Patterns for Solving Coding Problems

In the last 15 years, I have interviewed 800+ Software Engineers across Google, Paytm, Amazon & various startups. Here are the most actionable tips I can give you on how to approach  solving coding problems in Interviews  (My DMs are always flooded with this particular question) 1. Use a Heap for K Elements      - When finding the top K largest or smallest elements, heaps are your best tool.      - They efficiently handle priority-based problems with O(log K) operations.      - Example: Find the 3 largest numbers in an array.   2. Binary Search or Two Pointers for Sorted Inputs      - Sorted arrays often point to Binary Search or Two Pointer techniques.      - These methods drastically reduce time complexity to O(log n) or O(n).      - Example: Find two numbers in a sorted array that add up to a target.   3. Backtracking    - Use Backtracking to explore all combinations or permutations.      - They’re great for generating subsets or solving puzzles.      - Example: Generate all possible subsets of a given set.   4. BFS or DFS for Trees and Graphs      - Trees and graphs are often solved using BFS for shortest paths or DFS for traversals.      - BFS is best for level-order traversal, while DFS is useful for exploring paths.      - Example: Find the shortest path in a graph.   5. Convert Recursion to Iteration with a Stack      - Recursive algorithms can be converted to iterative ones using a stack.      - This approach provides more control over memory and avoids stack overflow.      - Example: Iterative in-order traversal of a binary tree.   6. Optimize Arrays with HashMaps or Sorting      - Replace nested loops with HashMaps for O(n) solutions or sorting for O(n log n).      - HashMaps are perfect for lookups, while sorting simplifies comparisons.      - Example: Find duplicates in an array.   7. Use Dynamic Programming for Optimization Problems      - DP breaks problems into smaller overlapping sub-problems for optimization.      - It's often used for maximization, minimization, or counting paths.      - Example: Solve the 0/1 knapsack problem.   8. HashMap or Trie for Common Substrings      - Use HashMaps or Tries for substring searches and prefix matching.      - They efficiently handle string patterns and reduce redundant checks.      - Example: Find the longest common prefix among multiple strings.   9. Trie for String Search and Manipulation      - Tries store strings in a tree-like structure, enabling fast lookups.      - They’re ideal for autocomplete or spell-check features.      - Example: Implement an autocomplete system.   10. Fast and Slow Pointers for Linked Lists      - Use two pointers moving at different speeds to detect cycles or find midpoints.      - This approach avoids extra memory usage and works in O(n) time.      - Example: Detect if a linked list has a loop.   💡 Save this for your next interview prep!

🎯 400 LeetCode problems solved - but this isn't your typical "grinding problems" post. Like training a machine learning model, I approached algorithmic problem-solving with a focus on data quality and diversity. Just as ML models need varied, high-quality data points to generalize well, I found that solving diverse problems across different patterns and domains builds better problem-solving intuition. My systematic approach: Pre-coding Analysis (Link to sample doc in comments) • Document multiple potential approaches • Analyze time & space complexity for each approach • Think through tradeoffs before writing any code • Consider edge cases and constraints Practice Execution • Used stopwatch to measure performance • Aimed to solve while explaining clearly within: - Easy: 10 minutes - Medium: 15 minutes - Hard: 25 minutes • Focus on thinking aloud - crucial for interviews Deep Dive Process • Rigorous complexity analysis • Explore optimization opportunities • Document learnings and patterns • Regular mock interviews on Pramp The goal wasn't to solve all 3000+ problems, but to build a robust "model" that could generalize to new problems effectively. Each solved problem is like a new training data point, helping my brain recognize patterns and edge cases. Key learning: The magic happens in the pre-coding analysis. Writing down different approaches and analyzing tradeoffs before coding helped me: - Build stronger problem-solving intuition - Communicate my thought process clearly - Make better engineering decisions - Save time during actual coding I'll share a sample doc in the comments. It's been crucial for building a systematic approach to problem-solving. To those on this journey: Keep your head down, document your thinking, and remember - you're not just solving problems, you're building a framework for approaching any technical challenge.

10 pieces of advice that helped me get good at solving problems and cracking coding interviews, I wish someone had told me when I was 20 and grinding at BITS Pilani.... 1. How do I pick which problems to solve? - Start with easy problems in each topic (arrays, strings, etc.) - Use curated lists like “Top 150 Interview Questions” - Don’t waste time on random, obscure problems - Repeat the basics till they’re muscle memory 2. What to do when you’re stuck on a problem for hours? - Spend 30–60 minutes thinking hard - If still stuck, check hints or partial solutions, not full code - Try writing the solution from scratch after reading it - Make a note to revisit after a week 3. How to avoid feeling overwhelmed by 3000+ LeetCode problems? - Ignore the total count - Focus on a shortlist of 250–300 core problems - One topic at a time, one pattern at a time - Track your progress on a sheet 4. What’s the best way to learn a new data structure or algorithm? - Watch a trusted YouTube video (not 10!) - Implement it from scratch - Solve 3–5 related easy/medium problems - Teach it to a friend or write a summary note 5. How do I actually retain what I learn? - Make a habit of revisiting solved problems - Explain your solution out loud or to someone else - Maintain a notebook with your ‘aha’ moments - Practice old topics every 2 weeks 6. How to get faster at solving problems? - Set a timer: 15 mins for easy, 30 for medium, 1 hour for hard - Focus on pattern recognition, not brute force - Analyze time/space complexity for every problem - Practice under interview conditions (mock tests, contests) 7. What language should I use? - Stick to what you’re comfortable with (Python/Java/C++) - Learn built-in libraries for arrays, maps, queues, etc. - Don’t switch languages unless you’re stuck 8. How do I stop memorizing solutions and actually understand them? - Re-write solutions from memory after learning - Break down why each step works - Relate the approach to real-world problems - Never copy-paste, always type it out 9. How do I tackle hard problems or topics? - Break into smaller subproblems - Use pen & paper to draw out tricky inputs - Start with a brute-force solution, then optimize - Look for similar solved patterns 10. How to not lose motivation when it gets tough? - Celebrate small wins (accepted solution, a new pattern cracked) - Take breaks and walk away when you’re frustrated - Join a group or community for accountability - Remember: Growth compounds, it gets easier with time

𝐇𝐨𝐰 𝐦𝐮𝐜𝐡 𝐃𝐒𝐀 𝐢𝐬 𝐞𝐧𝐨𝐮𝐠𝐡 𝐭𝐨 𝐜𝐫𝐚𝐜𝐤 𝐌𝐢𝐜𝐫𝐨𝐬𝐨𝐟𝐭, 𝐆𝐨𝐨𝐠𝐥𝐞 𝐨𝐫 𝐖𝐚𝐥𝐦𝐚𝐫𝐭? This is the most common DM which I get from juniors are, "Ma’am, I’ve solved 300+ questions but still can’t solve new ones. How many do I really need to do?" When I started, I had the same doubt. Some seniors said 300 questions, others said 500+ to be safe. So I rushed to hit those numbers. But here’s the truth it’s not about the count, it’s about the patterns. Once you master patterns, every new problem feels familiar. Here are the 15 patterns you must know for placements: 1. 𝐓𝐰𝐨 𝐏𝐨𝐢𝐧𝐭𝐞𝐫 𝐓𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞 – Solve pair/relationship problems in arrays/linked lists. 2. 𝐒𝐥𝐢𝐝𝐢𝐧𝐠 𝐖𝐢𝐧𝐝𝐨𝐰 – Efficiently handle subarray/substring problems. 3. 𝐇𝐚𝐬𝐡𝐢𝐧𝐠 / 𝐅𝐫𝐞𝐪𝐮𝐞𝐧𝐜𝐲 𝐂𝐨𝐮𝐧𝐭𝐢𝐧𝐠 – O(1) lookups for counts, duplicates, mapping. 4. 𝐏𝐫𝐞𝐟𝐢𝐱 𝐒𝐮𝐦𝐬 – Answer range queries fast. 5. 𝐁𝐢𝐧𝐚𝐫𝐲 𝐒𝐞𝐚𝐫𝐜𝐡 (𝐚𝐧𝐝 𝐯𝐚𝐫𝐢𝐚𝐧𝐭𝐬) – For sorted arrays or monotonic conditions. 6. 𝐆𝐫𝐞𝐞𝐝𝐲 – Local choices that lead to global solutions. 7. 𝐃𝐲𝐧𝐚𝐦𝐢𝐜 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 – Break down overlapping subproblems. 8. 𝐁𝐚𝐜𝐤𝐭𝐫𝐚𝐜𝐤𝐢𝐧𝐠 – Explore all possibilities (subsets, permutations). 9. 𝐁𝐅𝐒 – Shortest paths, level-by-level traversals. 10. 𝐃𝐅𝐒 – Explore all paths, detect cycles. 11. 𝐇𝐞𝐚𝐩 / 𝐓𝐨𝐩-𝐊 – Manage largest/smallest efficiently. 12. 𝐌𝐞𝐫𝐠𝐞 𝐈𝐧𝐭𝐞𝐫𝐯𝐚𝐥𝐬 – Handle overlaps in schedules. 13. 𝐔𝐧𝐢𝐨𝐧-𝐅𝐢𝐧𝐝 – Manage connectivity in graphs. 14. 𝐓𝐫𝐢𝐞 – Prefix-based search and storage. 15. 𝐌𝐨𝐧𝐨𝐭𝐨𝐧𝐢𝐜 𝐒𝐭𝐚𝐜𝐤 / 𝐐𝐮𝐞𝐮𝐞 – Solve next/previous greater/smaller problems. And remember, DSA is not a sprint, it is a marathon. Rejections will happen, and that is normal. But every attempt makes you sharper, and every failure teaches you a pattern in life too. #DSA #Placements #CodingInterviews #ProblemSolving #CareerAdvice

I analyzed the top 50 coding interview questions, and they all boil down to these 18 patterns. People ask me how to crack technical interviews without spending years on LeetCode. My secret? I don't memorize solutions. People ask how I identify the right approach for a brand new problem instantly. My secret? I look for the underlying pattern, not the specific question. But the truth is... There is no secret. Just pure Pattern Recognition. To master DSA, you have to stop treating every problem as unique. You need to map them to these 18 fundamental branches. Here is the technical breakdown to get you started: 1. Optimization & Pointers - Two Pointers: Essential for sorted arrays and linked lists. Use this to detect cycles (Floyd's algorithm), remove elements, or find target pairs in O(N) time. - Sliding Window: The standard for subarray problems. Perfect for calculating running averages, finding the longest substring under constraints, or optimization within a linear data structure. - Intervals: When dealing with time ranges or overlaps, use Merge Intervals or Catalan logic. 2. Search & Traversal - Binary Search: Beyond finding numbers. Use on rotated sorted arrays or to find "first/last occurrence" boundaries in O(log N). - Tree Traversal: Master the recursion. Know when to use Level-order (BFS) vs. Pre/In/Post-order (DFS) for tasks like serialization or finding the Lowest Common Ancestor. - Graph Traversal: Solves island counting, cycle detection, and topological sorting. 3. Complex Structures & Logic - Heaps: The most efficient way to handle "Top K" elements, scheduling tasks, or finding medians in a data stream. - Tries: The go-to design pattern for autocomplete systems, spell checkers, and prefix searches. - Backtracking: For when you need all possibilities. Used in generating permutations, N-Queens, and Sudoku solvers. 4. The Heavy Hitters - Dynamic Programming (DP): For overlapping subproblems. Includes 1D/2D arrays, Longest Common Subsequence (LCS), and the Knapsack problem. - Graph Optimization (Union Find): Critical for network connectivity. Uses Disjoint Set Union and MST algorithms like Kruskal’s or Prim’s. Want to be a software engineer? Stop memorizing. Start recognizing. Remember, seeing the pattern is 90% of the solution. The code is just syntax. Which of these patterns do you find most difficult to implement? ♻️ Repost to help a connection ace their technical interview.

Three years ago, I was deep in interview prep for roles at Amazon, Google and Meta. And Leetcode almost broke me. I remember staring at problems for hours, wondering if I'd ever get it. I tried solving everything randomly, Arrays, Graphs, DP — hoping something would click. But solving 3500+ questions felt impossible. Then I realised something that changed everything: It’s not about solving thousands of problems. It’s about learning the patterns behind them. Once I focused on the core data structure and algorithm (DSA) patterns, everything started to make sense. Here are 25 DSA coding patterns that cover most interview questions: 1. Two Pointers 2. Fast & Slow Pointers 3. Sliding Window 4. Prefix Sum 5. Monotonic Stack / Queue 6. Merge Intervals 7. Sorting-Based Patterns 8. Divide & Conquer 9. Binary Search (and Variants) 10. Overlapping Intervals 11. Greedy Algorithms 12. Linked List Techniques (Dummy Node, In-place Reversal) 13. Top-K Elements (Heap / QuickSelect) 14. Kth Largest/Smallest Elements 15. Hashmaps & Frequency Counting 16. Dynamic Programming (1D, 2D, Knapsack, Range DP) 17. Backtracking & Recursive Search 18. Graph Traversals (BFS, DFS) 19. Topological Sort 20. Binary Tree Traversals (Preorder, Inorder, Postorder, Level Order) 21. Path Sum & Root-to-Leaf Techniques 22. Matrix Traversal 23. Expression Evaluation (Stacks & Queues) 24. Design Problems (LRU Cache, Twitter, Rate Limiter, etc.) 25. Trie / Prefix Tree Once you understand these patterns, you’ll start recognising them everywhere — from Leetcode to real-world systems. You don’t need to solve thousands of questions to land great offers. You just need to master these patterns and practice them deliberately. If you’re starting today: → Learn the core data structures first: Arrays, Linked Lists, Stacks, Queues, Hashmaps, Heaps, Trees, Graphs, Tries, and Matrices. → Check out Taro (YC S22) 75 questions by Alex and Rahul. Curated beautifully around these patterns. → Give NeetCode a go — Navdeep did a cracking job explaining the patterns above. This is how you go from frustration to confidence. And from endless grinding to real results. #softwareengineering #leetcode

🚀 𝗟𝗲𝗲𝘁𝗖𝗼𝗱𝗲 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀 𝗜 𝗙𝗼𝗰𝘂𝘀 𝗼𝗻 𝗳𝗼𝗿 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀 Over time, I’ve noticed that most problems follow certain patterns. Identifying the right pattern early makes solving problems much easier. Here are the ones I focus on the most: ✅ 𝗜𝗳 𝘁𝗵𝗲 𝗶𝗻𝗽𝘂𝘁 𝗮𝗿𝗿𝗮𝘆 𝗶𝘀 𝘀𝗼𝗿𝘁𝗲𝗱, 𝘁𝗵𝗲𝗻: Binary search (O(log N) for efficient searching) Two pointers (Optimizing sum/pair problems) ✅ 𝗜𝗳 𝗮𝘀𝗸𝗲𝗱 𝗳𝗼𝗿 𝗮𝗹𝗹 𝗽𝗲𝗿𝗺𝘂𝘁𝗮𝘁𝗶𝗼𝗻𝘀/𝘀𝘂𝗯𝘀𝗲𝘁𝘀, 𝘁𝗵𝗲𝗻: Backtracking (Explore all possible solutions) ✅ 𝗜𝗳 𝗴𝗶𝘃𝗲𝗻 𝗮 𝘁𝗿𝗲𝗲, 𝘁𝗵𝗲𝗻: DFS (Depth-first for deep traversal) BFS (Level-order traversal) ✅ 𝗜𝗳 𝗴𝗶𝘃𝗲𝗻 𝗮 𝗴𝗿𝗮𝗽𝗵, 𝘁𝗵𝗲𝗻: DFS (Finding connected components, cycles) BFS (Shortest path, traversals) ✅ 𝗜𝗳 𝗴𝗶𝘃𝗲𝗻 𝗮 𝗹𝗶𝗻𝗸𝗲𝗱 𝗹𝗶𝘀𝘁, 𝘁𝗵𝗲𝗻: Two pointers (Fast & slow for cycle detection, middle node, etc.) ✅ 𝗜𝗳 𝗿𝗲𝗰𝘂𝗿𝘀𝗶𝗼𝗻 𝗶𝘀 𝗯𝗮𝗻𝗻𝗲𝗱, 𝘁𝗵𝗲𝗻: Stack (Simulate recursion iteratively) ✅ 𝗜𝗳 𝗺𝘂𝘀𝘁 𝘀𝗼𝗹𝘃𝗲 𝗶𝗻-𝗽𝗹𝗮𝗰𝗲, 𝘁𝗵𝗲𝗻: Swap corresponding values (For sorting or reordering) Store multiple values in the same pointer (Memory optimization) ✅ 𝗜𝗳 𝗮𝘀𝗸𝗲𝗱 𝗳𝗼𝗿 𝗺𝗮𝘅𝗶𝗺𝘂𝗺/𝗺𝗶𝗻𝗶𝗺𝘂𝗺 𝘀𝘂𝗯𝗮𝗿𝗿𝗮𝘆/𝘀𝘂𝗯𝘀𝗲𝘁/𝗼𝗽𝘁𝗶𝗼𝗻𝘀, 𝘁𝗵𝗲𝗻: Dynamic Programming (DP) (Break into overlapping subproblems) ✅ 𝗜𝗳 𝗮𝘀𝗸𝗲𝗱 𝗳𝗼𝗿 𝘁𝗼𝗽/𝗹𝗲𝗮𝘀𝘁 𝗞 𝗶𝘁𝗲𝗺𝘀, 𝘁𝗵𝗲𝗻: Heap (Efficient priority-based extraction) QuickSelect (Optimized selection of Kth smallest/largest) ✅ 𝗜𝗳 𝗮𝘀𝗸𝗲𝗱 𝗳𝗼𝗿 𝗰𝗼𝗺𝗺𝗼𝗻 𝘀𝘁𝗿𝗶𝗻𝗴𝘀, 𝘁𝗵𝗲𝗻: Map (HashMap) (Quick lookup & frequency counting) Trie (Efficient prefix-based searching) ✅ 𝗘𝗹𝘀𝗲: Map/Set (O(1) lookup, O(N) space) Sort input (O(N log N) time, O(1) space) These patterns help break down complex problems and quickly guide me toward an optimal solution. 🚀 💬 Which pattern do you find most useful in interviews? Let’s discuss! #LeetCode #DSA #JobSearch #CodingInterview #FAANG #MAANG #Amazon #Meta #Google #Microsoft #Netflix #Apple #Blind75 #Grind75 #TechInterview #SoftwareEngineering #Algorithms #DataStructures #InterviewPreparation #ProblemSolving #CrackingTheCodingInterview #Careers #SystemDesign #CompetitiveProgramming #FullStackDeveloper #DevCommunity #CodeNewbie #TechCareers #JobSearch #Python #Java #CodingLife #TechIndustry

Not able to recognize patterns even after solving hundreds of Leetcode problems? With this pattern recognition cheatsheet you will be able to solve more than 80% of the problems: ➡️Array / String Inputs 1. Is the array sorted? - Yes → Think: Binary Search, Two Pointers, or Prefix Sums. - No → Move to structural/intent-based decisions. 2. What's the intent of the problem? - Optimization (Max/Min, count of ways, min cost) - Interdependent choices? → Use Dynamic Programming (Top-Down or Bottom-Up). - Independent decisions? → Consider a Greedy approach (with proof of correctness). - Feasibility / Existence Check - Use Backtracking, DFS with pruning, or Binary Search on Answer. - Does it involve String Manipulation? - Prefix/Suffix logic? → Trie, Rolling Hash, or Z-Algorithm. - Sequential manipulation? → Stack, Monotonic Stack, or Deque. - Frequent lookups / duplicates / uniqueness - Use Hash Maps, Hash Sets, or Counting Arrays. - Sliding window behavior - Use Two Pointers, Deque, or Counting Hash Map. - Frequent Min/Max retrieval - Use Heap, Monotonic Queue, or Segment Tree (for range queries). ➡️ Graph Inputs 1. Pathfinding / Traversals Shortest path / fewest steps → BFS (Unweighted), Dijkstra (Weighted). Exhaustive exploration / component discovery → DFS, Union-Find. 2. Cycle Detection / Topological Order Use DFS with visited tracking, Kahn’s Algorithm, or Disjoint Sets. 3. Optimization on graphs Minimum Spanning Tree? → Kruskal, Prim. Connectivity? → Tarjan's Algorithm, Bridge Finding, etc. ➡️ Tree Inputs (Typically Binary Trees) 1. Traversals / Depth Calculations Level-order traversal / Lowest Common Ancestor → Use BFS / DFS with depth tracking. Recursive divide-and-conquer logic → Postorder traversal is your friend. 2. Balancing / Ordering Constraints Use BST properties, AVL / Red-Black trees, or Segment Trees if mutable. ➡️ Linked List Inputs 1. Cycle detection → Fast/Slow Pointers 2. Structural changes Reversal? → Track prev, curr, next pointers. Head/edge cases? → Use a dummy node strategy. Follow me Suresh G. for more content around System Design and Interview Prep

From leaving TCS in Jan 2025… to landing an SDE-1 (L59) offer from Microsoft India in April 2025… This is the story of one of my mentees. (He’s asked me not to tag him, but I’m sharing his full DSA + LeetCode strategy because his approach fixed what most candidates get wrong.) Most students struggle with DSA, not because their logic is weak… but because their approach is broken.  ✅ Step 1: Choose Your Language Based on Speed  Don’t waste time debating Java vs C++ vs Python.  Pick what you’re fastest at when stressed.  Tip: If you’re solving string-heavy questions, Python will save time.  ✅ Step 2: Learn Pattern-Wise This was the biggest game-changer. Instead of solving problems randomly, he studied DSA patterns. → Sliding Window → Two Pointers → Hashing → Recursion + DP → Backtracking → Binary Search on Answers → Monotonic Stack / Queue Every new question became easier to recognize, not just solve. 👉 Here’s my pattern-based learning guide he followed: https://bit.ly/3SmlAzr More on learning DSA pattern-wise here: https://lnkd.in/dkW_CkH5  ✅ Step 3: Get Solid on Time + Space You’re expected to:  Estimate time/space complexity fast  Start with brute force, then optimize  Know tradeoffs (code clarity vs performance) 📌 Before interviews, revise:  Time/space of all core data structures  Big-O notations (keep a printed cheat sheet)  ✅ Step 4: Master the Fundamentals First Don’t rush to DP or Graphs if your basics aren’t solid. He spent his first 4 weeks on: — Arrays — LinkedLists — HashMaps — Stack/Queue operations — Custom implementations like LRU Cache, HashMap 📌 These showed up in his interviews at Microsoft.  ✅ Step 5: Use a Clear, Ordered Roadmap His DSA roadmap looked like this: 1. Arrays, LinkedLists, HashMaps 2. Searching + Sorting 3. Trees (Pre, In, Post order) 4. Graphs, Heaps, Backtracking 5. Recursion 6. DP, Tries, Union-Find He didn’t jump between 10s of DSA sheets out there  ✅ Step 6: Practice With Depth — 10 problems done deeply >> 50 done shallowly — Always ask: “What pattern does this question follow?” Examples:  Max sum of subarray? → Sliding Window  Repeated sub-problems? → Try Recursion → then DP  Multiple paths? → Backtracking  ✅ Step 7: Interview Like a Problem Solver — Start with brute force — Explain your thinking out loud — Clarify the question before writing code — Know your edge cases (nulls, empty inputs, one element) Your goal isn’t to become a LeetCode machine. It’s to become the person who knows how to think, debug, and deliver when it matters. And that’s what this offer from Microsoft proved.

Back in 2020, I was grinding LeetCode problem after problem, 100s of them, hoping sheer volume would somehow prepare me for FAANG interviews. But when I came to the US for my Master’s, everything changed. Between assignments, projects, and job search, I barely had time to breathe, let alone solve 10 problems a day. That’s when it hit me: I didn’t need more problems. I needed a 𝗯𝗲𝘁𝘁𝗲𝗿 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆. Instead of solving 500+ questions, I began 𝗴𝗿𝗼𝘂𝗽𝗶𝗻𝗴 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀 𝗯𝘆 𝘁𝗵𝗲 𝗶𝗱𝗲𝗮𝘀 𝗯𝗲𝗵𝗶𝗻𝗱 𝘁𝗵𝗲𝗺: sliding window, DP, recursion trees, fast & slow pointers, backtracking, and more. Different questions started to feel like variations of the same puzzle. And my interview performance skyrocketed. This journey is what inspired me to put together a complete guide for DSA PATTERNS: https://lnkd.in/gnXy4XNn This roadmap shows you: 🔹 The exact DSA patterns companies test 🔹 How to identify repeating subproblems 🔹 How to practice smart instead of practicing endlessly I hope this guide helps anyone overwhelmed with leetcode questions! If you want more such roadmaps in your mailbox every week, consider subscribing to my 𝗙𝗥𝗘𝗘 𝗡𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿: https://lnkd.in/gSN9buQ7 #leetcode #dsa #faang #interviewprep #tech #hiring #datastructures