🎯Master DSA in 90 Days: My Exact Roadmap for Tech Interviews

This was the exact strategy that helped me crack product-based companies like Google and Walmart as a Software Engineer 3 in Silicon Valley, USA!

🧑‍💻If you're serious about cracking tech interviews, especially at product-based companies, you can't afford to prepare DSA randomly.

I’ve seen too many folks burn out solving 300+ questions in random order, only to feel underconfident when faced with a new pattern. So let me walk you through the structured path that I followed.

This roadmap is battle-tested, beginner-friendly, and scalable to advanced levels.

🚀 You should begin learning data structures in an order that aligns with how easily you can visualize them and understand operations like insertion, deletion, and traversal.

🗓️ Month 1: The Core Foundation (Weeks 1–4)

✅ Week 1–2: Arrays

• Foundation of everything in DSA: Learn how memory is allocated, how elements are accessed in O(1), inserted, deleted and how to traverse efficiently.

• Techniques:

  • Two Pointer Technique: Use two indices moving toward each other to find pairs or remove elements.

  • Sliding Window: Use a moving subarray window for problems involving subarrays of size k or dynamic length.

  • Prefix Sum: Precompute running totals to answer range sum queries efficiently.

  • Sorting + Binary Search: Sort the array to help optimize the search

  • Kadane’s Algorithm: Track current and global max to find maximum subarray sum in O(n).

• Goal: Get comfortable with indices, time complexity, and brute-force vs optimal.

✅ Week 2–3: Strings

• Strings are basically character arrays, but they come with their own set of operations.

• Techniques:

  • Two Pointer: Useful in palindrome checks, string reversal, and trimming.

  • Sliding Window: Track substrings or character frequency windows.

  • Hashing: Store character counts or indices using hash maps.

  • Character Frequency Array: Optimize space using fixed arrays for lowercase/uppercase.

  • Prefix Matching: Use prefix comparisons or KMP algorithm for pattern matching.

✅ Week 4: Hashing / Maps

• Once you get hashing, your brute-force mindset will shift to O(n) solutions.

• Techniques:

  • Frequency Count: Track occurrence of elements in O(n).

  • Fast Lookup: Check existence or uniqueness with O(1) lookup.

  • Key-Pair Mapping: Store extra context (indices, complements, etc.) in the map for quick pairing.

🗓️ Month 2: Problem Solving Booster (Weeks 5–8)

✅ Week 5: Sorting & Searching

• Goal: Arrange data in a specific order (ascending/descending) to improve searchability, readability, and efficiency. Understand time and space trade-offs

• Techniques:

  • Sorting: Bubble < Selection < Insertion < Merge < Quick < Heap < Inbuilt

  • Searching: Linera Search < Binary Search

  • Divide and Conquer: Break the problem into smaller subproblems, solve them independently, and combine the results.

  • Two Pointer Post-Sort: Use two indices moving toward each other to find pairs or remove elements.

✅ Week 6: Linked Lists

• Goal: Build intuition for non-contiguous memory data structures and prepare for pointer-heavy interview problems. These will serve as a foundation for stacks, queues, LRU cache, and graph adjacency lists.

• Techniques:

  • Pointer Manipulation: Learn how to move head, next, and prev pointers. Key for inserting, deleting, or traversing nodes, reversing.

  • Dummy Node Technique: Use a fake starting node to simplify edge cases.

  • Slow & Fast Pointer (Tortoise & Hare): Fast pointer moves 2 steps, slow moves 1 step. Detect cycles, find middle node, or find k-th from end.

  • Iterative & Recursive techniques: Good for solving in-place or stack-based problems.

✅ Week 7: Stacks & Queues

• Learn operations from scratch:

  • Push, pop, peek

  • Implement using arrays/linked list

• Techniques:

  • Monotonic Stack: Maintain increasing or decreasing order to solve next/previous element problems.

  • Two Stacks: Emulate a queue or track min/max values.

  • Circular Queue: Efficient wrap-around queue with fixed size.

  • Deque for Sliding Window: Maintain useful window of elements in O(n).

✅ Week 8: Trees

• Start with basics:

  • Inorder, Preorder, Postorder traversal (recursive & iterative)

  • Height of tree

  • Max depth

• Then learn:

  • Binary Search Trees (insert, search, delete)

  • Balanced Trees (AVL, Red-Black – just theory is fine)

  • LCA, Diameter, Path Sum problems

• Techniques:

  • DFS Recursion: Explore each branch deeply before backtracking.

  • BFS with Queue: Traverse level by level.

  • Post-order for Cleanup: Use it to delete or free nodes bottom-up.

  • In-order for BSTs: Sorted output or kth smallest element.

🗓️ Month 3: Advanced Concepts (Weeks 9–12)

✅ Week 9: Recursion & Backtracking

• Understand base case + recursion call = full solution.

• Start with simple: Factorial, Fibonacci, String reversal

• Then move to backtracking patterns: Subsets, Permutations, N-Queens

• Techniques:

  • Backtracking: Explore all combinations by undoing decisions.

  • Memoization: Cache function calls to avoid recomputation.

  • Tail Recursion: Optimization-friendly form of recursion.

✅ Week 10: Heaps / Priority Queue

• Goal: Use heaps when you need quick access to the smallest or largest element at any point in time.

• Min-heap —> smallest at top. Max-heap —> largest at top. Every insertion/removal reorders elements using "heapify.

• Techniques:

  • Heapify: Convert unsorted array to heap in O(n).

  • Priority Queue: Use to always fetch the highest/lowest priority item.

  • Custom Comparator: Define your own rules to sort complex objects (e.g., by frequency, distance).

  • Heap + Sliding Window: Track top-k or running min/max values efficiently.

✅ Week 11: Graphs

• Start with the difference between DFS and BFS. Learn when to use adjacency matrix vs list.

• Techniques:

  • BFS/DFS Traversal: For connected components, cycles, and paths.

  • Topological Sorting: Handle dependency ordering.

  • Dijkstra’s Algorithm: Shortest path from source using priority queue.

  • Union-Find: For detecting cycles or connected components.

✅ Week 12: Dynamic Programming

• Build slowly. You’re not supposed to get it in one go.

• Two Strategies: Memoization vs Tabulation

• Techniques:

  • Overlapping Subproblems: Solve once and store.

  • State Definition: Clearly define parameters representing the problem.

  • Recurrence Relation: Build solution from smaller subproblems.

  • Tabulation: Bottom-up approach storing previous results.

✅ Final Note

I know DSA can be overwhelming — but with the right plan and consistency, you can crack top product companies and build a standout profile for MS abroad too.

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