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Название: Math for Programming (Early Access)
Автор: Ronald T. Kneusel
Издательство: No Starch Press
Год: 2025
Страниц: 498
Язык: английский
Формат: pdf (true)
Размер: 16.1 MB
A one-stop-shop for all the math you should have learned for your programming career.Math for Programming summarizes all the core math topics a typical professional software engineer needs to know. The book condenses the various mathematics concepts covered in an undergraduate computer science program into a single volume, providing a starting point for independent study or a refresher for those who are some years removed from the classroom. The book first covers preliminary subjects like number representation systems, set theory, and Boolean algebra. Then it dives into the field of discrete mathematics, including functions, induction proofs, number theory, combinatorics, graphs, and trees. The book also examines essential topics in probability, statistics, linear algebra, and calculus. Rather than confine itself to abstract theory, the book focuses on practical application and numerical methods at the level typically encountered by working developers. Hands-on code examples in Python and C also make the topics concrete. Brush up on all the math you should have learned and level-up your career today.
Автор: Ronald T. Kneusel
Издательство: No Starch Press
Год: 2025
Страниц: 498
Язык: английский
Формат: pdf (true)
Размер: 16.1 MB
A one-stop-shop for all the math you should have learned for your programming career.Math for Programming summarizes all the core math topics a typical professional software engineer needs to know. The book condenses the various mathematics concepts covered in an undergraduate computer science program into a single volume, providing a starting point for independent study or a refresher for those who are some years removed from the classroom. The book first covers preliminary subjects like number representation systems, set theory, and Boolean algebra. Then it dives into the field of discrete mathematics, including functions, induction proofs, number theory, combinatorics, graphs, and trees. The book also examines essential topics in probability, statistics, linear algebra, and calculus. Rather than confine itself to abstract theory, the book focuses on practical application and numerical methods at the level typically encountered by working developers. Hands-on code examples in Python and C also make the topics concrete. Brush up on all the math you should have learned and level-up your career today.