Daniel Zingaro

Algorithmic Thinking, 2nd Edition (eBook, ePUB)

Learn Algorithms to Level Up Your Coding Skills

• Format: ePub

Produktbeschreibung

Get in the game and learn essential computer algorithms by solving competitive programming problems, in the fully revised second edition of the bestselling original. (Still no math required!) Are you hitting a wall with data structures and algorithms? Whether you're a student prepping for coding interviews or an independent learner, this book is your essential guide to efficient problem-solving in programming. UNLOCK THE POWER OF DATA STRUCTURES & ALGORITHMS: Learn the intricacies of hash tables, recursion, dynamic programming, trees, graphs, and heaps. Become proficient in choosing and implementing the best solutions for any coding challenge. REAL-WORLD, COMPETITION-PROVEN CODE EXAMPLES: The programs and challenges in this book aren't just theoretical-they're drawn from real programming competitions. Train with problems that have tested and honed the skills of coders around the world. GET INTERVIEW-READY: Prepare yourself for coding interviews with practice exercises that help you think algorithmically, weigh different solutions, and implement the best choices efficiently. WRITTEN IN C, USEFUL ACROSS LANGUAGES: The code examples are written in C and designed for clarity and accessibility to those familiar with languages like C++, Java, or Python. If you need help with the C code, no problem: We've got recommended reading, too. Algorithmic Thinking is the complete package, providing the solid foundation you need to elevate your coding skills to the next level.

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Produktdetails

• Verlag: No Starch Press
• Seitenzahl: 480
• Erscheinungstermin: 23. Januar 2024
• Englisch
• ISBN-13: 9781718503236
• Artikelnr.: 67661332

Autorenporträt

Dr. Daniel Zingaro is an award-winning associate professor of mathematical and computational sciences at the University of Toronto Mississauga. He is well known for his uniquely interactive approach to teaching and internationally recognized for his expertise in active learning. He is also the author of Learn to Code by Solving Problems (No Starch Press) and co-author of Learn AI-Assisted Python Programming.

Inhaltsangabe

Foreword
Introduction
Acknowledgments
Chapter 1: Hash Tables
Chapter 2: Trees and Recursion
Chapter 3: Memoization and Dynamic Programming
Chapter 4: Advanced Memoization and Dynamic Programming
Chapter 5: Graphs and Breadth-First Search
Chapter 6: Shortest Paths in Weighted Graphs
Chapter 7: Binary Search
Chapter 8: Heaps and Segment Trees
Chapter 9: Union-Find
Chapter 10: Randomization
Afterword
Appendix A: Algorithm Runtime
Appendix B: Because I Can’t Resist
Appendix C: Problem Credits
Index