Practical Data Science with R (2014)
Part 1. Introduction to data science
Chapter 1. The data science process
Chapter 2. Loading data into R
Part 2. Modeling methods
Chapter 5. Choosing and evaluating models
Chapter 6. Memorization methods
Chapter 7. Linear and logistic regression
Chapter 8. Unsupervised methods
Chapter 9. Exploring advanced methods
Part 3. Delivering results
Chapter 10. Documentation and deployment
Chapter 11. Producing effective presentations
Appendix A. Working with R and other tools
Appendix B. Important statistical concepts
Appendix C. More tools and ideas worth exploring