Data Structures & Algorithms
DSA, explained visually
Every important data structure and algorithm — from arrays to dynamic programming — with the intuition, the Big-O, the code, and a live animation you can poke at. Built for interviews and for actually understanding how things work.
Data Structures
10 lessonsArrays
Contiguous memory, O(1) indexed access, and the shifting cost behind insert and delete.
8 minLinked Lists
Nodes chained by pointers: O(1) insert/delete where you hold a reference, but no random access.
9 minStacks
A LIFO container: push and pop from one end in O(1). The structure behind function calls and undo.
8 minQueues
A FIFO line: enqueue at the back, dequeue from the front in O(1). The engine behind BFS and buffering.
8 minHash Tables
O(1) average lookups via a hash function — plus how collisions and load factor are handled.
9 minTrees & Binary Search Trees
Hierarchical data, binary trees, and the BST invariant that gives O(log n) search.
10 minHeaps
A complete binary tree in an array that keeps the min (or max) at the root — the engine of priority queues.
10 minTries (Prefix Trees)
A tree keyed by characters that shares prefixes — the structure behind autocomplete and fast prefix search.
8 minGraphs
Nodes joined by edges — directed or not, weighted or not — and the two ways to store them: adjacency list vs matrix.
9 minUnion-Find (Disjoint Set Union)
Track which elements share a group with near-constant-time union and find — the engine behind connected components, cycle detection, and Kruskal.
9 minAlgorithms
12 lessonsBig-O & Complexity Analysis
How Big-O measures growth, why we drop constants, and the common complexity classes from O(1) to O(2ⁿ).
9 minTwo Pointers
Two indices sweeping an array in coordination turn O(n²) brute force into a single O(n) pass.
9 minSliding Window
Maintain a moving range over an array or string, reusing work between steps to hit O(n) instead of O(n·k).
10 minPrefix Sums
Precompute running totals once so any range-sum query answers in O(1) — plus 2D grids, difference arrays, and subarray-sum tricks.
7 minBinary Search
Halve the search space every step to find an element in a sorted array in O(log n).
8 minSorting Algorithms
From O(n²) bubble sort to O(n log n) merge and quick sort — how they work and when each wins.
11 minRecursion & Backtracking
Solve a problem by solving smaller copies of itself, then explore every choice with choose → explore → un-choose.
11 minTree Traversals
Depth-first (pre/in/post-order) and breadth-first (level-order) ways to visit every node — recursive and iterative.
9 minGraph Traversal (BFS & DFS)
Explore a graph without looping forever — BFS for shortest hops, DFS for reachability, and a visited set to keep both honest.
10 minDynamic Programming
Beat exponential recursion by remembering overlapping subproblems — memoization and tabulation.
12 minGreedy Algorithms
Take the best-looking choice at every step and never look back — fast and simple, but only correct when you can prove it with an exchange argument.
8 minBit Manipulation
Work directly on the binary representation of integers — AND, OR, XOR, shifts, and a toolbox of O(1) tricks that replace whole loops.
9 minLearn DSA the intuitive way
Most DSA resources throw definitions and code at you and hope it sticks. This course leads with intuition — the mental model behind each structure and algorithm — then backs it with an interactive visualizer, an honest Big-O analysis, and clean, idiomatic code you can actually use.
The curriculum follows the path top interviewers expect: foundational structures (arrays, linked lists, stacks, queues, hash tables), then trees, heaps, and graphs, then the algorithmic patterns that solve most problems — two pointers, sliding window, binary search, recursion and backtracking, and dynamic programming.
When you're ready to practice, the interactive Labs let you run sorting and pathfinding visualizers, a real SQL engine, and data-structure sandboxes — and the roadmaps show where DSA fits in your broader prep.