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pope j discrete mathematics data science 2026

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Textbook in PDF format 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] Prerequisite Knowledge: This book assumes no prior background in computer programming or statistics. The student should be comfortable with algebra at the college level. Some prior knowledge of the Linux command-line terminal would be helpful as some example programs may be run from the command-line. This book refers from time to time to the Unix operating system. Internet servers hosting Big Data are overwhelmingly Linux/Unix systems. These, in turn, are accessed by analysts remotely through various types of application interfaces, including database connections and Internet browsers. Derived from Unix is Linux, the most ubiquitous operating system in the world today. Of the top 500 supercomputers in the world, all run the Linux operating system.3 Most Internet servers are also hosted on Unix or Linux. More data is being both generated and stored in a Linux/Unix environment than people realize. But it is easy to take for granted the architecture of our programming environment. When “properly” coded, we expect our programs to work smoothly, often oblivious to the extraordinary yet beautiful complexity of the underlying system. Some low-level aspects of such system complexity are illuminated by this book, which should be of particular interest to students of data science planning to work in data engineering. Coding is used in this book as a vehicle for learning the subject matter. You can learn the streets of the big city by studying a map. You can additionally walk the streets yourself and learn your way around through experience. To program is to walk the streets. Likewise, programming a statistics routine is a learning-by-doing approach that seems to be effective for many students. Coding allows us to condense otherwise verbose descriptions of concepts into a relatively compact, albeit abstract, perspective. For example, code that tests conditions is used to augment concepts of mathematical proof. To an extent, this book uses a Forth-like code called epop for its compact, yet natural language structure. epop is designed as a teaching tool to help students learn discrete math for Data Science, with data processing in mind. epop's postfix notation allows fairly concise mathematical expressions, which leads to relatively compact syntax. More about this coding convention is presented in Appendix A, as well as in the online resources noted in Appendix C. Arguably, Forth has characteristics of the functional programming paradigm. Functional programming emphasizes what task is to be accomplished, in contrast to procedural programming, which deals more with how a task is to be accomplished. Long before the fad of functional programming, Forth was functional. Being a work of Charles (Chuck) Moore in the late 1960s, Forth may have taken inspiration from what might be considered the first functional programming language, Lisp, which was originated by John McCarthy at the Massachusetts Institute of Technology in 1958. Chuck Moore was a student of John McCarthy. Why not use Python or R? With Python and R, the student can possess the capabilities of built-in abstract functions, without necessarily understanding the problem at hand. Because Python and R libraries exist for just about any problem, these libraries are recommended for use only after fundamentals are understood by wording them in a more natural language, such as Forth

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pope j discrete mathematics data science 2026

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