Data Structures and Algorithms

Complete collection of fundamental CS concepts with clean implementations and detailed explanations.

📁 Structure

├── 01-Object-Oriented-Programming/    # OOP theory and examples (Markdown)
├── 02-Time-Space-Complexity/          # Algorithm analysis theory (Markdown) 
├── 03-Sorting-Algorithms/             # 5 sorting algorithms (Python)
├── 04-Searching-Algorithms/           # Binary search + variants (Python)
├── 05-Algorithms/                     # Important algorithms (Python)
└── README.md

📚 Contents

1. Object-Oriented Programming

Theory-focused with practical examples

• 01-classes-objects-inheritance.md - Classes, objects, inheritance types
• 02-encapsulation-polymorphism.md - Data hiding, operator overloading

2. Time & Space Complexity

Algorithm analysis fundamentals

• 01-complexity-fundamentals.md - Big O notation, common complexities
• 02-analysis-techniques.md - Step-by-step analysis methods

3. Sorting Algorithms

Clean implementations with visualizations

Algorithm	Time	Space	Stable	Best For
Selection Sort	O(n²)	O(1)	❌	Educational
Bubble Sort	O(n²)	O(1)	✅	Educational
Insertion Sort	O(n²)	O(1)	✅	Small/nearly sorted
Merge Sort	O(n log n)	O(n)	✅	Large datasets
Quick Sort	O(n log n) avg	O(log n)	❌	General purpose

4. Searching Algorithms

Binary search with interview variants

• Standard binary search
• First/last occurrence in duplicates
• Search in rotated sorted array
• Find insertion point
• Peak element finding

5. Important Algorithms

Essential algorithms for interviews

• Euclidean Algorithm: GCD computation, cryptographic applications
• Kadane's Algorithm: Maximum subarray + variants (2D, circular, product)

🎯 Quick Reference

Complexity Hierarchy

O(1) < O(log n) < O(n) < O(n log n) < O(n²) < O(2ⁿ)

Performance Guide

Input Size	O(log n)	O(n)	O(n log n)	O(n²)
1,000	✅	✅	✅	⚠️
10,000	✅	✅	✅	❌
100,000+	✅	✅	✅	❌

💡 Key Features

• Clean Code: Focused implementations without unnecessary complexity
• Step-by-step Visualizations: See algorithms in action
• Interview Variants: Important variations frequently asked
• Complexity Analysis: Theoretical and practical performance insights
• Real Examples: Practical applications and use cases