Principles of Data Science

A step by step handbook that walks through the full data analysis cycle, from planning what to collect to interpreting and reporting results. Written for program staff with no statistics background, it uses plain language and worksheets at every stage.

Migrant & Seasonal Head Start

A university course book that builds data analysis from the ground up, structured in three acts: exploring data, modelling it, and drawing conclusions. Covers variability, study design, distributions, hypothesis testing, and regression with worked examples throughout.

Michael Bulmer

The mathematical toolkit behind modern data analysis, covering probability, linear algebra, distances, clustering, regression, and dimensionality reduction. Built from two university courses designed to prepare students for rigorous machine learning work.

Jeff M. Phillips

An advanced treatment of topological data analysis, covering persistent homology, Reeb graphs, and discrete Morse theory, with algorithms for applying them to real datasets. Aimed at readers who already have a solid mathematical background.

Tamal Krishna Dey, Yusu Wang