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 Classificationproposes different ways of visualizing high-dimensional data to unveil hidden internal structures, andincludes graphical explanations and computed examples using publicly available data sets in nearly every chapter to highlight similarities and differences among the algorithms.
