Cookiecutter Conda Data Science

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

Requirements

Conda
Cookiecutter Python package: This can be installed with pip by or conda depending on how you manage your Python packages:

bash
pip install cookiecutter

or

bash
conda install -c conda-forge cookiecutter

Create a new project

In a folder where you want your project generated:

cookiecutter https://github.com/jvelezmagic/cookiecutter-conda-data-science

Resulting directory structure

├── LICENSE ├── tasks.py <- Invoke with commands like notebook. ├── README.md <- The top-level README for developers using this project. ├── install.md <- Detailed instructions to set up 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. │ ├── 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 <- 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. │ ├── environment.yml <- The requirements file for reproducing the analysis environment. │ ├── .here <- File that will stop the search if none of the other criteria │ apply when searching head of project. │ ├── setup.py <- Makes project pip installable (pip install -e .) │ so {{ cookiecutter.projectmodulename }} can be imported. │ └── {{ cookiecutter.projectmodulename }} <- Source code for use in this project. ├── init.py <- Makes {{ cookiecutter.projectmodulename }} 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 │ ├── utils <- Scripts to help with common tasks. └── paths.py <- Helper functions to relative file referencing across project. │ └── visualization <- Scripts to create exploratory and results oriented visualizations. └── visualize.py