Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/badr-moufad/cookiecutter-simple-ds-project
A simple cookiecutter template to structure your Data Science projects.
https://github.com/badr-moufad/cookiecutter-simple-ds-project
cookiecutter data-science project-structure python simple-ds-project
Last synced: 2 months ago
JSON representation
A simple cookiecutter template to structure your Data Science projects.
- Host: GitHub
- URL: https://github.com/badr-moufad/cookiecutter-simple-ds-project
- Owner: Badr-MOUFAD
- License: mit
- Created: 2021-10-07T18:59:06.000Z (about 3 years ago)
- Default Branch: master
- Last Pushed: 2021-12-05T16:58:02.000Z (about 3 years ago)
- Last Synced: 2024-01-28T23:12:42.901Z (11 months ago)
- Topics: cookiecutter, data-science, project-structure, python, simple-ds-project
- Language: Jupyter Notebook
- Homepage: https://towardsdatascience.com/its-time-to-structure-your-data-science-project-1fa064fbe46
- Size: 12.7 KB
- Stars: 6
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# cookiecutter Simple DS project
A simple template project to stucture your Data Science projects.
This template is inspired by "cookiecutter data science" to provide an entry-level structure to organize data science projects.
Check my article ["It's time to structure your data science project"](https://towardsdatascience.com/its-time-to-structure-your-data-science-project-1fa064fbe46) to find out more about "Simple DS project", namely the motivation behind it as well as the purpose of every component of its structure.
## Getting started
to get started,
1. First, install cookiecutter by running
```
pip install cookiecutter
```
2. Then execute the following command to generate the project structure
```
cookiecutter https://github.com/Badr-MOUFAD/cookiecutter-simple-DS-project.git
```
## Template structure
After executing the previous commands, the project structure will be generated on your local machine.
The project directory contains the following directory tree
```
|
├── data <- where to put and save
| |── dataset_1.csv your data
| |
│
├── notebooks_exploration_cleaning <- notebooks related to data
| |── exploration_1.py exploration and cleaning
| |
│
├── notebooks_models <- notebooks to train
| |── model_1.py and evaluate models
| |
│
├── py_scripts <- where to put repetitive code
│ ├── __init__.py
| |
| |── script_1.py
| |
│
├── README.md <- where to describe your project
|
├── .gitignore <- where to mention folder and files
| to not to be synchronized
| with your remote repository
|
└── environment.yml <- project dependencies
(Anaconda environment)
```
## Note
This structure is not to be literally followed. You can adjust it and refine it based on your needs. Remember that the way you organize your work keeps evolving as you work on projects.