Discrete Mathematics for Data Science
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 mathematics relevant to students of data science, offering an 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. 

 

1148364131
Discrete Mathematics for Data Science
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 mathematics relevant to students of data science, offering an 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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Discrete Mathematics for Data Science

Discrete Mathematics for Data Science

by Jack Pope
Discrete Mathematics for Data Science
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Discrete Mathematics for Data Science

Discrete Mathematics for Data Science

by Jack Pope

Overview

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 mathematics relevant to students of data science, offering an 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. 

 

Product Details

ISBN-13: 9781032687735
Publisher: CRC Press
Publication date: 03/31/2026
Pages: 400
Product dimensions: 6.12(w) x 9.19(h) x (d)

About the Author

Jack Pope has wrangled financial data since Big Data meant a big pile of floppy disks. He works at Investment Economics (aka, System Goats) providing system configuration, guidance, and training for organizations interested in data science infrastructure. He is also department coordinator for Computer Science and Data Science at North Hennepin Community College and chairman of the Twin Cities IEEE Computer Society.

Table of Contents

List of Figures List of Tables Preface Section I Problem Solving Chapter 1 Your Mind: A Programming Environment Section II Elements Chapter 2 Atoms & Abstractions Chapter 3 Numbers Chapter 4 Number Conversion Chapter 5 Digital Arithmetic & Logic Section III Computational Logic Chapter 6 Propositional Logic Chapter 7 Set Quantification Chapter 8 Proof Chapter 9 Computability Section IV Functions Chapter 10 Functions & Abstractions Chapter 11 Repetition & Recursion Chapter 12 Lambda Calculus Chapter 13  Algorithm Complexity Section V Data Organization Chapter 14 Data Organization Chapter 15 Unconnected Data Chapter 16 Linear Structures Chapter 17 Branched Structures Section VI Data Analysis Chapter 18 Counting: Permutations & Combinations Chapter 19  Probability & Statistics Chapter 20 Multivariate Analysis Chapter 21 Resampling Chapter 22 Information Theory Chapter 23 Data Dimensions Section VII Appendix Bibliography Index

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