This repository contains my structured study of Data Structures and Algorithms (DSA) using Python.
The goal is not competitive programming or solving hundreds of random problems.
It is focused on building the problem-solving skills needed for full-stack development, backend engineering, and AI development.
- Problem-solving and Big-O
- Arrays and strings
- Two pointers and sliding window
- Linked lists
- Stacks and queues
- Hash maps
- Recursion and backtracking
- Sorting and searching
- Trees and Binary Search Trees
- Heaps and priority queues
- Graphs and graph algorithms
- Greedy algorithms
- Dynamic programming
- Problem-solving patterns and complexity analysis
- Real-world and interview applications
Each topic focuses on:
- Understanding the problem
- Building the intuition and theory
- Starting with a brute-force approach
- Identifying the bottleneck
- Optimizing the solution
- Analyzing time and space complexity
- Testing edge cases
- Re-explaining the solution without notes
I don't want to memorize solutions. The goal is to understand why a data structure or algorithm fits a problem and recognize the pattern when facing a new problem.
DSA/
├── docs/
│ └── road-map.md
└── README.md
The full learning plan is available in docs/road-map.md.
As I progress, solutions and exercises will be organized by phase and topic rather than added as a random collection of problems.
Build practical DSA skills that transfer to real software engineering:
- Choosing the right data structure
- Writing efficient and maintainable solutions
- Understanding time and space complexity
- Recognizing common problem-solving patterns
- Connecting DSA concepts to backend, frontend, databases, and AI systems
Understand the problem. Understand the pattern. Build the solution. Know why it works.