Books for Data Science in R

Data Science = Data + Statistics + Machine Learning

After being a data practitioner for many years, I’m realizing that I’ve developed quite a number of gaps in my knowledge of statistics and modeling. All the MOOCs that have come out with the data boom have been great but personally I still prefer reading books to learn theory.

As part of my belated New Year’s resolutions, I selected 5 core books that I’m going to try to read (and do the exercises for!) in 2016 to fill in those gaps. Here’s the list.

1. An Introduction to Statistical Learning with Applications in R (ISLR) by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani — sixth edition is free to download here.
2. The Elements of Statistical Learning (ESL) by Trevor Hastie, Robert Tibshirani, Jerome Friedman — second edition is free to download here.
3. Forecasting: Principles and Practice by Rob Hyndman, George Athanasopoulos — free to read online here.
4. Applied Predictive Modeling by Max Kuhn, Kjell Johnson
5. Practical Data Science with R by Nina Zumel, John Mount

I feel very strongly about getting through the first three on the list but could be convinced to replace my 4th or 5th with another suggestion. Feel free to leave comments with recommendations!