Master datastructures.Watch them move. Then build them.
Every concept has an interactive animated visualizer — watch algorithms run step by step, not just read about them. Free, beginner-to-advanced, no fluff.
Stop reading about algorithms.
Press play and watch a sort actually run — compares, swaps, and all. Every page on this course has a widget like this.
The crash course
Never studied DSA before? Start here. Twelve focused lessons that take you from “what is an array?” to confidently reading Big-O notation and writing your first recursive function. Each one is short, punchy, and has a built-in visualizer.
What Is a Data Structure?
A data structure is a deal you strike — you give up something to make one operation fast. Here is the big picture before you dive into any of them.
Big-O Notation, Demystified
Big-O notation is the one tool every engineer needs to reason about performance. Learn to read it, use it, and stop fearing it — with concrete op-counts that make the curves click.
Arrays and How Memory Really Works
Go under the hood of the most fundamental data structure — contiguous bytes, O(1) index addressing, cache lines, and why a sequential scan beats a random walk every time.
Recursion: Thinking in Smaller Selves
Recursion explained from first principles — base case, recursive case, the call stack, and why your function sometimes blows up. The mental model every programmer needs before touching trees, graphs, or dynamic programming.
Data structures
One article per structure, each with an interactive visualizer you can actually play with. Insert nodes, delete edges, watch the rebalancing happen live. Then read the explanation that makes sense of what you just saw.
Union-Find (Disjoint Set Union)
The near-magic data structure for connectivity queries — are these two nodes in the same component? Nearly O(1) per operation with union by rank and path compression. Kruskal's MST, redundant connections, account merging, and more.
Tries (Prefix Trees)
The data structure powering autocomplete, spell-check, and IP routing — a tree where each edge is a character and every path from the root spells a prefix. Insert, search, and prefix queries all run in O(L) on word length alone, completely independent of how many words you store.
Strings
Strings look like simple text, but they hide a brutal trap: naive concatenation in a loop is O(n²). Learn why, how encoding actually works, and the handful of patterns — sliding window, two pointers, hashing — that solve 80% of string interview problems.
Segment Trees
Segment trees answer range queries AND handle point or range updates in O(log n) — the structure to reach for when prefix sums aren't enough and you need both reads and writes at scale.
LRU Cache
The definitive guide to the LRU cache — how to combine a hash map and a doubly linked list to get O(1) get and put, why neither structure alone is enough, and how to walk the full implementation in an interview.
Hash Sets
A hash set is a hash table with the values ripped out — pure O(1) membership, dedup, and "have I seen this?" tracking. Master it and a whole class of O(n²) problems collapse to O(n).
Techniques & patterns
Sliding window, two pointers, binary search on the answer, backtracking, dynamic programming — the fifteen patterns that show up in 80% of coding interviews. Each guide walks through the template, then applies it to real problems.
Topological Sort
Order the nodes of a DAG so every edge points forward — the algorithm that drives build systems, package managers, course schedulers, and anything else that lives and dies by dependency ordering.
The Top-K Elements Pattern
How a heap of size k gives you the k largest (or smallest, or most frequent) items in O(n log k) — and why a min-heap is the counterintuitive tool for finding the biggest things.
Subsets, Permutations & Combinations
Generate every subset, permutation, and combination with the choose/explore/un-choose backtracking template. Understand the 2^n and n! complexity ceilings, why sorting kills duplicate branches, and how to recognize which variant a problem is actually asking for.
Shortest Paths (Dijkstra, Bellman-Ford, BFS)
Pick the right shortest-path algorithm the first time: BFS for unweighted graphs, Dijkstra with a heap for non-negative weights, Bellman-Ford when negative edges appear. Concrete worked examples, a decision table, and every gotcha that costs people the interview.
Prefix Sums
Precompute running totals so any range sum becomes one subtraction. The prefix sum technique turns O(n) per query into O(1) — and the hashmap combo unlocks a whole class of subarray counting problems.
Monotonic Stacks
The monotonic stack is the O(n) trick for finding the next greater or smaller element — a sorted stack you maintain on the fly, with one deceptively simple pop-while loop at its core. Used in Daily Temperatures, Largest Rectangle in Histogram, and Trapping Rain Water.
Find what you need, fast
Every article is tagged. Jump straight to the topic you want to study or review.
See it. Understand it.
Keep it.
The fastest way to learn a data structure is to watch it work. Ironclad Academy gives you interactive visualizers for everything, paired with explanations written by people who actually care about teaching well. It's free. Always will be.