Data Science Project Template
This repo holds a template data science project
Installation Instructions
# 1. Clone this repository
git clone git@github.com:<USERNAME>/datasci <NEWPROJ>
# or clone with -b <BRANCH> if you have more then one template
# 2. We will have a new "origin", rename this to "upstream"
# so we can still pull updates to the template, disable push
git remote rename origin upstream
git remote set-url --push upstream no_push
# 3. Create a <NEWPROJ> repository on GitHub
# 4. Set <NEWPROJ> as the origin and push the code
git remote add origin git@github.com:<USERNAME>/<NEWPROJ>.git
git push -u origin master
Template Directory Structure
├── LICENSE
├── Makefile <- Makefile with commands like make data or make train
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ ├── refdata <- Data dictionaries
│ └── raw <- The original, immutable data dump.
│
├── docs <- A default Sphinx project; see sphinx-doc.org for details
│
├── 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 <- Articles, manuals, and all other explanatory materials.
│
├── 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 <- Make this project pip installable with pip install -e
├── src <- Source code for use in this project.
│ ├── init.py <- Makes src 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
│ │
│ └── visualization <- Scripts to create exploratory and results oriented visualizations
│ └── visualize.py
│
└── venv <- directory to hold the virtualenv
Sources
https://www.kdnuggets.com/2018/07/cookiecutter-data-science-organize-data-project.html https://medium.com/@smrgrace/having-a-git-repo-that-is-a-template-for-new-projects-148079b7f178