Mathematics of Data Science

A comprehensive book by Bandeira, Singer, and Strohmer examines the mathematical foundations of data science, covering topics including linear algebra, optimization, dimension reduction, classification, deep learning, and matrix recovery. The work spans 16 chapters that integrate classical mathematical theory with modern data science applications, addressing both theoretical concepts and practical computational methods.