From a14719beaa220fd2a6fecd60dedc0f42b4cb58e0 Mon Sep 17 00:00:00 2001 From: Frederick Legaspi Date: Mon, 8 Nov 2021 13:18:10 -0500 Subject: [PATCH 01/10] Update README.md Replaced "in order words" with "in other words" --- 1-Introduction/01-defining-data-science/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/1-Introduction/01-defining-data-science/README.md b/1-Introduction/01-defining-data-science/README.md index bedbb1e..6606d0f 100644 --- a/1-Introduction/01-defining-data-science/README.md +++ b/1-Introduction/01-defining-data-science/README.md @@ -24,7 +24,7 @@ In [Wikipedia](https://en.wikipedia.org/wiki/Data_science), **Data Science** is This definition highlights the following important aspects of data science: -* The main goal of data science is to **extract knowledge** from data, in order words - to **understand** data, find some hidden relationships and build a **model**. +* The main goal of data science is to **extract knowledge** from data, in other words - to **understand** data, find some hidden relationships and build a **model**. * Data science uses **scientific methods**, such as probability and statistics. In fact, when the term *data science* was first introduced, some people argued that data science is just a new fancy name for statistics. Nowadays it has become evident that the field is much broader. * Obtained knowledge should be applied to produce some **actionable insights**. * We should be able to operate on both **structured** and **unstructured** data. We will come back to discuss different types of data later in the course. From 4e0524d0892d8e57c4e91d79f737619ba6a2173d Mon Sep 17 00:00:00 2001 From: Frederick Legaspi Date: Mon, 8 Nov 2021 14:04:02 -0500 Subject: [PATCH 02/10] Create README.md Replace "Examples of unstructured data" with "Examples of semi-structured data" in the Semi-structured paragraph. --- 1-Introduction/03-defining-data/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/1-Introduction/03-defining-data/README.md b/1-Introduction/03-defining-data/README.md index 0582ebb..62b1848 100644 --- a/1-Introduction/03-defining-data/README.md +++ b/1-Introduction/03-defining-data/README.md @@ -32,7 +32,7 @@ Examples of unstructured data: text files, text messages, video files ### Semi-structured Semi-structured data has features that make it a combination of structured and unstructured data. It doesn't typically conform to a format of rows and columns but is organized in a way that is considered structured and may follow a fixed format or set of rules. The structure will vary between sources, such as a well defined hierarchy to something more flexible that allows for easy integration of new information. Metadata are indicators that help decide how the data is organized and stored and will have various names, based on the type of data. Some common names for metadata are tags, elements, entities and attributes. For example, a typical email message will have a subject, body and a set of recipients and can be organized by whom or when it was sent. -Examples of unstructured data: HTML, CSV files, JavaScript Object Notation (JSON) +Examples of semi-structured data: HTML, CSV files, JavaScript Object Notation (JSON) ## Sources of Data From 97717d44fc4aa929c96568e443dc0df8a55e4e14 Mon Sep 17 00:00:00 2001 From: Frederick Legaspi Date: Wed, 17 Nov 2021 09:23:08 -0500 Subject: [PATCH 03/10] Fix typo fix typo: "basic repoduction number" to "basic reproduction number" --- 2-Working-With-Data/07-python/notebook-covidspread.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/2-Working-With-Data/07-python/notebook-covidspread.ipynb b/2-Working-With-Data/07-python/notebook-covidspread.ipynb index babd77f..f745150 100644 --- a/2-Working-With-Data/07-python/notebook-covidspread.ipynb +++ b/2-Working-With-Data/07-python/notebook-covidspread.ipynb @@ -2264,7 +2264,7 @@ "\r\n", "## Computing $R_t$\r\n", "\r\n", - "To see how infectuous is the disease, we look at the **basic repoduction number** $R_0$, which indicated the number of people that an infected person would further infect. When $R_0$ is more than 1, the epidemic is likely to spread.\r\n", + "To see how infectuous is the disease, we look at the **basic reproduction number** $R_0$, which indicated the number of people that an infected person would further infect. When $R_0$ is more than 1, the epidemic is likely to spread.\r\n", "\r\n", "$R_0$ is a property of the disease itself, and does not take into account some protective measures that people may take to slow down the pandemic. During the pandemic progression, we can estimate the reproduction number $R_t$ at any given time $t$. It has been shown that this number can be roughly estimated by taking a window of 8 days, and computing $$R_t=\\frac{I_{t-7}+I_{t-6}+I_{t-5}+I_{t-4}}{I_{t-3}+I_{t-2}+I_{t-1}+I_t}$$\r\n", "where $I_t$ is the number of newly infected individuals on day $t$.\r\n", @@ -2447,4 +2447,4 @@ }, "nbformat": 4, "nbformat_minor": 2 -} \ No newline at end of file +} From df8ae4140f7125906978a80dc0c31a88dae6bc83 Mon Sep 17 00:00:00 2001 From: Frederick Legaspi Date: Fri, 19 Nov 2021 08:17:11 -0500 Subject: [PATCH 04/10] Update README.md Fix matplotlib Cheatsheets link. Original link returns Page not found. --- 3-Data-Visualization/09-visualization-quantities/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/3-Data-Visualization/09-visualization-quantities/README.md b/3-Data-Visualization/09-visualization-quantities/README.md index c8d2c92..10c62ba 100644 --- a/3-Data-Visualization/09-visualization-quantities/README.md +++ b/3-Data-Visualization/09-visualization-quantities/README.md @@ -21,7 +21,7 @@ An excellent library to create both simple and sophisticated plots and charts of If you have a dataset and need to discover how much of a given item is included, one of the first tasks you have at hand will be to inspect its values. -✅ There are very good 'cheat sheets' available for Matplotlib [here](https://github.com/matplotlib/cheatsheets/blob/master/cheatsheets-1.png) and [here](https://github.com/matplotlib/cheatsheets/blob/master/cheatsheets-2.png). +✅ There are very good 'cheat sheets' available for Matplotlib [here](https://github.com/matplotlib/cheatsheets). ## Build a line plot about bird wingspan values From 04a422a2c6434c82a0dc2f84d70448aa3847d7a0 Mon Sep 17 00:00:00 2001 From: Frederick Legaspi Date: Mon, 22 Nov 2021 15:10:18 -0500 Subject: [PATCH 05/10] Add files via upload Change Plot Background to 'white' for better visibility with dark theme browser. --- .../images/max-wingspan-02.png | Bin 0 -> 11123 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 3-Data-Visualization/09-visualization-quantities/images/max-wingspan-02.png diff --git a/3-Data-Visualization/09-visualization-quantities/images/max-wingspan-02.png b/3-Data-Visualization/09-visualization-quantities/images/max-wingspan-02.png new file mode 100644 index 0000000000000000000000000000000000000000..bdbc458ea9a78c60f9db326a313d682cf97965d2 GIT binary patch literal 11123 zcma)i1yodB+x9`aOGR=hMMdclkVZNbgDy$w?vicX z@AJLi|NZM*|5`J%&YZK)&g;6bd++-WeX1-&h)0D7K@g#wtduGQp?QJpQyeVt3pwyb 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'white' for better visibility with dark theme browser. --- 3-Data-Visualization/09-visualization-quantities/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/3-Data-Visualization/09-visualization-quantities/README.md b/3-Data-Visualization/09-visualization-quantities/README.md index 10c62ba..33ac06b 100644 --- a/3-Data-Visualization/09-visualization-quantities/README.md +++ b/3-Data-Visualization/09-visualization-quantities/README.md @@ -52,7 +52,7 @@ Let's start by plotting some of the numeric data using a basic line plot. Suppos wingspan = birds['MaxWingspan'] wingspan.plot() ``` -![Max Wingspan](images/max-wingspan.png) +![Max Wingspan](images/max-wingspan-02.png) What do you notice immediately? There seems to be at least one outlier - that's quite a wingspan! A 2300 centimeter wingspan equals 23 meters - are there Pterodactyls roaming Minnesota? Let's investigate. 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z=AnGjPH~)2DVHZ=q964?oS Date: Mon, 22 Nov 2021 15:25:10 -0500 Subject: [PATCH 08/10] Update README.md Change Max-Wingspan-labels Plot Background to 'white' for better visibility with dark theme browser. --- 3-Data-Visualization/09-visualization-quantities/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/3-Data-Visualization/09-visualization-quantities/README.md b/3-Data-Visualization/09-visualization-quantities/README.md index 33ac06b..d8b0599 100644 --- a/3-Data-Visualization/09-visualization-quantities/README.md +++ b/3-Data-Visualization/09-visualization-quantities/README.md @@ -72,7 +72,7 @@ plt.plot(x, y) plt.show() ``` -![wingspan with labels](images/max-wingspan-labels.png) +![wingspan with labels](images/max-wingspan-labels-02.png) Even with the rotation of the labels set to 45 degrees, there are too many to read. Let's try a different strategy: label only those outliers and set the labels within the chart. You can use a scatter chart to make more room for the labeling: From 94f124cf6134edd86dac31ad1e361fb91c2db9e2 Mon Sep 17 00:00:00 2001 From: Frederick Legaspi Date: Mon, 22 Nov 2021 16:23:05 -0500 Subject: [PATCH 09/10] Add files via upload Change Plot Background to 'white' for better visibility with dark theme browser. --- .../images/category-counts-02.png | Bin 0 -> 73356 bytes .../images/category-length-02.png | Bin 0 -> 73447 bytes .../images/full-data-bar-02.png | Bin 0 -> 57494 bytes .../images/labeled-wingspan-02.png | Bin 0 -> 12183 bytes .../images/scatterplot-wingspan-02.png | Bin 0 -> 14771 bytes .../images/superimposed-02.png | Bin 0 -> 73889 bytes 6 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 3-Data-Visualization/09-visualization-quantities/images/category-counts-02.png create mode 100644 3-Data-Visualization/09-visualization-quantities/images/category-length-02.png create mode 100644 3-Data-Visualization/09-visualization-quantities/images/full-data-bar-02.png create mode 100644 3-Data-Visualization/09-visualization-quantities/images/labeled-wingspan-02.png create mode 100644 3-Data-Visualization/09-visualization-quantities/images/scatterplot-wingspan-02.png create mode 100644 3-Data-Visualization/09-visualization-quantities/images/superimposed-02.png diff --git a/3-Data-Visualization/09-visualization-quantities/images/category-counts-02.png b/3-Data-Visualization/09-visualization-quantities/images/category-counts-02.png new file mode 100644 index 0000000000000000000000000000000000000000..6c3cf0e2ab2111339e15f9791d81215f3c96b565 GIT binary patch literal 73356 zcmce3$-UJgs=X{3o*6s&q09A4a`_2`72ZCgk1@izxnmSt*O zTtQ2}?86nla_@_Rcjo&uPmD0i4{Oh3V;f+WuA#Mu)j;0M_aU*|2vyV{$e-Y>QfQ}E}s-@7O_KHR^IM~l` z(ZM_09KO6?ta#l*#nN*9$&)9qrSE4{kP*AH?WCf!^JLXokHv~In&IK$-VK{IDaHBx zyXm#QN>Sd?A08cCnSQ!1`s~qM7ug6YoAqf{rlxBmr0p0Czm-EPN z^WMCZp#_wC=86uxHPntgNgZj=zN|`I$+vq@<+J_4QRbmz+RdOMtDLyq@`o3x^!MzuM3n=6t%1t)=NLVZ({hz zSX+R+_vw+hH-%0%=g2x{^Dr_pszylBE0~)LW;+f=A5J*(yQel_@G*9{O~>${wN|fQ9qi^t;hC5fdM__6xgoGzNh25!Yr_WZBrP^2jayp`lB|Dx zIjq5^)b;skOi+0ElgY_n72Y>*-W0VcU-j*0<13Y$f(N}%?`kXZ3z59oA&ieOa2b2( z_+;l5hIKSFG%B{X>CwX8ACrzte;XZWBUg}fX@bqbzyP26ZG5Ol;MbU%nhNXc>e82!tHa7Eo#@L=#@8FNowUzeTWhIC$t9j{c%gwOmK_uk zk$5c2UL#&3#v|2bYFzP*-TCtxITJsn$%o*6N)wM}1Yf^C|Doehcj?%#?;+vgs|w%0 zKj`J}e+0Lgbo|w$TR)m}a|f%`X&v9%*%`M(N@~4%P0xD2 z*wgC=_h4^SuHxqQY0x+7qm9#hzTVy4UFYc0XJ;FwT&Bk?6nE{SdEqp&`FnZ9hV;{? 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J?>%n${{gU&BYFS; literal 0 HcmV?d00001 From 65cbffc2b84dbdd960a21e124d542bd6c2196526 Mon Sep 17 00:00:00 2001 From: Frederick Legaspi Date: Mon, 22 Nov 2021 16:25:30 -0500 Subject: [PATCH 10/10] Update README.md Change Plot Background to 'white' for better visibility with dark theme browser. --- .../09-visualization-quantities/README.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/3-Data-Visualization/09-visualization-quantities/README.md b/3-Data-Visualization/09-visualization-quantities/README.md index d8b0599..e45077f 100644 --- a/3-Data-Visualization/09-visualization-quantities/README.md +++ b/3-Data-Visualization/09-visualization-quantities/README.md @@ -94,7 +94,7 @@ What's going on here? You used `tick_params` to hide the bottom labels and then What did you discover? -![outliers](images/labeled-wingspan.png) +![outliers](images/labeled-wingspan-02.png) ## Filter your data Both the Bald Eagle and the Prairie Falcon, while probably very large birds, appear to be mislabeled, with an extra `0` added to their maximum wingspan. It's unlikely that you'll meet a Bald Eagle with a 25 meter wingspan, but if so, please let us know! Let's create a new dataframe without those two outliers: @@ -114,7 +114,7 @@ plt.show() By filtering out outliers, your data is now more cohesive and understandable. -![scatterplot of wingspans](images/scatterplot-wingspan.png) +![scatterplot of wingspans](images/scatterplot-wingspan-02.png) Now that we have a cleaner dataset at least in terms of wingspan, let's discover more about these birds. @@ -140,7 +140,7 @@ birds.plot(x='Category', title='Birds of Minnesota') ``` -![full data as a bar chart](images/full-data-bar.png) +![full data as a bar chart](images/full-data-bar-02.png) This bar chart, however, is unreadable because there is too much non-grouped data. You need to select only the data that you want to plot, so let's look at the length of birds based on their category. @@ -155,7 +155,7 @@ category_count = birds.value_counts(birds['Category'].values, sort=True) plt.rcParams['figure.figsize'] = [6, 12] category_count.plot.barh() ``` -![category and length](images/category-counts.png) +![category and length](images/category-counts-02.png) This bar chart shows a good view of the number of birds in each category. In a blink of an eye, you see that the largest number of birds in this region are in the Ducks/Geese/Waterfowl category. Minnesota is the 'land of 10,000 lakes' so this isn't surprising! @@ -171,7 +171,7 @@ plt.barh(y=birds['Category'], width=maxlength) plt.rcParams['figure.figsize'] = [6, 12] plt.show() ``` -![comparing data](images/category-length.png) +![comparing data](images/category-length-02.png) Nothing is surprising here: hummingbirds have the least MaxLength compared to Pelicans or Geese. It's good when data makes logical sense! @@ -189,7 +189,7 @@ plt.show() ``` In this plot, you can see the range per bird category of the Minimum Length and Maximum length. You can safely say that, given this data, the bigger the bird, the larger its length range. Fascinating! -![superimposed values](images/superimposed.png) +![superimposed values](images/superimposed-02.png) ## 🚀 Challenge