Coding Interview Resources

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Let’s dive into the frequently asked coding interview topic now: Hashing & Maps Hashing helps us access data quickly, making it a critical topic in coding interviews. HashMaps (or dictionaries) provide constant-time access and are used to solve problems like counting elements, finding duplicates, and mapping data efficiently. 3.1. Use Hash Maps for Fast Lookups Example: Check if two strings are anagrams. Given s1 = "listen", s2 = "silent", are they anagrams? Solution: Use a hash map to count the frequency of characters in both strings and compare the counts. Concept tested: Efficient searching and counting with O(1) average time complexity. 3.2. Count Frequency of Elements Example: Given an array nums = [1, 2, 2, 3, 3, 3, 4], count the frequency of each element. Solution: Use a hash map to store the counts: {1: 1, 2: 2, 3: 3, 4: 1} It tests how well you can group data efficiently and manage it using hash-based structures. 3.3. Find Duplicates Example: Given arr = [4, 5, 6, 7, 5, 8], find the first duplicate. Solution: Use a hash map to track seen elements as you traverse the array. If you encounter an element already in the map, it’s a duplicate. It tests your ability to solve problems with constant time lookups for duplicates. 3.4. Two Sum Problem Example: Given nums = [2, 7, 11, 15] and target = 9, return indices of the two numbers that add up to the target. Solution: Use a hash map to track the difference between the target and the current number. When you find a match, return the indices. Concept tested: Efficient search for pairs in a single pass with O(n) time complexity. 3.5. Implement LRU Cache Example: Design a cache that stores the most recently used items, evicting the least recently used when it exceeds its capacity. Solution: Use a hash map to store the cache and a doubly linked list to keep track of the order of usage. Combine both to make retrieval and eviction O(1). This tests your ability to combine hash maps with other data structures like linked lists, and to implement efficient solutions with constraints. Hash maps are frequently used in problems involving counting, grouping, and mapping, especially when you need to reduce time complexity from quadratic to linear. React with ❤️ once you're ready for the next topic: *Recursion & Backtracking* Top 7 Coding Interview Concepts: https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X/720 Top 7 Python Concepts: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L/1452 ENJOY LEARNING 👍👍

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Let's now move to next important concept asked in coding interviews: Linked Lists: Linked Lists test your ability to handle pointers, edge cases, and memory efficiency. They show up in both beginner and advanced interview rounds. 2.1. Reverse a Linked List Example: Reverse this list: 1 → 2 → 3 → 4 Output: 4 → 3 → 2 → 1 Concept tested: Rewiring the .next pointers — often asked with follow-ups like iterative vs. recursive solutions. 2.2. Detect Cycle in a Linked List Example: In 1 → 2 → 3 → 4 → 2 (back to second node), detect the cycle. Solution: Use Floyd’s Cycle Detection Algorithm (fast and slow pointers). It tests how well you manage infinite loops and pointer traversal without modifying the list. 2.3. Merge Two Sorted Linked Lists Example: Merge 1 → 3 → 5 and 2 → 4 → 6 Output: 1 → 2 → 3 → 4 → 5 → 6 Concept tested: Efficient pointer traversal with dummy nodes or recursion. A classic sub-task in linked list sorting. 2.4. Find the Middle of a Linked List Example: In 1 → 2 → 3 → 4 → 5 → 6, the middle node is 4. Solution: Fast and slow pointer — when the fast pointer reaches the end, the slow one is at the middle. 2.5. Remove N-th Node from End Example: Remove the 2nd node from the end of 1 → 2 → 3 → 4 → 5 Output: 1 → 2 → 3 → 5 Trick: Create a gap of n between two pointers and move them together — when the first hits the end, the second is at the right spot. You’ll see linked lists hidden in many real-world structures — like undo-redo functionality, LRU cache, or browser history stacks. React with ❤️ once you're ready for the next concept Hashing & Maps Top 7 Coding Interview Concepts: https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X/720 ENJOY LEARNING 👍👍

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Let's understand how Arrays & Strings can be asked in coding interviews Arrays and strings are the building blocks of most coding interview problems. They test your logic, optimization skills, and your ability to recognize patterns — and they pop up in everything from system design to algorithm rounds. *1.1. Rotation* You may be asked to rotate an array left or right by k positions, in-place and with O(1) space. Example: > Rotate [1, 2, 3, 4, 5] right by 2 → Output: [4, 5, 1, 2, 3] It tests how well you manage array indices and edge cases like k > n. *1.2. Sliding Window* Used to reduce brute-force O(n²) solutions to O(n). Interviewers love this for problems around subarrays, substrings, or fixed windows. *Example* : > Find the max sum of a subarray of size 3 in [4, 2, 1, 7, 8, 1, 2, 8, 1, 0] → Output: 17 It's commonly used in anagram detection, maximum subarray sum, and longest substring without repeating characters. *1.3. Two Pointers* Two indices scanning the array — from start and end or moving in sync. Great for reducing space/time complexity. Example: > Given [1, 2, 4, 4] and target = 8, return true if two numbers sum up to target → Output: True (4+4) Common interview problems: - Reverse a string/array - Check for palindrome - Remove duplicates in-place - Merge two sorted arrays *1.4. Prefix Sum* Precompute cumulative sums to answer range queries in O(1) instead of O(n). Example: > For nums = [1, 2, 3, 4, 5], find sum from index 1 to 3 quickly → Output: 9 (2+3+4) *Popular problems:* - Subarray sum equals k - Range sum queries - Balanced subarrays React with ❤️ once you're ready for the next concept Linked Lists Top 7 Coding Interview Concepts: https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X/720

Tips for solving leetcode codings interview problems If input array is sorted then - Binary search - Two pointers If asked for all permutations/subsets then - Backtracking If given a tree then - DFS - BFS If given a graph then - DFS - BFS If given a linked list then - Two pointers If recursion is banned then - Stack If must solve in-place then - Swap corresponding values - Store one or more different values in the same pointer If asked for maximum/minimum subarray/subset/options then - Dynamic programming If asked for top/least K items then - Heap If asked for common strings then - Map - Trie Else - Map/Set for O(1) time & O(n) space - Sort input for O(nlogn) time and O(1) space

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