The book "Mathematics of Data Science" covers the mathematical foundations essential for data science applications. Key topics include high-dimensional analysis, singular value decomposition, principal component analysis, linear regression, clustering, and dimension reduction techniques.

The Singular Value Decomposition (SVD) is a mathematical technique used in linear algebra for decomposing a matrix into singular values and vectors. It has applications in various fields, including statistics, signal processing, and machine learning.