Overview
Bridge the gap between theoretical concepts and their practical applications with this rigorous introduction to the mathematics underpinning data science. It covers essential topics in linear algebra, calculus and optimization, and probability and statistics, demonstrating their relevance in the context of data analysis. Key application topics include clustering, regression, classification, dimensionality reduction, network analysis, and neural networks. What sets this text apart is its focus on hands-on learning. Each chapter combines mathematical insights with practical examples, using Python to implement algorithms and solve problems. Self-assessment quizzes, warm-up exercises and theoretical problems foster both mathematical understanding and computational skills. Designed for advanced undergraduate students and beginning graduate students, this textbook serves as both an invitation to data science for mathematics majors and as a deeper excursion into mathematics for data science students.
Full Product Details
Author: Sébastien Roch (University of Wisconsin, Madison)
Publisher: Cambridge University Press
Imprint: Cambridge University Press
ISBN: 9781009509404
ISBN 10: 1009509403
Pages: 499
Publication Date: 31 October 2025
Audience:
College/higher education
,
Undergraduate
,
Postgraduate, Research & Scholarly
Format: Paperback
Publisher's Status: Forthcoming
Availability: Not yet available, will be POD
This item is yet to be released. You can pre-order this item and we will dispatch it to you upon it's release. This is a print on demand item which is still yet to be released.
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).
