Cookiecutter Data Science

A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.

Project homepage

Requirements to use the cookiecutter template:

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• pyenv - manage python versions
• pipenv - manage python dependencies
• Cookiecutter Python package >= 1.4.0
• dvc - data version control and s3 syncing

To install on mac you can use homebrew:

$ brew upgrade
$ brew install pyenv
$ brew install pipenv
$ pip install cookiecutter
$ pip install dvc

To start a new project, run:

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cookiecutter https://github.com/zegocover/cookiecutter-zego-data-science

The resulting directory structure

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The directory structure of your new project looks like this:

    ├── Makefile           <- Makefile with commands 
    ├── README.md          <- The top-level README for developers using this project.
    ├── data
    │   ├── external       <- Data from third party sources.
    │   ├── interim        <- Intermediate data that has been transformed.
    │   ├── processed      <- The final, canonical data sets for modeling.
    │   └── raw            <- The original, immutable data dump.
    │
    ├── docs               <- A default Sphinx project; see sphinx-doc.org for details
    │
    ├── dvc                <- Contains dvc files specifying pipeline steps
    │
    ├── models             <- Trained and serialized models, model predictions, or model summaries
    │
    ├── notebooks          <- Jupyter notebooks. Naming convention is (separated by '_') 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.
    │
    ├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
    │   └── figures        <- Generated graphics and figures to be used in reporting
    │
    ├── Pipfile            <- The requirements file for reproducing the analysis environment using pipenv
    │
    ├── setup.py           <- makes project pip installable (pipenv install -e .) so src can be imported
    └── src                <- Source code for use in this project.
        ├── __init__.py    <- Makes src a Python module
        │
        ├── data           <- Scripts to download or generate data
        │   └── make_dataset.py
        │
        ├── features       <- Scripts to turn raw data into features for modeling
        │   └── build_features.py
        │
        ├── models         <- Scripts to train models and then use trained models to make
        │   │                 predictions
        │   ├── predict_model.py
        │   └── train_model.py
        │
        └── visualization  <- Scripts to create exploratory and results oriented visualizations
            └── visualize.py