DSA Mastery Roadmap for Product-Based Companies
Follow this structured 13-milestone roadmap from ground zero. Each of the 23 standalone tutorials teaches the concept in plain English, walks through interactive visual drawings at the exact point of explanation, and builds up to 4-language implementations and 94 curated interview problems.
Distinct patterns with at least 1 solved problem
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All 13 Milestones & 23 Interactive Tutorials
Arrays, Memory & In-Place Matrix Operations
Start from zero: learn what an Array actually is in computer memory, why reading is instant while shifting is slow, and how to manipulate arrays and 2D matrices in place.
Hashing (Hash Sets & Hash Maps)
Understand what a Hash Function is, how Hash Sets and Hash Maps give instant O(1) value lookups, and how to turn slow O(N^2) searches into single-pass solutions.
Prefix Sums & Kadane's Subarray Algorithm
Learn what a contiguous subarray is, how Prefix Sums answer any range sum in O(1), and how Kadane's Algorithm finds the maximum subarray sum in one pass.
Two Pointers
Understand what a Pointer index is, why Two Pointers eliminates O(N^2) nested loops using O(1) memory, and master opposite-end, read/write, and 3-way pointers.
Sliding Window
Learn what a Sliding Window is, why re-scanning overlapping subarrays wastes work, and master fixed, variable, and frequency-counted windows.
Stacks (LIFO) & Queues (FIFO)
Learn what Stacks and Queues are from scratch, why restricting access to LIFO or FIFO makes code cleaner and faster, and how to solve bracket and Min-Stack problems.
Monotonic Stack & Monotonic Deque
Understand what 'Monotonic' means, why keeping a stack sorted turns O(N^2) 'next greater' searches into O(N), and how a Monotonic Deque tracks window maximums.
Recursion Fundamentals & Subsets
Understand how Recursion and the Call Stack actually work, why Backtracking needs an 'Undo' step, and how to generate all 2^N Subsets.
Combinations, Permutations & Grid Backtracking
Master the difference between Combinations (order doesn't matter) and Permutations (order matters), plus 2D Grid Backtracking for Word Search and N-Queens.
Binary Trees (DFS, BFS & Bottom-Up Recursion)
Learn what a Binary Tree is from scratch, master Preorder/Inorder/Postorder DFS and Level-Order BFS, and solve Height, Diameter, and LCA problems.
Binary Search Trees (BST)
Understand what makes a Binary Tree a Binary Search Tree (BST), why it gives O(log N) search AND insertion, and how to validate and query BSTs.
Intervals (Sorting, Merging & Sweep Line)
Understand what an Interval is, why sorting by start time or end time is the universal first step, and how to merge or remove overlapping intervals.
Greedy Algorithms
Understand what a Greedy Algorithm is, when making the best local choice guarantees the global best answer, and when you must use DP instead.
Graph Fundamentals, BFS & DFS
Understand what a Graph is (Vertices, Edges, Directed/Undirected), how to build an Adjacency List, and when to use Graph DFS vs. Multi-Source BFS.
Topological Sort & Directed Cycle Detection
Understand what a Directed Acyclic Graph (DAG) and Indegree are, and how Kahn's Algorithm orders tasks with prerequisites or detects deadlocks.
Dijkstra's Shortest Path & Disjoint Set Union (Union-Find)
Master Dijkstra's Min-Heap Shortest Path for weighted graphs and Disjoint Set Union (Union-Find) for near-O(1) dynamic connectivity.
1D Linear & Knapsack Dynamic Programming
Master Dynamic Programming for product-company interviews: learn how to spot DP vs. Greedy, the 4-stage Brute-Force → Memoization → Tabulation → O(1) Space blueprint, and 0/1 vs. Unbounded Knapsack.
2D Grid, String & Subsequence DP
Master 2D Dynamic Programming for product-company interviews: Grid Path DP, Two-String Alignment (Longest Common Subsequence & Edit Distance), and O(N log N) Longest Increasing Subsequence.