Discovering Computer Science: Interdisciplinary Problems, Principles, and Python Programming

Discovering Computer Science: Interdisciplinary Problems, Principles, and Python Programming introduces computational problem solving as a vehicle of discovery in a wide variety of disciplines. With a principles-oriented introduction to computational thinking, the text provides a broader and deeper introduction to computer science than typical introductory programming books.

Organized around interdisciplinary problem domains, rather than programming language features, each chapter guides students through increasingly sophisticated algorithmic and programming techniques. The author uses a spiral approach to introduce Python language features in increasingly complex contexts as the book progresses.

The text places programming in the context of fundamental computer science principles, such as abstraction, efficiency, and algorithmic techniques, and offers overviews of fundamental topics that are traditionally put off until later courses.

The book includes thirty well-developed independent projects that encourage students to explore questions across disciplinary boundaries. Each is motivated by a problem that students can investigate by developing algorithms and implementing them as Python programs.

The book’s accompanying website ― http://discoverCS.denison.edu ― includes sample code and data files, pointers for further exploration, errata, and links to Python language references.

Containing over 600 homework exercises and over 300 integrated reflection questions, this textbook is appropriate for a first computer science course for computer science majors, an introductory scientific computing course or, at a slower pace, any introductory computer science course.

Table of Contents

Chapter 1: What is computation?
Chapter 2: Elementary computations
Chapter 3: Visualizing abstraction
Chapter 4: Growth and decay
Chapter 5: Forks in the road
Chapter 6: Text, documents, and DNA
Chapter 7: Designing programs
Chapter 8: Data analysis
Chapter 9: Flatland
Chapter 10: Self-similarity and recursion
Chapter 11: Organizing data
Chapter 12: Networks
Chapter 13: Abstract data types
Appendix A: Installing Python
Appendix B: Python library reference