Cookiecutter Simple Data Science

A simple project structure for doing and sharing data science work.

The easy way to start a data science:

• pet project
• competition
• homework
• etc

Requirements to use the cookiecutter template:

• Python 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:

cookiecutter https://github.com/mitrofanov-m/cookiecutter-simple-data-science

The resulting directory structure

The directory structure of your new project looks like this:

├── LICENSE
├── README.md          <- The top-level README for developers using this project.
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`.
│
├── setup.py           <- makes project pip installable (pip install -e .) so src can be
│                         imported.
│
├── 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.
│
├── models             <- Trained and serialized models, model predictions, or model summaries.
│
├── 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`.
│
├── misc               <- Miscellaneous files: figures, docker files, additional markdown files, etc.
│
└── src                <- Source code for use in this project. The written name of the project
   │                      will be used.
   ├── __init__.py     <- Makes src a Python module.
   │
   ├── data            <- Module to download, generate data or turn raw data into features
   │   │                  for modeling.
   │   ├── make_dataset.py
   │   └── build_features.py
   │
   ├── models          <- Module to train models and then use trained models to make
   │   │                  predictions.
   │   └── baseline.py
   │
   └── visualization   <- Scripts to create exploratory and results oriented visualizations.
       └── visualize.py

Contributing

We welcome contributions!

Installing development requirements

pip install -r requirements.txt