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Linear Algebra for Data Science

Coming soon in January 2027

Authors

Michael Kirby
, Colorado State University

Description

This accessible yet rigorous textbook introduces the fundamentals of linear algebra in the context of real-world data science applications. Including the latest developments in the field, clear and detailed mathematical explanations. and extensive examples, it offers a comprehensive and approachable introduction to the subject, focusing on the foundations of the singular value decomposition and its many uses. Key topics include matrix subspaces, reduced-rank matrix approximation, angles between subspaces, averaging subspaces, spectral embedding algorithms including Laplacian eigenmaps and multidimensional scaling, the…

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Key features

  • Builds strong comprehension through detailed mathematical explanations and numerous examples, supporting students from diverse backgrounds while providing depth for mathematics majors
  • Connects theory with practice by closely tying core concepts to real-world data science applications, showing why each method is important
  • Provides a wealth of practice opportunities with more than 600 end-of-chapter exercises for students to check their understanding and knowledge
  • Includes figures in full colour to facilitate data clarity and interpretation
  • Enables hands-on experience by providing data sets and MATLAB and Python code in an accompanying GitHub online, allowing students to focus on linear algebra concepts without the need for extensive programming

About the book