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 VisualizationWhy a chart works (or doesn’t): histograms, boxplots, scatterplots, faceted views, and the perceptual rules behind picking the right encoding for the question you’re actually trying to answer.
Chapter 3 Regression & InferenceFitting a line is easy; interpreting it honestly is not. Ordinary least squares, residual diagnostics, confidence intervals, p-values done right, and the boundary between description and inference.
Chapter 4 Clustering & SegmentationUnsupervised learning for business: k-means, hierarchical clustering, the elbow and silhouette tests, and how to turn raw segments into a story a manager can act on.