cookiecutter-data-science-min

Minimal cookiecutter template for data science projects

This is forked from https://github.com/drivendata/cookiecutter-data-science. The rationales of the Cookiecutter Data Science project are well explained at http://drivendata.github.io/cookiecutter-data-science/.

However, not all projects involve writing a new software module. When the activity of a researcher is centered on data analysis using existing tools, a minimal project template may be more suitable. This is what this template is for.

In these projects, all code should be in the Jupyter notebooks. If there's need for a new software module, the module should be its own software project and the data analysis project should import the said software module.

Requirements to use the cookiecutter template:

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• Python 2.7 or 3.5
• 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

or

$ conda config --add channels conda-forge
$ conda install cookiecutter

To start a new project, run:

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cookiecutter https://github.com/shuzhao-li/cookiecutter-data-science-min

cookiecutter will ask four questions:

    project_name [project_name]: 
    repo_name [project_name]: 
    author_name [Your name (or your organization/company/team)]: 
    description [A short description of the project.]: 

It's okay not to have a repo_name here.

The resulting directory structure

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The directory structure of your new project looks like this:

├── README.md
├── Reproducibility-checklist.txt
├── data
│   ├── external
│   ├── interim
│   ├── processed
│   └── raw
├── notebooks
├── references
└── reports
    └── figures

Contributing

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Contributions are welcome!

Acknowledgement

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Forked from https://github.com/drivendata/cookiecutter-data-science.

Inspired by:

http://drivendata.github.io/cookiecutter-data-science/

https://github.com/cookiecutter/cookiecutter