Discrete Mathematics for Data Science
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Estimated delivery time: In stock at the publisher, but not at Prospero's office. Delivery time approx. 3-5 weeks.
Not in stock at Prospero.
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Product details:
- Edition number 1
- Publisher Chapman and Hall
- Date of Publication 31 March 2026
- ISBN 9781032687735
- Binding Paperback
- No. of pages404 pages
- Size 234x156 mm
- Weight 750 g
- Language English
- Illustrations 72 Illustrations, black & white; 72 Line drawings, black & white; 16 Tables, black & white 699
Categories
Short 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.
MoreLong 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
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 Appendix A. Appendix B. Appendix C.
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