What this book covers
Part I – Basic Concepts of Discrete Math
Chapter 1, Key Concepts, Notation, Set Theory, Relations, and Functions, is an introduction to the basic vocabulary, concepts, and notation of discrete mathematics.
Chapter 2, Formal Logic and Constructing Mathematical Proofs, covers formal logic and binary and explains how to prove mathematical results.
Chapter 3, Computing with Base-n Numbers, discusses arithmetic in different numbering systems, including hexadecimal and binary.
Chapter 4, Combinatorics Using SciPy, explains how to count the elements in certain types of discrete structures.
Chapter 5, Elements of Discrete Probability, covers measuring chance and the basics of Google's PageRank algorithm.
Part II – Implementing Discrete Mathematics in Data and Computer Science
Chapter 6, Computational Algorithms in Linear Algebra, explains how to solve algebra problems with Python using NumPy.
Chapter 7, Computational Requirements for Algorithms, gives you the tools to determine how long algorithms take to run and how much space they require.
Chapter 8, Storage and Feature Extraction of Graphs, Trees, and Networks, covers storing graph structures and finding information about them with code.
Chapter 9, Searching Data Structures and Finding Shortest Paths, explains how to traverse graphs and figure out efficient paths between vertices.
Part III – Real-World Applications of Discrete Mathematics
Chapter 10, Regression Analysis with NumPy, is a discussion on the prediction of variables in datasets containing multiple variables.
Chapter 11, Web Searches with PageRank, shows you how to rank the results of web searches to find the most relevant web pages.
Chapter 12, Principal Component Analysis with Scikit-Learn, explains how to reduce the dimensionality of high-dimensional datasets to save space and speed up machine learning.
