Overview

Data Structures and Algorithms are the foundation of how I think about problem solving. I don’t treat DSA as something limited to interviews — I apply the same logic while building backend systems, optimizing APIs, and handling large datasets. I naturally consider time complexity, space usage, and edge cases before finalizing an approach.

🧠 Core Strength Areas

Concept	Topics & Techniques
Data Structures	Arrays, Strings, Linked Lists, Hash Maps
Advanced Structures	Stacks, Queues, Trees (BST), Heaps, Graphs
Algorithmic Patterns	Sliding Window, Two Pointers, Fast & Slow Pointers
Core Algorithms	Binary Search, Recursion, Backtracking
Optimization	Greedy Algorithms, Dynamic Programming (Basic-Intermediate)
Traversal & Sorting	BFS, DFS, Sorting & Searching Techniques

🧩 How I Think About Problems

Whenever I face a problem, I first understand the constraints and possible edge cases. I usually begin with a simple approach and then refine it step by step by analyzing time and space complexity.

Instead of jumping to the most complex solution, I focus on clarity first, then optimization. My aim is to write solutions that are efficient, readable, and scalable.

⚡ Practical Impact

Strong DSA fundamentals help me optimize database-heavy operations, reduce API response times, design rate limiters, implement caching strategies, and structure scalable features.

For me, algorithmic thinking is not separate from development — it strengthens every technical decision I make.

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Good engineering starts with strong fundamentals.