Chapter 1. The data science process
Chapter 2. Starting with R and data
Chapter 3. Exploring data
Table 3.1. Data dictionary entry for gas_usage
Chapter 6. Choosing and evaluating models
Table 6.1. From problem to approach
Table 6.2. Two-by-two confusion matrix
Table 6.3. Classifier performance measures business stories.
Chapter 7. Linear and logistic regression
Chapter 9. Unsupervised methods
Table 9.1. A database of library transactions
Table 9.2. Looking for The Hobbit and The Princess Bride
Table 9.3. The five most confident rules discovered in the data
Chapter 11. Documentation and deployment
Table 11.2. Buzz data description
Table 11.3. Maintenance tasks made easier by R markdown
Table 11.4. Some useful knitr options
Chapter 12. Producing effective presentations
Appendix A. Starting with R and other tools
Appendix B. Important statistical concepts