Most programmers pick up statistics in fragments, if at all. Gary Sutton's guide organizes the subject properly, moving from foundational ideas to techniques you'll actually use when data drives a decision.
Core Methods Covered
- Descriptive statistics and summary visualizations
- Hypothesis testing
- Linear regression
- Time series analysis
- Markov chains
- Optimization techniques for constrained problems
Learning by Example
Each topic comes with standalone, well-documented Python examples built around real problems, including predicting splits in ultramarathons, classifying raisins by shape, and analyzing system reliability. The exercises are designed to move you from statistical theory to code you can adapt for your own data.
