Mathematical Methods in Data Science: Bridging Theory and Applications with Python

Author:   Sébastien Roch (University of Wisconsin, Madison)
Publisher:   Cambridge University Press
ISBN:  

9781009509459


Pages:   499
Publication Date:   30 September 2025
Format:   Hardback
Availability:   Not yet available   Availability explained
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Mathematical Methods in Data Science: Bridging Theory and Applications with Python


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Author:   Sébastien Roch (University of Wisconsin, Madison)
Publisher:   Cambridge University Press
Imprint:   Cambridge University Press
ISBN:  

9781009509459


ISBN 10:   1009509454
Pages:   499
Publication Date:   30 September 2025
Audience:   College/higher education ,  Undergraduate ,  Postgraduate, Research & Scholarly
Format:   Hardback
Publisher's Status:   Forthcoming
Availability:   Not yet available   Availability explained
This item is yet to be released. You can pre-order this item and we will dispatch it to you upon its release.

Table of Contents

1. Introduction: a first data science problem; 2. Least squares: geometric, algebraic, and numerical aspects; 3. Optimization theory and algorithms; 4. Singular value decomposition; 5. Spectral graph theory; 6. Probabilistic models: from simple to complex; 7. Random walks on graphs and Markov chains; 8. Neural networks, backpropagation and stochastic gradient descent.

Reviews

'Mathematical Methods in Data Science provides a clear and accessible primer on key concepts central to data science and machine learning. Through engaging examples from neural networks, recommender systems, and data visualization, Roch illuminates myriad foundational topics and methods. Designed for readers from a broad range of backgrounds, this text is an indispensable resource for students and professionals.' Rebecca Willett, University of Chicago 'This book is an outstanding introduction to the fundamentals of data science by an expert educator and researcher in the area. Its choice of topics, its use of Python, its plentiful examples and exercises, and its battle-testing in the classroom make it a top choice for students and educators seeking a mathematically rigorous yet practical entrée into data science.' Stephen J. Wright, University of Wisconsin


Author Information

Sébastien Roch is a Vilas Distinguished Achievement Professor of Mathematics at the University of Wisconsin, Madison. At UW-Madison, he helped establish the Data Science Major and has developed several courses on the mathematics of data. He is the author of Modern Discrete Probability: An Essential Toolkit (2023).

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