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:
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.
Inhaltsverzeichnis
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.
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