Mathematical Foundations for Data Analysis

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

A complete introductory textbook covering the data science workflow end to end: collecting and cleaning data, descriptive and inferential statistics, visualization, regression, and an introduction to machine learning. Includes exercises and examples in both Python and R.

OpenStax

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