Well structured and tested data science project template. You can use this template when creating the data sicence repository.
📁 Organized: The project structure is refereed to Cookiecutter Data Science
🚀 Prepared: Major libraries are prepared in environment.yml
. If you are familiar with Colaboratory environment, please use environment-colab.yml
.
✅ Tested: scripts
are checked by common linter when pre-commit.
Here is the notebook link to provide the quick access to your analysis. You can create the conda environment by Right click Build Conda Environment
or conda create -f environment.yml
in Studio Lab.
.
├── data
│ ├── external # data from third party sources.
│ ├── processed # data after processing
│ ├── interim # data that transformed
│ └── raw # raw data
├── models # store models
├── notebooks # store notebooks
├── docs # documentation for your project
├── .gitignore # ignore files that cannot commit to Git
├── .pre-commit-config.yaml # configurations for pre-commit
├── pyproject.toml # dependencies for poetry
├── README.md # describe your project
├── scripts # store source code used in notebook
│ └── __init__.py # make src a Python module
└── tests # store tests
└── __init__.py # make tests a Python module
environment.yml
: Please specify the packages and versions. As a default, no version is specified..pre-commit-config.yaml
: Please check therev
to check the code.- Change the Notebook url for
Open in Studio Lab
.