Math for Programming

by
Format: Paperback
Pub. Date: 2025-04-22
Publisher(s): No Starch Press
List Price: $53.00

Buy New

Usually Ships in 2-3 Business Days
$50.48

Rent Book

Select for Price
There was a problem. Please try again later.

Rent Digital

Rent Digital Options
Online:1825 Days access
Downloadable:Lifetime Access
$29.99
*To support the delivery of the digital material to you, a non-refundable digital delivery fee of $3.99 will be charged on each digital item.
$29.99*

Used Book

We're Sorry
Sold Out

How Marketplace Works:

  • This item is offered by an independent seller and not shipped from our warehouse
  • Item details like edition and cover design may differ from our description; see seller's comments before ordering.
  • Sellers much confirm and ship within two business days; otherwise, the order will be cancelled and refunded.
  • Marketplace purchases cannot be returned to eCampus.com. Contact the seller directly for inquiries; if no response within two days, contact customer service.
  • Additional shipping costs apply to Marketplace purchases. Review shipping costs at checkout.

Summary

Learn all of the core mathematical topics that professional software engineers need to know—in a single book!

This book summarizes all the core mathematical topics a typical professional software engineer needs to know. In condensing the various concepts covered in an undergraduate computer science program into a single volume, it provides an excellent starting point for independent study, or a refresher for those who haven’t been in a classroom for years. Early chapters cover preliminary subjects like number representation systems, set theory, and Boolean algebra, followed by a dive into the field of discrete mathematics, including functions, induction proofs, number theory, combinatorics, graphs, and trees. Later sections examine essential topics in probability, statistics, linear algebra, and calculus.

Rather than confine itself to abstract theory, the book focuses on practical applications and numerical methods at the level typically encountered by working software developers. In addition, hands-on code examples in Python and C make the topics concrete.

Author Biography

Ronald T. Kneusel is a data scientist who builds deep-learning (AI) systems, as well as extensive experience with medical imaging and the development of medical devices. He earned a PhD in machine learning from the University of Colorado, Boulder, has nearly 20 years of machine learning experience in industry, and is presently pursuing deep-learning projects with L3Harris Technologies, Inc. Kneusel is also the author of Random Numbers and Computers (Springer 2018), in addition to Math for Deep Learning, Practical Deep Learning, and Strange Code—all published by No Starch Press.

Table of Contents

Introduction
Chapter 1. Computers and Numbers
Chapter 2. Sets and Abstract Algebra
Chapter 3. Boolean Algebra
Chapter 4. Functions and Relations
Chapter 5. Induction
Chapter 6. Recurrence and Recursion
Chapter 7. Number Theory
Chapter 8. Counting and Combinatorics
Chapter 9. Graphs
Chapter 10. Trees
Chapter 11. Probability
Chapter 12. Statistics
Chapter 13. Linear Algebra
Chapter 14. Differential Calculus
Chapter 15. Integral Calculus
Chapter 16. Differential Equations

An electronic version of this book is available through VitalSource.

This book is viewable on PC, Mac, iPhone, iPad, iPod Touch, and most smartphones.

By purchasing, you will be able to view this book online, as well as download it, for the chosen number of days.

Digital License

You are licensing a digital product for a set duration. Durations are set forth in the product description, with "Lifetime" typically meaning five (5) years of online access and permanent download to a supported device. All licenses are non-transferable.

More details can be found here.

A downloadable version of this book is available through the eCampus Reader or compatible Adobe readers.

Applications are available on iOS, Android, PC, Mac, and Windows Mobile platforms.

Please view the compatibility matrix prior to purchase.