tl;dr: A textbook on the mathematical foundations of data science, covering 16 chapters spanning high-dimensional geometry, SVD/PCA, linear regression, graph-based methods, dimension reduction, optimization, classification, and deep learning. It also treats more theoretical topics including concentration of measure, matrix concentration inequalities, compressive sensing, and low-rank matrix recovery. Authored by Thomas Strohmer and posted to arXiv.