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如果要学R的话,推荐你Hadley大神的《R for Data Science》,有电子版:https://r4ds.had.co.nz/
会教你一整套流程,可以学习统计知识的同时,把这本书刷完。
Hadley也推荐了一些统计的进阶数据,但是比较偏向数据科学,统计学习方面的内容。
*Statistical Modeling: A Fresh Approach* by Danny Kaplan, [http://www.mosaic-web.org/go/StatisticalModeling/]. This book provides a gentle introduction to modelling, where you build your intuition, mathematical tools, and R skills in parallel. The book replaces a traditional "introduction to statistics" course, providing a curriculum that is up-to-date and relevant to data science.
*An Introduction to Statistical Learning* by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani, [http://www-bcf.usc.edu/\~gareth/ISL/] (available online for free). This book presents a family of modern modelling techniques collectively known as statistical learning. For an even deeper understanding of the math behind the models, read the classic *Elements of Statistical Learning* by Trevor Hastie, Robert Tibshirani, and Jerome Friedman, [https://web.stanford.edu/\~hastie/Papers/ESLII.pdf] (also available online for free).
*Applied Predictive Modeling* by Max Kuhn and Kjell Johnson, [http://appliedpredictivemodeling.com]. This book is a companion to the **caret** package and provides practical tools for dealing with real-life predictive modelling challenges. |
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