Chap 2: What to teach about data? Basics about the structure of data needed to do the statistics and graphics, but not (extensive) wrangling.

Chap 3: Data and statistical graphics Introduces a tight but expressive framework for thinking about graphics.

Chap 4: Summary & prediction Single variables. Format for prediction leading to summary descriptions

Chap 5: Stratification Providing a basis for multiple explanatory variables very early on.

Chap 6: Process of investigation State a pretty comprehensive workflow for statistical investigations.

Chap 7: Case study of the process of investigation An illustration of how outside knowledge comes into play

Chap 8: Bayes' Rule Bayesian thinking is part of data science

Chap 9: Modeling functions The prime intermediary between data and information

Chap 10: Models that learn Introducing a simple and compelling machine-learning technology

Chap 12: Causal networks