This handbook is a curated guide to Data Structures and Algorithms (DSA) designed for last-minute coding interview preparation at top tech companies. It includes each topic into a concise, high-impact format covering: key definitions, time complexities, lesser-known concepts, edge cases, common patterns, and the most relevant problems & resources to help you revise DSA smarter, not harder.
- 🎯 About This Handbook
- ⚡ Quick Start
- 📚 DSA Topics Covered
- 🎯 What You'll Learn
- 📖 Must-Know References
- 📺 Video Resources
- 🤝 Contributing
- ⭐ Support the Project
This handbook is a curated guide to Data Structures and Algorithms (DSA) designed for last-minute coding interview preparation at top tech companies. Each topic is presented in a concise, high-impact format covering:
- 🔑 Key Definitions - Core concepts explained clearly
- ⏱️ Time Complexities - Performance analysis for all operations
- 💡 Lesser-Known Concepts - Advanced techniques and optimizations
⚠️ Edge Cases - Common pitfalls and how to handle them- 🎯 Common Patterns - Reusable problem-solving strategies
- 📝 Relevant Problems - Curated practice questions
- 🔗 Additional Resources - Further reading and practice materials
- Choose your topic from the list below
- Review the key concepts and time complexities
- Practice with the provided problems
- Use the references for deeper understanding
- Watch the video resources for visual learning
- 📌 Arrays - Linear data structure with contiguous memory
- 🧵 Strings - Character sequences and text processing
- 🔗 Linked List - Dynamic linear data structure
- 🧱 Stack - LIFO (Last In, First Out) structure
- ⏩ Queue - FIFO (First In, First Out) structure
- 🗺️ Hash Map / Hash Set - Key-value storage with O(1) access
- 🌲 Tree - Hierarchical data structure
- 🔤 Trie - Prefix tree for string operations
- 🔺 Heap / Priority Queue - Complete binary tree with ordering
- 🧮 Matrix - 2D array operations and patterns
- 🌐 Graph - Network of nodes and edges
- 📆 Intervals - Range-based problems
- 🔃 Sorting - Arranging elements in order
- 💰 Greedy - Local optimal choices
- 🔁 Recursion - Self-referential problem solving
- 🧠 Dynamic Programming - Optimal substructure problems
- 🔙 Backtracking - Systematic search with pruning
- 🔄 Cyclic Sort - In-place array sorting technique
- 🧮 Bit Manipulation - Binary operations and tricks
- ➗ Math - Mathematical concepts and number theory
- Big-O notation understanding
- Space vs Time trade-offs
- Algorithm optimization techniques
- Two Pointers technique
- Sliding Window approach
- Binary Search variations
- DFS/BFS strategies
- Topological Sorting
- And many more...
- How to approach new problems
- Communication techniques
- Code optimization tips
- Edge case handling
- Big-O Cheatsheet - Complete complexity reference
- AlgoMonster: Runtime Complexity Summary - Quick lookup guide
- AlgoMonster: Keyword to Algo - Problem pattern recognition
- AlgoMaster DSA Patterns - Pattern-based learning
- Tech Interview Handbook - Comprehensive study guide
- Python
collectionsmodule - Built-in data structures - CP Algorithms - Bit Manipulation - Advanced bit tricks
- Python Sorted Containers - Efficient sorted data structures
- Dunder Methods in Python - Python magic methods
- 70 Leetcode problems in 5+ hours (every data structure) - Rapid-fire problem solving
- Neetcode 150 Youtube Playlist - Detailed explanations for 150 Most asked problems
We welcome contributions to make this handbook even better! Please check our Contributing Guidelines for details on:
- 📝 How to contribute
- 🎨 Style guidelines
- 💬 Commit message conventions
- 🔄 Pull request process
- 📚 Topic guidelines
- 🐛 Reporting issues
Before contributing, please discuss your ideas with Tharun Kumar Reddy Polu to ensure they align with the project's goals.
If this handbook helps you in your coding interview preparation, please consider:
Inspired by practical coding interview prep with key insights from resources like AlgoMonster, LeetCode, GFG, CP-Algorithms, and personal DSA preparation notes. Happy Coding Interviews! 🚀