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.
Sprache
Verlagsort
Verlagsgruppe
Zielgruppe
Für höhere Schule und Studium
Undergraduate Advanced
Illustrationen
16 s/w Tabellen, 72 s/w Zeichnungen, 72 s/w Abbildungen
16 Tables, black and white; 72 Line drawings, black and white; 72 Illustrations, black and white
Maße
Höhe: 234 mm
Breite: 156 mm
ISBN-13
978-1-032-68722-3 (9781032687223)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
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.
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
