Cookiecutter Data Science GDS

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

Project homepage

Acknowledgment

This project builds on drivendata's cookiecutter-data-science project template #cookiecutterdatascience

Requirements to use the cookiecutter template:

Python 3.6 or above
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

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

To start a new project, run:

cookiecutter https://github.com/ukgovdatascience/cookiecutter-data-science-gds

The resulting directory structure

The directory structure of your new project looks like this:

├── LICENSE
│
├── Makefile                <- Makefile with commands like `make data` or `make train`
│
├── README.md               <- The top-level README for developers using this project.
│
├── CONTRIBUTING.md         <- Guide to how potential contributors can help with your project
│
├── .env                    <- Where to declare individual user environment variables
│
├── .gitignore              <- Files and directories to be ignored by git
│
├── test_environment.py     <- Python environment tester
│
├── 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
│   └── pull_request_template.md    <- Pull request template
│
├── 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`.
│
├── references               <- AQA plan, Assumptions log, data dictionaries, and all other explanatory materials
│   ├── aqa_plan.md          <- AQA plan for the project
│   └── assumptions_log.md   <- where to log key assumptions to data / models / analyses
│
├── reports                  <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures              <- Generated graphics and figures to be used in reporting
│
├── 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
│
├── src                      <- Source code for use in this project.
    ├── __init__.py          <- Makes src a Python module
    │
    ├── make_data            <- Scripts to download or generate data
    │
    ├── make_features        <- Scripts to turn raw data into features for modeling
    │
    ├── make_models          <- Scripts to train models and then use trained models to make predictions
    │
    ├── make_visualisations  <- Scripts to create exploratory and results oriented visualizations
    │
    └── tools                <- Any helper scripts go here
      
--------

<p><small>Project based on the <a target="_blank" href="https://drivendata.github.io/cookiecutter-data-science/">cookiecutter data science project template</a>. #cookiecutterdatascience</small></p>

Installing development requirements

pip install -r requirements.txt

Running the tests

py.test tests