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E-raamat: Discrete Mathematics for Data Science

  • Formaat: PDF+DRM
  • Ilmumisaeg: 31-Mar-2026
  • Kirjastus: Chapman & Hall/CRC
  • Keel: eng
  • ISBN-13: 9781040517536
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Discrete Mathematics for Data ScienceSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 31-Mar-2026
  • Kirjastus: Chapman & Hall/CRC
  • Keel: eng
  • ISBN-13: 9781040517536
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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.



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.

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.
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.
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