🚀 Data Structures & Algorithms Project

A comprehensive collection of Data Structures & Algorithms implementations and solutions from the Udacity Computer Science program. This project demonstrates efficient algorithmic thinking, optimal data structure usage, and modern Python development practices.

📚 Project Overview

This repository contains solutions to algorithmic challenges organized into 4 progressive modules:

🔍 Module 1: Unscramble Computer Science Problems

Focus: Time & Space Complexity Analysis

• Task 0: Data extraction and basic operations
• Task 1: Set operations for unique telephone numbers
• Task 2: Longest duration call analysis
• Task 3: Area code pattern analysis
• Task 4: Telemarketer detection algorithm

🏗️ Module 2: Show Me The Data Structures

Focus: Custom Data Structure Implementation

• Problem 1: LRU Cache (Least Recently Used)
• Problem 2: File Recursion (Directory traversal)
• Problem 3: Huffman Coding (Compression algorithm)
• Problem 4: Active Directory (User group management)
• Problem 5: Blockchain (Distributed ledger)
• Problem 6: Union & Intersection (Linked lists)

⚡ Module 3: Problems vs. Algorithms

Focus: Advanced Algorithm Design

• Problem 1: Square Root (Binary search implementation)
• Problem 2: Search in Rotated Array
• Problem 3: Rearrange Array Digits
• Problem 4: Dutch National Flag Problem
• Problem 5: Autocomplete with Tries
• Problem 6: Unsorted Integer Array (Min/Max)
• Problem 7: HTTP Router using Trie

🗺️ Module 4: Route Planner

Focus: Graph Algorithms & Pathfinding

• Implementation: A* Search Algorithm
• Application: GPS-style shortest path finder
• Visualization: Interactive map navigation

🚀 Quick Start

Prerequisites

• Python 3.8 or higher
• pip package manager

Installation

# Clone the repository
git clone https://github.com/yourusername/Data-Structures-Algorithms.git
cd Data-Structures-Algorithms

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

Running Solutions

# Execute specific problems
python "01 Unscramble Computer Science Problems/submit/Task0.py"
python "02 Show Me The Data Structures/single/problem_1.py"

# Run tests
pytest tests/ -v --cov=src/

# Format code
black src/ tests/
isort src/ tests/

# Run Route Planner (Jupyter Notebook)
cd "04 Route Planner/home"
jupyter notebook project_notebook.ipynb

🧪 Testing & Validation

Comprehensive test suite covering:

• ✅ Unit Tests: Individual function validation
• ✅ Integration Tests: End-to-end workflows
• ✅ Performance Tests: Time & space complexity verification
• ✅ Edge Cases: Boundary conditions and error handling

# Run all tests
pytest

# Run with coverage report
pytest --cov=src/ --cov-report=html

# Run specific module tests
pytest tests/test_lru_cache.py -v

📊 Performance Analysis

Each solution includes:

• Time Complexity: Big O analysis
• Space Complexity: Memory usage assessment
• Benchmarking: Performance comparisons
• Optimization Notes: Trade-offs and improvements

🔧 Development Tools

• Code Formatting: Black, isort
• Linting: flake8, mypy
• Testing: pytest, coverage
• Documentation: Jupyter notebooks
• Pre-commit Hooks: Automated quality checks

📁 Project Structure

Data-Structures-Algorithms/
├── 01 Unscramble Computer Science Problems/
│   ├── submit/           # Solution files
│   └── data/            # Input datasets
├── 02 Show Me The Data Structures/
│   ├── single/          # Implementation files
│   └── explanations/    # Algorithm analysis
├── 03 Problems vs. Algorithms/
│   ├── single/          # Advanced solutions
│   └── tests/           # Unit tests
├── 04 Route Planner/
│   ├── home/            # A* implementation
│   └── maps/            # Graph data
├── src/                 # Enhanced implementations
├── tests/               # Comprehensive test suite
├── docs/                # Documentation
├── requirements.txt     # Dependencies
└── README.md           # This file

🎯 Key Learning Outcomes

• Algorithm Design: Problem decomposition and solution strategies
• Data Structure Mastery: Choosing optimal structures for specific use cases
• Complexity Analysis: Understanding time/space trade-offs
• Code Quality: Writing maintainable, efficient Python code
• Testing: Comprehensive validation and edge case handling

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/enhancement)
3. Commit changes (git commit -m 'Add new feature')
4. Push to branch (git push origin feature/enhancement)
5. Open a Pull Request

📄 License

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

🙏 Acknowledgments

• Udacity: Course structure and problem sets
• Python Community: Libraries and best practices
• Contributors: Code reviews and enhancements

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📈 Project Status: ✅ Active Development | 🧪 Tests Passing | 📚 Well Documented