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# 視覺化
![一隻蜜蜂停在薰衣草花上](../../../translated_images/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.tw.jpg)
> 照片由 <a href="https://unsplash.com/@jenna2980?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Jenna Lee</a> 提供,來自 <a href="https://unsplash.com/s/photos/bees-in-a-meadow?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
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。
在這五節課中,你將探索來自自然界的數據,並使用各種技術創建有趣且美觀的視覺化圖表。
| 主題編號 | 主題 | 相關課程 | 作者 |
| :-----------: | :--: | :-----------: | :----: |
| 1. | 數量視覺化 | <ul> <li> [Python](09-visualization-quantities/README.md)</li> <li>[R](../../../3-Data-Visualization/R/09-visualization-quantities) </li> </ul>|<ul> <li> [Jen Looper](https://twitter.com/jenlooper)</li><li> [Vidushi Gupta](https://github.com/Vidushi-Gupta)</li> <li>[Jasleen Sondhi](https://github.com/jasleen101010)</li></ul> |
| 2. | 分佈視覺化 | <ul> <li> [Python](10-visualization-distributions/README.md)</li> <li>[R](../../../3-Data-Visualization/R/10-visualization-distributions) </li> </ul>|<ul> <li> [Jen Looper](https://twitter.com/jenlooper)</li><li> [Vidushi Gupta](https://github.com/Vidushi-Gupta)</li> <li>[Jasleen Sondhi](https://github.com/jasleen101010)</li></ul> |
| 3. | 比例視覺化 | <ul> <li> [Python](11-visualization-proportions/README.md)</li> <li>[R](../../../3-Data-Visualization) </li> </ul>|<ul> <li> [Jen Looper](https://twitter.com/jenlooper)</li><li> [Vidushi Gupta](https://github.com/Vidushi-Gupta)</li> <li>[Jasleen Sondhi](https://github.com/jasleen101010)</li></ul> |
| 4. | 關係視覺化 | <ul> <li> [Python](12-visualization-relationships/README.md)</li> <li>[R](../../../3-Data-Visualization) </li> </ul>|<ul> <li> [Jen Looper](https://twitter.com/jenlooper)</li><li> [Vidushi Gupta](https://github.com/Vidushi-Gupta)</li> <li>[Jasleen Sondhi](https://github.com/jasleen101010)</li></ul> |
| 5. | 創建有意義的視覺化 | <ul> <li> [Python](13-meaningful-visualizations/README.md)</li> <li>[R](../../../3-Data-Visualization) </li> </ul>|<ul> <li> [Jen Looper](https://twitter.com/jenlooper)</li><li> [Vidushi Gupta](https://github.com/Vidushi-Gupta)</li> <li>[Jasleen Sondhi](https://github.com/jasleen101010)</li></ul> |
### 致謝
這些視覺化課程由 [Jen Looper](https://twitter.com/jenlooper)、[Jasleen Sondhi](https://github.com/jasleen101010) 和 [Vidushi Gupta](https://github.com/Vidushi-Gupta) 用 🌸 精心撰寫。
🍯 美國蜂蜜生產數據來自 Jessica Li 在 [Kaggle](https://www.kaggle.com/jessicali9530/honey-production) 上的項目。該[數據](https://usda.library.cornell.edu/concern/publications/rn301137d)來源於[美國農業部](https://www.nass.usda.gov/About_NASS/index.php)。
🍄 蘑菇數據同樣來自 [Kaggle](https://www.kaggle.com/hatterasdunton/mushroom-classification-updated-dataset),由 Hatteras Dunton 修訂。該數據集包括對假設樣本的描述,這些樣本對應於伞菌科和口蘑科的 23 種有鰓蘑菇。數據來源於《Audubon Society Field Guide to North American Mushrooms》(1981)。該數據集於 1987 年捐贈給 UCI ML 27。
🦆 明尼蘇達州鳥類數據來自 [Kaggle](https://www.kaggle.com/hannahcollins/minnesota-birds),由 Hannah Collins 從 [Wikipedia](https://en.wikipedia.org/wiki/List_of_birds_of_Minnesota) 抓取。
所有這些數據集均以 [CC0: Creative Commons](https://creativecommons.org/publicdomain/zero/1.0/) 授權。
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