Contents
Contents
Tap any chapter to start reading.
Chapter 0 Setup & Your First ProgramInstall the tools, run Python in the browser, and ship a working data-analysis cell on day one. The minimum viable workflow every analyst uses, demystified.
Chapter 1 Data & DistributionsFrom raw numbers to insight: types of data, summary statistics, the shape of a distribution, and the intuition behind mean, median, variance, and skew — all rendered live with pandas and matplotlib.
Chapter 2 Working with DataFrames in PandasSelect business records, handle missing data, create useful measures, compare groups, and calculate rolling and cumulative results. Eight sections include runnable Python, charts, and practice questions.
Chapter 3 Predicting with Linear RegressionFit and interpret OLS and CAPM; quantify uncertainty, inspect residuals and multicollinearity, and evaluate later-data predictions. Nine sections include interactive diagrams, runnable Python, and 100 practice questions.
Chapter 4 Finding Hidden Groups — Clustering MethodsDefine similarity, choose scaling, understand K-means, compare plausible group counts, and profile real wholesale customers. Six sections include interactive diagrams and 70 practice questions; hierarchical trees are an extension.