Curated collection of Data Structures & Algorithms solutions for interview preparation and competitive programming.
This repository tracks solved Data Structures and Algorithms (DSA) problems, meticulously organized by algorithmic category. It serves as a comprehensive learning dashboard and progress tracker.
The goal of this repository is to:
- Strengthen algorithmic thinking and problem-solving fundamentals.
- Prepare comprehensively for coding interviews at top tech companies.
- Master common algorithm patterns and recognize when to apply them.
All problems are implemented with clean, optimized Python solutions, focusing on readability, time complexity, and space efficiency.
This repository covers a wide spectrum of essential data structures and algorithms patterns:
- Searching Algorithms: Linear Search, Binary Search, Quick Select.
- Graph Traversal: Breadth-First Search (BFS), Depth-First Search (DFS), Dijkstra's, Topological Sort.
- Recursion & Backtracking: Generating subsets, permutations, combinations, and solving constraint satisfaction problems.
- Dynamic Programming: Memoization, tabulation, knapsack problems, sequence alignment.
- Greedy Algorithms: Local optimal choices to find global optimums.
- Sliding Window Technique: Fixed and variable length window optimizations for arrays/strings.
- Tree Traversal Algorithms: Pre-order, in-order, post-order, level-order traversal, and BST operations.
- Heap & Priority Queue: Top K elements, merging sorted lists, scheduling.
- Bit Manipulation: XOR tricks, masking, and optimizing space-time complexity.
Each solution is designed to quickly demonstrate efficient implementations and reusable algorithm patterns.
Continually updated to reflect an ongoing professional DSA learning journey and algorithm mastery tracker.