60 days — 2 hours a day — Python — 0 to 100%

DAY 1 / 60

Your 60-day run from zero to hero hasn't started yet. Day 1 is one click away — two focused hours, one lesson, one visualizer.

DSA mastery — one lesson moves it 2.5 points0%

streak

hours logged

0/80

lessons

Browse curriculum

The 60 days

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

filled = lesson complete · outlined = today · days 46–60 begin the advanced track (runs to day 90)

Core — the 0→100% track

Module 1 · days 1–110%

OOP via Analogies

Objects are just nouns with skills. Learn the four pillars through houses, genetics and multi-tools.

□ 1. Classes as BlueprintsThe architect's drawing □ 2. Objects as HousesBuilt from the drawing □ 3. Attributes vs MethodsWhat it has vs what it does □ 4. ConstructorsThe build crew's checklist □ 5. EncapsulationWalls and a doorbell □ 6. Inheritance as GeneticsChildren of the blueprint □ 7. Method OverridingThe child who cooks differently □ 8. Polymorphism as a Multi-toolOne handle, many heads □ 9. AbstractionThe steering wheel □ 10. Composition over InheritanceLego, not a family tree

Module 2 · days 12–220%

🔒 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 23–340%

🔒 Algorithms in Motion

Watch the work happen frame by frame. Compare, swap, split, merge, halve, traverse.

□ 1. Bubble Sort□ 2. Selection Sort□ 3. Insertion Sort□ 4. Merge Sort□ 5. Quick Sort□ 6. Linear Search□ 7. Binary Search□ 8. Breadth-First Search□ 9. Depth-First Search□ 10. Recursion

Module 4 · days 35–450%

🔒 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

Advanced — beyond the core

Module 5 · days 46–560%

🔒 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 57–670%

🔒 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 68–790%

🔒 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 80–900%

🔒 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