The Good Tech Companies - Mastering Top LeetCode Patterns for Coding Interviews: A 2025 Guide and Strategy

Episode Date: July 2, 2025

This story was originally published on HackerNoon at: https://hackernoon.com/mastering-top-leetcode-patterns-for-coding-interviews-a-2025-guide-and-strategy. Master 7 key LeetCode patterns (Sliding Window, Two Pointers, DP & more) for 2025 coding interviews. Includes code examples & 12-week study plan. Check more stories related to programming at: https://hackernoon.com/c/programming. You can also check exclusive content about #leetcode, #leetcode-practice, #leetcode-patterns, #solving-leetcode-problems, #leetcode-python, #1000-leetcode-problems, #leetcode-solutions, #good-company, and more. This story was written by: @rabbitrank. Learn more about this writer by checking @rabbitrank's about page, and for more stories, please visit hackernoon.com. Master key LeetCode patterns, not random problems, for 2025 interviews. Top patterns: Sliding Window, Two Pointers, Fast/Slow Pointers, Tree/Graph (DFS/BFS), Binary Search, Dynamic Programming, Backtracking. Use the 12-week study plan: focus 1-2 patterns weekly. Practice explaining solutions & edge cases, communication matters as much as code. Crush interviews with pattern recognition!

This audio is presented by Hacker Noon, where anyone can learn anything about any technology. Mastering top Leetcode patterns for coding interviews, a 2025 guide and strategy, by RabidRank. So you're prepping for those dreaded technical interviews, huh? Trust me, I've been there, drowning in an ocean of 2000 plus Leetcode problems, wondering if I'd ever make it to the other side. But here's the secret I wish someone had told me earlier. It's not about how many problems you solve, it's about recognizing the patterns behind them. After banging my head against the algorithmic wall for months, and nursing the resulting

headaches with way too much caffeine cold sweat smile, I finally cracked the code. Today, I'm sharing the most effective lead code patterns that'll have you solving problems like a pro in 2025. Why patterns matter more than grinding random problems? Look, I used to be that person who'd randomly pick leadcode problems and pray had seen enough variations to ace my interviews. Spoiler alert. It didn't work out great. The truth? Technical interviews in 2025 are less about memorizing solutions and more about applying problem-solving frameworks. According to recent studies, candidates who master key patterns perform significantly better than those who just grind through hundreds of random problems.

As one senior engineer at a FAANG company told me, greater than, I don't care if you've solved that exact problem before. I want to see if you greater than can recognize the pattern and apply the right approach. So let's get into the patterns that'll give you the highest ROI for your precious study time. 1. Sliding window. Your BFF for array and string problems. The sliding window technique is clutch for array, string problems where you need to find

a sub array or substring that meets certain conditions. Instead of brute forcing with nested loops and making the interviewer cringe at your o n squared solution, this pattern lets you solve these problems in o n time. When to use it, working with linear data structures like arrays or strings, need to find a subarray substring that meets a condition, looking for min max longest shortest subarray substring that meets a condition, looking for min, max, longest, shortest subarray with specific properties. The technique. We use two pointers, let's call them i and j, to create a window that can expand or shrink. There are two flavors of sliding window. 1. Fixed size window. When the subarray size is fixed, like, find max sum of subarray of size k. 2. Dynamic size window. When the size changes based on a condition, like, shortest subarray

with sum greater than equals target. Here's how you'd implement a dynamic window for finding the smallest subarray with a sum is greater than or equal to target. Honestly, once I got this pattern down, so many, hard, problems suddenly became manageable. It's like having a secret weapon. Gun practice problems maximum sum sub array of size k longest substring with k distinct characters. Fruits into baskets, leadcode number 904, longest substring without repeating characters, leadcode number 3, 2, 2 pointers, double the fun, 2 pointers is another game changer, especially when working with sorted arrays. This pattern involves using 2 pointers to iterate through an array and find elements

that satisfy certain conditions. When to use it, working with sorted arrays, need to find pairs that satisfy a condition, problems involving reversing or palindromes. Basic implementation. I swear, this technique has saved me in so many interviews. One time I was blanking until I realized, oh wait, this is just a two-pointer problem. The interviewer's face lit up and I knew I was back in the game. Cool practice problems. Two sum two, Leap code number 167. Remove duplicates, Leet code number 26. Squares of a sorted array, Leet code number 977. 3 sum, Leet code number 15.

3. Fast and slow pointers. The tortoise and the hare. This pattern uses two pointers that move at different speeds. Super useful for cycle detection in linked lists or arrays. When to use it? Linked list problems, especially cycle detection. Finding the middle of a linked list. Determining if a number is a happy number. When I first encountered this technique, my mind was blown. Exploding head like. How does moving at different speeds help? But once you see it in action, especially with the Floyd's cycle finding algorithm, it's pure magic. Practice problems linked list cycle, Leet code number 141

middle of the linked list, Leet code number 876 palindrome linked list, Leet code number 234 happy number, Leet code number 202 4, tree and graph travers traversal DFS and BFS trees and graphs are everywhere in technical interviews especially at companies like Meta and Amazon mastering both depth first search DFS and breadth first search BFS is non-negotiable when to use DFS. Finding a path between two nodes. Detecting cycles. Topological sorting. Exploring all possibilities. Backtracking. DFS implementation. When to use BFS. Finding

the shortest path. Level order traversal. Finding nodes closest to the starting node. BFS implementation. The first time I tried implementing these algorithms in an interview, I mixed up my stack and queue operations. Talk about an embarrassing moment. Flushed pro tip. Practice these until they're second nature. Practice problems. Binary tree level order traversal, Leet code number 102.

Number of islands, Leet code number 200. Course schedule, Leet code number 207. Word ladder, Leet code number 200. Course schedule, Leet code number 207. Word ladder, Leet code number 127. Five, binary search. Not just for sorted arrays. Binary search is a classic divide and conquer algorithm that's often underestimated. It's not just for finding elements in sorted arrays,

it can be applied to various problems with a monotonic search space. When to use it, searching in a sorted arrays, it can be applied to various problems with a monotonic search space. When to use it? Searching in a sorted array. Finding a specific value or a range. Problems where the solution space can be divided in half. I used to think binary search was trivial until I started seeing all the clever variations

in interviews. Now it's one of my go-to techniques. Remember, binary search isn't just about finding an element it's about eliminating half of the possibilities in each step. Practice problems. Search in rotated sorted array. Leet code number 33. Find first and last position of element. Leet code number 34. Median of two sorted arrays. Leet code number 4. Coco eating bananas. leap code number 875, 6, dynamic programming, breaking problems down. Dynamic programming, DP, is often the most intimidating pattern for candidates, but it's

incredibly powerful once you get the hang of it. The key is breaking down problems into smaller, overlapping subproblems. When to use it? Optimization problems, max per minute, longest, shortest. Counting problems, number of ways to. Problems with overlapping subproblems and optimal substructure. I'm not gonna lie, DP problems used to make me want to curl up in a ball and cry. Loudly crying but once I understood the pattern of defining states and transitions, they became much more approachable. Practice problems. Coin change, Le became much more approachable. Practice problems.

Coin change, Leap code number 322. Longest common subsequence, Leap code number 1143. Longest increasing subsequence, Leap code number 300. House robber, Leap code number 198. 7. Backtracking. Exploring all possibilities. Backtracking is essentially recursion with

a twist it explores all possible solutions by building candidates incrementally and abandoning them. Backtracking. Once it's clear they won't work. When to use it. Combinatorial problems. Combinations. Permutations. Puzzle solving. Sudoku. N queens. Constraint satisfaction problems. Backtracking problems can be a bit mind bending at first. I remember staring at my whiteboard for an embarrassingly long time trying to visualize the recursion tree. But once you get it, they become really satisfying to solve, practice problems. Subsets, Leet code number 78.

Permutations, Leet code number 46. N Queens, Leet code number 46, N queens, Leet code number 51, word search, Leet code number 79, study plan, how to master these patterns by 2025. Now that we've covered the most important patterns, let's talk strategy. Horesho I'd approach mastering these patterns if I were starting from scratch. 1. Week 1 to 2. Focus on sliding window and two pointers start with easy problems for each pattern. Move to medium problems once comfortable. Review solutions and optimize. Two.

Week three to four. Tree, graph traversal. DFS and BFS. Practice both recursive and iterative implementations. Apply to tree problems first, then graph problems. Make sure you understand the differences between DFS and BFS. Three, week five to six, binary search and fast and slow pointers master the basic template first.

Then tackle variations in edge cases. Focus on problems that aren't obviously binary search at first glance. Four, week seven to nine, dynamic programming start with simple one-DDP problems. Practice on problems that aren't obviously binary search at first glance. 4. Week 7-9. Dynamic programming start with simple 1-DDP problems. Move to 2-DDP problems. Practice recognizing when to use DP. 5.

Week 10-12. Backtracking in advanced patterns combine multiple patterns in complex problems. Time yourself to simulate interview conditions. Practice explaining your approach out loud. Remember, consistency beats intensity. I'd rather see you solve one problem thoroughly each day than binge ten problems without understanding them. Brain, it's not just about the code. Let me get real with you for a second. While these patterns are crucial, technical interviews are also about communication, problem-solving

process, and handling pressure. I've bombed interviews where I actually knew the solution, just because I got nervous and couldn't articulate my thought process. And I've passed interviews where I didn't get the optimal solution but clearly explained my reasoning. So as you practice, don't just code explain your approach out loud, consider trade-offs, analyze time and space complexity, and handle edge cases. These soft skills matter just as much as your algorithmic knowledge. Well, that's all I've got for you today.

I hope this guide helps you crush your coding interviews in 2025. Remember, you're not alone in this journey we're all suffering through Leetcode together. Now go forth and conquer those patterns, happy coding, and may the algorithmic gods be ever in your favor. Thank you for listening to this Hacker Noon story, read by Artificial Intelligence. Visit HackerNoon.com to read, write, learn and publish.