Learning Algorithms: A Programmer’s Guide to Writing Better Code

Good code is not only about getting the right answer; it is also about choosing a method that keeps working as the problem grows. Learning Algorithms offers a Python-based route into that way of thinking, explaining how common algorithms and data structures work and how their performance can be evaluated.

Understand the cost of an algorithm

The book begins with approachable list problems and uses them to introduce key ideas such as counting operations, time complexity, and space complexity. That foundation gives readers a practical lens for comparing solutions—not just by whether they work, but by how they behave as the input gets larger.

From hash tables to sorting

Heineman develops a range of essential techniques, including hash tables and collision handling, binary heaps and priority queues, and sorting methods from selection sort to merge sort, quicksort, heap sort, and Tim Sort. Performance analysis accompanies the algorithms, helping readers see the trade-offs behind different approaches.

Explore trees, graphs, and paths

Later chapters turn to binary search trees and self-balancing trees, followed by graph traversal and shortest-path methods. Depth-first search, breadth-first search, Dijkstra’s algorithm, and Floyd–Warshall show how algorithmic tools can address problems involving connected structures and routes.

Python examples, with room to practice

Python is the book’s working language, and a final section considers built-in data types alongside implementations of stacks, queues, heaps, and priority queues. Chapter summaries and challenge exercises give readers a chance to review ideas and test their understanding.

For programmers building a stronger grounding in algorithms, this book connects code with the reasoning behind it: how a technique works, how to analyze its performance, and when a different approach may be worth considering.