This repository serves as a comprehensive roadmap for mastering data structures, algorithms, and various advanced topics in computer science and software development. Whether you're a beginner looking to start your journey or an experienced developer aiming to brush up on your skills, this guide has something for everyone.
Table of Contents
- Lists
- Arrays
- Stacks
- Queues
- Linked lists
- Searching Algorithms
- Linear Search
- Binary Search
- Sorting
- Bubble Sort
- Selection Sort
- Insertion Sort
- Merge Sort
- Quick Sort
- Heap Sort
- Counting Sort
- Radix Sort
- Bucket Sort
- Recursion
- Backtracking
- Hash Tables
- Trees
- Binary Trees
- Binary Search Trees
- AVL Trees
- Red-Black Trees
- B-Trees -Heaps
- Min Heap
- Max Heap
- Graphs
- Graph Representation
- Graph Traversal
- Tries
- Segment Trees
- Bloom Filters
- Disjoint-Set (Union-Find)
- Depth-First Search (DFS)
- Breadth-First Search (BFS)
- Dynamic Programming
- Divide and Conquer
- Greedy Algorithms
- Sliding Window Technique
- Two-Pointer Technique
- Bit Manipulation
- Recursion with Memoization
- In-place Algorithms
- Time Complexity (Big O notation)
- Space Complexity
- Amortized Analysis
- Best, Worst, and Average Case Analysis
- Utilize platforms like LeetCode, HackerRank, Codeforces, and others for practice.
- Solve problems by category (e.g., arrays, strings, trees).
- Participate in coding contests and challenges.
- String Algorithms (Longest Common Subsequence, Edit Distance, etc.)
- Computational Geometry
- Network Flow Algorithms (Ford-Fulkerson, Edmonds-Karp)
- NP-Completeness and Approximation Algorithms
- Singleton
- Factory
- Observer
- Strategy
- Decorator
- Command
- State
- Scalability
- Load Balancing
- Caching
- Database Sharding
- Microservices
Explore the following resources to further enhance your knowledge and skills:
- Python Crash Course by Eric Matthes - A comprehensive and accessible introduction to Python programming, including data structures and algorithms.
- Grokking Algorithms by Aditya Bhargava - Utilizes a unique visual approach to teach algorithms, making them easier to understand and remember. Includes Python code examples.
- Data Structures and Algorithms in Python by Goodrich, Tamassia, and Goldwasser - Offers comprehensive and in-depth coverage of data structures and algorithms in Python, suitable for students familiar with Python basics.
Additional resources:
- GeeksforGeeks - Provides tutorials, articles, and practice problems on various computer science topics, including data structures and algorithms in Python.
- LeetCode - Offers a wide range of coding challenges and practice problems on data structures and algorithms, ideal for testing your skills and learning new concepts.
- HackerRank - Features coding challenges and practice problems on data structures and algorithms, along with tutorials and articles on various computer science topics.
This structure should provide a clear and systematic organization of your topics related to data structures and algorithms in Python.
