{{ cookiecutter.project_name }}

{{ cookiecutter.description }}

This Cookiecutter template is designed to help you quickly start a new data science project. It provides a flexible and adaptable structure suitable for various types of data science projects, including general-purpose projects, computer vision, NLP, time series analysis, and more.

Table of Contents

• {{ cookiecutter.project_name }}
  • Table of Contents
  • Features
  • Requirements
  • Installation
    • Install Cookiecutter
  • Usage
    • Generate a New Project
    • Navigate to Your Project
    • Set Up the Environment and Dependencies
    • Run Makefile Commands
  • Project Structure
  • Customization Options
  • Contributing
  • License

Features

• Flexible Project Structure: Supports various data science domains (e.g., NLP, computer vision, time series).
• Modular Code Organization: Easily extendable and maintainable codebase.
• Integrated Testing: Includes unit testing with pytest.
• Automation with Makefile: Automate tasks like environment setup, testing, and running models.
• Optional SonarQube Integration: For code quality and security analysis.

Requirements

To use this Cookiecutter template, you need the following installed:

• Python >= {{ cookiecutter.python_version }}
• Cookiecutter >= 2.2.0
• Conda (optional, for environment management)
• pipx (optional, for isolated package installation)

Installation

Install Cookiecutter

pip install cookiecutter

Or using pipx:

pipx install cookiecutter

Usage

Generate a New Project

Run the following command to generate a new project using this template:

cookiecutter [email protected]:simonherlin/cookiecutter-datascience.git

You will be prompted to enter various details like project_name, author_name, description, and more.

Navigate to Your Project

After the project is generated, navigate to your new project directory:

cd {{ cookiecutter.project_name }}

Set Up the Environment and Dependencies

Use the Makefile to automate the environment setup and dependency installation:

make install

Run Makefile Commands

Other tasks can also be automated using the Makefile:

• Run tests: make test
• Lint project: make lint
• Use sonarqube: make sonar
• Read the documentation for more information

Project Structure

The generated project structure is designed to be adaptable to various types of data science projects:

{{ cookiecutter.project_name }}/
├── data/
├── notebooks/
├── docs/
├── src/
├── config/
├── scripts/
├── reports/
├── logs/
├── environment.yml
├── requirements.txt
├── README.md
└── Makefile

Customization Options

During project generation, you can customize various aspects of the project:

• project_name: The name of your project.
• author_name: Your name or your team’s name.
• description: A brief description of the project.
• project_type: Choose from "General", "Computer Vision", "NLP", "Time Series".
• use_sonarqube: Optionally integrate with SonarQube for code quality analysis.

Contributing

Contributions are welcome! If you find a bug or have a feature request, please open an issue or submit a pull request.

License

This project is licensed under the {{ cookiecutter.license }} License.