Practical Data Science with R (2014)

Practical Data Science with R (2014)

Part 1. Introduction to data science

Chapter 1. The data science process

Chapter 2. Loading data into R

Chapter 3. Exploring data

Chapter 4. Managing data

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