kikejimenez / cookiecutter-docker-data-science

Cookiecutter & Docker data science

Cookiecutter Data Science & Docker

Simplified version of Cookiecutter Data Science that includes Docker Jupyter Image.

Requirements to use the cookiecutter template:

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• Python 3.7
• Docker
• Cookiecutter Python package >= 1.4.0: This can be installed with pip by or conda depending on how you manage your Python packages:

$ pip install cookiecutter

To start a new project, run:

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cookiecutter https://github.com/kikejimenez/cookiecutter-docker-data-science

The resulting directory structure

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The directory structure of your new project looks like this:

├── MIT LICENSE
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
├── presentation       <- Presentation slides
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, 
│
└── src                <- Source code for use in this project.
    ├── __init__.py    <- Makes src a Python module

To start Jupyter Lab:

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• Enter command 'docker-compose up'

Credits

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Enrique Jimenez