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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.
List 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.
About the author
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).
