Discrete Mathematics for Data Science
Abstract
Discrete Mathematics for Data Science provides an early course in both Data Science and Discrete Mathematics, focusing on how a deeper understanding of the former can unlock a more effective implementation of the latter. Students of Data Science come from a variety of disciplines, with Business, Statistics, Computer Science, Economics, and Psychology among the departments offering courses on the subject. Therefore, for many students, Data Science is considered a means of insight into a particular field of interest, with the study of its underlying discrete mathematics not a primary objective. This book covers the topics of Discrete Mathematical Structures relevant to students of Data Science, offering a relevant and gentle introduction to both the theoretical and practical elements required to be a successful data scientist. The relaxed, accessible style makes it a perfect textbook for undergraduates. Features • Numerous exercises and examples. • Ideal as a textbook for a Discrete Mathematics course for data science and computer science students. • Source code and solutions provided as a supplementary resource.
Fields of Study & Subjects
Markov Chains and Monte Carlo Methods
Advanced Statistical Modeling Techniques
Statistics Education and Methodologies
Publication details
- Journal / source
- Chapman & Hall/CRC eBooks
- Publication date
- 9 January 2026
- Language
- EN
- Document type
- Book