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A simple data science project template
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README.md

DataBake - A Simple Data Science Project Template

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DataBake is a simple data science project template based on the Cookiecutter templating engine and heavily inspired by the excellent Cookiecutter Data Science.

Assumptions

This project template makes certain assumptions. These are:

  • Your data science project uses Python.
  • You're working in a *nix environment. Cookiecutter will give you project structure in Windows, but some features of this template may not work.

Requirements

Usage

To start a new project, run:

cookiecutter https://github.com/smoothml/data-bake

You will be asked to provide the variables described below.

Variable Description
project_name Name of your project.
repo_name Name of your repository. Defaults to lower case project_name with spaces and underscores replaced with hyphens.
package_name Name of your source code package. Defaults to repo_name with underscores instead of hyphens.
author_name Your name, or name of your project or organization.
description A short description of your project.
dvc_remote_type Remote location for DVC to use for data storage.
license A choice of several open source licenses. Choose "None" if your project is not open source.

The resulting project structure is:

├ Makefile              <- Makefile with helpful make commands.
├ README.md             <- Top-level README for project developers.
├ LICENSE               <- License file (unless no license was specified).
├ .env                  <- Secrets. DO NOT SOURCE CONTROL!
├ .gitignore            <- Files to ignore.
├ pytest.ini            <- PyTest configuration.
├ setup.cfg             <- Project configuration.
├ data
│   ├ external          <- Data from external sources.
│   ├ interim           <- Intermediate, transformed data.
│   ├ processed         <- Final, canonical data sets from modelling.
│   ├ raw               <- Original, immutable raw data sets.
│   ├ results           <- Results of modelling and analysis.
│   └ resources         <- Useful resources (e.g. relevant papers).
├ models                <- Trained and serialized models, model predictions, or model summaries.
├ notebooks             <- Jupyter notebooks. Suggested naming convention is a of the form
│                          <step>.<version>-<initials>-<description>.ipynb
│                          For example 01.0-PH-really-interesting-analysis.ipynb.
├ outputs               <- Generated outputs, such as figures or reports.
├ requirements.txt      <- Python requirements file for reproducing the analysis environment. 
└ src                   <- Source code for use in the project.
    ├ package_name
    │   └ __init__.py
    ├ tests
    │   └ __init__.py
    └ setup.py
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