Pope J Discrete Mathematics for Data Science 2026 | 16.37 MB
Title: Discrete Mathematics for Data Science
Author: Jack Pope;
Description:
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.
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.
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