Data Structures and Algorithms Study Repository

Welcome to the Data Structures and Algorithms Study Repository! This project serves as a comprehensive collection of my studies and practice with various data structures and algorithms. The goal is to deepen understanding, improve problem-solving skills, and prepare for technical interviews and real-world applications.

Table of Contents

• Introduction
• Technologies Used
• Project Structure
• Data Structures Covered
• Algorithms Covered
• How to Use
• Contributing
• License

Introduction

This repository is a personal study guide for mastering data structures and algorithms. It includes implementations, explanations, and examples of various concepts. The code is primarily written in [Primary Programming Language(s)], and each topic is documented with comments and explanations.

Technologies Used

• Programming Languages: [List of languages used, e.g., Python, JavaScript]

Project Structure

├── data_structures/
│   ├── arrays/
│   ├── linked_lists/
│   ├── stacks/
│   ├── queues/
│   ├── trees/
│   ├── graphs/
│   └── hash_tables/
├── algorithms/
│   ├── sorting/
│   ├── searching/
│   ├── dynamic_programming/
│   ├── greedy_algorithms/
│   ├── graph_algorithms/
│   └── backtracking/
├── tests/
│   ├── test_arrays.py
│   ├── test_linked_lists.py
│   └── ...
├── docs/
│   ├── arrays.md
│   ├── linked_lists.md
│   └── ...
└── README.md

Data Structures Covered

• Arrays
• Linked Lists
• Stacks
• Queues
• Trees
• Graphs
• Hash Tables

Algorithms Covered

• Sorting Algorithms: Bubble Sort, Quick Sort, Merge Sort, etc.
• Searching Algorithms: Binary Search, Linear Search, etc.
• Dynamic Programming: Fibonacci, Knapsack, etc.
• Greedy Algorithms: Huffman Coding, Prim's Algorithm, etc.
• Graph Algorithms: Dijkstra's Algorithm, Depth-First Search, etc.
• Backtracking: N-Queens, Sudoku Solver, etc.

How to Use

1. Clone the repository:

   git clone https://github.com/brunousml/data-structures-and-algorithms.git

2. Navigate to the project directory:

   cd data-structures-and-algorithms

3. Run the code: Depending on the language, use the appropriate command to run the scripts, e.g., python file_name.py for Python.

4. Run tests:

   # Example using pytest for Python
   pytest tests/