This clearly structured and easy to read textbook explains the concepts and techniques required to write programs that can handle large amounts of data efficiently.

Project-oriented and classroom-tested, the book presents a number of important algorithms supported by motivating examples that bring meaning to the problems faced by computer programmers. The idea of computational complexity is also introduced, demonstrating what can and cannot be computed efficiently so that the programmer can make informed judgements about the algorithms they use. The text assumes some basic experience in computer programming and familiarity in an object-oriented language, but not necessarily with Python.

Topics and features: includes both introductory and advanced data structures and algorithms topics, with suggested chapter sequences for those respective courses provided in the preface; provides learning goals, review questions and programming exercises in each chapter, as well as numerous illustrative examples; offers downloadable programs and supplementary files at an associated website, with instructor materials available from the author; presents a primer on Python for those coming from a different language background; reviews the use of hashing in sets and maps, along with an examination of binary search trees and tree traversals, and material on depth first search of graphs; discusses topics suitable for an advanced course, such as membership structures, heaps, balanced binary search trees, B-trees and heuristic search.

Students of computer science will find this clear and concise textbook to be invaluable for undergraduate courses on data structures and algorithms, at both introductory and advanced levels. The book is also suitable as a refresher guide for computer programmers starting new jobs working with Python.

Includes broad coverage of both introductory and advanced data structures topics, supported by examples

Guides the reader through the concepts of computational complexity, from the basics to amortized complexity

Makes learning fun, using the development of graphical user interface programs to illustrate concepts

Includes supplementary material: sn.pub/extras

Autorentext

Dr. Kent D. Lee is Professor of Computer Science at Luther College, Decorah, Iowa, USA. He is the author of the successful Springer textbook Python Programming Fundamentals and the forthcoming Foundations of Programming Languages.

Dr. Steve Hubbard is Professor of Mathematics and Computer Science at Luther College.

Inhalt

1: Python Programming 101.- 2: Computational Complexity.- 3: Recursion.- Sequences.- 4: Sets and Maps.- 5: Trees.- 6: Graphs.- 7: Membership Structures.- 8: Heaps.- 9: Balanced Binary Search Trees.- 10: B-Trees.- 11: Heuristic Search.- Appendix A: Integer Operators.- Appendix B: Float Operators.- Appendix C: String Operators and Methods.- Appendix D: List Operators and Methods.- Appendix E: Dictionary Operators and Methods.- Appendix F: Turtle Methods.- Appendix G: TurtleScreen Methods.- Appendix H: Complete Programs.