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Simplified data science project template

A template for data science projects. Simplified, based on datadriven.org's cookiecutter.

Project Organization

├── LICENSE
├── README.md                 <- The top-level README for developers using this project.
├── TODO.md                   <- A helpful TODO list for project related tasks.
├── .env                      <- contains usernames/passwords to access database, etc (file is ignored with .gitignore)
├── .env_template             <- template to make your own .env file
├── certificates              <- eg. ssl certificate needed for sql access (contents ignored with .gitignore)
├── data
│   ├── external              <- Data from third party sources.
│   ├── interim               <- Intermediate data that has been transformed.
│   ├── processed             <- The final, canonical data sets for modeling.
│   └── raw                   <- The original, immutable data dump.
│
├── notebooks                 <- Jupyter notebooks. Naming convention is a number (for ordering),
│                                and a short `_` delimited description, e.g.
│                                `01_initial_data_exploration`.
│
├── reports                   <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures               <- Generated graphics and figures to be used in reporting
│
├── environment.yml           <- The requirements file for reproducing the analysis environment, e.g.
│                                generated with `conda env export --no-builds -n Simplified data science project template > environment.yml`.
├── environment_simple.yml    <- A simpler version of the environment with only the packages directly 
│                                installed. Ideally generated through `conda env export --from-history -f environment_simple.yml`.
│
├── src                       <- Source code for use in this project.
│   ├── __init__.py           <- Makes src a Python module
│   ├── data                  <- Scripts to download or generate data
│   ├── utilities             <- Scripts or modules for miscellaneous functions
│   └── visualization         <- Scripts to create exploratory and results oriented visualizations
│
└── Anything else?

Contributing

Some guidelines should be observed consistently across all that contribute to this project, so that this simplified version of a data science structure is still viable.

  • Make sure your notebook works in the repository.

    • If you refer to files in notebooks/scripts, they have to exist in the repository - and not just your own computer. Ideally use the dotenv path system to refer to files in the repository.
    • Your scripts will need certain libraries to run. Make sure they are listed in the environment_simple.yml file. Some requirements are tricky to resolve due to version conflicts. A full environment export can be put on environment.yml. Make sure to built up on the previously established environment and/or test/update the rest of the notebooks/scripts accordingly.

  • Keep the formatting standard.

    • Use the black formatter to format your code.

  • Keep data in the structure presented.

    • Raw data should always be traceable to sources.
    • Interim data need not be well-tracked. (#TODO: Consider adding it to gitignore?)
    • Processed data should be easily regeneratable from raw data through scripts or notebooks.

Sources of external files:

Any data processed/generated outside this repository (and prone to change) should go under data/external. When new data file is added to external, the source should be catalogued here.

Requirements:

environment_simple.yml file lists all packages used/needed in scripts and notebooks. This environment can be created by conda using:

conda env create -f environment_simple.yml

Project based on the cookiecutter data science project template. #cookiecutterdatascience