Well structured and tested data science project template. You can use this template when creating the data sicence repository.
π Organized: The project structure is refereed to Cookiecutter Data Science
π Prepared: Major libraries are prepared in environment.yml
. If you are familiar with Colaboratory environment, please use environment-colab.yml
.
β
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.
.
βββ data
β βββ external # data from third party sources.
β βββ processed # data after processing
β βββ interim # data that transformed
β βββ raw # raw data
βββ models # store models
βββ notebooks # store notebooks
βββ docs # documentation for your project
βββ .gitignore # ignore files that cannot commit to Git
βββ .pre-commit-config.yaml # configurations for pre-commit
βββ pyproject.toml # dependencies for poetry
βββ README.md # describe your project
βββ scripts # store source code used in notebook
β βββ __init__.py # make src a Python module
βββ tests # store tests
βββ __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
.