Data Project Template

Cookiecutter Data Science

This project template is a simplified version of the Cookiecutter Data Science template, created to suit the needs of Datalumina and made available as a GitHub template.

Adjusting .gitignore

Ensure you adjust the .gitignore file according to your project needs. For example, since this is a template, the /data/ folder is commented out and data will not be exlucded from source control:

# exclude data from source control by default
# /data/

Typically, you want to exclude this folder if it contains either sensitive data that you do not want to add to version control or large files.

Duplicating the .env File

To set up your environment variables, you need to duplicate the .env.example file and rename it to .env. You can do this manually or using the following terminal command:

cp .env.example .env # Linux, macOS, Git Bash, WSL
copy .env.example .env # Windows Command Prompt

This command creates a copy of .env.example and names it .env, allowing you to configure your environment variables specific to your setup.

Project Organization

├── LICENSE            <- Open-source license if one is chosen
├── 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
│   └── raw            <- The original, immutable data dump
│
├── 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         <- 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
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
└── src                         <- Source code for this project
    │
    ├── __init__.py             <- Makes src a Python module
    │
    ├── config.py               <- Store useful variables and configuration
    │
    ├── dataset.py              <- Scripts to download or generate data
    │
    ├── features.py             <- Code to create features for modeling
    │
    │    
    ├── modeling                
    │   ├── __init__.py 
    │   ├── predict.py          <- Code to run model inference with trained models          
    │   └── train.py            <- Code to train models
    │
    ├── plots.py                <- Code to create visualizations 
    │
    └── services                <- Service classes to connect with external platforms, tools, or APIs
        └── __init__.py 

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