Synopsis
Programming is about using a computer to solve problems, and algorithms and data structures are the building blocks of computer programs. For each problem that a programmer wants to solve, they employ an algorithm: a sequence of steps for solving the problem. Many books teach algorithms independently of specific problems, but this book uses careful explanations, examples, and arguments, rather than formal mathematics and proofs which make it difficult for the reader to connect what they are learning to what they can do with that learning. Algorithmic Thinking: A Problem-Based Introduction teaches the reader to use the best algorithms and data structures for a given situation by walking them through solving real-world problems pulled from international programming competitions, such as how to determine whether snowflakes are unique; how to win a game in the minimum number of moves; how to find the number of ways to get to someone's house; how to escape a cave in as few steps as possible; and so on. Readers tackle challenging topics like recursion, dynamic programming, graphs, greedy algorithms, heaps, hash tables, segment trees, and other data structures for efficiently handling data. The book contains no pseudocode: all code is written in C and is thoroughly explained in the text (C is a de facto programming language for programming competitions). Zingaro also shows how several problems can be reduced to algorithms on graphs. By the end of the book, readers should understand the importance of modeling, how to carefully work through a problem, and why it pays to organize data using data structures.
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About the Author
Dr. Daniel Zingaro is an assistant teaching professor of computer science and award-winning teacher at the University of Toronto. His main area of research is computer science education research, where he studies how students learn (and sometimes don't learn) computer science material.
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