Summary Data Science Introduction Computational Tools Statistical Techniques Why Data Science? Plotting the Classics Literary Characters Another Kind of Character Causality and Experiments John Snow and the Broad Street Pump Snow’s “Grand Experiment” Establishing Causality Randomization Endnote Programming in Python Expressions Numbers Names Example: Growth Rates Call Expressions Data Types Strings String Methods Comparisons Sequences Arrays Ranges More on Arrays Tables Sorting Rows Selecting Rows Example: Population Trends Example: Trends in Gender Visualization Categorical Distributions Numerical Distributions Overlaid Graphs Functions and Tables Applying Functions to Columns Classifying by One Variable Cross-Classifying Joining Tables by Columns Bike Sharing in the Bay Area Randomness Conditional Statements Iteration The Monty Hall Problem Finding Probabilities Sampling Empirical Distributions Sampling from a Population At the Roulette Table Empirical Distibution of a Statistic Testing Hypotheses Jury Selection Terminology of Testing Error Probabilities Example: Deflategate Estimation Percentiles The Bootstrap Confidence Intervals Using Confidence Intervals Why the Mean Matters Properties of the Mean Variability The SD and the Normal Curve The Central Limit Theorem The Variability of the Sample Mean Choosing a Sample Size Prediction Correlation The Regression Line The Method of Least Squares Least Squares Regression Visual Diagnostics Numerical Diagnostics Inference for Regression A Regression Model Inference for the True Slope Prediction Intervals Classification Nearest Neighbors Training and Testing Rows of Tables Implementing the Classifier The Accuracy of the Classifier Multiple Regression Comparing Two Samples Two Categorical Distributions A/B Testing Causality Updating Predictions A "More Likely Than Not" Binary Classifier Making Decisions