилежне: -![погана діаграма 3](../../../../../translated_images/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.uk.jpg) +![погана діаграма 3](../../../../../translated_images/uk/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.jpg) Ця дивна діаграма показує, як пропорції можуть бути маніпульовані до комічного ефекту: -![погана діаграма 4](../../../../../translated_images/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.uk.jpg) +![погана діаграма 4](../../../../../translated_images/uk/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.jpg) Порівняння непорівнюваного — ще один хитрий трюк. Існує [чудовий вебсайт](https://tylervigen.com/spurious-correlations), присвячений "хибним кореляціям", який демонструє "факти", що пов'язують, наприклад, рівень розлучень у штаті Мен із споживанням маргарину. Група на Reddit також збирає [приклади поганого використання даних](https://www.reddit.com/r/dataisugly/top/?t=all). @@ -100,13 +100,13 @@ CO_OP_TRANSLATOR_METADATA: Якщо ваші дані текстові та довгі на осі X, ви можете нахилити текст для кращої читабельності. [plot3D](https://cran.r-project.org/web/packages/plot3D/index.html) пропонує 3D-графіки, якщо ваші дані це підтримують. Використовуючи його, можна створювати складні візуалізації. -![3D графіки](../../../../../translated_images/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.uk.png) +![3D графіки](../../../../../translated_images/uk/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.png) ## Анімація та 3D-відображення діаграм Деякі з найкращих візуалізацій даних сьогодні є анімованими. Ширлі Ву створила дивовижні приклади за допомогою D3, такі як '[квітки фільмів](http://bl.ocks.org/sxywu/raw/d612c6c653fb8b4d7ff3d422be164a5d/)', де кожна квітка є візуалізацією фільму. Інший приклад для Guardian — 'Bussed Out', інтерактивний досвід, що поєднує візуалізації з Greensock і D3, а також формат статті з прокруткою, щоб показати, як Нью-Йорк вирішує проблему бездомних, відправляючи їх за межі міста. -![перевезення](../../../../../translated_images/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.uk.png) +![перевезення](../../../../../translated_images/uk/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.png) > "Bussed Out: Як Америка переміщує своїх бездомних" від [Guardian](https://www.theguardian.com/us-news/ng-interactive/2017/dec/20/bussed-out-america-moves-homeless-people-country-study). Візуалізації Наді Бремер і Ширлі Ву @@ -116,7 +116,7 @@ CO_OP_TRANSLATOR_METADATA: Ви завершите веб-додаток, який відображатиме анімований вигляд цієї соціальної мережі. Він використовує бібліотеку, створену для [візуалізації мережі](https://github.com/emiliorizzo/vue-d3-network) за допомогою Vue.js і D3. Коли додаток працює, ви можете переміщати вузли на екрані, щоб змінювати розташування даних. -![зв'язки](../../../../../translated_images/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.uk.png) +![зв'язки](../../../../../translated_images/uk/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.png) ## Проєкт: Створіть діаграму для відображення мережі за допомогою D3.js diff --git a/translations/uk/3-Data-Visualization/README.md b/translations/uk/3-Data-Visualization/README.md index f93e5f51..b630bf02 100644 --- a/translations/uk/3-Data-Visualization/README.md +++ b/translations/uk/3-Data-Visualization/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Візуалізації -![бджола на квітці лаванди](../../../translated_images/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.uk.jpg) +![бджола на квітці лаванди](../../../translated_images/uk/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.jpg) > Фото Дженни Лі на Unsplash Візуалізація даних — одна з найважливіших задач для дата-сайєнтиста. Зображення варті тисячі слів, а візуалізація може допомогти вам виявити різноманітні цікаві аспекти ваших даних, такі як піки, аномалії, групування, тенденції та багато іншого, що допоможе вам зрозуміти історію, яку ваші дані намагаються розповісти. diff --git a/translations/uk/4-Data-Science-Lifecycle/14-Introduction/README.md b/translations/uk/4-Data-Science-Lifecycle/14-Introduction/README.md index 6b236683..15dc58a0 100644 --- a/translations/uk/4-Data-Science-Lifecycle/14-Introduction/README.md +++ b/translations/uk/4-Data-Science-Lifecycle/14-Introduction/README.md @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: Цей урок зосереджується на трьох частинах життєвого циклу: зборі, обробці та підтримці. -![Діаграма життєвого циклу науки про дані](../../../../translated_images/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.uk.jpg) +![Діаграма життєвого циклу науки про дані](../../../../translated_images/uk/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.jpg) > Фото від [Berkeley School of Information](https://ischoolonline.berkeley.edu/data-science/what-is-data-science/) ## Збір @@ -101,7 +101,7 @@ CO_OP_TRANSLATOR_METADATA: |Процес команди науки про дані (TDSP)|Стандартний процес для добування даних у різних галузях (CRISP-DM)| |--|--| -|![Життєвий цикл TDSP](../../../../translated_images/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.uk.png)|![Зображення CRISP-DM](../../../../translated_images/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.uk.png)| +|![Життєвий цикл TDSP](../../../../translated_images/uk/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.png)|![Зображення CRISP-DM](../../../../translated_images/uk/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.png)| | Зображення від [Microsoft](https://docs.microsoft.comazure/architecture/data-science-process/lifecycle) | Зображення від [Data Science Process Alliance](https://www.datascience-pm.com/crisp-dm-2/) | ## [Післялекційний тест](https://ff-quizzes.netlify.app/en/ds/quiz/27) diff --git a/translations/uk/4-Data-Science-Lifecycle/README.md b/translations/uk/4-Data-Science-Lifecycle/README.md index 0f1f745f..15590c14 100644 --- a/translations/uk/4-Data-Science-Lifecycle/README.md +++ b/translations/uk/4-Data-Science-Lifecycle/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Життєвий цикл науки про дані -![communication](../../../translated_images/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.uk.jpg) +![communication](../../../translated_images/uk/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.jpg) > Фото від Headway на Unsplash У цих уроках ви дослідите деякі аспекти життєвого циклу науки про дані, включаючи аналіз і комунікацію навколо даних. diff --git a/translations/uk/5-Data-Science-In-Cloud/README.md b/translations/uk/5-Data-Science-In-Cloud/README.md index 7c0aadb0..5d51d757 100644 --- a/translations/uk/5-Data-Science-In-Cloud/README.md +++ b/translations/uk/5-Data-Science-In-Cloud/README.md @@ -9,13 +9,13 @@ CO_OP_TRANSLATOR_METADATA: --> # Наука про дані в хмарі -![cloud-picture](../../../translated_images/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.uk.jpg) +![cloud-picture](../../../translated_images/uk/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.jpg) > Фото [Jelleke Vanooteghem](https://unsplash.com/@ilumire) з [Unsplash](https://unsplash.com/s/photos/cloud?orientation=landscape) Коли справа доходить до роботи з великими даними у сфері науки про дані, хмара може стати справжнім проривом. У наступних трьох уроках ми розглянемо, що таке хмара і чому вона може бути дуже корисною. Ми також дослідимо набір даних про серцеву недостатність і створимо модель, яка допоможе оцінити ймовірність виникнення серцевої недостатності у людини. Ми використаємо потужність хмари для навчання, розгортання та використання моделі двома різними способами. Один спосіб — використовуючи лише інтерфейс користувача в стилі Low code/No code, інший — за допомогою набору інструментів для розробників Azure Machine Learning (Azure ML SDK). -![project-schema](../../../translated_images/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.uk.png) +![project-schema](../../../translated_images/uk/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.png) ### Теми diff --git a/translations/uk/6-Data-Science-In-Wild/20-Real-World-Examples/README.md b/translations/uk/6-Data-Science-In-Wild/20-Real-World-Examples/README.md index 731efb5e..07038c78 100644 --- a/translations/uk/6-Data-Science-In-Wild/20-Real-World-Examples/README.md +++ b/translations/uk/6-Data-Science-In-Wild/20-Real-World-Examples/README.md @@ -41,7 +41,7 @@ CO_OP_TRANSLATOR_METADATA: * [Наука про дані у сфері охорони здоров’я](https://data-flair.training/blogs/data-science-in-healthcare/) - підкреслює такі додатки, як медична візуалізація (наприклад, МРТ, рентген, КТ-сканування), геноміка (секвенування ДНК), розробка ліків (оцінка ризиків, прогноз успіху), прогнозна аналітика (догляд за пацієнтами та логістика постачання), відстеження та профілактика захворювань тощо. -![Застосування науки про дані у реальному світі](../../../../translated_images/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.uk.png) Джерело зображення: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) +![Застосування науки про дані у реальному світі](../../../../translated_images/uk/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.png) Джерело зображення: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) На малюнку показані інші галузі та приклади застосування технік науки про дані. Хочете дослідити інші застосування? Ознайомтеся з розділом [Огляд і самостійне навчання](../../../../6-Data-Science-In-Wild/20-Real-World-Examples) нижче. diff --git a/translations/uk/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md b/translations/uk/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md index 1cf5271d..320b185d 100644 --- a/translations/uk/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md +++ b/translations/uk/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md @@ -22,7 +22,7 @@ CO_OP_TRANSLATOR_METADATA: 2. Дослідити [Каталог](https://planetarycomputer.microsoft.com/catalog) наборів даних — дізнатися про їхнє призначення. 3. Використати Explorer — вибрати цікавий набір даних, обрати відповідний запит і варіант візуалізації. -![The Planetary Computer Explorer](../../../../translated_images/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.uk.png) +![The Planetary Computer Explorer](../../../../translated_images/uk/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.png) `Ваше завдання:` Тепер вивчіть візуалізацію, яка з’явиться у браузері, і дайте відповіді на наступні запитання: diff --git a/translations/uk/CONTRIBUTING.md b/translations/uk/CONTRIBUTING.md index e4e92a22..18078eca 100644 --- a/translations/uk/CONTRIBUTING.md +++ b/translations/uk/CONTRIBUTING.md @@ -316,7 +316,7 @@ def calculate_mean(data): ``` ```` -- Додавайте alt-текст до зображень: `![Alt text](../../translated_images/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.uk.png)` +- Додавайте alt-текст до зображень: `![Alt text](../../translated_images/uk/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.png)` - Зберігайте розумну довжину рядків (близько 80-100 символів) ### Python diff --git a/translations/uk/README.md b/translations/uk/README.md index d8e18dd5..02dbf7ad 100644 --- a/translations/uk/README.md +++ b/translations/uk/README.md @@ -33,7 +33,7 @@ Azure Cloud Advocates at Microsoft are pleased to offer a 10-week, 20-lesson cur **🙏 Особлива подяка 🙏 нашим [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/) авторам, рецензентам та контриб’юторам вмісту,** зокрема Aaryan Arora, [Aditya Garg](https://github.com/AdityaGarg00), [Alondra Sanchez](https://www.linkedin.com/in/alondra-sanchez-molina/), [Ankita Singh](https://www.linkedin.com/in/ankitasingh007), [Anupam Mishra](https://www.linkedin.com/in/anupam--mishra/), [Arpita Das](https://www.linkedin.com/in/arpitadas01/), ChhailBihari Dubey, [Dibri Nsofor](https://www.linkedin.com/in/dibrinsofor), [Dishita Bhasin](https://www.linkedin.com/in/dishita-bhasin-7065281bb), [Majd Safi](https://www.linkedin.com/in/majd-s/), [Max Blum](https://www.linkedin.com/in/max-blum-6036a1186/), [Miguel Correa](https://www.linkedin.com/in/miguelmque/), [Mohamma Iftekher (Iftu) Ebne Jalal](https://twitter.com/iftu119), [Nawrin Tabassum](https://www.linkedin.com/in/nawrin-tabassum), [Raymond Wangsa Putra](https://www.linkedin.com/in/raymond-wp/), [Rohit Yadav](https://www.linkedin.com/in/rty2423), Samridhi Sharma, [Sanya Sinha](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200), [Sheena Narula](https://www.linkedin.com/in/sheena-narua-n/), [Tauqeer Ahmad](https://www.linkedin.com/in/tauqeerahmad5201/), Yogendrasingh Pawar , [Vidushi Gupta](https://www.linkedin.com/in/vidushi-gupta07/), [Jasleen Sondhi](https://www.linkedin.com/in/jasleen-sondhi/) -|![Скетчноут від @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.uk.png)| +|![Скетчноут від @sketchthedocs https://sketchthedocs.dev](../../translated_images/uk/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.png)| |:---:| | Data Science For Beginners - _Скетчноут від [@nitya](https://twitter.com/nitya)_ | @@ -52,7 +52,7 @@ Azure Cloud Advocates at Microsoft are pleased to offer a 10-week, 20-lesson cur У нас триває серія «Навчайся з ШІ» у Discord — дізнайтеся більше та приєднуйтесь до нас на [Серія «Навчайся з ШІ»](https://aka.ms/learnwithai/discord) з 18 по 30 вересня 2025 року. Ви отримаєте поради та хитрощі щодо використання GitHub Copilot для Data Science. -![Серія «Навчайся з ШІ»](../../translated_images/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.uk.jpg) +![Серія «Навчайся з ШІ»](../../translated_images/uk/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.jpg) # Ви студент? @@ -132,7 +132,7 @@ Azure Cloud Advocates at Microsoft are pleased to offer a 10-week, 20-lesson cur ## Уроки -|![ Скетчноут від @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.uk.png)| +|![ Скетчноут від @sketchthedocs https://sketchthedocs.dev](../../translated_images/uk/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png)| |:---:| | Data Science For Beginners: Roadmap - _Скетчноут від [@nitya](https://twitter.com/nitya)_ | diff --git a/translations/uk/sketchnotes/README.md b/translations/uk/sketchnotes/README.md index a8013151..1c6d79de 100644 --- a/translations/uk/sketchnotes/README.md +++ b/translations/uk/sketchnotes/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: Нітія Нарасімхан, художниця -![скетчноут дорожньої карти](../../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.uk.png) +![скетчноут дорожньої карти](../../../translated_images/uk/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png) --- diff --git a/translations/ur/1-Introduction/01-defining-data-science/README.md b/translations/ur/1-Introduction/01-defining-data-science/README.md index 1e544d70..6f32b535 100644 --- a/translations/ur/1-Introduction/01-defining-data-science/README.md +++ b/translations/ur/1-Introduction/01-defining-data-science/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: --- -[![ڈیٹا سائنس کی تعریف ویڈیو](../../../../translated_images/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.ur.png)](https://youtu.be/beZ7Mb_oz9I) +[![ڈیٹا سائنس کی تعریف ویڈیو](../../../../translated_images/ur/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.png)](https://youtu.be/beZ7Mb_oz9I) ## [لیکچر سے پہلے کا کوئز](https://ff-quizzes.netlify.app/en/ds/quiz/0) @@ -153,7 +153,7 @@ CO_OP_TRANSLATOR_METADATA: اس چیلنج میں، ہم ڈیٹا سائنس کے میدان سے متعلق تصورات کو متن کے ذریعے تلاش کرنے کی کوشش کریں گے۔ ہم ڈیٹا سائنس پر ایک ویکیپیڈیا مضمون لیں گے، متن کو ڈاؤنلوڈ اور پروسیس کریں گے، اور پھر ایک ورڈ کلاؤڈ بنائیں گے جیسا کہ یہ: -![ڈیٹا سائنس کے لیے ورڈ کلاؤڈ](../../../../translated_images/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.ur.png) +![ڈیٹا سائنس کے لیے ورڈ کلاؤڈ](../../../../translated_images/ur/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.png) کوڈ کو پڑھنے کے لیے [`notebook.ipynb`](../../../../1-Introduction/01-defining-data-science/notebook.ipynb ':ignore') پر جائیں۔ آپ کوڈ کو چلا بھی سکتے ہیں، اور دیکھ سکتے ہیں کہ یہ حقیقی وقت میں تمام ڈیٹا تبدیلیاں کیسے انجام دیتا ہے۔ diff --git a/translations/ur/1-Introduction/04-stats-and-probability/README.md b/translations/ur/1-Introduction/04-stats-and-probability/README.md index 21d324c9..8f1e3911 100644 --- a/translations/ur/1-Introduction/04-stats-and-probability/README.md +++ b/translations/ur/1-Introduction/04-stats-and-probability/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: شماریات اور احتمال کا نظریہ ریاضی کے دو ایسے شعبے ہیں جو ڈیٹا سائنس کے لیے انتہائی اہم ہیں۔ ڈیٹا کے ساتھ کام کرنا ممکن ہے چاہے آپ کو ریاضی کی گہری سمجھ نہ ہو، لیکن کچھ بنیادی تصورات جاننا ہمیشہ بہتر ہوتا ہے۔ یہاں ہم ایک مختصر تعارف پیش کریں گے جو آپ کو آغاز کرنے میں مدد دے گا۔ -[![تعارفی ویڈیو](../../../../translated_images/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.ur.png)](https://youtu.be/Z5Zy85g4Yjw) +[![تعارفی ویڈیو](../../../../translated_images/ur/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.png)](https://youtu.be/Z5Zy85g4Yjw) ## [لیکچر سے پہلے کا کوئز](https://ff-quizzes.netlify.app/en/ds/quiz/6) @@ -39,7 +39,7 @@ CO_OP_TRANSLATOR_METADATA: ہم صرف اس بات کے احتمال کے بارے میں بات کر سکتے ہیں کہ کوئی متغیر کسی دیے گئے وقفے میں آتا ہے، مثلاً P(t1≤X2)۔ اس صورت میں، احتمال کی تقسیم کو **احتمال کثافت فنکشن** p(x) کے ذریعے بیان کیا جاتا ہے، اس طرح کہ -![P(t_1\le X تعلق اور کوویریئنس کی مزید مثالیں [ساتھ دی گئی نوٹ بک](notebook.ipynb) میں دیکھی جا سکتی ہیں۔ diff --git a/translations/ur/1-Introduction/README.md b/translations/ur/1-Introduction/README.md index 284838b8..7c7cae5e 100644 --- a/translations/ur/1-Introduction/README.md +++ b/translations/ur/1-Introduction/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # ڈیٹا سائنس کا تعارف -![عملی ڈیٹا](../../../translated_images/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.ur.jpg) +![عملی ڈیٹا](../../../translated_images/ur/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.jpg) > تصویر از اسٹیفن ڈاسن، Unsplash پر ان اسباق میں، آپ جانیں گے کہ ڈیٹا سائنس کو کیسے بیان کیا جاتا ہے اور ان اخلاقی پہلوؤں کے بارے میں سیکھیں گے جنہیں ایک ڈیٹا سائنسدان کو مدنظر رکھنا چاہیے۔ آپ یہ بھی سیکھیں گے کہ ڈیٹا کو کیسے بیان کیا جاتا ہے اور شماریات اور احتمال کے بارے میں کچھ جانیں گے، جو کہ ڈیٹا سائنس کے بنیادی تعلیمی شعبے ہیں۔ diff --git a/translations/ur/2-Working-With-Data/07-python/README.md b/translations/ur/2-Working-With-Data/07-python/README.md index db2b902b..2bd3525e 100644 --- a/translations/ur/2-Working-With-Data/07-python/README.md +++ b/translations/ur/2-Working-With-Data/07-python/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: | :-------------------------------------------------------------------------------------------------------: | | پائتھون کے ساتھ کام کرنا - _[@nitya](https://twitter.com/nitya) کی طرف سے اسکیچ نوٹ_ | -[![تعارفی ویڈیو](../../../../translated_images/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.ur.png)](https://youtu.be/dZjWOGbsN4Y) +[![تعارفی ویڈیو](../../../../translated_images/ur/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.png)](https://youtu.be/dZjWOGbsN4Y) اگرچہ ڈیٹا بیسز ڈیٹا کو محفوظ کرنے اور انہیں کوئری لینگویجز کے ذریعے تلاش کرنے کے لیے بہت مؤثر طریقے فراہم کرتے ہیں، ڈیٹا پروسیسنگ کا سب سے لچکدار طریقہ اپنا پروگرام لکھ کر ڈیٹا کو تبدیل کرنا ہے۔ اکثر اوقات، ڈیٹا بیس کوئری کرنا زیادہ مؤثر ہوگا۔ لیکن کچھ معاملات میں جب زیادہ پیچیدہ ڈیٹا پروسیسنگ کی ضرورت ہو، تو یہ کام آسانی سے SQL کے ذریعے نہیں کیا جا سکتا۔ ڈیٹا پروسیسنگ کسی بھی پروگرامنگ زبان میں کی جا سکتی ہے، لیکن کچھ زبانیں ڈیٹا کے ساتھ کام کرنے کے لحاظ سے زیادہ اعلیٰ سطح کی ہوتی ہیں۔ ڈیٹا سائنسدان عام طور پر درج ذیل زبانوں میں سے کسی ایک کو ترجیح دیتے ہیں: @@ -73,7 +73,7 @@ print(f"Length of index is {len(idx)}") items_sold = pd.Series(np.random.randint(25,50,size=len(idx)),index=idx) items_sold.plot() ``` -![ٹائم سیریز پلاٹ](../../../../translated_images/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.ur.png) +![ٹائم سیریز پلاٹ](../../../../translated_images/ur/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.png) اب فرض کریں کہ ہر ہفتے ہم دوستوں کے لیے ایک پارٹی کا اہتمام کرتے ہیں، اور پارٹی کے لیے آئس کریم کے اضافی 10 پیک لیتے ہیں۔ ہم ایک اور سیریز بنا سکتے ہیں، جو ہفتے کے انڈیکس کے ذریعے ظاہر ہو: ```python @@ -84,7 +84,7 @@ additional_items = pd.Series(10,index=pd.date_range(start_date,end_date,freq="W" total_items = items_sold.add(additional_items,fill_value=0) total_items.plot() ``` -![ٹائم سیریز پلاٹ](../../../../translated_images/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.ur.png) +![ٹائم سیریز پلاٹ](../../../../translated_images/ur/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.png) > **نوٹ** کہ ہم سادہ سینٹیکس `total_items+additional_items` استعمال نہیں کر رہے ہیں۔ اگر ہم ایسا کرتے، تو ہمیں نتیجہ میں بہت سے `NaN` (*Not a Number*) اقدار ملتی۔ اس کی وجہ یہ ہے کہ `additional_items` سیریز میں انڈیکس پوائنٹس کے لیے کچھ اقدار غائب ہیں، اور کسی بھی چیز میں `NaN` شامل کرنے سے نتیجہ `NaN` ہوتا ہے۔ اس لیے ہمیں جمع کرتے وقت `fill_value` پیرامیٹر کی وضاحت کرنے کی ضرورت ہوتی ہے۔ @@ -93,7 +93,7 @@ total_items.plot() monthly = total_items.resample("1M").mean() ax = monthly.plot(kind='bar') ``` -![ماہانہ ٹائم سیریز اوسط](../../../../translated_images/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.ur.png) +![ماہانہ ٹائم سیریز اوسط](../../../../translated_images/ur/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.png) ### ڈیٹا فریم @@ -219,7 +219,7 @@ df = pd.read_csv('file.csv') چونکہ ہم یہ دکھانا چاہتے ہیں کہ ڈیٹا کے ساتھ کیسے کام کیا جائے، ہم آپ کو دعوت دیتے ہیں کہ [`notebook-covidspread.ipynb`](notebook-covidspread.ipynb) کھولیں اور اسے شروع سے آخر تک پڑھیں۔ آپ سیلز کو چلا سکتے ہیں اور آخر میں دیے گئے کچھ چیلنجز کو حل کر سکتے ہیں۔ -![COVID Spread](../../../../translated_images/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.ur.png) +![COVID Spread](../../../../translated_images/ur/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.png) > اگر آپ کو Jupyter Notebook میں کوڈ چلانے کا طریقہ معلوم نہیں ہے، تو [اس مضمون](https://soshnikov.com/education/how-to-execute-notebooks-from-github/) کو دیکھیں۔ @@ -241,7 +241,7 @@ df = pd.read_csv('file.csv') [`notebook-papers.ipynb`](notebook-papers.ipynb) کھولیں اور اسے شروع سے آخر تک پڑھیں۔ آپ سیلز کو چلا سکتے ہیں اور آخر میں دیے گئے کچھ چیلنجز کو حل کر سکتے ہیں۔ -![Covid Medical Treatment](../../../../translated_images/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.ur.png) +![Covid Medical Treatment](../../../../translated_images/ur/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.png) ## تصویری ڈیٹا کی پروسیسنگ diff --git a/translations/ur/2-Working-With-Data/README.md b/translations/ur/2-Working-With-Data/README.md index 54b4b7c1..aff15a9b 100644 --- a/translations/ur/2-Working-With-Data/README.md +++ b/translations/ur/2-Working-With-Data/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # ڈیٹا کے ساتھ کام کرنا -![data love](../../../translated_images/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.ur.jpg) +![data love](../../../translated_images/ur/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.jpg) > تصویر از الیکزینڈر سن on Unsplash ان اسباق میں، آپ سیکھیں گے کہ ڈیٹا کو کس طرح منظم، تبدیل، اور ایپلیکیشنز میں استعمال کیا جا سکتا ہے۔ آپ ریلیشنل اور نان ریلیشنل ڈیٹا بیسز کے بارے میں جانیں گے اور یہ کہ ڈیٹا ان میں کس طرح محفوظ کیا جا سکتا ہے۔ آپ Python کے ساتھ ڈیٹا کو منظم کرنے کے بنیادی اصول سیکھیں گے، اور آپ دریافت کریں گے کہ Python کے ذریعے ڈیٹا کو منظم اور مائن کرنے کے کئی طریقے موجود ہیں۔ diff --git a/translations/ur/3-Data-Visualization/12-visualization-relationships/README.md b/translations/ur/3-Data-Visualization/12-visualization-relationships/README.md index dd94b2ea..937adf9e 100644 --- a/translations/ur/3-Data-Visualization/12-visualization-relationships/README.md +++ b/translations/ur/3-Data-Visualization/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ honey.head() ```python sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ``` -![scatterplot 1](../../../../translated_images/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.ur.png) +![scatterplot 1](../../../../translated_images/ur/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.png) اب، اسی ڈیٹا کو شہد کے رنگ سکیم کے ساتھ دکھائیں تاکہ یہ ظاہر ہو کہ قیمت سال بہ سال کیسے بدلی ہے۔ آپ یہ 'hue' پیرامیٹر شامل کرکے کر سکتے ہیں تاکہ سال بہ سال تبدیلی کو دکھایا جا سکے: @@ -60,7 +60,7 @@ sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ```python sns.relplot(x="priceperlb", y="state", hue="year", palette="YlOrBr", data=honey, height=15, aspect=.5); ``` -![scatterplot 2](../../../../translated_images/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.ur.png) +![scatterplot 2](../../../../translated_images/ur/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.png) اس رنگ سکیم کی تبدیلی کے ساتھ، آپ واضح طور پر دیکھ سکتے ہیں کہ شہد کی فی پاؤنڈ قیمت میں سال بہ سال ایک مضبوط ترقی ہو رہی ہے۔ درحقیقت، اگر آپ ڈیٹا کے ایک نمونے کو جانچنے کے لیے دیکھیں (مثال کے طور پر، ایریزونا ریاست کو منتخب کریں) تو آپ دیکھ سکتے ہیں کہ قیمت میں سال بہ سال اضافے کا ایک نمونہ موجود ہے، چند استثنائی صورتوں کے ساتھ: @@ -89,7 +89,7 @@ sns.relplot(x="priceperlb", y="state", size="year", data=honey, height=15, aspec ``` آپ دیکھ سکتے ہیں کہ نقطوں کے سائز میں بتدریج اضافہ ہو رہا ہے۔ -![scatterplot 3](../../../../translated_images/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.ur.png) +![scatterplot 3](../../../../translated_images/ur/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.png) کیا یہ صرف طلب اور رسد کا معاملہ ہے؟ موسمیاتی تبدیلی اور کالونی کولیپس جیسے عوامل کی وجہ سے، کیا سال بہ سال خریداری کے لیے کم شہد دستیاب ہے، اور اس لیے قیمت بڑھ رہی ہے؟ @@ -104,7 +104,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); ``` جواب: ہاں، کچھ استثنائی صورتوں کے ساتھ، خاص طور پر 2003 کے آس پاس: -![line chart 1](../../../../translated_images/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.ur.png) +![line chart 1](../../../../translated_images/ur/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.png) ✅ چونکہ Seaborn ایک لائن کے ارد گرد ڈیٹا کو جمع کر رہا ہے، یہ "ہر x ویلیو پر متعدد پیمائشوں کو اوسط اور اوسط کے ارد گرد 95% اعتماد کے وقفے کو پلاٹ کر کے ظاہر کرتا ہے"۔ [ماخذ](https://seaborn.pydata.org/tutorial/relational.html)۔ اس وقت لینے والے رویے کو `ci=None` شامل کر کے غیر فعال کیا جا سکتا ہے۔ @@ -114,7 +114,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); sns.relplot(x="year", y="totalprod", kind="line", data=honey); ``` -![line chart 2](../../../../translated_images/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.ur.png) +![line chart 2](../../../../translated_images/ur/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.png) جواب: واقعی نہیں۔ اگر آپ کل پیداوار کو دیکھیں، تو یہ خاص سال میں درحقیقت بڑھتی ہوئی نظر آتی ہے، حالانکہ عمومی طور پر ان سالوں کے دوران شہد کی پیداوار میں کمی ہو رہی ہے۔ @@ -139,7 +139,7 @@ sns.relplot( ``` اس بصری نمائندگی میں، آپ فی کالونی پیداوار اور کالونیوں کی تعداد کو سال بہ سال، ریاست بہ ریاست، 3 کالمز کے ساتھ موازنہ کر سکتے ہیں: -![facet grid](../../../../translated_images/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.ur.png) +![facet grid](../../../../translated_images/ur/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.png) اس ڈیٹا سیٹ کے لیے، کالونیوں کی تعداد اور ان کی پیداوار کے حوالے سے سال بہ سال اور ریاست بہ ریاست کچھ خاص نمایاں نہیں ہوتا۔ کیا ان دو متغیرات کے درمیان تعلق تلاش کرنے کے لیے دیکھنے کا کوئی مختلف طریقہ ہے؟ @@ -162,7 +162,7 @@ sns.despine(right=False) plt.ylabel('colony yield') ax.figure.legend(); ``` -![superimposed plots](../../../../translated_images/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.ur.png) +![superimposed plots](../../../../translated_images/ur/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.png) اگرچہ 2003 کے آس پاس آنکھ کو کچھ خاص نظر نہیں آتا، لیکن یہ ہمیں اس سبق کو ایک خوشگوار نوٹ پر ختم کرنے کی اجازت دیتا ہے: اگرچہ مجموعی طور پر کالونیوں کی تعداد میں کمی ہو رہی ہے، کالونیوں کی تعداد مستحکم ہو رہی ہے، چاہے ان کی فی کالونی پیداوار کم ہو رہی ہو۔ diff --git a/translations/ur/3-Data-Visualization/R/09-visualization-quantities/README.md b/translations/ur/3-Data-Visualization/R/09-visualization-quantities/README.md index 4d849c4b..b10092cf 100644 --- a/translations/ur/3-Data-Visualization/R/09-visualization-quantities/README.md +++ b/translations/ur/3-Data-Visualization/R/09-visualization-quantities/README.md @@ -66,7 +66,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` یہاں، آپ `ggplot2` پیکج انسٹال کرتے ہیں اور پھر اسے `library("ggplot2")` کمانڈ کے ذریعے ورک اسپیس میں درآمد کرتے ہیں۔ ggplot میں کوئی بھی چارٹ پلاٹ کرنے کے لیے، `ggplot()` فنکشن استعمال کیا جاتا ہے اور آپ ڈیٹا سیٹ، x اور y متغیرات کو صفات کے طور پر تفویض کرتے ہیں۔ اس صورت میں، ہم `geom_line()` فنکشن استعمال کرتے ہیں کیونکہ ہمارا مقصد ایک لائن چارٹ بنانا ہے۔ -![MaxWingspan-lineplot](../../../../../translated_images/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.ur.png) +![MaxWingspan-lineplot](../../../../../translated_images/ur/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.png) آپ فوراً کیا نوٹ کرتے ہیں؟ کم از کم ایک آؤٹ لائر موجود ہے - یہ پروں کا پھیلاؤ کافی حیران کن ہے! 2000+ سینٹی میٹر پروں کا پھیلاؤ 20 میٹر سے زیادہ کے برابر ہے - کیا منیسوٹا میں پیٹروڈیکٹائلز گھوم رہے ہیں؟ آئیے تحقیق کریں۔ @@ -84,7 +84,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` ہم `theme` میں زاویہ تفویض کرتے ہیں اور `xlab()` اور `ylab()` میں x اور y محور کے لیبلز تفویض کرتے ہیں۔ `ggtitle()` چارٹ/گراف کو ایک نام دیتا ہے۔ -![MaxWingspan-lineplot-improved](../../../../../translated_images/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.ur.png) +![MaxWingspan-lineplot-improved](../../../../../translated_images/ur/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.png) لیبلز کو 45 ڈگری پر گھمانے کے باوجود، پڑھنے کے لیے بہت زیادہ ہیں۔ آئیے ایک مختلف حکمت عملی آزمائیں: صرف آؤٹ لائرز کو لیبل کریں اور لیبلز کو چارٹ کے اندر سیٹ کریں۔ آپ لیبلنگ کے لیے اسکیٹر چارٹ استعمال کر سکتے ہیں: @@ -100,7 +100,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + آپ کیا دریافت کرتے ہیں؟ -![MaxWingspan-scatterplot](../../../../../translated_images/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.ur.png) +![MaxWingspan-scatterplot](../../../../../translated_images/ur/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.png) ## اپنے ڈیٹا کو فلٹر کریں @@ -119,7 +119,7 @@ ggplot(data=birds_filtered, aes(x=Name, y=MaxWingspan,group=1)) + ``` ہم نے ایک نیا ڈیٹا فریم `birds_filtered` بنایا اور پھر ایک اسکیٹر چارٹ پلاٹ کیا۔ آؤٹ لائرز کو فلٹر کرنے سے، آپ کا ڈیٹا اب زیادہ مربوط اور قابل فہم ہو گیا ہے۔ -![MaxWingspan-scatterplot-improved](../../../../../translated_images/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.ur.png) +![MaxWingspan-scatterplot-improved](../../../../../translated_images/ur/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.png) اب جب کہ ہمارے پاس پروں کے پھیلاؤ کے لحاظ سے کم از کم ایک صاف شدہ ڈیٹا سیٹ موجود ہے، آئیے ان پرندوں کے بارے میں مزید دریافت کریں۔ @@ -160,7 +160,7 @@ birds_filtered %>% group_by(Category) %>% ``` مندرجہ ذیل کوڈ میں، ہم [dplyr](https://www.rdocumentation.org/packages/dplyr/versions/0.7.8) اور [lubridate](https://www.rdocumentation.org/packages/lubridate/versions/1.8.0) پیکجز انسٹال کرتے ہیں تاکہ ڈیٹا کو جوڑنے اور گروپ کرنے میں مدد ملے تاکہ ایک اسٹیکڈ بار چارٹ پلاٹ کیا جا سکے۔ پہلے، آپ ڈیٹا کو پرندے کے `Category` کے لحاظ سے گروپ کرتے ہیں اور پھر `MinLength`, `MaxLength`, `MinBodyMass`, `MaxBodyMass`, `MinWingspan`, `MaxWingspan` کالمز کو خلاصہ کرتے ہیں۔ پھر، `ggplot2` پیکج کا استعمال کرتے ہوئے بار چارٹ پلاٹ کریں اور مختلف زمرے کے لیے رنگ اور لیبلز تفویض کریں۔ -![Stacked bar chart](../../../../../translated_images/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.ur.png) +![Stacked bar chart](../../../../../translated_images/ur/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.png) یہ بار چارٹ، تاہم، ناقابل پڑھائی ہے کیونکہ بہت زیادہ غیر گروپ شدہ ڈیٹا موجود ہے۔ آپ کو صرف وہ ڈیٹا منتخب کرنے کی ضرورت ہے جسے آپ پلاٹ کرنا چاہتے ہیں، تو آئیے پرندے کے زمرے کی بنیاد پر لمبائی کا جائزہ لیں۔ @@ -175,7 +175,7 @@ ggplot(birds_count,aes(Category,n))+geom_bar(stat="identity")+coord_flip() ``` آپ پہلے `Category` کالم میں منفرد اقدار کی گنتی کرتے ہیں اور پھر انہیں ایک نئے ڈیٹا فریم `birds_count` میں ترتیب دیتے ہیں۔ یہ ترتیب شدہ ڈیٹا پھر اسی سطح پر فیکٹر کیا جاتا ہے تاکہ اسے ترتیب شدہ طریقے سے پلاٹ کیا جا سکے۔ `ggplot2` کا استعمال کرتے ہوئے آپ پھر ڈیٹا کو بار چارٹ میں پلاٹ کرتے ہیں۔ `coord_flip()` افقی بارز پلاٹ کرتا ہے۔ -![category-length](../../../../../translated_images/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.ur.png) +![category-length](../../../../../translated_images/ur/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.png) یہ بار چارٹ پرندوں کے ہر زمرے میں تعداد کا ایک اچھا نظارہ دکھاتا ہے۔ ایک نظر میں، آپ دیکھتے ہیں کہ اس علاقے میں سب سے زیادہ تعداد میں پرندے بطخ/گیز/واٹر فاول کے زمرے میں ہیں۔ منیسوٹا '10,000 جھیلوں کی زمین' ہے، لہذا یہ حیرت انگیز نہیں ہے! @@ -198,7 +198,7 @@ ggplot(birds_grouped,aes(Category,MaxLength))+geom_bar(stat="identity")+coord_fl ``` ہم `birds_filtered` ڈیٹا کو `Category` کے لحاظ سے گروپ کرتے ہیں اور پھر ایک بار چارٹ پلاٹ کرتے ہیں۔ -![comparing data](../../../../../translated_images/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.ur.png) +![comparing data](../../../../../translated_images/ur/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.png) یہاں کچھ حیران کن نہیں ہے: ہمینگ برڈز کی زیادہ سے زیادہ لمبائی پیلیکنز یا گیز کے مقابلے میں سب سے کم ہے۔ یہ اچھا ہے جب ڈیٹا منطقی طور پر سمجھ میں آتا ہے! @@ -210,7 +210,7 @@ ggplot(data=birds_grouped, aes(x=Category)) + geom_bar(aes(y=MinLength), stat="identity", position="identity", fill='orange')+ coord_flip() ``` -![super-imposed values](../../../../../translated_images/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.ur.png) +![super-imposed values](../../../../../translated_images/ur/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.png) ## 🚀 چیلنج diff --git a/translations/ur/3-Data-Visualization/R/10-visualization-distributions/README.md b/translations/ur/3-Data-Visualization/R/10-visualization-distributions/README.md index f71584fb..290b386f 100644 --- a/translations/ur/3-Data-Visualization/R/10-visualization-distributions/README.md +++ b/translations/ur/3-Data-Visualization/R/10-visualization-distributions/README.md @@ -45,7 +45,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + geom_point() + ggtitle("Max Length per order") + coord_flip() ``` -![آرڈر کے لحاظ سے زیادہ سے زیادہ لمبائی](../../../../../translated_images/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.ur.png) +![آرڈر کے لحاظ سے زیادہ سے زیادہ لمبائی](../../../../../translated_images/ur/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.png) یہ پرندوں کے آرڈر کے لحاظ سے جسمانی لمبائی کی عمومی تقسیم کا جائزہ دیتا ہے، لیکن یہ حقیقی تقسیم کو ظاہر کرنے کا بہترین طریقہ نہیں ہے۔ یہ کام عام طور پر ہسٹوگرام بنا کر کیا جاتا ہے۔ ## ہسٹوگرام کے ساتھ کام کرنا @@ -56,7 +56,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=10)+ylab('Frequency') ``` -![پورے ڈیٹا سیٹ پر تقسیم](../../../../../translated_images/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.ur.png) +![پورے ڈیٹا سیٹ پر تقسیم](../../../../../translated_images/ur/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.png) جیسا کہ آپ دیکھ سکتے ہیں، اس ڈیٹا سیٹ میں موجود 400+ پرندوں میں سے زیادہ تر کا زیادہ سے زیادہ جسمانی وزن 2000 سے کم ہے۔ ڈیٹا کے بارے میں مزید بصیرت حاصل کرنے کے لیے `bins` پیرامیٹر کو زیادہ تعداد، جیسے 30 میں تبدیل کریں: @@ -64,7 +64,7 @@ ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![30 بِنز کے ساتھ تقسیم](../../../../../translated_images/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.ur.png) +![30 بِنز کے ساتھ تقسیم](../../../../../translated_images/ur/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.png) یہ چارٹ تقسیم کو تھوڑا زیادہ تفصیلی انداز میں دکھاتا ہے۔ ایک چارٹ جو بائیں طرف کم جھکا ہوا ہو، اس وقت بنایا جا سکتا ہے جب آپ صرف ایک دیے گئے حد کے اندر ڈیٹا کو منتخب کریں: @@ -76,7 +76,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![فلٹر شدہ ہسٹوگرام](../../../../../translated_images/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.ur.png) +![فلٹر شدہ ہسٹوگرام](../../../../../translated_images/ur/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.png) ✅ کچھ دوسرے فلٹرز اور ڈیٹا پوائنٹس آزمائیں۔ ڈیٹا کی مکمل تقسیم دیکھنے کے لیے، `['MaxBodyMass']` فلٹر کو ہٹا دیں تاکہ لیبل شدہ تقسیمات دکھائی دیں۔ @@ -90,7 +90,7 @@ ggplot(data=birds_filtered_1, aes(x=MaxBodyMass, y=MaxLength) ) + ``` ایسا لگتا ہے کہ ان دو عناصر کے درمیان ایک متوقع محور کے ساتھ ایک متوقع تعلق ہے، جس میں ہم آہنگی کا ایک خاص طور پر مضبوط نقطہ ہے: -![2D پلاٹ](../../../../../translated_images/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.ur.png) +![2D پلاٹ](../../../../../translated_images/ur/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.png) ہسٹوگرامز عددی ڈیٹا کے لیے ڈیفالٹ کے طور پر اچھی طرح کام کرتے ہیں۔ اگر آپ کو متن کے ڈیٹا کے مطابق تقسیمات دیکھنے کی ضرورت ہو تو کیا ہوگا؟ ## متن کے ڈیٹا کا استعمال کرتے ہوئے ڈیٹا سیٹ کے لیے تقسیمات کو دریافت کریں @@ -121,7 +121,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + scale_fill_manual(name="Conservation Status",values=c("red","green","blue","pink"),labels=c("Endangered","Near Threathened","Vulnerable","Least Concern")) ``` -![پروں کا پھیلاؤ اور تحفظ کی حالت کا موازنہ](../../../../../translated_images/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.ur.png) +![پروں کا پھیلاؤ اور تحفظ کی حالت کا موازنہ](../../../../../translated_images/ur/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.png) کم از کم پروں کے پھیلاؤ اور تحفظ کی حالت کے درمیان کوئی اچھا تعلق نظر نہیں آتا۔ اس طریقے کا استعمال کرتے ہوئے ڈیٹا سیٹ کے دوسرے عناصر کو آزمائیں۔ آپ مختلف فلٹرز بھی آزما سکتے ہیں۔ کیا آپ کو کوئی تعلق نظر آتا ہے؟ @@ -135,7 +135,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + geom_density() ``` -![کثافت پلاٹ](../../../../../translated_images/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.ur.png) +![کثافت پلاٹ](../../../../../translated_images/ur/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.png) آپ دیکھ سکتے ہیں کہ یہ پلاٹ کم از کم پروں کے پھیلاؤ کے ڈیٹا کے لیے پچھلے پلاٹ کی عکاسی کرتا ہے؛ یہ صرف تھوڑا سا ہموار ہے۔ اگر آپ اس قدموں والے MaxBodyMass لائن کو دوبارہ دیکھنا چاہتے ہیں جو آپ نے دوسرے چارٹ میں بنایا تھا، تو آپ اسے اس طریقے کا استعمال کرتے ہوئے بہت اچھی طرح سے ہموار کر سکتے ہیں: @@ -143,7 +143,7 @@ ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density() ``` -![جسمانی وزن کی کثافت](../../../../../translated_images/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.ur.png) +![جسمانی وزن کی کثافت](../../../../../translated_images/ur/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.png) اگر آپ ایک ہموار، لیکن زیادہ ہموار نہ ہونے والی لائن چاہتے ہیں، تو `adjust` پیرامیٹر میں ترمیم کریں: @@ -151,7 +151,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density(adjust = 1/5) ``` -![کم ہموار جسمانی وزن](../../../../../translated_images/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.ur.png) +![کم ہموار جسمانی وزن](../../../../../translated_images/ur/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.png) ✅ اس قسم کے پلاٹ کے لیے دستیاب پیرامیٹرز کے بارے میں پڑھیں اور تجربہ کریں! @@ -161,7 +161,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + ggplot(data=birds_filtered_1,aes(x = MaxBodyMass, fill = Order)) + geom_density(alpha=0.5) ``` -![آرڈر کے لحاظ سے جسمانی وزن](../../../../../translated_images/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.ur.png) +![آرڈر کے لحاظ سے جسمانی وزن](../../../../../translated_images/ur/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.png) ## 🚀 چیلنج diff --git a/translations/ur/3-Data-Visualization/R/11-visualization-proportions/README.md b/translations/ur/3-Data-Visualization/R/11-visualization-proportions/README.md index 3ed77c7d..4e93d9f1 100644 --- a/translations/ur/3-Data-Visualization/R/11-visualization-proportions/README.md +++ b/translations/ur/3-Data-Visualization/R/11-visualization-proportions/README.md @@ -97,7 +97,7 @@ pie(grouped$count,grouped$class, main="Edible?") ``` دیکھیں، ایک پائی چارٹ جو اس ڈیٹا کو ان دو مشرومز کی کلاسز کے مطابق تناسبات دکھاتا ہے۔ لیبلز کی ترتیب کو درست رکھنا خاص طور پر یہاں بہت اہم ہے، لہذا لیبل آرے کی ترتیب کو ضرور چیک کریں! -![پائی چارٹ](../../../../../translated_images/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.ur.png) +![پائی چارٹ](../../../../../translated_images/ur/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.png) ## ڈونٹس! @@ -132,7 +132,7 @@ library(webr) PieDonut(habitat, aes(habitat, count=count)) ``` -![ڈونٹ چارٹ](../../../../../translated_images/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.ur.png) +![ڈونٹ چارٹ](../../../../../translated_images/ur/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.png) یہ کوڈ دو لائبریریوں - ggplot2 اور webr - استعمال کرتا ہے۔ webr لائبریری کے PieDonut فنکشن کا استعمال کرتے ہوئے، ہم آسانی سے ایک ڈونٹ چارٹ بنا سکتے ہیں! @@ -169,7 +169,7 @@ waffle((cap_color$count/10), rows = 7, title = "Waffle Chart")+scale_fill_manual وافل چارٹ کا استعمال کرتے ہوئے، آپ مشرومز کے اس ڈیٹا سیٹ میں ٹوپی کے رنگوں کے تناسب کو واضح طور پر دیکھ سکتے ہیں۔ دلچسپ بات یہ ہے کہ بہت سے سبز ٹوپی والے مشرومز موجود ہیں! -![وافل چارٹ](../../../../../translated_images/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.ur.png) +![وافل چارٹ](../../../../../translated_images/ur/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.png) اس سبق میں، آپ نے تناسبات کو بصری طور پر سمجھنے کے تین طریقے سیکھے۔ پہلے، آپ کو اپنے ڈیٹا کو کیٹیگریز میں گروپ کرنا ہوگا اور پھر فیصلہ کرنا ہوگا کہ ڈیٹا کو دکھانے کا بہترین طریقہ کون سا ہے - پائی، ڈونٹ، یا وافل۔ یہ سب مزیدار ہیں اور صارف کو ڈیٹا سیٹ کا فوری جائزہ فراہم کرتے ہیں۔ diff --git a/translations/ur/3-Data-Visualization/R/12-visualization-relationships/README.md b/translations/ur/3-Data-Visualization/R/12-visualization-relationships/README.md index 0ca9dfe9..d964c254 100644 --- a/translations/ur/3-Data-Visualization/R/12-visualization-relationships/README.md +++ b/translations/ur/3-Data-Visualization/R/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ library(ggplot2) ggplot(honey, aes(x = priceperlb, y = state)) + geom_point(colour = "blue") ``` -![scatterplot 1](../../../../../translated_images/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.ur.png) +![scatterplot 1](../../../../../translated_images/ur/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.png) اب، وہی ڈیٹا شہد کے رنگ سکیم کے ساتھ دکھائیں تاکہ یہ ظاہر ہو کہ قیمت سال بہ سال کیسے بدلی ہے۔ آپ یہ 'scale_color_gradientn' پیرامیٹر شامل کر کے کر سکتے ہیں تاکہ تبدیلی کو ظاہر کیا جا سکے: @@ -61,7 +61,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ggplot(honey, aes(x = priceperlb, y = state, color=year)) + geom_point()+scale_color_gradientn(colours = colorspace::heat_hcl(7)) ``` -![scatterplot 2](../../../../../translated_images/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.ur.png) +![scatterplot 2](../../../../../translated_images/ur/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.png) اس رنگ سکیم کی تبدیلی کے ساتھ، آپ واضح طور پر دیکھ سکتے ہیں کہ سال بہ سال شہد کی فی پاؤنڈ قیمت میں ایک مضبوط ترقی ہوئی ہے۔ درحقیقت، اگر آپ ڈیٹا کے ایک نمونے کو جانچنے کے لیے دیکھیں (مثال کے طور پر، ایریزونا کی ریاست)، تو آپ دیکھ سکتے ہیں کہ قیمت میں سال بہ سال اضافے کا ایک نمونہ موجود ہے، چند استثناء کے ساتھ: @@ -92,7 +92,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ``` آپ دیکھ سکتے ہیں کہ نقطوں کا سائز بتدریج بڑھ رہا ہے۔ -![scatterplot 3](../../../../../translated_images/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.ur.png) +![scatterplot 3](../../../../../translated_images/ur/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.png) کیا یہ صرف طلب اور رسد کا معاملہ ہے؟ موسمیاتی تبدیلی اور کالونی کولیپس جیسے عوامل کی وجہ سے، کیا سال بہ سال خریداری کے لیے کم شہد دستیاب ہے، اور اس وجہ سے قیمت بڑھ رہی ہے؟ @@ -107,7 +107,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab ``` جواب: ہاں، کچھ استثناء کے ساتھ، خاص طور پر 2003 کے آس پاس: -![line chart 1](../../../../../translated_images/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.ur.png) +![line chart 1](../../../../../translated_images/ur/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.png) سوال: ٹھیک ہے، کیا 2003 میں ہم شہد کی فراہمی میں بھی کوئی اضافہ دیکھ سکتے ہیں؟ اگر آپ کل پیداوار کو سال بہ سال دیکھیں تو کیا ہوگا؟ @@ -115,7 +115,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab qplot(honey$year,honey$totalprod, geom='smooth', span =0.5, xlab = "year",ylab = "totalprod") ``` -![line chart 2](../../../../../translated_images/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.ur.png) +![line chart 2](../../../../../translated_images/ur/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.png) جواب: واقعی نہیں۔ اگر آپ کل پیداوار کو دیکھیں، تو ایسا لگتا ہے کہ اس خاص سال میں یہ درحقیقت بڑھ گئی ہے، حالانکہ عمومی طور پر ان سالوں کے دوران شہد کی پیداوار میں کمی ہو رہی ہے۔ @@ -135,7 +135,7 @@ ggplot(honey, aes(x=yieldpercol, y = numcol,group = 1)) + ``` اس بصری نمائندگی میں، آپ فی کالونی پیداوار اور کالونیوں کی تعداد کو سال بہ سال، ریاست بہ ریاست، 3 کالمز کے ساتھ موازنہ کر سکتے ہیں: -![facet grid](../../../../../translated_images/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.ur.png) +![facet grid](../../../../../translated_images/ur/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.png) اس ڈیٹا سیٹ کے لیے، کالونیوں کی تعداد اور ان کی پیداوار کے لحاظ سے سال بہ سال اور ریاست بہ ریاست کچھ خاص نمایاں نہیں ہوتا۔ کیا ان دو متغیرات کے درمیان تعلق تلاش کرنے کے لیے دیکھنے کا کوئی مختلف طریقہ ہے؟ @@ -152,7 +152,7 @@ plot(honey$year, honey$yieldpercol, pch = 17, col = 3, axis(side = 4, at = pretty(range(y2))) mtext("colony yield", side = 4, line = 3) ``` -![superimposed plots](../../../../../translated_images/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.ur.png) +![superimposed plots](../../../../../translated_images/ur/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.png) جبکہ 2003 کے آس پاس کچھ خاص نظر نہیں آتا، یہ ہمیں سبق کو ایک خوشگوار نوٹ پر ختم کرنے کی اجازت دیتا ہے: اگرچہ مجموعی طور پر کالونیوں کی تعداد میں کمی ہو رہی ہے، کالونیوں کی تعداد مستحکم ہو رہی ہے، چاہے ان کی فی کالونی پیداوار کم ہو رہی ہو۔ diff --git a/translations/ur/3-Data-Visualization/R/13-meaningful-vizualizations/README.md b/translations/ur/3-Data-Visualization/R/13-meaningful-vizualizations/README.md index 2b65ef49..c5e985ef 100644 --- a/translations/ur/3-Data-Visualization/R/13-meaningful-vizualizations/README.md +++ b/translations/ur/3-Data-Visualization/R/13-meaningful-vizualizations/README.md @@ -47,25 +47,25 @@ CO_OP_TRANSLATOR_METADATA: چاہے ایک ڈیٹا سائنسدان صحیح ڈیٹا کے لیے صحیح چارٹ منتخب کرنے میں محتاط ہو، پھر بھی ڈیٹا کو اس طرح پیش کرنے کے کئی طریقے ہیں جو کسی نکتے کو ثابت کرنے کے لیے استعمال کیے جا سکتے ہیں، اکثر ڈیٹا کی سالمیت کو نقصان پہنچا کر۔ دھوکہ دہی والے چارٹس اور انفراگرافکس کی کئی مثالیں موجود ہیں! -[![البرٹو کائرو کی "How Charts Lie"](../../../../../translated_images/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.ur.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "How charts lie") +[![البرٹو کائرو کی "How Charts Lie"](../../../../../translated_images/ur/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "How charts lie") > 🎥 دھوکہ دہی والے چارٹس کے بارے میں ایک کانفرنس ٹاک کے لیے اوپر دی گئی تصویر پر کلک کریں یہ چارٹ X محور کو الٹا کر کے تاریخ کی بنیاد پر حقیقت کے برعکس دکھاتا ہے: -![خراب چارٹ 1](../../../../../translated_images/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.ur.png) +![خراب چارٹ 1](../../../../../translated_images/ur/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.png) [یہ چارٹ](https://media.firstcoastnews.com/assets/WTLV/images/170ae16f-4643-438f-b689-50d66ca6a8d8/170ae16f-4643-438f-b689-50d66ca6a8d8_1140x641.jpg) اور بھی زیادہ دھوکہ دہی والا ہے، کیونکہ آنکھ دائیں طرف کھینچی جاتی ہے تاکہ یہ نتیجہ اخذ کیا جا سکے کہ وقت کے ساتھ، مختلف کاؤنٹیز میں COVID کیسز کم ہو گئے ہیں۔ درحقیقت، اگر آپ تاریخوں کو قریب سے دیکھیں تو آپ کو پتہ چلے گا کہ انہیں اس دھوکہ دہی والے نیچے کے رجحان کو ظاہر کرنے کے لیے دوبارہ ترتیب دیا گیا ہے۔ -![خراب چارٹ 2](../../../../../translated_images/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.ur.jpg) +![خراب چارٹ 2](../../../../../translated_images/ur/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.jpg) یہ بدنام زمانہ مثال رنگ اور الٹے Y محور کا استعمال کرتی ہے تاکہ دھوکہ دیا جا سکے: بندوق کے حامی قانون سازی کے نفاذ کے بعد بندوق سے ہونے والی اموات میں اضافے کے بجائے، آنکھ کو دھوکہ دیا جاتا ہے کہ اس کے برعکس سچ ہے: -![خراب چارٹ 3](../../../../../translated_images/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.ur.jpg) +![خراب چارٹ 3](../../../../../translated_images/ur/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.jpg) یہ عجیب چارٹ تناسب کو مزاحیہ انداز میں جوڑ توڑ کر دکھاتا ہے: -![خراب چارٹ 4](../../../../../translated_images/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.ur.jpg) +![خراب چارٹ 4](../../../../../translated_images/ur/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.jpg) ناقابل موازنہ چیزوں کا موازنہ کرنا ایک اور مشکوک چال ہے۔ ایک [شاندار ویب سائٹ](https://tylervigen.com/spurious-correlations) 'غلط تعلقات' کے بارے میں ہے جو 'حقائق' کو ظاہر کرتی ہے جیسے مین میں طلاق کی شرح اور مارجرین کے استعمال کے درمیان تعلق۔ ایک Reddit گروپ بھی ڈیٹا کے [بدصورت استعمالات](https://www.reddit.com/r/dataisugly/top/?t=all) کو جمع کرتا ہے۔ @@ -100,13 +100,13 @@ CO_OP_TRANSLATOR_METADATA: اگر آپ کا ڈیٹا X محور پر متنی اور تفصیلی ہے، تو بہتر پڑھنے کے لیے متن کو زاویہ دے سکتے ہیں۔ [plot3D](https://cran.r-project.org/web/packages/plot3D/index.html) 3D پلاٹنگ پیش کرتا ہے، اگر آپ کا ڈیٹا اس کی حمایت کرتا ہو۔ اس کا استعمال کرتے ہوئے نفیس بصری نمائیاں تیار کی جا سکتی ہیں۔ -![3D پلاٹس](../../../../../translated_images/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.ur.png) +![3D پلاٹس](../../../../../translated_images/ur/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.png) ## متحرک اور 3D چارٹ ڈسپلے آج کل کی بہترین بصری نمائیاں متحرک ہوتی ہیں۔ Shirley Wu نے D3 کے ساتھ حیرت انگیز نمائیاں بنائی ہیں، جیسے '[فلم کے پھول](http://bl.ocks.org/sxywu/raw/d612c6c653fb8b4d7ff3d422be164a5d/)'، جہاں ہر پھول ایک فلم کی نمائندگی کرتا ہے۔ Guardian کے لیے ایک اور مثال 'بَسڈ آؤٹ' ہے، جو بصری نمائیاں، Greensock اور D3 کے ساتھ ایک انٹرایکٹو تجربہ ہے، اور ایک اسکرولی ٹیلنگ آرٹیکل فارمیٹ کے ساتھ دکھاتا ہے کہ NYC اپنے بے گھر افراد کو شہر سے باہر بھیج کر کیسے سنبھالتا ہے۔ -![بَسڈ آؤٹ](../../../../../translated_images/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.ur.png) +![بَسڈ آؤٹ](../../../../../translated_images/ur/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.png) > "بَسڈ آؤٹ: امریکہ اپنے بے گھر افراد کو کیسے منتقل کرتا ہے" [دی گارڈین](https://www.theguardian.com/us-news/ng-interactive/2017/dec/20/bussed-out-america-moves-homeless-people-country-study) سے۔ بصری نمائیاں: Nadieh Bremer & Shirley Wu @@ -116,7 +116,7 @@ CO_OP_TRANSLATOR_METADATA: آپ ایک ویب ایپ مکمل کریں گے جو اس سوشل نیٹ ورک کا متحرک منظر دکھائے گی۔ یہ ایک لائبریری کا استعمال کرتی ہے جو Vue.js اور D3 کا استعمال کرتے ہوئے ایک [نیٹ ورک کی نمائیاں](https://github.com/emiliorizzo/vue-d3-network) بنانے کے لیے تیار کی گئی تھی۔ جب ایپ چل رہی ہو، تو آپ اسکرین پر نوڈز کو گھسیٹ کر ڈیٹا کو ادھر ادھر کر سکتے ہیں۔ -![liaisons](../../../../../translated_images/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.ur.png) +![liaisons](../../../../../translated_images/ur/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.png) ## پروجیکٹ: D3.js کا استعمال کرتے ہوئے نیٹ ورک دکھانے کے لیے ایک چارٹ بنائیں diff --git a/translations/ur/3-Data-Visualization/README.md b/translations/ur/3-Data-Visualization/README.md index 59976a43..791522ce 100644 --- a/translations/ur/3-Data-Visualization/README.md +++ b/translations/ur/3-Data-Visualization/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # تصورات -![ایک شہد کی مکھی لیونڈر کے پھول پر](../../../translated_images/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.ur.jpg) +![ایک شہد کی مکھی لیونڈر کے پھول پر](../../../translated_images/ur/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.jpg) > تصویر جینا لی کی جانب سے Unsplash پر ڈیٹا کو بصری طور پر پیش کرنا ایک ڈیٹا سائنسدان کے سب سے اہم کاموں میں سے ایک ہے۔ تصاویر ہزاروں الفاظ کے برابر ہوتی ہیں، اور ایک تصور آپ کو اپنے ڈیٹا کے مختلف دلچسپ پہلوؤں جیسے کہ اسپائکس، آؤٹ لائرز، گروپنگز، رجحانات، اور مزید کو پہچاننے میں مدد دے سکتا ہے، جو آپ کو یہ سمجھنے میں مدد دیتا ہے کہ آپ کا ڈیٹا کیا کہانی بیان کر رہا ہے۔ diff --git a/translations/ur/4-Data-Science-Lifecycle/14-Introduction/README.md b/translations/ur/4-Data-Science-Lifecycle/14-Introduction/README.md index e01998e7..c728ecc3 100644 --- a/translations/ur/4-Data-Science-Lifecycle/14-Introduction/README.md +++ b/translations/ur/4-Data-Science-Lifecycle/14-Introduction/README.md @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: یہ سبق لائف سائیکل کے تین حصوں پر مرکوز ہے: ڈیٹا حاصل کرنا، پراسیسنگ اور دیکھ بھال۔ -![ڈیٹا سائنس کے لائف سائیکل کا خاکہ](../../../../translated_images/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.ur.jpg) +![ڈیٹا سائنس کے لائف سائیکل کا خاکہ](../../../../translated_images/ur/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.jpg) > تصویر [Berkeley School of Information](https://ischoolonline.berkeley.edu/data-science/what-is-data-science/) کی جانب سے ## ڈیٹا حاصل کرنا @@ -101,7 +101,7 @@ CO_OP_TRANSLATOR_METADATA: |Team Data Science Process (TDSP)|Cross-industry standard process for data mining (CRISP-DM)| |--|--| -|![Team Data Science Lifecycle](../../../../translated_images/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.ur.png) | ![Data Science Process Alliance Image](../../../../translated_images/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.ur.png) | +|![Team Data Science Lifecycle](../../../../translated_images/ur/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.png) | ![Data Science Process Alliance Image](../../../../translated_images/ur/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.png) | | تصویر [Microsoft](https://docs.microsoft.comazure/architecture/data-science-process/lifecycle) کی جانب سے | تصویر [Data Science Process Alliance](https://www.datascience-pm.com/crisp-dm-2/) کی جانب سے | ## [لیکچر کے بعد کا کوئز](https://ff-quizzes.netlify.app/en/ds/quiz/27) diff --git a/translations/ur/4-Data-Science-Lifecycle/README.md b/translations/ur/4-Data-Science-Lifecycle/README.md index e694ddb9..cb1abe21 100644 --- a/translations/ur/4-Data-Science-Lifecycle/README.md +++ b/translations/ur/4-Data-Science-Lifecycle/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # ڈیٹا سائنس کا لائف سائیکل -![communication](../../../translated_images/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.ur.jpg) +![communication](../../../translated_images/ur/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.jpg) > تصویر از Headway on Unsplash ان اسباق میں، آپ ڈیٹا سائنس کے لائف سائیکل کے کچھ پہلوؤں کو دریافت کریں گے، جن میں ڈیٹا کے تجزیے اور اس کے بارے میں بات چیت شامل ہے۔ diff --git a/translations/ur/5-Data-Science-In-Cloud/README.md b/translations/ur/5-Data-Science-In-Cloud/README.md index d3574dbc..582ebcb8 100644 --- a/translations/ur/5-Data-Science-In-Cloud/README.md +++ b/translations/ur/5-Data-Science-In-Cloud/README.md @@ -9,13 +9,13 @@ CO_OP_TRANSLATOR_METADATA: --> # کلاؤڈ میں ڈیٹا سائنس -![cloud-picture](../../../translated_images/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.ur.jpg) +![cloud-picture](../../../translated_images/ur/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.jpg) > تصویر [Jelleke Vanooteghem](https://unsplash.com/@ilumire) کی جانب سے [Unsplash](https://unsplash.com/s/photos/cloud?orientation=landscape) پر دستیاب ہے۔ جب بڑے ڈیٹا کے ساتھ ڈیٹا سائنس کرنے کی بات آتی ہے، تو کلاؤڈ ایک انقلابی تبدیلی ثابت ہو سکتا ہے۔ اگلے تین اسباق میں، ہم دیکھیں گے کہ کلاؤڈ کیا ہے اور یہ کیوں مددگار ثابت ہو سکتا ہے۔ ہم دل کی ناکامی کے ڈیٹا سیٹ کا جائزہ لیں گے اور ایک ماڈل بنائیں گے جو کسی شخص کے دل کی ناکامی کے امکانات کا اندازہ لگانے میں مدد کرے گا۔ ہم کلاؤڈ کی طاقت کا استعمال کرتے ہوئے ماڈل کو دو مختلف طریقوں سے تربیت دیں گے، تعینات کریں گے اور استعمال کریں گے۔ ایک طریقہ صرف یوزر انٹرفیس کے ذریعے، جسے "لو کوڈ/نو کوڈ" انداز کہا جاتا ہے، اور دوسرا طریقہ Azure Machine Learning Software Developer Kit (Azure ML SDK) کا استعمال کرتے ہوئے۔ -![project-schema](../../../translated_images/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.ur.png) +![project-schema](../../../translated_images/ur/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.png) ### موضوعات diff --git a/translations/ur/6-Data-Science-In-Wild/20-Real-World-Examples/README.md b/translations/ur/6-Data-Science-In-Wild/20-Real-World-Examples/README.md index ea74926e..93be0c6f 100644 --- a/translations/ur/6-Data-Science-In-Wild/20-Real-World-Examples/README.md +++ b/translations/ur/6-Data-Science-In-Wild/20-Real-World-Examples/README.md @@ -41,7 +41,7 @@ AI کی جمہوریت کی بدولت، ڈویلپرز کے لیے AI سے چل * [صحت کی دیکھ بھال میں ڈیٹا سائنس](https://data-flair.training/blogs/data-science-in-healthcare/) - طبی امیجنگ (جیسے MRI، ایکس رے، CT-Scan)، جینومکس (DNA سیکوینسنگ)، دوا کی ترقی (خطرے کی تشخیص، کامیابی کی پیش گوئی)، پیش گوئی تجزیات (مریض کی دیکھ بھال اور سپلائی لاجسٹکس)، بیماری کی ٹریکنگ اور روک تھام وغیرہ جیسے اطلاقات کو اجاگر کرتا ہے۔ -![حقیقی دنیا میں ڈیٹا سائنس کے اطلاقات](../../../../translated_images/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.ur.png) تصویر کا کریڈٹ: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) +![حقیقی دنیا میں ڈیٹا سائنس کے اطلاقات](../../../../translated_images/ur/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.png) تصویر کا کریڈٹ: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) یہ شکل دیگر ڈومینز اور ڈیٹا سائنس تکنیکوں کے اطلاقات کی مثالیں دکھاتی ہے۔ کیا آپ دیگر اطلاقات کو دریافت کرنا چاہتے ہیں؟ نیچے [جائزہ اور خود مطالعہ](../../../../6-Data-Science-In-Wild/20-Real-World-Examples) سیکشن دیکھیں۔ diff --git a/translations/ur/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md b/translations/ur/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md index f99146e5..9f3ed44f 100644 --- a/translations/ur/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md +++ b/translations/ur/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md @@ -22,7 +22,7 @@ CO_OP_TRANSLATOR_METADATA: 2. ڈیٹا سیٹ [کیٹلاگ](https://planetarycomputer.microsoft.com/catalog) کو دریافت کریں - ہر ایک کا مقصد سیکھیں۔ 3. ایکسپلورر استعمال کریں - دلچسپی کا ایک ڈیٹا سیٹ منتخب کریں، ایک متعلقہ کوئری اور رینڈرنگ آپشن منتخب کریں۔ -![سیاروی کمپیوٹر ایکسپلورر](../../../../translated_images/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.ur.png) +![سیاروی کمپیوٹر ایکسپلورر](../../../../translated_images/ur/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.png) `آپ کا کام:` اب براؤزر میں رینڈر کی گئی ویژولائزیشن کا مطالعہ کریں اور درج ذیل سوالات کے جواب دیں: diff --git a/translations/ur/CONTRIBUTING.md b/translations/ur/CONTRIBUTING.md index 9552b5d8..98a1c6e0 100644 --- a/translations/ur/CONTRIBUTING.md +++ b/translations/ur/CONTRIBUTING.md @@ -312,7 +312,7 @@ def calculate_mean(data): import pandas as pd ``` ```` -- تصاویر کے لیے alt متن شامل کریں: `![Alt text](../../translated_images/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.ur.png)` +- تصاویر کے لیے alt متن شامل کریں: `![Alt text](../../translated_images/ur/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.png)` - لائن کی لمبائی معقول رکھیں (تقریباً 80-100 حروف) ### Python diff --git a/translations/ur/README.md b/translations/ur/README.md index 895108aa..f23e4279 100644 --- a/translations/ur/README.md +++ b/translations/ur/README.md @@ -28,7 +28,7 @@ Azure Cloud Advocates at Microsoft خوش ہیں کہ وہ ایک 10 ہفتوں **🙏 خصوصی شکریہ 🙏 ہمارے [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/) مصنفین، جائزہ نگاروں اور مواد کے حصہ ڈالنے والوں کو،** خاص طور پر Aaryan Arora, [Aditya Garg](https://github.com/AdityaGarg00), [Alondra Sanchez](https://www.linkedin.com/in/alondra-sanchez-molina/), [Ankita Singh](https://www.linkedin.com/in/ankitasingh007), [Anupam Mishra](https://www.linkedin.com/in/anupam--mishra/), [Arpita Das](https://www.linkedin.com/in/arpitadas01/), ChhailBihari Dubey, [Dibri Nsofor](https://www.linkedin.com/in/dibrinsofor), [Dishita Bhasin](https://www.linkedin.com/in/dishita-bhasin-7065281bb), [Majd Safi](https://www.linkedin.com/in/majd-s/), [Max Blum](https://www.linkedin.com/in/max-blum-6036a1186/), [Miguel Correa](https://www.linkedin.com/in/miguelmque/), [Mohamma Iftekher (Iftu) Ebne Jalal](https://twitter.com/iftu119), [Nawrin Tabassum](https://www.linkedin.com/in/nawrin-tabassum), [Raymond Wangsa Putra](https://www.linkedin.com/in/raymond-wp/), [Rohit Yadav](https://www.linkedin.com/in/rty2423), Samridhi Sharma, [Sanya Sinha](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200), [Sheena Narula](https://www.linkedin.com/in/sheena-narua-n/), [Tauqeer Ahmad](https://www.linkedin.com/in/tauqeerahmad5201/), Yogendrasingh Pawar , [Vidushi Gupta](https://www.linkedin.com/in/vidushi-gupta07/), [Jasleen Sondhi](https://www.linkedin.com/in/jasleen-sondhi/) -|![سکیچ نوٹ بذریعہ @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.ur.png)| +|![سکیچ نوٹ بذریعہ @sketchthedocs https://sketchthedocs.dev](../../translated_images/ur/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.png)| |:---:| | ڈیٹا سائنس برائے مبتدی - _سکیچ نوٹ بذریعہ [@nitya](https://twitter.com/nitya)_ | @@ -47,7 +47,7 @@ Azure Cloud Advocates at Microsoft خوش ہیں کہ وہ ایک 10 ہفتوں ہماری Discord پر "AI کے ساتھ سیکھیں" سیریز جاری ہے، مزید جاننے اور شامل ہونے کے لیے [Learn with AI Series](https://aka.ms/learnwithai/discord) پر آئیں از 18 - 30 ستمبر، 2025۔ آپ کو GitHub Copilot کو ڈیٹا سائنس کے لیے استعمال کرنے کے ٹپس اور ٹرکس ملیں گے۔ -![AI کے ساتھ سیکھنے کی سیریز](../../translated_images/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.ur.jpg) +![AI کے ساتھ سیکھنے کی سیریز](../../translated_images/ur/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.jpg) # کیا آپ طالب علم ہیں؟ @@ -127,7 +127,7 @@ Azure Cloud Advocates at Microsoft خوش ہیں کہ وہ ایک 10 ہفتوں ## اسباق -|![ اسکیچ نوٹ بذریعہ @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.ur.png)| +|![ اسکیچ نوٹ بذریعہ @sketchthedocs https://sketchthedocs.dev](../../translated_images/ur/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png)| |:---:| | ڈیٹا سائنس برائے ابتدائی افراد: روڈ میپ - _Sketchnote by [@nitya](https://twitter.com/nitya)_ | diff --git a/translations/ur/sketchnotes/README.md b/translations/ur/sketchnotes/README.md index 360e6f10..36987338 100644 --- a/translations/ur/sketchnotes/README.md +++ b/translations/ur/sketchnotes/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: نیتیا نرسمہن، آرٹسٹ -![روڈمیپ اسکیچ نوٹ](../../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.ur.png) +![روڈمیپ اسکیچ نوٹ](../../../translated_images/ur/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png) --- diff --git a/translations/vi/1-Introduction/01-defining-data-science/README.md b/translations/vi/1-Introduction/01-defining-data-science/README.md index 1c1b51bb..1fff0aa5 100644 --- a/translations/vi/1-Introduction/01-defining-data-science/README.md +++ b/translations/vi/1-Introduction/01-defining-data-science/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: --- -[![Video Định nghĩa Khoa học Dữ liệu](../../../../translated_images/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.vi.png)](https://youtu.be/beZ7Mb_oz9I) +[![Video Định nghĩa Khoa học Dữ liệu](../../../../translated_images/vi/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.png)](https://youtu.be/beZ7Mb_oz9I) ## [Câu hỏi trước bài giảng](https://ff-quizzes.netlify.app/en/ds/quiz/0) @@ -153,7 +153,7 @@ Nếu muốn phức tạp hơn, chúng ta có thể vẽ biểu đồ thời gia Trong thử thách này, chúng ta sẽ cố gắng tìm các khái niệm liên quan đến lĩnh vực Khoa học Dữ liệu bằng cách xem xét các văn bản. Chúng ta sẽ lấy một bài viết trên Wikipedia về Khoa học Dữ liệu, tải xuống và xử lý văn bản, sau đó tạo một đám mây từ như thế này: -![Đám mây từ cho Khoa học Dữ liệu](../../../../translated_images/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.vi.png) +![Đám mây từ cho Khoa học Dữ liệu](../../../../translated_images/vi/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.png) Truy cập [`notebook.ipynb`](../../../../1-Introduction/01-defining-data-science/notebook.ipynb ':ignore') để đọc qua mã. Bạn cũng có thể chạy mã và xem cách nó thực hiện tất cả các chuyển đổi dữ liệu trong thời gian thực. diff --git a/translations/vi/1-Introduction/04-stats-and-probability/README.md b/translations/vi/1-Introduction/04-stats-and-probability/README.md index a156664d..0ac5f436 100644 --- a/translations/vi/1-Introduction/04-stats-and-probability/README.md +++ b/translations/vi/1-Introduction/04-stats-and-probability/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: Lý thuyết Thống kê và Xác suất là hai lĩnh vực liên quan chặt chẽ trong Toán học và rất quan trọng đối với Khoa học Dữ liệu. Bạn có thể làm việc với dữ liệu mà không cần kiến thức sâu về toán học, nhưng vẫn tốt hơn nếu biết ít nhất một số khái niệm cơ bản. Dưới đây là phần giới thiệu ngắn gọn để giúp bạn bắt đầu. -[![Video Giới thiệu](../../../../translated_images/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.vi.png)](https://youtu.be/Z5Zy85g4Yjw) +[![Video Giới thiệu](../../../../translated_images/vi/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.png)](https://youtu.be/Z5Zy85g4Yjw) ## [Câu hỏi trước bài giảng](https://ff-quizzes.netlify.app/en/ds/quiz/6) @@ -39,7 +39,7 @@ Mô tả phân phối xác suất của một biến liên tục khó hơn, vớ Chúng ta chỉ có thể nói về xác suất của một biến nằm trong một khoảng giá trị nhất định, ví dụ P(t1≤X2). Trong trường hợp này, phân phối xác suất được mô tả bằng **hàm mật độ xác suất** p(x), sao cho: -![P(t_1\le X Nhiều ví dụ hơn về tương quan và hiệp phương sai có thể được tìm thấy trong [notebook đi kèm](notebook.ipynb). diff --git a/translations/vi/1-Introduction/README.md b/translations/vi/1-Introduction/README.md index 41abe1da..04eedb75 100644 --- a/translations/vi/1-Introduction/README.md +++ b/translations/vi/1-Introduction/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Giới thiệu về Khoa học Dữ liệu -![dữ liệu trong hành động](../../../translated_images/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.vi.jpg) +![dữ liệu trong hành động](../../../translated_images/vi/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.jpg) > Ảnh của Stephen Dawson trên Unsplash Trong các bài học này, bạn sẽ khám phá cách Khoa học Dữ liệu được định nghĩa và tìm hiểu về các cân nhắc đạo đức mà một nhà khoa học dữ liệu cần lưu ý. Bạn cũng sẽ học cách dữ liệu được định nghĩa và tìm hiểu một chút về thống kê và xác suất, những lĩnh vực học thuật cốt lõi của Khoa học Dữ liệu. diff --git a/translations/vi/2-Working-With-Data/07-python/README.md b/translations/vi/2-Working-With-Data/07-python/README.md index 027d2296..90652c4f 100644 --- a/translations/vi/2-Working-With-Data/07-python/README.md +++ b/translations/vi/2-Working-With-Data/07-python/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: | :-------------------------------------------------------------------------------------------------------: | | Làm việc với Python - _Sketchnote của [@nitya](https://twitter.com/nitya)_ | -[![Video Giới thiệu](../../../../translated_images/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.vi.png)](https://youtu.be/dZjWOGbsN4Y) +[![Video Giới thiệu](../../../../translated_images/vi/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.png)](https://youtu.be/dZjWOGbsN4Y) Mặc dù cơ sở dữ liệu cung cấp cách lưu trữ dữ liệu và truy vấn chúng rất hiệu quả bằng ngôn ngữ truy vấn, cách linh hoạt nhất để xử lý dữ liệu là viết chương trình của riêng bạn để thao tác dữ liệu. Trong nhiều trường hợp, việc thực hiện truy vấn cơ sở dữ liệu sẽ hiệu quả hơn. Tuy nhiên, trong một số trường hợp khi cần xử lý dữ liệu phức tạp hơn, điều này không thể thực hiện dễ dàng bằng SQL. Xử lý dữ liệu có thể được lập trình bằng bất kỳ ngôn ngữ lập trình nào, nhưng có một số ngôn ngữ ở cấp độ cao hơn khi làm việc với dữ liệu. Các nhà khoa học dữ liệu thường ưu tiên một trong các ngôn ngữ sau: @@ -73,7 +73,7 @@ print(f"Length of index is {len(idx)}") items_sold = pd.Series(np.random.randint(25,50,size=len(idx)),index=idx) items_sold.plot() ``` -![Biểu đồ chuỗi thời gian](../../../../translated_images/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.vi.png) +![Biểu đồ chuỗi thời gian](../../../../translated_images/vi/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.png) Bây giờ giả sử rằng mỗi tuần chúng ta tổ chức một bữa tiệc cho bạn bè và lấy thêm 10 hộp kem cho bữa tiệc. Chúng ta có thể tạo một series khác, được index theo tuần, để minh họa điều đó: ```python @@ -84,7 +84,7 @@ Khi chúng ta cộng hai series lại với nhau, chúng ta sẽ có tổng số total_items = items_sold.add(additional_items,fill_value=0) total_items.plot() ``` -![Biểu đồ chuỗi thời gian](../../../../translated_images/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.vi.png) +![Biểu đồ chuỗi thời gian](../../../../translated_images/vi/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.png) > **Lưu ý** rằng chúng ta không sử dụng cú pháp đơn giản `total_items+additional_items`. Nếu làm vậy, chúng ta sẽ nhận được rất nhiều giá trị `NaN` (*Not a Number*) trong series kết quả. Điều này là do có các giá trị bị thiếu cho một số điểm index trong series `additional_items`, và việc cộng `NaN` với bất kỳ giá trị nào sẽ dẫn đến `NaN`. Do đó, chúng ta cần chỉ định tham số `fill_value` trong quá trình cộng. @@ -93,7 +93,7 @@ Với chuỗi thời gian, chúng ta cũng có thể **resample** series với c monthly = total_items.resample("1M").mean() ax = monthly.plot(kind='bar') ``` -![Biểu đồ trung bình chuỗi thời gian hàng tháng](../../../../translated_images/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.vi.png) +![Biểu đồ trung bình chuỗi thời gian hàng tháng](../../../../translated_images/vi/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.png) ### DataFrame @@ -219,7 +219,7 @@ Vấn đề đầu tiên chúng ta sẽ tập trung là mô hình hóa sự lây Vì chúng ta muốn minh họa cách xử lý dữ liệu, hãy mở [`notebook-covidspread.ipynb`](notebook-covidspread.ipynb) và đọc từ đầu đến cuối. Bạn cũng có thể thực thi các ô lệnh và thực hiện một số thử thách mà chúng tôi đã để lại cho bạn ở cuối. -![COVID Spread](../../../../translated_images/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.vi.png) +![COVID Spread](../../../../translated_images/vi/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.png) > Nếu bạn không biết cách chạy mã trong Jupyter Notebook, hãy xem [bài viết này](https://soshnikov.com/education/how-to-execute-notebooks-from-github/). @@ -241,7 +241,7 @@ Một ví dụ đầy đủ về việc phân tích bộ dữ liệu này bằng Hãy mở [`notebook-papers.ipynb`](notebook-papers.ipynb) và đọc từ đầu đến cuối. Bạn cũng có thể thực thi các ô lệnh và thực hiện một số thử thách mà chúng tôi đã để lại cho bạn ở cuối. -![Covid Medical Treatment](../../../../translated_images/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.vi.png) +![Covid Medical Treatment](../../../../translated_images/vi/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.png) ## Xử Lý Dữ Liệu Hình Ảnh diff --git a/translations/vi/2-Working-With-Data/README.md b/translations/vi/2-Working-With-Data/README.md index 73d31520..6f7fb2c7 100644 --- a/translations/vi/2-Working-With-Data/README.md +++ b/translations/vi/2-Working-With-Data/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Làm việc với Dữ liệu -![data love](../../../translated_images/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.vi.jpg) +![data love](../../../translated_images/vi/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.jpg) > Ảnh của Alexander Sinn trên Unsplash Trong các bài học này, bạn sẽ tìm hiểu một số cách mà dữ liệu có thể được quản lý, xử lý và sử dụng trong các ứng dụng. Bạn sẽ học về cơ sở dữ liệu quan hệ và phi quan hệ, cũng như cách dữ liệu có thể được lưu trữ trong chúng. Bạn sẽ nắm được các kiến thức cơ bản về cách làm việc với Python để quản lý dữ liệu, và khám phá một số cách mà Python có thể được sử dụng để quản lý và khai thác dữ liệu. diff --git a/translations/vi/3-Data-Visualization/12-visualization-relationships/README.md b/translations/vi/3-Data-Visualization/12-visualization-relationships/README.md index b45dabf0..bedecee1 100644 --- a/translations/vi/3-Data-Visualization/12-visualization-relationships/README.md +++ b/translations/vi/3-Data-Visualization/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ Tạo một biểu đồ phân tán cơ bản để hiển thị mối quan hệ ```python sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ``` -![scatterplot 1](../../../../translated_images/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.vi.png) +![scatterplot 1](../../../../translated_images/vi/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.png) Bây giờ, hiển thị cùng dữ liệu với một bảng màu mật ong để thể hiện cách giá thay đổi theo năm. Bạn có thể làm điều này bằng cách thêm tham số 'hue' để hiển thị sự thay đổi theo năm: @@ -60,7 +60,7 @@ Bây giờ, hiển thị cùng dữ liệu với một bảng màu mật ong đ ```python sns.relplot(x="priceperlb", y="state", hue="year", palette="YlOrBr", data=honey, height=15, aspect=.5); ``` -![scatterplot 2](../../../../translated_images/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.vi.png) +![scatterplot 2](../../../../translated_images/vi/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.png) Với sự thay đổi bảng màu này, bạn có thể thấy rõ ràng rằng có một sự tiến triển mạnh mẽ theo năm về giá mật ong mỗi pound. Thực tế, nếu bạn xem xét một tập mẫu trong dữ liệu để xác minh (chọn một bang cụ thể, ví dụ Arizona), bạn có thể thấy một xu hướng tăng giá theo năm, với một vài ngoại lệ: @@ -89,7 +89,7 @@ sns.relplot(x="priceperlb", y="state", size="year", data=honey, height=15, aspec ``` Bạn có thể thấy kích thước của các điểm tăng dần. -![scatterplot 3](../../../../translated_images/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.vi.png) +![scatterplot 3](../../../../translated_images/vi/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.png) Đây có phải là một trường hợp đơn giản của cung và cầu? Do các yếu tố như biến đổi khí hậu và sự sụp đổ của đàn ong, liệu có ít mật ong hơn để mua theo năm, và do đó giá tăng lên? @@ -104,7 +104,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); ``` Trả lời: Có, với một số ngoại lệ vào khoảng năm 2003: -![line chart 1](../../../../translated_images/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.vi.png) +![line chart 1](../../../../translated_images/vi/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.png) ✅ Vì Seaborn đang tổng hợp dữ liệu xung quanh một đường, nó hiển thị "các phép đo nhiều lần tại mỗi giá trị x bằng cách vẽ trung bình và khoảng tin cậy 95% xung quanh trung bình". [Nguồn](https://seaborn.pydata.org/tutorial/relational.html). Hành vi tốn thời gian này có thể bị vô hiệu hóa bằng cách thêm `ci=None`. @@ -114,7 +114,7 @@ Câu hỏi: Vậy, vào năm 2003, liệu chúng ta có thể thấy sự tăng sns.relplot(x="year", y="totalprod", kind="line", data=honey); ``` -![line chart 2](../../../../translated_images/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.vi.png) +![line chart 2](../../../../translated_images/vi/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.png) Trả lời: Không hẳn. Nếu bạn xem xét tổng sản lượng, thực tế nó dường như đã tăng trong năm đó, mặc dù nhìn chung lượng mật ong được sản xuất đang giảm trong những năm này. @@ -139,7 +139,7 @@ sns.relplot( ``` Trong hình dung này, bạn có thể so sánh sản lượng mỗi đàn và số lượng đàn ong theo năm, cạnh nhau với wrap được đặt là 3 cho các cột: -![facet grid](../../../../translated_images/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.vi.png) +![facet grid](../../../../translated_images/vi/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.png) Đối với tập dữ liệu này, không có gì đặc biệt nổi bật liên quan đến số lượng đàn ong và sản lượng của chúng theo năm và theo bang. Có cách nào khác để tìm mối tương quan giữa hai biến này không? @@ -162,7 +162,7 @@ sns.despine(right=False) plt.ylabel('colony yield') ax.figure.legend(); ``` -![superimposed plots](../../../../translated_images/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.vi.png) +![superimposed plots](../../../../translated_images/vi/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.png) Mặc dù không có gì nổi bật vào khoảng năm 2003, nó cho phép chúng ta kết thúc bài học này với một ghi chú tích cực hơn: mặc dù số lượng đàn ong đang giảm, số lượng đàn ong đang ổn định ngay cả khi sản lượng mỗi đàn đang giảm. diff --git a/translations/vi/3-Data-Visualization/R/09-visualization-quantities/README.md b/translations/vi/3-Data-Visualization/R/09-visualization-quantities/README.md index b7dc04a2..ff6636f2 100644 --- a/translations/vi/3-Data-Visualization/R/09-visualization-quantities/README.md +++ b/translations/vi/3-Data-Visualization/R/09-visualization-quantities/README.md @@ -66,7 +66,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` Ở đây, bạn cài đặt gói `ggplot2` và sau đó nhập nó vào workspace bằng lệnh `library("ggplot2")`. Để vẽ bất kỳ biểu đồ nào trong ggplot, hàm `ggplot()` được sử dụng và bạn chỉ định tập dữ liệu, các biến x và y làm thuộc tính. Trong trường hợp này, chúng ta sử dụng hàm `geom_line()` vì mục tiêu là vẽ biểu đồ đường. -![MaxWingspan-lineplot](../../../../../translated_images/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.vi.png) +![MaxWingspan-lineplot](../../../../../translated_images/vi/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.png) Bạn nhận thấy điều gì ngay lập tức? Có vẻ như có ít nhất một giá trị ngoại lai - đó là một sải cánh khá lớn! Một sải cánh hơn 2000 cm tương đương hơn 20 mét - liệu có phải khủng long bay đang lang thang ở Minnesota? Hãy điều tra. @@ -84,7 +84,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` Chúng ta chỉ định góc trong `theme` và chỉ định nhãn cho trục x và y trong `xlab()` và `ylab()` tương ứng. Hàm `ggtitle()` đặt tên cho biểu đồ. -![MaxWingspan-lineplot-improved](../../../../../translated_images/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.vi.png) +![MaxWingspan-lineplot-improved](../../../../../translated_images/vi/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.png) Ngay cả khi xoay nhãn ở góc 45 độ, vẫn có quá nhiều nhãn để đọc. Hãy thử một chiến lược khác: chỉ gắn nhãn cho các giá trị ngoại lai và đặt nhãn trong biểu đồ. Bạn có thể sử dụng biểu đồ scatter để có thêm không gian cho việc gắn nhãn: @@ -100,7 +100,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + Bạn phát hiện ra điều gì? -![MaxWingspan-scatterplot](../../../../../translated_images/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.vi.png) +![MaxWingspan-scatterplot](../../../../../translated_images/vi/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.png) ## Lọc dữ liệu của bạn @@ -119,7 +119,7 @@ ggplot(data=birds_filtered, aes(x=Name, y=MaxWingspan,group=1)) + ``` Chúng ta đã tạo một dataframe mới `birds_filtered` và sau đó vẽ một biểu đồ scatter. Bằng cách lọc các giá trị ngoại lai, dữ liệu của bạn giờ đây trở nên gắn kết và dễ hiểu hơn. -![MaxWingspan-scatterplot-improved](../../../../../translated_images/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.vi.png) +![MaxWingspan-scatterplot-improved](../../../../../translated_images/vi/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.png) Bây giờ chúng ta đã có một tập dữ liệu sạch hơn ít nhất là về sải cánh, hãy khám phá thêm về những loài chim này. @@ -161,7 +161,7 @@ birds_filtered %>% group_by(Category) %>% ``` Trong đoạn mã sau, chúng ta cài đặt các gói [dplyr](https://www.rdocumentation.org/packages/dplyr/versions/0.7.8) và [lubridate](https://www.rdocumentation.org/packages/lubridate/versions/1.8.0) để hỗ trợ thao tác và nhóm dữ liệu nhằm vẽ biểu đồ cột xếp chồng. Đầu tiên, bạn nhóm dữ liệu theo `Category` của chim và sau đó tóm tắt các cột `MinLength`, `MaxLength`, `MinBodyMass`, `MaxBodyMass`, `MinWingspan`, `MaxWingspan`. Sau đó, vẽ biểu đồ cột bằng gói `ggplot2` và chỉ định màu sắc cho các loại khác nhau và nhãn. -![Stacked bar chart](../../../../../translated_images/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.vi.png) +![Stacked bar chart](../../../../../translated_images/vi/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.png) Tuy nhiên, biểu đồ cột này khó đọc vì có quá nhiều dữ liệu không được nhóm. Bạn cần chọn chỉ dữ liệu mà bạn muốn vẽ, vì vậy hãy xem xét chiều dài của chim dựa trên loại của chúng. @@ -176,7 +176,7 @@ ggplot(birds_count,aes(Category,n))+geom_bar(stat="identity")+coord_flip() ``` Bạn đầu tiên đếm các giá trị duy nhất trong cột `Category` và sau đó sắp xếp chúng vào một dataframe mới `birds_count`. Dữ liệu đã sắp xếp này sau đó được đưa vào cùng cấp để nó được vẽ theo cách đã sắp xếp. Sử dụng `ggplot2`, bạn sau đó vẽ dữ liệu trong một biểu đồ cột. Hàm `coord_flip()` vẽ các cột theo chiều ngang. -![category-length](../../../../../translated_images/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.vi.png) +![category-length](../../../../../translated_images/vi/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.png) Biểu đồ cột này cho thấy một cái nhìn tốt về số lượng chim trong mỗi loại. Trong nháy mắt, bạn thấy rằng số lượng chim lớn nhất trong khu vực này thuộc loại Vịt/ngỗng/chim nước. Minnesota là "vùng đất của 10.000 hồ" nên điều này không có gì ngạc nhiên! @@ -199,7 +199,7 @@ ggplot(birds_grouped,aes(Category,MaxLength))+geom_bar(stat="identity")+coord_fl ``` Chúng ta nhóm dữ liệu `birds_filtered` theo `Category` và sau đó vẽ một biểu đồ cột. -![comparing data](../../../../../translated_images/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.vi.png) +![comparing data](../../../../../translated_images/vi/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.png) Không có gì ngạc nhiên ở đây: chim ruồi có chiều dài tối đa nhỏ nhất so với bồ nông hoặc ngỗng. Thật tốt khi dữ liệu có ý nghĩa logic! @@ -211,7 +211,7 @@ ggplot(data=birds_grouped, aes(x=Category)) + geom_bar(aes(y=MinLength), stat="identity", position="identity", fill='orange')+ coord_flip() ``` -![super-imposed values](../../../../../translated_images/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.vi.png) +![super-imposed values](../../../../../translated_images/vi/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.png) ## 🚀 Thử thách diff --git a/translations/vi/3-Data-Visualization/R/10-visualization-distributions/README.md b/translations/vi/3-Data-Visualization/R/10-visualization-distributions/README.md index 935f6204..6d6e6da7 100644 --- a/translations/vi/3-Data-Visualization/R/10-visualization-distributions/README.md +++ b/translations/vi/3-Data-Visualization/R/10-visualization-distributions/README.md @@ -45,7 +45,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + geom_point() + ggtitle("Max Length per order") + coord_flip() ``` -![chiều dài tối đa theo bộ](../../../../../translated_images/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.vi.png) +![chiều dài tối đa theo bộ](../../../../../translated_images/vi/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.png) Điều này cung cấp một cái nhìn tổng quan về phân bố chung của chiều dài cơ thể theo từng bộ chim, nhưng đây không phải là cách tối ưu để hiển thị phân bố thực sự. Nhiệm vụ này thường được thực hiện bằng cách tạo biểu đồ Histogram. @@ -57,7 +57,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=10)+ylab('Frequency') ``` -![phân bố trên toàn bộ bộ dữ liệu](../../../../../translated_images/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.vi.png) +![phân bố trên toàn bộ bộ dữ liệu](../../../../../translated_images/vi/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.png) Như bạn có thể thấy, hầu hết hơn 400 loài chim trong bộ dữ liệu này nằm trong phạm vi dưới 2000 cho khối lượng cơ thể tối đa của chúng. Tìm hiểu thêm về dữ liệu bằng cách thay đổi tham số `bins` thành một số lớn hơn, chẳng hạn như 30: @@ -65,7 +65,7 @@ Như bạn có thể thấy, hầu hết hơn 400 loài chim trong bộ dữ li ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![phân bố-30bins](../../../../../translated_images/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.vi.png) +![phân bố-30bins](../../../../../translated_images/vi/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.png) Biểu đồ này hiển thị phân bố một cách chi tiết hơn. Một biểu đồ ít lệch về bên trái hơn có thể được tạo bằng cách đảm bảo rằng bạn chỉ chọn dữ liệu trong một phạm vi nhất định: @@ -77,7 +77,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![biểu đồ histogram đã lọc](../../../../../translated_images/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.vi.png) +![biểu đồ histogram đã lọc](../../../../../translated_images/vi/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.png) ✅ Thử một số bộ lọc và điểm dữ liệu khác. Để xem toàn bộ phân bố của dữ liệu, loại bỏ bộ lọc `['MaxBodyMass']` để hiển thị các phân bố có nhãn. @@ -91,7 +91,7 @@ ggplot(data=birds_filtered_1, aes(x=MaxBodyMass, y=MaxLength) ) + ``` Có vẻ như có một mối tương quan dự kiến giữa hai yếu tố này dọc theo một trục dự kiến, với một điểm hội tụ đặc biệt mạnh: -![biểu đồ 2d](../../../../../translated_images/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.vi.png) +![biểu đồ 2d](../../../../../translated_images/vi/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.png) Biểu đồ Histogram hoạt động tốt theo mặc định cho dữ liệu dạng số. Vậy nếu bạn cần xem phân bố theo dữ liệu dạng văn bản thì sao? @@ -123,7 +123,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + scale_fill_manual(name="Conservation Status",values=c("red","green","blue","pink"),labels=c("Endangered","Near Threathened","Vulnerable","Least Concern")) ``` -![sải cánh và tình trạng bảo tồn](../../../../../translated_images/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.vi.png) +![sải cánh và tình trạng bảo tồn](../../../../../translated_images/vi/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.png) Dường như không có mối tương quan tốt giữa sải cánh tối thiểu và tình trạng bảo tồn. Thử nghiệm các yếu tố khác của bộ dữ liệu bằng phương pháp này. Bạn có thể thử các bộ lọc khác nhau. Bạn có tìm thấy mối tương quan nào không? @@ -137,7 +137,7 @@ Hãy làm việc với biểu đồ mật độ ngay bây giờ! ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + geom_density() ``` -![biểu đồ mật độ](../../../../../translated_images/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.vi.png) +![biểu đồ mật độ](../../../../../translated_images/vi/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.png) Bạn có thể thấy cách biểu đồ phản ánh biểu đồ trước đó về dữ liệu sải cánh tối thiểu; nó chỉ mượt mà hơn một chút. Nếu bạn muốn xem lại đường gấp khúc của MaxBodyMass trong biểu đồ thứ hai mà bạn đã tạo, bạn có thể làm mượt nó rất tốt bằng cách tạo lại nó bằng phương pháp này: @@ -145,7 +145,7 @@ Bạn có thể thấy cách biểu đồ phản ánh biểu đồ trước đó ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density() ``` -![mật độ khối lượng cơ thể](../../../../../translated_images/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.vi.png) +![mật độ khối lượng cơ thể](../../../../../translated_images/vi/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.png) Nếu bạn muốn một đường mượt mà nhưng không quá mượt, hãy chỉnh sửa tham số `adjust`: @@ -153,7 +153,7 @@ Nếu bạn muốn một đường mượt mà nhưng không quá mượt, hãy ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density(adjust = 1/5) ``` -![khối lượng cơ thể ít mượt hơn](../../../../../translated_images/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.vi.png) +![khối lượng cơ thể ít mượt hơn](../../../../../translated_images/vi/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.png) ✅ Đọc về các tham số có sẵn cho loại biểu đồ này và thử nghiệm! @@ -163,7 +163,7 @@ Loại biểu đồ này cung cấp các hình ảnh trực quan giải thích r ggplot(data=birds_filtered_1,aes(x = MaxBodyMass, fill = Order)) + geom_density(alpha=0.5) ``` -![khối lượng cơ thể theo bộ](../../../../../translated_images/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.vi.png) +![khối lượng cơ thể theo bộ](../../../../../translated_images/vi/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.png) ## 🚀 Thử thách diff --git a/translations/vi/3-Data-Visualization/R/11-visualization-proportions/README.md b/translations/vi/3-Data-Visualization/R/11-visualization-proportions/README.md index 723d2354..7f5628e9 100644 --- a/translations/vi/3-Data-Visualization/R/11-visualization-proportions/README.md +++ b/translations/vi/3-Data-Visualization/R/11-visualization-proportions/README.md @@ -93,7 +93,7 @@ pie(grouped$count,grouped$class, main="Edible?") ``` Voila, một biểu đồ tròn hiển thị tỷ lệ của dữ liệu này theo hai lớp nấm. Điều rất quan trọng là phải sắp xếp đúng thứ tự của các nhãn, đặc biệt ở đây, vì vậy hãy chắc chắn kiểm tra thứ tự mà mảng nhãn được xây dựng! -![biểu đồ tròn](../../../../../translated_images/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.vi.png) +![biểu đồ tròn](../../../../../translated_images/vi/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.png) ## Biểu đồ donut! @@ -128,7 +128,7 @@ library(webr) PieDonut(habitat, aes(habitat, count=count)) ``` -![biểu đồ donut](../../../../../translated_images/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.vi.png) +![biểu đồ donut](../../../../../translated_images/vi/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.png) Đoạn mã này sử dụng hai thư viện - ggplot2 và webr. Sử dụng hàm PieDonut của thư viện webr, chúng ta có thể dễ dàng tạo biểu đồ donut! @@ -166,7 +166,7 @@ waffle((cap_color$count/10), rows = 7, title = "Waffle Chart")+scale_fill_manual Sử dụng biểu đồ waffle, bạn có thể dễ dàng thấy tỷ lệ màu sắc của mũ nấm trong bộ dữ liệu này. Thật thú vị, có rất nhiều nấm có mũ màu xanh lá cây! -![biểu đồ waffle](../../../../../translated_images/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.vi.png) +![biểu đồ waffle](../../../../../translated_images/vi/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.png) Trong bài học này, bạn đã học ba cách để trực quan hóa tỷ lệ. Đầu tiên, bạn cần nhóm dữ liệu của mình thành các danh mục và sau đó quyết định cách tốt nhất để hiển thị dữ liệu - biểu đồ tròn, donut, hoặc waffle. Tất cả đều hấp dẫn và mang lại cho người dùng một cái nhìn nhanh về bộ dữ liệu. diff --git a/translations/vi/3-Data-Visualization/R/12-visualization-relationships/README.md b/translations/vi/3-Data-Visualization/R/12-visualization-relationships/README.md index 7c3073e9..48d8d0f6 100644 --- a/translations/vi/3-Data-Visualization/R/12-visualization-relationships/README.md +++ b/translations/vi/3-Data-Visualization/R/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ library(ggplot2) ggplot(honey, aes(x = priceperlb, y = state)) + geom_point(colour = "blue") ``` -![scatterplot 1](../../../../../translated_images/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.vi.png) +![scatterplot 1](../../../../../translated_images/vi/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.png) Bây giờ, hiển thị cùng dữ liệu với bảng màu mật ong để thể hiện cách giá thay đổi theo năm. Bạn có thể làm điều này bằng cách thêm tham số 'scale_color_gradientn' để hiển thị sự thay đổi theo năm: @@ -61,7 +61,7 @@ Bây giờ, hiển thị cùng dữ liệu với bảng màu mật ong để th ggplot(honey, aes(x = priceperlb, y = state, color=year)) + geom_point()+scale_color_gradientn(colours = colorspace::heat_hcl(7)) ``` -![scatterplot 2](../../../../../translated_images/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.vi.png) +![scatterplot 2](../../../../../translated_images/vi/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.png) Với sự thay đổi bảng màu này, bạn có thể thấy rõ ràng rằng có một sự tiến triển mạnh mẽ theo năm về giá mật ong mỗi pound. Thực tế, nếu bạn xem xét một tập mẫu trong dữ liệu để xác minh (chọn một bang cụ thể, ví dụ Arizona), bạn có thể thấy một xu hướng tăng giá theo năm, với một vài ngoại lệ: @@ -92,7 +92,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ``` Bạn có thể thấy kích thước của các điểm tăng dần. -![scatterplot 3](../../../../../translated_images/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.vi.png) +![scatterplot 3](../../../../../translated_images/vi/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.png) Đây có phải là một trường hợp đơn giản của cung và cầu? Do các yếu tố như biến đổi khí hậu và sự sụp đổ của đàn ong, liệu có ít mật ong hơn để mua theo năm, dẫn đến giá tăng? @@ -107,7 +107,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab ``` Trả lời: Có, với một số ngoại lệ vào khoảng năm 2003: -![line chart 1](../../../../../translated_images/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.vi.png) +![line chart 1](../../../../../translated_images/vi/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.png) Câu hỏi: Vậy vào năm 2003, liệu chúng ta có thể thấy sự tăng đột biến trong nguồn cung mật ong không? Nếu bạn xem xét tổng sản lượng theo năm thì sao? @@ -115,7 +115,7 @@ Câu hỏi: Vậy vào năm 2003, liệu chúng ta có thể thấy sự tăng qplot(honey$year,honey$totalprod, geom='smooth', span =0.5, xlab = "year",ylab = "totalprod") ``` -![line chart 2](../../../../../translated_images/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.vi.png) +![line chart 2](../../../../../translated_images/vi/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.png) Trả lời: Không hẳn. Nếu bạn xem xét tổng sản lượng, thực tế nó dường như đã tăng trong năm đó, mặc dù nhìn chung lượng mật ong được sản xuất đang giảm trong những năm này. @@ -135,7 +135,7 @@ ggplot(honey, aes(x=yieldpercol, y = numcol,group = 1)) + ``` Trong hình ảnh hóa này, bạn có thể so sánh sản lượng mỗi đàn và số lượng đàn ong theo năm, cạnh nhau với wrap được đặt là 3 cho các cột: -![facet grid](../../../../../translated_images/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.vi.png) +![facet grid](../../../../../translated_images/vi/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.png) Đối với tập dữ liệu này, không có điều gì đặc biệt nổi bật liên quan đến số lượng đàn ong và sản lượng của chúng theo năm và theo bang. Liệu có cách khác để tìm mối tương quan giữa hai biến này? @@ -152,7 +152,7 @@ plot(honey$year, honey$yieldpercol, pch = 17, col = 3, axis(side = 4, at = pretty(range(y2))) mtext("colony yield", side = 4, line = 3) ``` -![superimposed plots](../../../../../translated_images/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.vi.png) +![superimposed plots](../../../../../translated_images/vi/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.png) Mặc dù không có điều gì nổi bật vào khoảng năm 2003, điều này cho phép chúng ta kết thúc bài học với một ghi chú vui vẻ hơn: mặc dù số lượng đàn ong đang giảm, số lượng đàn ong đang ổn định ngay cả khi sản lượng mỗi đàn đang giảm. diff --git a/translations/vi/3-Data-Visualization/R/13-meaningful-vizualizations/README.md b/translations/vi/3-Data-Visualization/R/13-meaningful-vizualizations/README.md index 0e6d8175..b89b7aad 100644 --- a/translations/vi/3-Data-Visualization/R/13-meaningful-vizualizations/README.md +++ b/translations/vi/3-Data-Visualization/R/13-meaningful-vizualizations/README.md @@ -47,25 +47,25 @@ Trong các bài học trước, bạn đã thử nghiệm xây dựng nhiều lo Ngay cả khi một nhà khoa học dữ liệu cẩn thận chọn đúng biểu đồ cho dữ liệu, vẫn có nhiều cách để dữ liệu được trình bày nhằm chứng minh một quan điểm, thường là làm tổn hại đến tính chính xác của dữ liệu. Có rất nhiều ví dụ về các biểu đồ và đồ họa thông tin gây hiểu lầm! -[![How Charts Lie của Alberto Cairo](../../../../../translated_images/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.vi.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "How charts lie") +[![How Charts Lie của Alberto Cairo](../../../../../translated_images/vi/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "How charts lie") > 🎥 Nhấp vào hình ảnh trên để xem một bài nói chuyện về các biểu đồ gây hiểu lầm Biểu đồ này đảo ngược trục X để hiển thị điều ngược lại với sự thật, dựa trên ngày tháng: -![bad chart 1](../../../../../translated_images/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.vi.png) +![bad chart 1](../../../../../translated_images/vi/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.png) [Biểu đồ này](https://media.firstcoastnews.com/assets/WTLV/images/170ae16f-4643-438f-b689-50d66ca6a8d8/170ae16f-4643-438f-b689-50d66ca6a8d8_1140x641.jpg) thậm chí còn gây hiểu lầm hơn, vì mắt người bị thu hút về phía bên phải để kết luận rằng, theo thời gian, số ca COVID đã giảm ở các quận khác nhau. Thực tế, nếu bạn nhìn kỹ vào các ngày tháng, bạn sẽ thấy chúng đã được sắp xếp lại để tạo ra xu hướng giảm giả. -![bad chart 2](../../../../../translated_images/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.vi.jpg) +![bad chart 2](../../../../../translated_images/vi/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.jpg) Ví dụ nổi tiếng này sử dụng màu sắc VÀ trục Y bị lật để đánh lừa: thay vì kết luận rằng số ca tử vong do súng tăng vọt sau khi thông qua luật thân thiện với súng, mắt người lại bị đánh lừa để nghĩ điều ngược lại: -![bad chart 3](../../../../../translated_images/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.vi.jpg) +![bad chart 3](../../../../../translated_images/vi/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.jpg) Biểu đồ kỳ lạ này cho thấy cách tỷ lệ có thể bị thao túng, dẫn đến hiệu ứng hài hước: -![bad chart 4](../../../../../translated_images/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.vi.jpg) +![bad chart 4](../../../../../translated_images/vi/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.jpg) So sánh những thứ không thể so sánh là một thủ thuật mờ ám khác. Có một [trang web tuyệt vời](https://tylervigen.com/spurious-correlations) về 'mối tương quan giả' hiển thị các 'sự thật' như tỷ lệ ly hôn ở Maine và mức tiêu thụ bơ thực vật. Một nhóm trên Reddit cũng thu thập [các ví dụ xấu](https://www.reddit.com/r/dataisugly/top/?t=all) về việc sử dụng dữ liệu. @@ -100,13 +100,13 @@ Gắn nhãn các trục, cung cấp chú giải nếu cần thiết và cung c Nếu dữ liệu của bạn là văn bản và dài dòng trên trục X, bạn có thể xoay góc văn bản để dễ đọc hơn. [plot3D](https://cran.r-project.org/web/packages/plot3D/index.html) cung cấp khả năng vẽ biểu đồ 3D nếu dữ liệu của bạn hỗ trợ. Các biểu đồ trực quan phức tạp có thể được tạo ra bằng cách sử dụng nó. -![3d plots](../../../../../translated_images/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.vi.png) +![3d plots](../../../../../translated_images/vi/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.png) ## Biểu đồ động và hiển thị 3D Một số biểu đồ trực quan tốt nhất hiện nay là biểu đồ động. Shirley Wu có những biểu đồ tuyệt vời được thực hiện bằng D3, chẳng hạn như '[film flowers](http://bl.ocks.org/sxywu/raw/d612c6c653fb8b4d7ff3d422be164a5d/)', nơi mỗi bông hoa là một biểu đồ trực quan của một bộ phim. Một ví dụ khác cho Guardian là 'bussed out', một trải nghiệm tương tác kết hợp biểu đồ trực quan với Greensock và D3 cùng với định dạng bài viết cuộn để kể câu chuyện về cách NYC xử lý vấn đề người vô gia cư bằng cách đưa họ ra khỏi thành phố. -![busing](../../../../../translated_images/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.vi.png) +![busing](../../../../../translated_images/vi/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.png) > "Bussed Out: How America Moves its Homeless" từ [the Guardian](https://www.theguardian.com/us-news/ng-interactive/2017/dec/20/bussed-out-america-moves-homeless-people-country-study). Biểu đồ trực quan bởi Nadieh Bremer & Shirley Wu @@ -116,7 +116,7 @@ Mặc dù bài học này không đủ để đi sâu vào việc dạy các th Bạn sẽ hoàn thành một ứng dụng web hiển thị một cái nhìn động về mạng xã hội này. Nó sử dụng một thư viện được xây dựng để tạo [biểu đồ mạng](https://github.com/emiliorizzo/vue-d3-network) bằng Vue.js và D3. Khi ứng dụng đang chạy, bạn có thể kéo các nút trên màn hình để sắp xếp lại dữ liệu. -![liaisons](../../../../../translated_images/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.vi.png) +![liaisons](../../../../../translated_images/vi/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.png) ## Dự án: Tạo biểu đồ hiển thị mạng bằng D3.js diff --git a/translations/vi/3-Data-Visualization/README.md b/translations/vi/3-Data-Visualization/README.md index eea34a3e..f110a043 100644 --- a/translations/vi/3-Data-Visualization/README.md +++ b/translations/vi/3-Data-Visualization/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Hình ảnh trực quan -![một con ong trên hoa oải hương](../../../translated_images/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.vi.jpg) +![một con ong trên hoa oải hương](../../../translated_images/vi/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.jpg) > Ảnh của Jenna Lee trên Unsplash Việc hình ảnh hóa dữ liệu là một trong những nhiệm vụ quan trọng nhất của một nhà khoa học dữ liệu. Một hình ảnh có giá trị bằng 1000 từ, và hình ảnh trực quan có thể giúp bạn nhận ra nhiều khía cạnh thú vị của dữ liệu như đỉnh, giá trị ngoại lai, nhóm, xu hướng, và nhiều hơn nữa, giúp bạn hiểu câu chuyện mà dữ liệu đang cố gắng truyền tải. diff --git a/translations/vi/4-Data-Science-Lifecycle/14-Introduction/README.md b/translations/vi/4-Data-Science-Lifecycle/14-Introduction/README.md index 47544f57..9e022692 100644 --- a/translations/vi/4-Data-Science-Lifecycle/14-Introduction/README.md +++ b/translations/vi/4-Data-Science-Lifecycle/14-Introduction/README.md @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: Bài học này tập trung vào 3 phần của vòng đời: thu thập, xử lý và bảo trì. -![Sơ đồ vòng đời khoa học dữ liệu](../../../../translated_images/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.vi.jpg) +![Sơ đồ vòng đời khoa học dữ liệu](../../../../translated_images/vi/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.jpg) > Ảnh của [Berkeley School of Information](https://ischoolonline.berkeley.edu/data-science/what-is-data-science/) ## Thu thập @@ -101,7 +101,7 @@ Khám phá [Vòng đời Quy trình Khoa học Dữ liệu của Nhóm](https:// |Quy trình Khoa học Dữ liệu của Nhóm (TDSP)|Quy trình tiêu chuẩn liên ngành cho khai thác dữ liệu (CRISP-DM)| |--|--| -|![Vòng đời Khoa học Dữ liệu của Nhóm](../../../../translated_images/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.vi.png) | ![Hình ảnh Quy trình Khoa học Dữ liệu](../../../../translated_images/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.vi.png) | +|![Vòng đời Khoa học Dữ liệu của Nhóm](../../../../translated_images/vi/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.png) | ![Hình ảnh Quy trình Khoa học Dữ liệu](../../../../translated_images/vi/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.png) | | Hình ảnh của [Microsoft](https://docs.microsoft.comazure/architecture/data-science-process/lifecycle) | Hình ảnh của [Data Science Process Alliance](https://www.datascience-pm.com/crisp-dm-2/) | ## [Câu hỏi sau bài giảng](https://ff-quizzes.netlify.app/en/ds/quiz/27) diff --git a/translations/vi/4-Data-Science-Lifecycle/README.md b/translations/vi/4-Data-Science-Lifecycle/README.md index 38a92c81..ad871846 100644 --- a/translations/vi/4-Data-Science-Lifecycle/README.md +++ b/translations/vi/4-Data-Science-Lifecycle/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # Vòng đời Khoa học Dữ liệu -![communication](../../../translated_images/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.vi.jpg) +![communication](../../../translated_images/vi/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.jpg) > Ảnh của Headway trên Unsplash Trong các bài học này, bạn sẽ khám phá một số khía cạnh của vòng đời Khoa học Dữ liệu, bao gồm phân tích và giao tiếp xoay quanh dữ liệu. diff --git a/translations/vi/5-Data-Science-In-Cloud/README.md b/translations/vi/5-Data-Science-In-Cloud/README.md index c7afa1e8..a3c6ba6d 100644 --- a/translations/vi/5-Data-Science-In-Cloud/README.md +++ b/translations/vi/5-Data-Science-In-Cloud/README.md @@ -9,13 +9,13 @@ CO_OP_TRANSLATOR_METADATA: --> # Khoa học dữ liệu trên đám mây -![cloud-picture](../../../translated_images/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.vi.jpg) +![cloud-picture](../../../translated_images/vi/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.jpg) > Ảnh của [Jelleke Vanooteghem](https://unsplash.com/@ilumire) từ [Unsplash](https://unsplash.com/s/photos/cloud?orientation=landscape) Khi thực hiện khoa học dữ liệu với dữ liệu lớn, đám mây có thể là một yếu tố thay đổi cuộc chơi. Trong ba bài học tiếp theo, chúng ta sẽ tìm hiểu đám mây là gì và tại sao nó có thể rất hữu ích. Chúng ta cũng sẽ khám phá một tập dữ liệu về suy tim và xây dựng một mô hình để giúp đánh giá khả năng một người bị suy tim. Chúng ta sẽ sử dụng sức mạnh của đám mây để huấn luyện, triển khai và sử dụng mô hình theo hai cách khác nhau. Một cách sử dụng giao diện người dùng theo phong cách "Ít mã/Không mã", cách còn lại sử dụng Bộ công cụ phát triển phần mềm Azure Machine Learning (Azure ML SDK). -![project-schema](../../../translated_images/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.vi.png) +![project-schema](../../../translated_images/vi/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.png) ### Các chủ đề diff --git a/translations/vi/6-Data-Science-In-Wild/20-Real-World-Examples/README.md b/translations/vi/6-Data-Science-In-Wild/20-Real-World-Examples/README.md index 5ed44c42..108363e1 100644 --- a/translations/vi/6-Data-Science-In-Wild/20-Real-World-Examples/README.md +++ b/translations/vi/6-Data-Science-In-Wild/20-Real-World-Examples/README.md @@ -41,7 +41,7 @@ Nhờ sự phổ biến của AI, các nhà phát triển hiện nay dễ dàng * [Khoa học Dữ liệu trong Y tế](https://data-flair.training/blogs/data-science-in-healthcare/) - nhấn mạnh các ứng dụng như hình ảnh y tế (ví dụ: MRI, X-Ray, CT-Scan), genomics (giải trình tự DNA), phát triển thuốc (đánh giá rủi ro, dự đoán thành công), phân tích dự đoán (chăm sóc bệnh nhân & hậu cần cung cấp), theo dõi & phòng ngừa bệnh, v.v. -![Ứng dụng Khoa học Dữ liệu trong Thế giới Thực](../../../../translated_images/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.vi.png) Nguồn hình ảnh: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) +![Ứng dụng Khoa học Dữ liệu trong Thế giới Thực](../../../../translated_images/vi/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.png) Nguồn hình ảnh: [Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) Hình minh họa cho thấy các lĩnh vực và ví dụ khác về việc áp dụng các kỹ thuật khoa học dữ liệu. Muốn khám phá thêm các ứng dụng khác? Hãy xem phần [Ôn tập & Tự học](../../../../6-Data-Science-In-Wild/20-Real-World-Examples) bên dưới. diff --git a/translations/vi/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md b/translations/vi/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md index c081c76a..f16df8e1 100644 --- a/translations/vi/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md +++ b/translations/vi/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md @@ -22,7 +22,7 @@ Giao diện Explorer (hiển thị trong ảnh chụp màn hình bên dưới) c 2. Khám phá [Danh mục bộ dữ liệu](https://planetarycomputer.microsoft.com/catalog) - tìm hiểu mục đích của từng bộ dữ liệu. 3. Sử dụng Explorer - chọn một bộ dữ liệu bạn quan tâm, chọn một truy vấn và tùy chọn hiển thị phù hợp. -![Giao diện Explorer của Planetary Computer](../../../../translated_images/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.vi.png) +![Giao diện Explorer của Planetary Computer](../../../../translated_images/vi/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.png) `Nhiệm vụ của bạn:` Bây giờ hãy nghiên cứu trực quan hóa được hiển thị trong trình duyệt và trả lời các câu hỏi sau: diff --git a/translations/vi/CONTRIBUTING.md b/translations/vi/CONTRIBUTING.md index 06ec798f..171c4ec1 100644 --- a/translations/vi/CONTRIBUTING.md +++ b/translations/vi/CONTRIBUTING.md @@ -312,7 +312,7 @@ Bao gồm trong mô tả PR của bạn: import pandas as pd ``` ```` -- Thêm văn bản thay thế cho hình ảnh: `![Alt text](../../translated_images/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.vi.png)` +- Thêm văn bản thay thế cho hình ảnh: `![Alt text](../../translated_images/vi/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.png)` - Giữ độ dài dòng hợp lý (khoảng 80-100 ký tự) ### Python diff --git a/translations/vi/README.md b/translations/vi/README.md index f997da39..76c28784 100644 --- a/translations/vi/README.md +++ b/translations/vi/README.md @@ -33,7 +33,7 @@ Azure Cloud Advocates tại Microsoft rất vui được cung cấp một chươ **🙏 Lời cảm ơn đặc biệt 🙏 tới các tác giả, người đánh giá và những người đóng góp nội dung thuộc [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/),** đáng chú ý có Aaryan Arora, [Aditya Garg](https://github.com/AdityaGarg00), [Alondra Sanchez](https://www.linkedin.com/in/alondra-sanchez-molina/), [Ankita Singh](https://www.linkedin.com/in/ankitasingh007), [Anupam Mishra](https://www.linkedin.com/in/anupam--mishra/), [Arpita Das](https://www.linkedin.com/in/arpitadas01/), ChhailBihari Dubey, [Dibri Nsofor](https://www.linkedin.com/in/dibrinsofor), [Dishita Bhasin](https://www.linkedin.com/in/dishita-bhasin-7065281bb), [Majd Safi](https://www.linkedin.com/in/majd-s/), [Max Blum](https://www.linkedin.com/in/max-blum-6036a1186/), [Miguel Correa](https://www.linkedin.com/in/miguelmque/), [Mohamma Iftekher (Iftu) Ebne Jalal](https://twitter.com/iftu119), [Nawrin Tabassum](https://www.linkedin.com/in/nawrin-tabassum), [Raymond Wangsa Putra](https://www.linkedin.com/in/raymond-wp/), [Rohit Yadav](https://www.linkedin.com/in/rty2423), Samridhi Sharma, [Sanya Sinha](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200), [Sheena Narula](https://www.linkedin.com/in/sheena-narua-n/), [Tauqeer Ahmad](https://www.linkedin.com/in/tauqeerahmad5201/), Yogendrasingh Pawar , [Vidushi Gupta](https://www.linkedin.com/in/vidushi-gupta07/), [Jasleen Sondhi](https://www.linkedin.com/in/jasleen-sondhi/) -|![Sketchnote bởi @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.vi.png)| +|![Sketchnote bởi @sketchthedocs https://sketchthedocs.dev](../../translated_images/vi/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.png)| |:---:| | Khoa học dữ liệu cho người mới bắt đầu - _Sketchnote bởi [@nitya](https://twitter.com/nitya)_ | @@ -52,7 +52,7 @@ Azure Cloud Advocates tại Microsoft rất vui được cung cấp một chươ Chúng tôi đang có chuỗi Discord "Học cùng AI", tìm hiểu thêm và tham gia với chúng tôi tại [Chuỗi Học cùng AI](https://aka.ms/learnwithai/discord) từ 18 - 30 tháng 9, 2025. Bạn sẽ nhận được mẹo và thủ thuật khi sử dụng GitHub Copilot cho Khoa học Dữ liệu. -![Chuỗi Học cùng AI](../../translated_images/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.vi.jpg) +![Chuỗi Học cùng AI](../../translated_images/vi/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.jpg) # Bạn có phải là sinh viên? @@ -132,7 +132,7 @@ Mỗi ví dụ bao gồm các chú thích chi tiết giải thích từng bướ ## Bài học -|![ Sketchnote bởi @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.vi.png)| +|![ Sketchnote bởi @sketchthedocs https://sketchthedocs.dev](../../translated_images/vi/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png)| |:---:| | Khoa học Dữ liệu cho Người mới bắt đầu: Lộ trình - _Sketchnote bởi [@nitya](https://twitter.com/nitya)_ | diff --git a/translations/vi/sketchnotes/README.md b/translations/vi/sketchnotes/README.md index 16bd32fb..2b57bc53 100644 --- a/translations/vi/sketchnotes/README.md +++ b/translations/vi/sketchnotes/README.md @@ -13,7 +13,7 @@ Tìm tất cả các bản vẽ phác thảo tại đây! Nitya Narasimhan, nghệ sĩ -![bản vẽ phác thảo lộ trình](../../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.vi.png) +![bản vẽ phác thảo lộ trình](../../../translated_images/vi/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png) --- diff --git a/translations/zh/1-Introduction/01-defining-data-science/README.md b/translations/zh/1-Introduction/01-defining-data-science/README.md index 16454600..b7cc28a7 100644 --- a/translations/zh/1-Introduction/01-defining-data-science/README.md +++ b/translations/zh/1-Introduction/01-defining-data-science/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: --- -[![数据科学定义视频](../../../../translated_images/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.zh.png)](https://youtu.be/beZ7Mb_oz9I) +[![数据科学定义视频](../../../../translated_images/zh/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.png)](https://youtu.be/beZ7Mb_oz9I) ## [课前测验](https://ff-quizzes.netlify.app/en/ds/quiz/0) @@ -153,7 +153,7 @@ CO_OP_TRANSLATOR_METADATA: 在这个挑战中，我们将尝试通过分析文本来找到与数据科学领域相关的概念。我们将选取一篇关于数据科学的维基百科文章，下载并处理文本，然后构建一个像这样的词云： -![数据科学词云](../../../../translated_images/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.zh.png) +![数据科学词云](../../../../translated_images/zh/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.png) 访问 [`notebook.ipynb`](../../../../1-Introduction/01-defining-data-science/notebook.ipynb ':ignore') 阅读代码。你也可以运行代码，实时查看它如何执行所有数据转换。 diff --git a/translations/zh/1-Introduction/04-stats-and-probability/README.md b/translations/zh/1-Introduction/04-stats-and-probability/README.md index e9b14ab0..47845fbd 100644 --- a/translations/zh/1-Introduction/04-stats-and-probability/README.md +++ b/translations/zh/1-Introduction/04-stats-and-probability/README.md @@ -15,7 +15,7 @@ CO_OP_TRANSLATOR_METADATA: 统计学和概率论是数学中两个密切相关的领域，与数据科学高度相关。虽然在没有深厚数学知识的情况下也可以处理数据，但了解一些基本概念仍然是有益的。在这里，我们将提供一个简短的介绍，帮助您入门。 -[![介绍视频](../../../../translated_images/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.zh.png)](https://youtu.be/Z5Zy85g4Yjw) +[![介绍视频](../../../../translated_images/zh/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.png)](https://youtu.be/Z5Zy85g4Yjw) ## [课前测验](https://ff-quizzes.netlify.app/en/ds/quiz/6) @@ -39,7 +39,7 @@ CO_OP_TRANSLATOR_METADATA: 我们只能讨论变量落入某个值区间的概率，例如 P(t1≤X2)。在这种情况下，概率分布由 **概率密度函数** p(x) 描述，其满足： -![P(t_1\le X 更多关于相关性和协方差的示例可以在 [配套笔记本](notebook.ipynb) 中找到。 diff --git a/translations/zh/1-Introduction/README.md b/translations/zh/1-Introduction/README.md index 8392b804..4e569a4a 100644 --- a/translations/zh/1-Introduction/README.md +++ b/translations/zh/1-Introduction/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 数据科学简介 -![数据的实际应用](../../../translated_images/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.zh.jpg) +![数据的实际应用](../../../translated_images/zh/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.jpg) > 图片由 Stephen Dawson 提供，来自 Unsplash 在这些课程中，您将了解数据科学的定义，并学习数据科学家必须考虑的伦理问题。您还将学习数据的定义，并对统计学和概率论有一些初步了解，这些是数据科学的核心学术领域。 diff --git a/translations/zh/2-Working-With-Data/07-python/README.md b/translations/zh/2-Working-With-Data/07-python/README.md index f552050d..02b6a403 100644 --- a/translations/zh/2-Working-With-Data/07-python/README.md +++ b/translations/zh/2-Working-With-Data/07-python/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: | :-------------------------------------------------------------------------------------------------------: | | 使用Python - _Sketchnote by [@nitya](https://twitter.com/nitya)_ | -[![介绍视频](../../../../translated_images/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.zh.png)](https://youtu.be/dZjWOGbsN4Y) +[![介绍视频](../../../../translated_images/zh/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.png)](https://youtu.be/dZjWOGbsN4Y) 虽然数据库提供了非常高效的方式来存储数据并通过查询语言进行查询，但最灵活的数据处理方式是编写自己的程序来操作数据。在许多情况下，使用数据库查询可能更有效。然而，当需要更复杂的数据处理时，SQL可能无法轻松完成。 数据处理可以用任何编程语言编写，但有些语言在处理数据方面更高级。数据科学家通常偏好以下语言之一： @@ -72,7 +72,7 @@ print(f"Length of index is {len(idx)}") items_sold = pd.Series(np.random.randint(25,50,size=len(idx)),index=idx) items_sold.plot() ``` -![时间序列图](../../../../translated_images/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.zh.png) +![时间序列图](../../../../translated_images/zh/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.png) 假设每周我们都会举办一个朋友聚会，并额外拿出10盒冰淇淋用于聚会。我们可以创建另一个以周为索引的Series来展示这一点： ```python @@ -83,7 +83,7 @@ additional_items = pd.Series(10,index=pd.date_range(start_date,end_date,freq="W" total_items = items_sold.add(additional_items,fill_value=0) total_items.plot() ``` -![时间序列图](../../../../translated_images/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.zh.png) +![时间序列图](../../../../translated_images/zh/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.png) > **注意** 我们没有使用简单的语法 `total_items+additional_items`。如果使用这种方法，我们会在结果Series中得到许多`NaN`（*Not a Number*）值。这是因为在`additional_items`的某些索引点上缺少值，而将`NaN`与任何值相加都会得到`NaN`。因此，我们需要在相加时指定`fill_value`参数。 @@ -92,7 +92,7 @@ total_items.plot() monthly = total_items.resample("1M").mean() ax = monthly.plot(kind='bar') ``` -![每月时间序列平均值](../../../../translated_images/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.zh.png) +![每月时间序列平均值](../../../../translated_images/zh/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.png) ### DataFrame（数据框） @@ -218,7 +218,7 @@ df = pd.read_csv('file.csv') 由于我们想演示如何处理数据，我们邀请你打开 [`notebook-covidspread.ipynb`](notebook-covidspread.ipynb) 并从头到尾阅读。你也可以执行单元格，并完成我们在最后留下的一些挑战。 -![COVID 传播](../../../../translated_images/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.zh.png) +![COVID 传播](../../../../translated_images/zh/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.png) > 如果你不知道如何在 Jupyter Notebook 中运行代码，可以查看 [这篇文章](https://soshnikov.com/education/how-to-execute-notebooks-from-github/)。 @@ -240,7 +240,7 @@ df = pd.read_csv('file.csv') 打开 [`notebook-papers.ipynb`](notebook-papers.ipynb) 并从头到尾阅读。你也可以执行单元格，并完成我们在最后留下的一些挑战。 -![COVID 医疗处理](../../../../translated_images/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.zh.png) +![COVID 医疗处理](../../../../translated_images/zh/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.png) ## 处理图像数据 diff --git a/translations/zh/2-Working-With-Data/README.md b/translations/zh/2-Working-With-Data/README.md index 64b9ee29..b59bce68 100644 --- a/translations/zh/2-Working-With-Data/README.md +++ b/translations/zh/2-Working-With-Data/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 数据处理 -![data love](../../../translated_images/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.zh.jpg) +![data love](../../../translated_images/zh/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.jpg) > 图片由 Alexander Sinn 提供，来自 Unsplash 在这些课程中，您将学习一些管理、操作和在应用程序中使用数据的方法。您将了解关系型和非关系型数据库，以及数据如何存储在其中。您将学习使用 Python 管理数据的基础知识，并探索多种使用 Python 管理和挖掘数据的方法。 diff --git a/translations/zh/3-Data-Visualization/12-visualization-relationships/README.md b/translations/zh/3-Data-Visualization/12-visualization-relationships/README.md index f766978a..f30c8a1b 100644 --- a/translations/zh/3-Data-Visualization/12-visualization-relationships/README.md +++ b/translations/zh/3-Data-Visualization/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ honey.head() ```python sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ``` -![scatterplot 1](../../../../translated_images/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.zh.png) +![scatterplot 1](../../../../translated_images/zh/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.png) 现在，用蜂蜜色调展示同样的数据，显示价格如何逐年变化。你可以通过添加一个“hue”参数来展示逐年的变化： @@ -60,7 +60,7 @@ sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5); ```python sns.relplot(x="priceperlb", y="state", hue="year", palette="YlOrBr", data=honey, height=15, aspect=.5); ``` -![scatterplot 2](../../../../translated_images/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.zh.png) +![scatterplot 2](../../../../translated_images/zh/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.png) 通过这个颜色方案的变化，你可以明显看到蜂蜜每磅价格在逐年强劲增长。如果你查看数据中的一个样本集（例如选择亚利桑那州），你会发现价格逐年上涨的模式，虽然有少数例外： @@ -89,7 +89,7 @@ sns.relplot(x="priceperlb", y="state", size="year", data=honey, height=15, aspec ``` 你可以看到点的大小逐渐增加。 -![scatterplot 3](../../../../translated_images/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.zh.png) +![scatterplot 3](../../../../translated_images/zh/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.png) 这是否是一个简单的供需问题？由于气候变化和蜂群崩溃等因素，蜂蜜的供应逐年减少，因此价格上涨？ @@ -104,7 +104,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); ``` 答案：是的，除了2003年左右的一些例外： -![line chart 1](../../../../translated_images/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.zh.png) +![line chart 1](../../../../translated_images/zh/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.png) ✅ 由于Seaborn对数据进行聚合，它通过绘制均值和均值周围的95%置信区间来显示“每个x值的多个测量值”。[来源](https://seaborn.pydata.org/tutorial/relational.html)。这种耗时的行为可以通过添加`ci=None`来禁用。 @@ -114,7 +114,7 @@ sns.relplot(x="year", y="priceperlb", kind="line", data=honey); sns.relplot(x="year", y="totalprod", kind="line", data=honey); ``` -![line chart 2](../../../../translated_images/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.zh.png) +![line chart 2](../../../../translated_images/zh/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.png) 答案：并不完全。如果你查看总产量，实际上在那一年似乎有所增加，尽管总体而言蜂蜜的产量在这些年间呈下降趋势。 @@ -139,7 +139,7 @@ sns.relplot( ``` 在这个可视化中，你可以比较逐年的每群产量和蜂群数量，并将列的wrap设置为3： -![facet grid](../../../../translated_images/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.zh.png) +![facet grid](../../../../translated_images/zh/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.png) 对于这个数据集，逐年和各州之间的蜂群数量及其产量并没有特别显著的变化。是否有其他方法可以找到这两个变量之间的相关性？ @@ -162,7 +162,7 @@ sns.despine(right=False) plt.ylabel('colony yield') ax.figure.legend(); ``` -![superimposed plots](../../../../translated_images/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.zh.png) +![superimposed plots](../../../../translated_images/zh/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.png) 虽然2003年没有明显的异常，但这确实让我们以一个稍微乐观的结论结束这节课：尽管蜂群数量总体上在下降，但蜂群数量正在趋于稳定，尽管每群产量在减少。 diff --git a/translations/zh/3-Data-Visualization/R/09-visualization-quantities/README.md b/translations/zh/3-Data-Visualization/R/09-visualization-quantities/README.md index 02a28279..df1f85f0 100644 --- a/translations/zh/3-Data-Visualization/R/09-visualization-quantities/README.md +++ b/translations/zh/3-Data-Visualization/R/09-visualization-quantities/README.md @@ -67,7 +67,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` 在这里，你安装了 `ggplot2` 包并通过 `library("ggplot2")` 命令将其导入工作区。要在 ggplot 中绘制任何图表，使用 `ggplot()` 函数，并将数据集、x 和 y 变量作为属性指定。在这种情况下，我们使用 `geom_line()` 函数，因为我们要绘制折线图。 -![最大翼展折线图](../../../../../translated_images/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.zh.png) +![最大翼展折线图](../../../../../translated_images/zh/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.png) 你立即注意到了什么？似乎至少有一个异常值——那是一个相当惊人的翼展！2000+ 厘米的翼展超过了 20 米——难道明尼苏达州有翼龙在飞翔？让我们调查一下。 @@ -85,7 +85,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + ``` 我们在 `theme` 中指定了角度，并在 `xlab()` 和 `ylab()` 中分别指定了 x 和 y 轴的标签。`ggtitle()` 为图表命名。 -![改进后的最大翼展折线图](../../../../../translated_images/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.zh.png) +![改进后的最大翼展折线图](../../../../../translated_images/zh/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.png) 即使将标签旋转到 45 度，仍然太多了，难以阅读。让我们尝试另一种策略：仅标记那些异常值，并在图表内设置标签。你可以使用散点图来腾出更多空间进行标记： @@ -101,7 +101,7 @@ ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) + 你发现了什么？ -![最大翼展散点图](../../../../../translated_images/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.zh.png) +![最大翼展散点图](../../../../../translated_images/zh/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.png) ## 筛选数据 @@ -120,7 +120,7 @@ ggplot(data=birds_filtered, aes(x=Name, y=MaxWingspan,group=1)) + ``` 我们创建了一个新的数据框 `birds_filtered`，然后绘制了一个散点图。通过筛选掉异常值，你的数据现在更加连贯且易于理解。 -![改进后的最大翼展散点图](../../../../../translated_images/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.zh.png) +![改进后的最大翼展散点图](../../../../../translated_images/zh/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.png) 现在我们至少在翼展方面有了一个更干净的数据集，让我们进一步探索这些鸟类。 @@ -162,7 +162,7 @@ birds_filtered %>% group_by(Category) %>% ``` 在以下代码片段中，我们安装了 [dplyr](https://www.rdocumentation.org/packages/dplyr/versions/0.7.8) 和 [lubridate](https://www.rdocumentation.org/packages/lubridate/versions/1.8.0) 包，以帮助操作和分组数据，从而绘制堆叠条形图。首先，你按鸟类的 `Category` 分组数据，然后汇总 `MinLength`、`MaxLength`、`MinBodyMass`、`MaxBodyMass`、`MinWingspan`、`MaxWingspan` 列。接着，使用 `ggplot2` 包绘制条形图，并为不同类别指定颜色和标签。 -![堆叠条形图](../../../../../translated_images/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.zh.png) +![堆叠条形图](../../../../../translated_images/zh/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.png) 然而，这个条形图由于数据未分组过多而难以阅读。你需要选择要绘制的数据，因此让我们根据鸟类类别查看其长度。 @@ -177,7 +177,7 @@ ggplot(birds_count,aes(Category,n))+geom_bar(stat="identity")+coord_flip() ``` 你首先统计 `Category` 列中的唯一值，然后将它们排序到一个新的数据框 `birds_count` 中。接着，将这些排序后的数据按相同顺序分级，以便按排序方式绘制。使用 `ggplot2` 绘制条形图。`coord_flip()` 将条形图水平显示。 -![类别长度](../../../../../translated_images/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.zh.png) +![类别长度](../../../../../translated_images/zh/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.png) 这个条形图很好地展示了每个类别中鸟类的数量。一眼就能看出，这个地区数量最多的鸟类是鸭/鹅/水禽类别。明尼苏达州是“万湖之地”，这并不令人意外！ @@ -200,7 +200,7 @@ ggplot(birds_grouped,aes(Category,MaxLength))+geom_bar(stat="identity")+coord_fl ``` 我们按 `Category` 对 `birds_filtered` 数据进行分组，然后绘制条形图。 -![比较数据](../../../../../translated_images/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.zh.png) +![比较数据](../../../../../translated_images/zh/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.png) 这里没有什么令人意外的：蜂鸟的最大长度最小，而鹈鹕或鹅的最大长度较大。当数据符合逻辑时，这是好事！ @@ -212,7 +212,7 @@ ggplot(data=birds_grouped, aes(x=Category)) + geom_bar(aes(y=MinLength), stat="identity", position="identity", fill='orange')+ coord_flip() ``` -![叠加值](../../../../../translated_images/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.zh.png) +![叠加值](../../../../../translated_images/zh/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.png) ## 🚀 挑战 diff --git a/translations/zh/3-Data-Visualization/R/10-visualization-distributions/README.md b/translations/zh/3-Data-Visualization/R/10-visualization-distributions/README.md index 879aaeb3..2b19a7fa 100644 --- a/translations/zh/3-Data-Visualization/R/10-visualization-distributions/README.md +++ b/translations/zh/3-Data-Visualization/R/10-visualization-distributions/README.md @@ -45,7 +45,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + geom_point() + ggtitle("Max Length per order") + coord_flip() ``` -![每目最大长度](../../../../../translated_images/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.zh.png) +![每目最大长度](../../../../../translated_images/zh/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.png) 这提供了每个鸟类目身体长度的一般分布概览，但这并不是显示真实分布的最佳方式。通常通过创建直方图来完成这一任务。 @@ -57,7 +57,7 @@ ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) + ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=10)+ylab('Frequency') ``` -![整个数据集的分布](../../../../../translated_images/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.zh.png) +![整个数据集的分布](../../../../../translated_images/zh/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.png) 如你所见，这个数据集中的 400 多种鸟类大多数最大体重都在 2000 以下。通过将 `bins` 参数更改为更高的数字，例如 30，可以获得更多数据洞察： @@ -65,7 +65,7 @@ ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![30个区间的分布](../../../../../translated_images/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.zh.png) +![30个区间的分布](../../../../../translated_images/zh/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.png) 此图表以更细致的方式显示分布。通过确保仅选择特定范围内的数据，可以创建一个偏向左侧较少的图表： @@ -77,7 +77,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency') ``` -![过滤后的直方图](../../../../../translated_images/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.zh.png) +![过滤后的直方图](../../../../../translated_images/zh/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.png) ✅ 尝试其他过滤器和数据点。要查看数据的完整分布，请移除 `['MaxBodyMass']` 过滤器以显示带标签的分布。 @@ -91,7 +91,7 @@ ggplot(data=birds_filtered_1, aes(x=MaxBodyMass, y=MaxLength) ) + ``` 可以看到这两个元素沿预期轴存在预期的相关性，其中一个点的收敛特别强： -![二维图](../../../../../translated_images/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.zh.png) +![二维图](../../../../../translated_images/zh/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.png) 直方图默认适用于数值数据。如果需要根据文本数据查看分布该怎么办？ @@ -123,7 +123,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + scale_fill_manual(name="Conservation Status",values=c("red","green","blue","pink"),labels=c("Endangered","Near Threathened","Vulnerable","Least Concern")) ``` -![翼展与保护状态的关联](../../../../../translated_images/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.zh.png) +![翼展与保护状态的关联](../../../../../translated_images/zh/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.png) 最小翼展与保护状态之间似乎没有明显的相关性。使用此方法测试数据集中的其他元素。你可以尝试不同的过滤器。是否发现任何相关性？ @@ -137,7 +137,7 @@ ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) + ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + geom_density() ``` -![密度图](../../../../../translated_images/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.zh.png) +![密度图](../../../../../translated_images/zh/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.png) 你可以看到此图与之前的最小翼展数据图相呼应；它只是稍微平滑了一些。如果你想重新创建第二个图表中那个不平滑的最大体重线，可以通过这种方法很好地将其平滑化： @@ -145,7 +145,7 @@ ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density() ``` -![体重密度](../../../../../translated_images/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.zh.png) +![体重密度](../../../../../translated_images/zh/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.png) 如果你想要一个平滑但不过于平滑的线条，可以编辑 `adjust` 参数： @@ -153,7 +153,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + geom_density(adjust = 1/5) ``` -![较少平滑的体重](../../../../../translated_images/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.zh.png) +![较少平滑的体重](../../../../../translated_images/zh/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.png) ✅ 阅读有关此类图表可用参数的内容并进行实验！ @@ -163,7 +163,7 @@ ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + ggplot(data=birds_filtered_1,aes(x = MaxBodyMass, fill = Order)) + geom_density(alpha=0.5) ``` -![每目体重](../../../../../translated_images/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.zh.png) +![每目体重](../../../../../translated_images/zh/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.png) ## 🚀 挑战 diff --git a/translations/zh/3-Data-Visualization/R/11-visualization-proportions/README.md b/translations/zh/3-Data-Visualization/R/11-visualization-proportions/README.md index eef0bfe0..0be6b02a 100644 --- a/translations/zh/3-Data-Visualization/R/11-visualization-proportions/README.md +++ b/translations/zh/3-Data-Visualization/R/11-visualization-proportions/README.md @@ -93,7 +93,7 @@ pie(grouped$count,grouped$class, main="Edible?") ``` 瞧，一个饼图展示了根据蘑菇的两种类别的数据比例。在这里，确保标签数组的顺序正确非常重要，因此务必验证标签的构建顺序！ -![饼图](../../../../../translated_images/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.zh.png) +![饼图](../../../../../translated_images/zh/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.png) ## 环形图！ @@ -128,7 +128,7 @@ library(webr) PieDonut(habitat, aes(habitat, count=count)) ``` -![环形图](../../../../../translated_images/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.zh.png) +![环形图](../../../../../translated_images/zh/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.png) 此代码使用了两个库——ggplot2 和 webr。通过 webr 库的 PieDonut 函数，我们可以轻松创建环形图！ @@ -166,7 +166,7 @@ waffle((cap_color$count/10), rows = 7, title = "Waffle Chart")+scale_fill_manual 使用华夫图，你可以清楚地看到蘑菇数据集中帽颜色的比例。有趣的是，有许多绿色帽子的蘑菇！ -![华夫图](../../../../../translated_images/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.zh.png) +![华夫图](../../../../../translated_images/zh/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.png) 在本课中，你学习了三种可视化比例的方法。首先，你需要将数据分组为类别，然后决定哪种方式最适合显示数据——饼图、环形图或华夫图。所有这些都很有趣，并能让用户快速了解数据集。 diff --git a/translations/zh/3-Data-Visualization/R/12-visualization-relationships/README.md b/translations/zh/3-Data-Visualization/R/12-visualization-relationships/README.md index 7529f05a..bddf735a 100644 --- a/translations/zh/3-Data-Visualization/R/12-visualization-relationships/README.md +++ b/translations/zh/3-Data-Visualization/R/12-visualization-relationships/README.md @@ -51,7 +51,7 @@ library(ggplot2) ggplot(honey, aes(x = priceperlb, y = state)) + geom_point(colour = "blue") ``` -![scatterplot 1](../../../../../translated_images/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.zh.png) +![scatterplot 1](../../../../../translated_images/zh/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.png) 现在，用蜂蜜色调展示同样的数据，显示价格随年份的变化。你可以通过添加`scale_color_gradientn`参数来实现逐年变化的可视化： @@ -61,7 +61,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ggplot(honey, aes(x = priceperlb, y = state, color=year)) + geom_point()+scale_color_gradientn(colours = colorspace::heat_hcl(7)) ``` -![scatterplot 2](../../../../../translated_images/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.zh.png) +![scatterplot 2](../../../../../translated_images/zh/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.png) 通过这个颜色方案的变化，你可以明显看到蜂蜜每磅价格在这些年间逐年上涨。如果你查看数据中的一个样本集（例如亚利桑那州），你会发现价格逐年上涨的模式，虽然有少数例外： @@ -92,7 +92,7 @@ ggplot(honey, aes(x = priceperlb, y = state)) + ``` 你可以看到点的大小逐渐增大。 -![scatterplot 3](../../../../../translated_images/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.zh.png) +![scatterplot 3](../../../../../translated_images/zh/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.png) 这是否是一个简单的供需关系？由于气候变化和蜂群崩溃等因素，是否导致蜂蜜的供应逐年减少，从而价格上涨？ @@ -107,7 +107,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab ``` 答案：是的，除了2003年左右的一些例外： -![line chart 1](../../../../../translated_images/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.zh.png) +![line chart 1](../../../../../translated_images/zh/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.png) 问题：那么在2003年，我们是否也能看到蜂蜜供应的激增？如果你查看逐年的总产量呢？ @@ -115,7 +115,7 @@ qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab qplot(honey$year,honey$totalprod, geom='smooth', span =0.5, xlab = "year",ylab = "totalprod") ``` -![line chart 2](../../../../../translated_images/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.zh.png) +![line chart 2](../../../../../translated_images/zh/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.png) 答案：并不明显。如果你查看总产量，实际上在那一年似乎有所增加，尽管总体而言蜂蜜的产量在这些年间是下降的。 @@ -135,7 +135,7 @@ ggplot(honey, aes(x=yieldpercol, y = numcol,group = 1)) + ``` 在这个可视化中，你可以比较逐年蜂群产量和蜂群数量，并将列数设置为3： -![facet grid](../../../../../translated_images/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.zh.png) +![facet grid](../../../../../translated_images/zh/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.png) 对于这个数据集，逐年和各州之间，蜂群数量和产量并没有特别突出的变化。是否有其他方法可以发现这两个变量之间的相关性？ @@ -152,7 +152,7 @@ plot(honey$year, honey$yieldpercol, pch = 17, col = 3, axis(side = 4, at = pretty(range(y2))) mtext("colony yield", side = 4, line = 3) ``` -![superimposed plots](../../../../../translated_images/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.zh.png) +![superimposed plots](../../../../../translated_images/zh/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.png) 虽然2003年没有明显的异常，但这让我们可以以一个稍微乐观的结论结束这节课：尽管蜂群数量总体上在下降，但蜂群数量正在趋于稳定，尽管每群产量在减少。 diff --git a/translations/zh/3-Data-Visualization/R/13-meaningful-vizualizations/README.md b/translations/zh/3-Data-Visualization/R/13-meaningful-vizualizations/README.md index cc9ffd0e..88229d3d 100644 --- a/translations/zh/3-Data-Visualization/R/13-meaningful-vizualizations/README.md +++ b/translations/zh/3-Data-Visualization/R/13-meaningful-vizualizations/README.md @@ -47,25 +47,25 @@ CO_OP_TRANSLATOR_METADATA: 即使数据科学家小心选择了适合数据的正确图表，也有很多方法可以通过展示数据来证明某种观点，往往以牺牲数据本身为代价。有许多误导性图表和信息图的例子！ -[![Alberto Cairo 的《图表如何撒谎》](../../../../../translated_images/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.zh.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "图表如何撒谎") +[![Alberto Cairo 的《图表如何撒谎》](../../../../../translated_images/zh/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "图表如何撒谎") > 🎥 点击上方图片观看关于误导性图表的会议演讲 这个图表颠倒了 X 轴的顺序，根据日期显示了与事实相反的内容： -![错误图表 1](../../../../../translated_images/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.zh.png) +![错误图表 1](../../../../../translated_images/zh/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.png) [这个图表](https://media.firstcoastnews.com/assets/WTLV/images/170ae16f-4643-438f-b689-50d66ca6a8d8/170ae16f-4643-438f-b689-50d66ca6a8d8_1140x641.jpg) 更具误导性，因为视觉上会让人得出结论，随着时间推移，各县的 COVID 病例数在下降。实际上，如果仔细查看日期，你会发现它们被重新排列以制造这种误导性的下降趋势。 -![错误图表 2](../../../../../translated_images/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.zh.jpg) +![错误图表 2](../../../../../translated_images/zh/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.jpg) 这个臭名昭著的例子同时使用了颜色和颠倒的 Y 轴来误导：本应得出枪支死亡人数在通过支持枪支的立法后激增的结论，但实际上视觉上被误导认为相反的情况是真实的： -![错误图表 3](../../../../../translated_images/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.zh.jpg) +![错误图表 3](../../../../../translated_images/zh/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.jpg) 这个奇怪的图表展示了比例如何被操纵，效果令人啼笑皆非： -![错误图表 4](../../../../../translated_images/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.zh.jpg) +![错误图表 4](../../../../../translated_images/zh/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.jpg) 比较不可比的事物是另一种阴险的技巧。有一个[精彩的网站](https://tylervigen.com/spurious-correlations) 专门展示“虚假的相关性”，比如缅因州的离婚率与人造黄油消费之间的“事实”相关性。Reddit 上还有一个小组收集了[数据的丑陋用法](https://www.reddit.com/r/dataisugly/top/?t=all)。 @@ -100,13 +100,13 @@ CO_OP_TRANSLATOR_METADATA: 如果你的数据在 X 轴上是文本且较长，可以将文本倾斜以提高可读性。[plot3D](https://cran.r-project.org/web/packages/plot3D/index.html) 提供了 3D 绘图功能，如果你的数据支持的话，可以用它制作复杂的数据可视化。 -![3D 图表](../../../../../translated_images/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.zh.png) +![3D 图表](../../../../../translated_images/zh/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.png) ## 动画和 3D 图表展示 如今一些最佳的数据可视化是动画的。Shirley Wu 使用 D3 制作了许多惊艳的作品，例如“[电影之花](http://bl.ocks.org/sxywu/raw/d612c6c653fb8b4d7ff3d422be164a5d/)”，每朵花都是一部电影的可视化。另一个为《卫报》制作的例子是“Bussed Out”，一个结合了 Greensock 和 D3 的交互式体验，通过滚动叙事文章格式展示纽约市如何通过将无家可归者送出城市来处理其无家可归问题。 -![Bussed Out](../../../../../translated_images/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.zh.png) +![Bussed Out](../../../../../translated_images/zh/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.png) > “Bussed Out: 美国如何转移无家可归者” 来自 [卫报](https://www.theguardian.com/us-news/ng-interactive/2017/dec/20/bussed-out-america-moves-homeless-people-country-study)。可视化由 Nadieh Bremer 和 Shirley Wu 制作 @@ -116,7 +116,7 @@ CO_OP_TRANSLATOR_METADATA: 你将完成一个网络应用，展示这个社交网络的动画视图。它使用了一个库来创建[网络可视化](https://github.com/emiliorizzo/vue-d3-network)，基于 Vue.js 和 D3。当应用运行时，你可以在屏幕上拖动节点以重新排列数据。 -![危险关系](../../../../../translated_images/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.zh.png) +![危险关系](../../../../../translated_images/zh/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.png) ## 项目：使用 D3.js 构建一个展示网络的图表 diff --git a/translations/zh/3-Data-Visualization/README.md b/translations/zh/3-Data-Visualization/README.md index 900ffd91..91223cf0 100644 --- a/translations/zh/3-Data-Visualization/README.md +++ b/translations/zh/3-Data-Visualization/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 可视化 -![一只蜜蜂停在薰衣草花上](../../../translated_images/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.zh.jpg) +![一只蜜蜂停在薰衣草花上](../../../translated_images/zh/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.jpg) > 图片由 Jenna Lee 提供，来自 Unsplash 数据可视化是数据科学家最重要的任务之一。图片胜过千言万语，可视化可以帮助你识别数据中的各种有趣部分，例如峰值、异常值、分组、趋势等，从而帮助你理解数据背后的故事。 diff --git a/translations/zh/4-Data-Science-Lifecycle/14-Introduction/README.md b/translations/zh/4-Data-Science-Lifecycle/14-Introduction/README.md index 37ef179f..4725dcda 100644 --- a/translations/zh/4-Data-Science-Lifecycle/14-Introduction/README.md +++ b/translations/zh/4-Data-Science-Lifecycle/14-Introduction/README.md @@ -25,7 +25,7 @@ CO_OP_TRANSLATOR_METADATA: 本课程重点讲解生命周期中的三个部分：数据捕获、数据处理和数据维护。 -![数据科学生命周期图示](../../../../translated_images/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.zh.jpg) +![数据科学生命周期图示](../../../../translated_images/zh/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.jpg) > 图片来源：[伯克利信息学院](https://ischoolonline.berkeley.edu/data-science/what-is-data-science/) ## 数据捕获 @@ -98,7 +98,7 @@ CO_OP_TRANSLATOR_METADATA: |团队数据科学过程 (TDSP)|跨行业数据挖掘标准过程 (CRISP-DM)| |--|--| -|![团队数据科学生命周期](../../../../translated_images/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.zh.png) | ![数据科学过程联盟图片](../../../../translated_images/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.zh.png) | +|![团队数据科学生命周期](../../../../translated_images/zh/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.png) | ![数据科学过程联盟图片](../../../../translated_images/zh/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.png) | | 图片来源：[Microsoft](https://docs.microsoft.comazure/architecture/data-science-process/lifecycle) | 图片来源：[数据科学过程联盟](https://www.datascience-pm.com/crisp-dm-2/) | ## [课后测验](https://ff-quizzes.netlify.app/en/ds/quiz/27) diff --git a/translations/zh/4-Data-Science-Lifecycle/README.md b/translations/zh/4-Data-Science-Lifecycle/README.md index 984cfc4e..0d10e8c9 100644 --- a/translations/zh/4-Data-Science-Lifecycle/README.md +++ b/translations/zh/4-Data-Science-Lifecycle/README.md @@ -9,7 +9,7 @@ CO_OP_TRANSLATOR_METADATA: --> # 数据科学生命周期 -![communication](../../../translated_images/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.zh.jpg) +![communication](../../../translated_images/zh/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.jpg) > 图片由 Headway 提供，来自 Unsplash 在这些课程中，您将探索数据科学生命周期的一些方面，包括数据的分析和沟通。 diff --git a/translations/zh/5-Data-Science-In-Cloud/README.md b/translations/zh/5-Data-Science-In-Cloud/README.md index de93eb2d..1315d7c2 100644 --- a/translations/zh/5-Data-Science-In-Cloud/README.md +++ b/translations/zh/5-Data-Science-In-Cloud/README.md @@ -9,13 +9,13 @@ CO_OP_TRANSLATOR_METADATA: --> # 云中的数据科学 -![cloud-picture](../../../translated_images/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.zh.jpg) +![cloud-picture](../../../translated_images/zh/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.jpg) > 图片由 [Jelleke Vanooteghem](https://unsplash.com/@ilumire) 提供，来自 [Unsplash](https://unsplash.com/s/photos/cloud?orientation=landscape) 在处理大数据的数据科学时，云计算可以带来革命性的变化。在接下来的三节课中，我们将了解什么是云，以及为什么它非常有用。我们还将探索一个心力衰竭数据集，并构建一个模型来帮助评估某人发生心力衰竭的可能性。我们将利用云的强大功能，通过两种不同的方式来训练、部署和使用模型。一种方式是仅使用用户界面，以低代码/无代码的方式进行；另一种方式是使用 Azure Machine Learning 软件开发工具包 (Azure ML SDK)。 -![project-schema](../../../translated_images/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.zh.png) +![project-schema](../../../translated_images/zh/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.png) ### 主题 diff --git a/translations/zh/6-Data-Science-In-Wild/20-Real-World-Examples/README.md b/translations/zh/6-Data-Science-In-Wild/20-Real-World-Examples/README.md index 7b249ea3..76c8142c 100644 --- a/translations/zh/6-Data-Science-In-Wild/20-Real-World-Examples/README.md +++ b/translations/zh/6-Data-Science-In-Wild/20-Real-World-Examples/README.md @@ -41,7 +41,7 @@ CO_OP_TRANSLATOR_METADATA: * [医疗领域的数据科学](https://data-flair.training/blogs/data-science-in-healthcare/) - 强调应用包括医学影像（如 MRI、X光、CT扫描）、基因组学（DNA测序）、药物开发（风险评估、成功预测）、预测分析（患者护理和供应物流）、疾病追踪与预防等。 -![数据科学在现实世界中的应用](../../../../translated_images/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.zh.png) 图片来源：[Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) +![数据科学在现实世界中的应用](../../../../translated_images/zh/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.png) 图片来源：[Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/) 图中展示了其他领域和数据科学技术的应用案例。想探索更多应用？查看下面的[复习与自学](../../../../6-Data-Science-In-Wild/20-Real-World-Examples)部分。 diff --git a/translations/zh/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md b/translations/zh/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md index fc265e60..48479648 100644 --- a/translations/zh/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md +++ b/translations/zh/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md @@ -22,7 +22,7 @@ Explorer界面（如下图所示）允许你选择一个数据集（从提供的 2. 探索数据集[目录](https://planetarycomputer.microsoft.com/catalog)——了解每个数据集的用途。 3. 使用Explorer——选择一个感兴趣的数据集，选择一个相关的查询和渲染选项。 -![行星计算机Explorer](../../../../translated_images/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.zh.png) +![行星计算机Explorer](../../../../translated_images/zh/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.png) `你的任务：` 现在研究浏览器中渲染的可视化，并回答以下问题： diff --git a/translations/zh/CONTRIBUTING.md b/translations/zh/CONTRIBUTING.md index b5cd2346..de93cdeb 100644 --- a/translations/zh/CONTRIBUTING.md +++ b/translations/zh/CONTRIBUTING.md @@ -311,7 +311,7 @@ def calculate_mean(data): import pandas as pd ``` ```` -- 为图片添加替代文本：`![Alt text](../../translated_images/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.zh.png)` +- 为图片添加替代文本：`![Alt text](../../translated_images/zh/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.png)` - 保持合理的行长度（约 80-100 个字符） ### Python diff --git a/translations/zh/README.md b/translations/zh/README.md index 589ff621..aca4fb05 100644 --- a/translations/zh/README.md +++ b/translations/zh/README.md @@ -33,7 +33,7 @@ CO_OP_TRANSLATOR_METADATA: **🙏 特别感谢 🙏 我们的 [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/) 作者、审阅者和内容贡献者，** 尤其是 Aaryan Arora、[Aditya Garg](https://github.com/AdityaGarg00)、[Alondra Sanchez](https://www.linkedin.com/in/alondra-sanchez-molina/)、[Ankita Singh](https://www.linkedin.com/in/ankitasingh007)、[Anupam Mishra](https://www.linkedin.com/in/anupam--mishra/)、[Arpita Das](https://www.linkedin.com/in/arpitadas01/)、ChhailBihari Dubey、[Dibri Nsofor](https://www.linkedin.com/in/dibrinsofor)、[Dishita Bhasin](https://www.linkedin.com/in/dishita-bhasin-7065281bb)、[Majd Safi](https://www.linkedin.com/in/majd-s/)、[Max Blum](https://www.linkedin.com/in/max-blum-6036a1186/)、[Miguel Correa](https://www.linkedin.com/in/miguelmque/)、[Mohamma Iftekher (Iftu) Ebne Jalal](https://twitter.com/iftu119)、[Nawrin Tabassum](https://www.linkedin.com/in/nawrin-tabassum)、[Raymond Wangsa Putra](https://www.linkedin.com/in/raymond-wp/)、[Rohit Yadav](https://www.linkedin.com/in/rty2423)、Samridhi Sharma、[Sanya Sinha](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200), [Sheena Narula](https://www.linkedin.com/in/sheena-narua-n/)、[Tauqeer Ahmad](https://www.linkedin.com/in/tauqeerahmad5201/)、Yogendrasingh Pawar 、[Vidushi Gupta](https://www.linkedin.com/in/vidushi-gupta07/)、[Jasleen Sondhi](https://www.linkedin.com/in/jasleen-sondhi/) -|![素描笔记，由 @sketchthedocs 提供 https://sketchthedocs.dev](../../translated_images/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.zh.png)| +|![素描笔记，由 @sketchthedocs 提供 https://sketchthedocs.dev](../../translated_images/zh/00-Title.8af36cd35da1ac555b678627fbdc6e320c75f0100876ea41d30ea205d3b08d22.png)| |:---:| | 面向初学者的数据科学 - _素描笔记 由 [@nitya](https://twitter.com/nitya)_ | @@ -52,7 +52,7 @@ CO_OP_TRANSLATOR_METADATA: 我们正在进行 Discord 的“与 AI 学习”系列，了解更多并在 2025 年 9 月 18 日至 30 日加入我们，访问 [“与 AI 学习”系列](https://aka.ms/learnwithai/discord)。您将获得使用 GitHub Copilot 进行数据科学的小技巧和窍门。 -![“与 AI 学习”系列](../../translated_images/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.zh.jpg) +![“与 AI 学习”系列](../../translated_images/zh/1.2b28cdc6205e26fef6a21817fe5d83ae8b50fbd0a33e9fed0df05845da5b30b6.jpg) # 你是学生吗？ @@ -132,7 +132,7 @@ CO_OP_TRANSLATOR_METADATA: ## 课程 -|![ 速写笔记作者 @sketchthedocs https://sketchthedocs.dev](../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.zh.png)| +|![ 速写笔记作者 @sketchthedocs https://sketchthedocs.dev](../../translated_images/zh/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png)| |:---:| | 面向初学者的数据科学：路线图 - _速写笔记作者 [@nitya](https://twitter.com/nitya)_ | diff --git a/translations/zh/sketchnotes/README.md b/translations/zh/sketchnotes/README.md index 22c1fee8..1801b11f 100644 --- a/translations/zh/sketchnotes/README.md +++ b/translations/zh/sketchnotes/README.md @@ -13,7 +13,7 @@ CO_OP_TRANSLATOR_METADATA: Nitya Narasimhan，艺术家 -![路线图手绘笔记](../../../translated_images/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.zh.png) +![路线图手绘笔记](../../../translated_images/zh/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png) **免责声明**： 本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于重要信息，建议使用专业人工翻译。我们不对因使用此翻译而产生的任何误解或误读承担责任。 \ No newline at end of file