A structured engineering playbook designed to streamline advanced technical interview preparation.
This repository bridges the gap between theoretical computer science and production-ready engineering by providing multi-approach solutions, rigorous Time & Space Complexity breakdowns, and optimized implementations in both Java and Python. It serves as a quick-reference dsa cheetsheet to quickly recall solution and different solution for given problems in java and python.
This playbook is divided into two main tracks:
High-yield notes focusing on pattern recognition, complexity trade-offs, and language-specific idioms (Pythonic vs. Java conventions).
- String Manipulation & Hashing (e.g., Array Counting vs. Sorting)
- Tree & Graph Traversal
- Sliding Window & Two Pointers
- Dynamic Programming Patterns
Production-grade solutions categorized by their underlying pattern. Each problem directory contains dual-language solutions alongside detailed approach notes.
Example Structure:
/patterns/group-anagrams/
├── Solution.java # Java implementation (Focus on thread-safety & type strictness)
├── solution.py # Python implementation (Focus on succinct, idiomatic logic)
└── approach-notes.md # Time/Space complexity analysis and trade-offs