A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.
- Python 2.7 or 3.5
- Cookiecutter Python package >= 1.4.0: This can be installed with pip by or conda depending on how you manage your Python packages:
$ pip install cookiecutteror
$ conda config --add channels conda-forge
$ conda install cookiecutter
The directory structure of your new project looks like this:
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
|
| Naming convention is a number (for ordering),
| date in the format DD_MM_YYYY,
| the creator's first-name,
| and a short `-` delimited description, e.g.
| `1.0-16_07_2020-maruti-linkedin-dump-v1`.
│
├── models <- Trained and serialized models, model predictions, or model summaries.
| Naming convention is a number (for ordering),
| date in the format DD_MM_YYYY,
| the creator's first-name,
| and a short `-` delimited description, e.g.
| `1.0-16_07_2020-maruti-initial-data-exploration`.
│
├── notebooks <- Jupyter notebooks.
| Naming convention is a number (for ordering),
│ the creator's first-name,
| and a short `-` delimited description, e.g.
│ `1.0-maruti-initial-data-exploration`.
├── tests
│ ├── integration <- Place for testing scripts for integration tests
│ ├── test_data <- Place to store data for integration tests
│ ├── unit <- Place for testing scripts for unittests
│
├── 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
│
├── 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
│
└── tox.ini <- tox file with settings for running tox; see tox.readthedocs.io
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