Most algorithm books prove things. This one shows you what each choice costs in code you can run.
You will count operations before trusting a benchmark, then build dynamic arrays, hash tables, linked lists, stacks, queues and heaps in both Python and JavaScript and watch where each one falls over. You will write merge sort, quicksort and heapsort, get binary search boundaries right, balance a search tree, build a trie for prefix search, and traverse graphs with breadth-first and depth-first search before moving to Dijkstra, minimum spanning trees and union-find. Closing chapters cover greedy reasoning, memoisation and tabulation, and how to choose a structure for a real service under memory and latency budgets.
Written for self-taught developers, bootcamp graduates and interview candidates, this book bridges the gap between theoretical computer science and the practical decisions you make every day. Instead of abstract proofs, you will measure, compare, and implement. Each chapter pairs conceptual explanation with runnable code in two languages, so you can see exactly how the same algorithm behaves differently in Python and JavaScript. You will learn to reason about time and space complexity not as a notation exercise but as a tool for predicting performance before you ship.
What you will learn:
• Count operations and interpret benchmarks to compare algorithms fairly
• Build dynamic arrays, hash tables, linked lists, stacks, queues and heaps in Python and JavaScript
• Implement elementary and divide-and-conquer sorting, including merge sort, quicksort and heapsort
• Get binary search boundaries right and work effectively with sorted data
• Balance a binary search tree and understand the trade-offs of different balancing strategies
• Build a trie for prefix search and apply it to text processing tasks
• Traverse graphs with breadth-first and depth-first search, then move to Dijkstra, minimum spanning trees and union-find
• Apply greedy reasoning, memoisation and tabulation to optimisation problems
• Choose the right data structure for a production service under memory and latency budgets
If you are a self-taught developer, a bootcamp graduate, or an interview candidate who wants to move beyond memorising solutions, this book gives you the mental models and hands-on practice to make confident, evidence-based decisions about algorithms and data structures in real code.