Description

This is a simple organization templated to be used for multiple Data Science projects, it is a simplified and modified folder structure from the Cookiecutter Data Science Repository.

How to use

It is suggested to use some form to organize the installation of packages and such, my recomendation is to use a conda manager and create one enviroment for each project.

conda create --name ENV_NAME python=3.XX

Then activate and install the requirements.

conda env ENV_NAME
pip install -r requirements.txt

Creating the enviroment and installing the requirements must be done only the first time.

Folder structure

├── 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. (optional)
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
|
├── 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`.
│
├── other-programs     <- For saving files from other programs related to the project (QGIS, BI, etc...)
│   ├── subfolder      <- Program A
│   ├── ....           <- ....
|
├── 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 use in this project.