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Mathematics of Data Science [Bandeira] - mklabgr - 07-16-2026
Mathematics of Data Science
by Afonso S. Bandeira
Summary
Mathematics of Data Science offers a broad introduction to the mathematical ideas that power modern data science and machine learning. Rather than focusing on programming tools or software, the book explains the core mathematical principles that allow algorithms to analyze, interpret, and learn from data. It begins by exploring the opportunities and challenges of working with high-dimensional data before covering essential topics such as principal component analysis, linear regression, optimization, classification, graph theory, clustering, and dimensionality reduction.
The authors also introduce the mathematical foundations of deep learning, along with advanced subjects including concentration inequalities, graph Laplacians, compressive sensing, sparsity, and low-rank matrix recovery. Throughout the book, the emphasis is on developing intuition as well as rigorous understanding, showing how ideas from linear algebra, probability, optimization, and statistics come together to solve real-world data problems. By connecting theory with practical applications, the text provides readers with a solid foundation for understanding why modern data science methods work and how they can be applied effectively across a wide range of disciplines.
BOOK [PDF]