Choose Your Career Goal & Follow the Roadmap
Unlike standalone tutorials where you look up a single topic, a Roadmap is a complete, start-to-finish execution plan built around a dedicated goal—sequencing tutorials and practice problems in exact dependency order.
Crack Product-Based Company Coding Interviews (DSA Roadmap)
A complete, zero-to-offer roadmap that orders every Data Structure and Algorithmic pattern in strict prerequisite sequence so you never hit a concept before learning its foundation.
Senior Backend & High-Level System Design (HLD) Roadmap
Step-by-step goal path for scaling systems from 1,000 to 100M users: DNS, Load Balancers, Caching, Sharding, Kafka Event Streams, CAP Theorem, and real FAANG case studies.
Low-Level Design (LLD), SOLID & 90-Min Machine Coding Roadmap
Structured roadmap to master clean object-oriented modeling, GoF Design Patterns, thread-safe concurrency, and real 90-minute machine coding rounds (Splitwise, Parking Lot, Elevator).
DSA & Coding Interview Mastery Roadmap
Complete each milestone in order. Every step links directly to its interactive visual tutorial and tracks how many curated interview problems you have solved.
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.
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.
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.
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.
Learn what a Sliding Window is, why re-scanning overlapping subarrays wastes work, and master fixed, variable, and frequency-counted windows.
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.
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.
Understand how Recursion and the Call Stack actually work, why Backtracking needs an 'Undo' step, and how to generate all 2^N Subsets.
Master the difference between Combinations (order doesn't matter) and Permutations (order matters), plus 2D Grid Backtracking for Word Search and N-Queens.
Learn what a Binary Tree is from scratch, master Preorder/Inorder/Postorder DFS and Level-Order BFS, and solve Height, Diameter, and LCA problems.
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.
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.
Understand what a Greedy Algorithm is, when making the best local choice guarantees the global best answer, and when you must use DP instead.
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.
Understand what a Directed Acyclic Graph (DAG) and Indegree are, and how Kahn's Algorithm orders tasks with prerequisites or detects deadlocks.
Master Dijkstra's Min-Heap Shortest Path for weighted graphs and Disjoint Set Union (Union-Find) for near-O(1) dynamic connectivity.
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.
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.