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Contributing to Practical Data Science Course

We welcome contributions to make this course better! Whether you're fixing bugs, improving documentation, adding new content, or suggesting improvements, your help is appreciated.

How to Contribute

1. Fork and Clone

  1. Fork this repository
  2. Clone your fork locally: 
    git clone https://github.com/YOUR-USERNAME/Practical-Data-Science-Course.git
cd Practical-Data-Science-Course

2. Set up Development Environment

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

# Install dependencies
pip install -r requirements.txt

# Install pre-commit hooks
pre-commit install

3. Make Your Changes

  • Create a new branch for your feature/fix
  • Make your changes following our guidelines below
  • Test your changes thoroughly

4. Submit a Pull Request

  1. Push your changes to your fork
  2. Create a pull request with a clear description
  3. Reference any related issues

Contribution Guidelines

Code Style

  • Follow PEP 8 for Python code
  • Use meaningful variable and function names
  • Add docstrings to functions and classes
  • Keep lines under 88 characters (Black formatter)

Jupyter Notebooks

  • Clear cell outputs before committing
  • Use markdown cells for explanations
  • Include cell numbers for easy reference
  • Test all cells execute without errors

Documentation

  • Use clear, concise language
  • Include code examples where helpful
  • Update README files when adding new content
  • Ensure all links work correctly

Content Guidelines

  • Make content accessible to beginners
  • Include practical examples and projects
  • Provide real-world applications
  • Cite sources and references

Types of Contributions

🐛 Bug Reports

  • Use the issue template
  • Include steps to reproduce
  • Provide expected vs. actual behavior
  • Include environment details

💡 Feature Requests

  • Describe the problem you're solving
  • Explain why this feature would be valuable
  • Provide implementation suggestions if possible

📖 Content Improvements

  • Fix typos or grammatical errors
  • Improve explanations or examples
  • Add missing topics or concepts
  • Update outdated information

🔧 Code Contributions

  • Bug fixes
  • New features or algorithms
  • Performance improvements
  • Code refactoring

📊 Dataset Contributions

  • Add new interesting datasets
  • Improve existing dataset documentation
  • Create synthetic datasets for examples

Review Process

  1. Automated Checks: Your PR will run automated tests
  2. Content Review: Maintainers will review for accuracy and clarity
  3. Educational Value: We assess the learning value of contributions
  4. Integration: We ensure contributions fit well with existing content

Recognition

Contributors will be acknowledged in:

  • README.md contributors section
  • Individual topic acknowledgments
  • Course credits

Questions?

Code of Conduct

We are committed to providing a welcoming and inclusive environment. Please:

  • Be respectful and professional
  • Focus on constructive feedback
  • Help create a positive learning environment
  • Report any inappropriate behavior

Thank you for contributing to the Practical Data Science Course! 🎓