数据挖掘流程
此网址还提供了其他众多涉及数学科学的cheat sheet,非常推荐
![](https://img2020.cnblogs.com/blog/1977069/202003/1977069-20200321170907738-659299207.png)
算法的选择
![](https://img2020.cnblogs.com/blog/1977069/202003/1977069-20200321170746523-879767361.png)
![](https://img2020.cnblogs.com/blog/1977069/202003/1977069-20200321170930121-1367576580.png)
![](https://img2020.cnblogs.com/blog/1977069/202003/1977069-20200321170950375-100959906.png)
SIGAI算法地图
![](https://img2020.cnblogs.com/blog/1977069/202003/1977069-20200321171113251-181908389.png)
sklearn使用
此网页也提供了numpy,pandas, matplotlib,PySpark,Keras等module的使用方法
![](https://img2020.cnblogs.com/blog/1977069/202003/1977069-20200321171434154-1701106524.png)
模型评估指标
![](https://img2020.cnblogs.com/blog/1977069/202003/1977069-20200321171649438-2143700294.png)
更多关于数据科学的cheat sheet,可以参考:
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https://github.com/FavioVazquez/ds-cheatsheets
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https://github.com/soulmachine/machine-learning-cheat-sheet
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https://www.analyticsvidhya.com/blog/2017/02/top-28-cheat-sheets-for-machine-learning-data-science-probability-sql-big-data/
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https://medium.com/machine-learning-in-practice/cheat-sheet-of-machine-learning-and-python-and-math-cheat-sheets-a4afe4e791b6
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https://static.coggle.it/diagram/WHeBqDIrJRk-kDDY/t/categories-of-algorithms-non-exhaustive