Module 0 · days 1–90%
Programming Foundations
The alphabet before the poetry: variables, types, decisions, loops and functions — everything the rest of the course quietly assumes you know.
Module 1 · days 10–200%
🔒 OOP via Analogies
Objects are just nouns with skills. Learn the four pillars through houses, genetics and multi-tools.
□ 1. Classes as Blueprints□ 2. Objects as Houses□ 3. Attributes vs Methods□ 4. Constructors□ 5. Encapsulation□ 6. Inheritance as Genetics□ 7. Method Overriding□ 8. Polymorphism as a Multi-tool□ 9. Abstraction□ 10. Composition over Inheritance
Module 2 · days 21–310%
🔒 Data Structures Visualized
Every structure is a way of arranging things in a room. Bookshelves, treasure hunts, cafeteria lines, org charts.
□ 1. Arrays as Bookshelves□ 2. Dynamic Arrays□ 3. Linked Lists as Treasure Hunts□ 4. Doubly Linked Lists□ 5. Stacks as Tray Piles□ 6. Queues as Lunch Lines□ 7. Hash Maps as Dictionaries□ 8. Collisions□ 9. Trees as Org Charts□ 10. Graphs as Friendship Maps
Module 3 · days 32–430%
🔒 Algorithms in Motion
Watch the work happen frame by frame. Compare, swap, split, merge, halve, traverse.
□ 1. Linear Search□ 2. Binary Search□ 3. Bubble Sort□ 4. Selection Sort□ 5. Insertion Sort□ 6. Recursion□ 7. Merge Sort□ 8. Quick Sort□ 9. Breadth-First Search□ 10. Depth-First Search
Module 4 · days 44–540%
🔒 Time & Space Complexity
Big O is not maths homework. It is the answer to: what happens when the data gets big?
□ 1. What Big O Measures□ 2. O(1) — Constant□ 3. O(n) — Linear□ 4. O(n²) — Quadratic□ 5. O(log n) — Logarithmic□ 6. O(n log n) — The Sorting Floor□ 7. Comparing the Curves□ 8. Space Complexity□ 9. Amortised Analysis□ 10. Choosing the Right Tool
Module 5 · days 55–650%
🔒 Advanced Data Structures
The structures that separate a working solution from a fast one: heaps, tries, disjoint sets, segment trees, and the internals of the map you use every day.
□ 1. Heaps & Priority Queues□ 2. Heapify & Heapsort□ 3. Tries / Prefix Trees□ 4. Union-Find (Disjoint Set Union)□ 5. Segment Trees□ 6. Fenwick Tree (BIT)□ 7. Self-Balancing Trees & Rotations□ 8. Hash Map Internals: Probing & Resizing□ 9. Designing an LRU Cache□ 10. Bloom Filters
Module 6 · days 66–760%
🔒 Graph Algorithms
Weights change everything. Shortest paths, spanning trees, orderings and flow — with the reasoning for choosing between them.
□ 1. Weighted Graphs & Representations□ 2. Dijkstra's Algorithm□ 3. Bellman-Ford & Negative Edges□ 4. Floyd-Warshall (All-Pairs)□ 5. Topological Sort□ 6. Kruskal's MST□ 7. Prim's MST□ 8. A* Search□ 9. Strongly Connected Components□ 10. Max Flow & Min Cut
Module 7 · days 77–880%
🔒 Algorithmic Patterns
The reusable shapes. Once you can name the pattern, most problems stop being novel — this is the module that changes how you read a problem statement.
□ 1. Two Pointers□ 2. Sliding Window□ 3. Prefix Sums & Difference Arrays□ 4. Binary Search on the Answer□ 5. Backtracking□ 6. Greedy & the Exchange Argument□ 7. Divide & Conquer□ 8. Bit Manipulation□ 9. String Matching: KMP & Rabin-Karp□ 10. Intervals & Sweep Line
Module 8 · days 89–990%
🔒 Dynamic Programming
The module that breaks people, taught as state design rather than magic recurrences. If you can name the state and the transition, you can write the code.
□ 1. Memoisation vs Tabulation□ 2. 1D DP & State Design□ 3. 0/1 Knapsack□ 4. Coin Change & Unbounded Knapsack□ 5. Longest Common Subsequence□ 6. Edit Distance□ 7. Longest Increasing Subsequence□ 8. Grid DP & Path Counting□ 9. DP on Trees□ 10. Bitmask DP & State Compression