Welcome

Welcome to Mastering Data Structures & Algorithms in C++, a course that takes you from the basics of std::vector all the way to graph algorithms, dynamic programming, and the algorithmic patterns that power systems software, game engines, competitive programming, and technical interviews.

C++ is one of the best languages on the planet for learning data structures and algorithms. It is close enough to the hardware that you can reason about memory layout, but its Standard Template Library (STL) gives you battle-tested containers and algorithms so you never have to re-implement a hash table just to solve a problem. By the end of this course you will both use the STL fluently and be able to implement the same structures from scratch — the two skills are complementary.

What you will learn

Each page mixes prose with three kinds of interactive widgets:

1. Executable code blocks. Every C++ snippet compiles to WebAssembly with clang++ in your browser and runs as a tiny WASI binary. No installation, no toolchain setup. Edit any block and click Run.
2. Challenge cards. Hidden-test coding problems. Some are single-file, some span multiple translation units so you can practice header/implementation splitting.
3. Multiple choice questions. Quick checks at the end of most pages. Each option has its own explanation, so a wrong answer still teaches you something.

Why "data structures and algorithms"?

You cannot really separate them. A data structure is a way of arranging data so that the algorithms you care about become fast. Choose a hash table and contains? becomes O(1); choose a sorted array and you can binary-search in O(log n); choose a heap and you can always pull the minimum in O(log n). Every chapter of this course pairs a structure with the operations it makes cheap (and the ones it makes expensive).

Course outline

The pages are ordered so each chapter builds on earlier material, but each one stands alone if you want to jump to a specific topic.

1. Foundations

Why C++ is a great teaching language for DSA, the slice of modern C++ you need (references, pointers, templates, iterators), and the mathematics of running-time analysis with Big-O, Big-Ω, and Big-Θ.

2. Linear structures

Arrays, the STL containers built on top of them (std::vector, std::array, std::string), linked lists you implement yourself, then std::list and std::deque, and the abstract data types of stacks and queues.

3. Recursion, searching, and sorting

How to write and reason about recursive functions, why binary search is a 30-line algorithm with a lot of off-by-one traps, and the classic sorting algorithms: bubble, selection, insertion, merge, and quicksort — culminating in std::sort (introsort).

4. Hashing and trees

Hash tables and std::unordered_map, binary trees and their traversals, binary search trees, binary heaps and std::priority_queue, and the trie — a tree keyed by characters.

5. Graphs

Adjacency-list and adjacency-matrix representations, BFS and DFS, Dijkstra's shortest paths, and the union-find structure that powers Kruskal's MST and dynamic connectivity.

6. Algorithmic paradigms

Dynamic programming, greedy algorithms, and backtracking — three families of techniques that, together, solve a remarkable fraction of real-world combinatorial problems.

7. Going further

A roadmap for what to study after this course: advanced graph algorithms, string algorithms, computational geometry, randomized and parallel algorithms, and where to practice (competitive programming judges, system design, low-latency engineering).