• 0. Introduction
• 1. Data Science
  • 1.1 Introduction
    • 1.1.1 Computational Tools
    • 1.1.2 Statistical Techniques
  • 1.2 Why Data Science?
  • 1.3 Plotting the Classics
    • 1.3.1 Literary Characters
    • 1.3.2 Another Kind of Character
• 2. Causality and Experiments
  • 2.1 John Snow and the Broad Street Pump
  • 2.2 Snow’s “Grand Experiment”
  • 2.3 Establishing Causality
  • 2.4 Randomization
  • 2.5 Endnote
• 3. Programming in Python
  • 3.1 Expressions
  • 3.2 Names
    • 3.2.1 Example: Growth Rates
  • 3.3 Call Expressions
  • 3.4 Introduction to Tables
• 4. Data Types
  • 4.1 Numbers
  • 4.2 Strings
    • 4.2.1 String Methods
  • 4.3 Comparisons
• 5. Sequences
  • 5.1 Arrays
  • 5.2 Ranges
  • 5.3 More on Arrays
• 6. Tables
  • 6.1 Sorting Rows
  • 6.2 Selecting Rows
  • 6.3 Example: Population Trends
  • 6.4 Example: Trends in Gender
• 7. Visualization
  • 7.1 Categorical Distributions
  • 7.2 Numerical Distributions
  • 7.3 Overlaid Graphs
• 8. Functions and Tables
  • 8.1 Applying Functions to Columns
  • 8.2 Classifying by One Variable
  • 8.3 Cross-Classifying
  • 8.4 Joining Tables by Columns
  • 8.5 Bike Sharing in the Bay Area
• 9. Randomness
  • 9.1 Conditional Statements
  • 9.2 Iteration
  • 9.3 Simulation
  • 9.4 The Monty Hall Problem
  • 9.5 Finding Probabilities
• 10. Sampling and Empirical Distributions
  • 10.1 Empirical Distributions
  • 10.2 Sampling from a Population
  • 10.3 Empirical Distibution of a Statistic
• 11. Testing Hypotheses
  • 11.1 Assessing Models
  • 11.2 Multiple Categories
  • 11.3 Decisions and Uncertainty
• 12. Comparing Two Samples
  • 12.1 A/B Testing
  • 12.2 Deflategate
  • 12.3 Causality
• 13. Estimation
  • 13.1 Percentiles
  • 13.2 The Bootstrap
  • 13.3 Confidence Intervals
  • 13.4 Using Confidence Intervals
• 14. Why the Mean Matters
  • 14.1 Properties of the Mean
  • 14.2 Variability
  • 14.3 The SD and the Normal Curve
  • 14.4 The Central Limit Theorem
  • 14.5 The Variability of the Sample Mean
  • 14.6 Choosing a Sample Size
• 15. Prediction
  • 15.1 Correlation
  • 15.2 The Regression Line
  • 15.3 The Method of Least Squares
  • 15.4 Least Squares Regression
  • 15.5 Visual Diagnostics
  • 15.6 Numerical Diagnostics
• 16. Inference for Regression
  • 16.1 A Regression Model
  • 16.2 Inference for the True Slope
  • 16.3 Prediction Intervals
• 17. Classification
  • 17.1 Nearest Neighbors
  • 17.2 Training and Testing
  • 17.3 Rows of Tables
  • 17.4 Implementing the Classifier
  • 17.5 The Accuracy of the Classifier
  • 17.6 Multiple Regression
• 18. Updating Predictions
  • 18.1 A “More Likely Than Not” Binary Classifier
  • 18.2 Making Decisions