Algorithmic Thinking Examples
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The Cambridge Handbook of Computing Education Research
This is an authoritative introduction to Computing Education research written by over 50 leading researchers from academia and the industry.
Algorithmic Thinking
A hands-on, problem-based introduction to building algorithms and data structures to solve problems with a computer. Algorithmic Thinking will teach you how to solve challenging programming problems and design your own algorithms. Daniel Zingaro, a master teacher, draws his examples from world-class programming competitions like USACO and IOI. You'll learn how to classify problems, choose data structures, and identify appropriate algorithms. You'll also learn how your choice of data structure, whether a hash table, heap, or tree, can affect runtime and speed up your algorithms; and how to adopt powerful strategies like recursion, dynamic programming, and binary search to solve challenging problems. Line-by-line breakdowns of the code will teach you how to use algorithms and data structures like: The breadth-first search algorithm to find the optimal way to play a board game or find the best way to translate a book Dijkstra's algorithm to determine how many mice can exit a maze or the number of fastest routes between two locations The union-find data structure to answer questions about connections in a social network or determine who are friends or enemies The heap data structure to determine the amount of money given away in a promotion The hash-table data structure to determine whether snowflakes are unique or identify compound words in a dictionary NOTE: Each problem in this book is available on a programming-judge website. You'll find the site's URL and problem ID in the description. What's better than a free correctness check?
Anyone Can Code: Algorithmic Thinking
As the second book in the Anyone Can Code series, Algorithmic Thinking focuses on the logic behind computer programming and software design. With a data-centred approach, it starts with simple algorithms that work on simple data items and advances to more complex ones covering data structures and classes. Examples are given in C/C++ and Python and use both plain text and graphics applications to illustrate the concepts in different languages and forms. With the advances in artificial intelligence and automated code generators, it is essential to learn about the logic of what a code needs to do, not just how to write the code. Anyone Can Code: Algorithmic Thinking is suitable for anyone who aims to improve their programming skills and go beyond the simple craft of programming, stepping into the world of algorithm design. This book is independent of the first one in the series but assumes some basic familiarity with programming, such as language syntax.