DSA-in-Python

This repository is a comprehensive guide to mastering Data Structures and Algorithms (DSA) using Python. It covers Python basics, fundamental and advanced data structures, algorithms, and competitive programming, with practical projects and interview preparation. Contributions welcome!

Data Structures and Algorithms (DSA) in Python

Welcome to my Data Structures and Algorithms (DSA) in Python repository! This repository serves as a comprehensive guide and resource for mastering DSA concepts using Python, covering everything from basic to advanced topics.

Roadmap

Phase 1: Basics of Python

• Python Fundamentals
  • Variables and Data Types
  • Control Structures (if-else, loops)
  • Functions and Recursion
  • Modules and Libraries
  • File Handling
• Object-Oriented Programming (OOP)
  • Classes and Objects
  • Inheritance and Polymorphism
  • Encapsulation and Abstraction

Phase 2: Basic Data Structures

• Arrays and Lists
  • Introduction to Arrays and Lists
  • Common Operations (insert, delete, traverse)
  • Slicing and List Comprehensions
• Strings
  • String Manipulation
  • Pattern Matching and Regular Expressions
• Stacks and Queues
  • Implementation using Lists/Deques
  • Applications and Problems
• Linked Lists
  • Single, Double, and Circular Linked Lists
  • Operations and Applications

Phase 3: Advanced Data Structures

• Trees
  • Binary Trees and Binary Search Trees (BST)
  • Tree Traversal Techniques (In-order, Pre-order, Post-order)
  • AVL Trees and Red-Black Trees
• Heaps
  • Min Heap and Max Heap
  • Priority Queues
• Graphs
  • Representation (Adjacency Matrix, List)
  • Traversal Algorithms (DFS, BFS)
  • Shortest Path Algorithms (Dijkstra, Floyd-Warshall)
• Hashing
  • Hash Functions and Hash Tables
  • Collision Resolution Techniques (Chaining, Open Addressing)

Phase 4: Algorithms

• Sorting Algorithms
  • Bubble Sort, Selection Sort, Insertion Sort
  • Merge Sort, Quick Sort, Heap Sort
  • Counting Sort, Radix Sort
• Searching Algorithms
  • Linear Search, Binary Search
  • Search in Rotated Arrays, Ternary Search
• Dynamic Programming
  • Principles and Patterns
  • Common Problems (Knapsack, Fibonacci, LCS)
• Greedy Algorithms
  • Principles and Patterns
  • Common Problems (Activity Selection, Huffman Coding)
• Backtracking
  • Principles and Patterns
  • Common Problems (N-Queens, Sudoku Solver)

Phase 5: Advanced Topics and Competitive Programming

• Advanced Graph Algorithms
  • Minimum Spanning Tree (Kruskal, Prim)
  • Network Flow (Ford-Fulkerson)
• Advanced Data Structures
  • Segment Trees, Fenwick Trees
  • Trie, Suffix Tree, and Suffix Array
• Problem Solving and Competitive Programming
  • Practicing Problems on Platforms like LeetCode, Codeforces, HackerRank
  • Participating in Contests

Phase 6: Real-World Applications and Projects

• Building Projects
  • Implement real-world projects using DSA concepts
  • Example Projects: Search Engine, Social Network Analysis, Recommendation Systems
• Interviews and Coding Challenges
  • Mock Interviews
  • Solving Past Interview Questions from Top Tech Companies

Resources

A curated list of resources and references to aid in understanding and mastering each phase of the roadmap.

Contributions

Contributions are welcome! If you have suggestions, improvements, or additions, feel free to open an issue or submit a pull request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

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Feel free to explore, contribute, and utilize this repository to enhance your DSA skills in Python. Let's dive into the world of algorithms and data structures together!