- Why Learn DSA?
- Tools & Resources
- 6-Week DSA Master Plan
- Core Problem-Solving Patterns Summary
- How to Track Progress
- Final Thoughts
- License & Contribution
Before diving into Data Structures and Algorithms (DSA), it’s crucial to understand why you’re doing it. DSA isn’t just an academic requirement — it’s a foundational skill for any software engineer or developer.
- 💼 Interviews: Companies like Google, Amazon, Meta, and startups focus heavily on DSA during technical interviews.
- 🧠 Problem Solving: DSA improves your logic, reasoning, and critical thinking.
- ⚙️ Performance: A strong grasp of DSA helps you build faster, more efficient programs.
- 🧪 Real-World Coding: Everything from search engines to social media algorithms relies on efficient data structures.
| Category | Tool |
|---|---|
| 📘 Main Book | Hands-On Data Structures and Algorithms with Python by Basant Agarwal |
| 🧠 Video Support | NeetCode, Abdul Bari (YouTube) |
| 💻 Practice Sites | LeetCode, HackerRank, GeeksforGeeks |
| 📝 Tracking | Notion, Google Sheets, Notebook |
| 🧑💻 IDEs | VS Code, Jupyter Notebook, PyCharm |
Each week builds on the previous one, covering both theory and practical coding exercises. Aim for 2.5–3 hours daily, and regularly revisit difficult problems.
- Understand why DSA is essential.
- Review Python fundamentals.
- Learn Big-O and complexity basics.
- Lists, tuples, dictionaries, sets
- Functions, loops, recursion
- Time and space complexity (Big-O notation)
- Read Chapters 1–2 from the book.
- Solve 5 Python warm-up problems on HackerRank.
- Watch NeetCode’s “Big-O Notation Explained”.
- Learn and implement foundational data structures.
- Arrays & Strings
- Stacks & Queues
- Linked Lists (Singly, Doubly, Circular)
- Book: Chapters 3–5
- Implement each structure from scratch.
- Solve problems:
- Two Sum
- Valid Parentheses
- Reverse Linked List
- Merge Two Lists
- Focus on how data is added, removed, and accessed.
- Try solving problems without looking at solutions initially.
- Learn hashing for fast lookup.
- Master recursion and its stack behavior.
- Begin solving backtracking problems.
- Hash maps and sets
- Recursive functions and patterns
- Backtracking (exhaustive search)
- Book: Chapter 6
- Solve:
- Group Anagrams
- Longest Substring Without Repeating Characters
- Word Search
- Sudoku Solver
- Work with complex structures used in real systems.
- Binary Trees, Binary Search Trees (BST)
- Heaps (Min/Max)
- Graphs (BFS, DFS)
- Tries (Prefix Trees)
- Book: Chapters 7–10
- Implement:
- Tree traversals (Inorder, Preorder, Postorder)
- MinHeap/MaxHeap from scratch
- Graph using adjacency list and matrix
- Trie insert and search
- Diameter of Binary Tree
- Kth Largest Element in a Stream
- Clone Graph
- Longest Word in Dictionary (Trie)
- Recognize common coding patterns to solve problems efficiently.
- Two Pointers
- Sliding Window
- Binary Search
- Greedy
- Backtracking
- Dynamic Programming (Intro)
- Study 1–2 patterns per day.
- Solve key problems:
- Container With Most Water (Two Pointers)
- Longest Substring Without Repeat (Sliding Window)
- Search in Rotated Sorted Array (Binary Search)
- Jump Game (Greedy)
- Climbing Stairs (Dynamic Programming)
- Build a personal cheat sheet for each pattern.
- Always understand why the pattern works.
- Apply knowledge in real-world scenarios.
- Begin mock interview practice.
- ✅ Spell Checker → Trie + Hashing
- ✅ Pathfinding Visualizer → Graph (BFS/DFS)
- ✅ Event Scheduler → MinHeap for event tracking
- Solve the Blind 75 problems on LeetCode.
- Revisit tough problems from previous weeks.
- Join mock interviews (Pramp, Interviewing.io).
| Pattern | Example Problems |
|---|---|
| Sliding Window | Longest Substring Without Repeating |
| Two Pointers | Container With Most Water |
| Binary Search | Search in Rotated Sorted Array |
| Backtracking | N-Queens, Sudoku |
| Greedy | Gas Station, Jump Game |
| Dynamic Programming | Fibonacci, 0/1 Knapsack, Longest Common Subsequence |
| Method | Tool |
|---|---|
| Problem Log | Google Sheets / Notion |
| Revision | Weekly re-solving schedule |
| Code Notes | VS Code snippets / Notebook |
| Accountability | Join a Discord group or GitHub project |
- ✔️ Consistency beats speed. Even solving 2 problems daily is effective.
- 📈 Repetition = Retention — revisit challenging problems regularly.
- 🧱 Master the core patterns to solve 80% of interview problems faster.
- 🧑💻 DSA is a long-term investment that pays dividends in coding interviews and real projects.
This roadmap is publicly shared under the MIT License.
Feel free to fork, clone, remix, and contribute!
Happy coding! 🚀