DSA
From Big-O to hash maps, two pointers and sliding windows, through linked lists, trees, heaps, graphs and dynamic programming. Every lesson runs JavaScript in your browser and links to real Practice problems.
Every chapter is an island. Finish its lessons, beat its challenge, and sail on ⛵
Island 1
Foundations: measuring code
- Big-O: how code growsComparing two solutions without running them: O(1), O(n), O(n²) and O(log n).12 min3 🎮
- Space and the time–memory trade-offHow much memory a solution uses, and when to spend memory to save time.9 min3 🎮
- Arrays and strings under the hoodWhat's fast and slow on arrays, and why building a string in a loop can be costly.10 min3 🎮
- Hash maps and setsThe most useful interview tool: O(1) lookups and inserts, counting and grouping.12 min3 🎮
- How to approach any interview problemA repeatable process from understanding to code to testing, and how to talk while solving.10 min3 🎮
Island 2
Array patterns
- Two pointersTwo indexes moving through an array instead of nested loops: O(n²) becomes O(n).12 min3 🎮
- Sliding windowLongest or shortest contiguous part meeting a condition: a window that grows and shrinks in one pass.13 min3 🎮
- Prefix sumsAny range sum in O(1) after O(n) preparation, plus a hash map to count subarrays.11 min2 🎮
- Binary search, done rightSearching by halving, the boundary bugs that trip people up, and searching on the answer itself.14 min2 🎮
- Sorting: merge sort and quicksortHow sorting works inside, how to use `sort` correctly, and when sorting is the solution.14 min2 🎮
Island 3
Stacks, queues and linked lists
- Stacks and monotonic stacksLast in, first out: brackets, undo, and "the next greater number".13 min2 🎮
- Queues and dequesFirst in, first out, why `shift()` slows code down, and deques for sliding windows.11 min2 🎮
- Linked listsNodes pointing at each other, and the core tricks: the dummy node, reversal and fast/slow pointers.15 min2 🎮
Island 4
Recursion and trees
- Recursion without fearThe base case, the call stack, and how to trust a function that calls itself.13 min2 🎮
- Backtracking: try and undoOne template for every "all possibilities" problem: subsets, permutations and combinations.14 min2 🎮
- Binary treesNodes and children, depth-first visits (pre/in/post-order) and level-order (BFS).15 min2 🎮
- Binary search treesAn ordered tree: search and insert in O(h), and inorder visits values in sorted order.13 min3 🎮
- Heaps and priority queuesThe smallest or largest item in O(1), insert and remove in O(log n), and "top k" problems.15 min2 🎮
Island 5
Graphs and dynamic programming
- Graphs: BFS and DFSStoring a graph, walking it breadth-first and depth-first, and seeing every grid as a graph.15 min2 🎮
- Shortest paths, topological sort and union-findDijkstra for weights, Kahn for ordering tasks, and union-find for connected groups.16 min2 🎮
- Dynamic programming: the ideaProblems made of repeating smaller problems: top-down memoization and bottom-up tabulation.16 min2 🎮
- DP on grids and stringsA `dp[i][j]` table: grid paths, longest common subsequence and the knapsack.15 min2 🎮
- Greedy: the best choice right nowWhen the locally best choice leads to the global best, and how to be sure.12 min2 🎮