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📘 Computer Science – Algorithms Notebook

A personal Jupyter Notebook collection focused on algorithmic thinking, complexity analysis, and classic computer science algorithms.

🧠 About This Course

This repository contains my study notes, implementations, and experiments developed while taking the Computer Science course.

The main goal of this class is to strengthen:

  • Algorithmic thinking
  • Problem-solving skills
  • Understanding of computational complexity
  • Implementation of fundamental algorithms

All content is implemented and documented using Jupyter Notebooks to combine theory, code, and examples in a clear learning workflow.

🎯 Topics Covered

1. Algorithmic Thinking

  • Breaking problems into smaller steps
  • Designing logical procedures to solve tasks
  • Writing clear and efficient pseudocode

2. Complexity Analysis

  • Time complexity (Big-O notation)
  • Space complexity
  • Comparing algorithm efficiency
  • Optimizing naive solutions

3. Sorting Algorithms

Classic sorting techniques implemented and analyzed:

  • Bubble Sort
  • Selection Sort
  • Insertion Sort
  • Merge Sort
  • Quick Sort

Each notebook includes:

  • Step-by-step explanation
  • Python implementation
  • Complexity discussion
  • Example test cases

4. Mathematical & Recursive Algorithms

  • Fibonacci sequence generation
  • Euclidean algorithm for GCD
  • Recursive vs iterative solutions
  • Performance comparisons

5. Array & Search Problems

  • Finding indexes efficiently
  • Maximum pair product in arrays
  • Basic searching strategies
  • Optimization from O(n log n) to O(n) solutions

🛠️ Technologies Used

  • Python for algorithm implementation
  • Jupyter Notebook for interactive learning
  • GitHub for version control and documentation

📂 Repository Structure

computer-science/
│
├── notebooks/
├── practice/
├── workshops/
└── README.md