This textbook provides a solid mathematical basis for understanding popular data science algorithms for clustering and classification and shows that an in-depth understanding of the mathematics powering these algorithms gives insight into the underlying data. It presents a step-by-step derivation of these algorithms, outlining their implementation from scratch in a computationally sound way.

Mathematics of Data Science: A Computational Approach to Clustering and Classification

proposes different ways of visualizing high-dimensional data to unveil hidden internal structures, and

includes graphical explanations and computed examples using publicly available data sets in nearly every chapter to highlight similarities and differences among the algorithms.