Data Science Project Template

Minimal template repository for Data Science Projects.

Instructions:

• Fork this repo.
• Rename the src directory to the name you wish to give to this project's python package
• Run a "search and replace all" over the entire project from src to the python package name chosen (make sure setup.py was updated).
• Rename the environment name from "dev" to the desired name in the environment.yml file
• Go through the installation section below to create an appropriate environment with all the necessary python dependencies installed.
• Replace this section and the title with something minigful to your project.

Installation

Install the conda environment by running:

conda env create -f environment.yml

alternatively if you wish to update an existing one simply run

conda env update --file environment.yml --prune

Usage

Running the data processing pipeline

python -m src.data.make_dataset

...

Project Organization

---

├── LICENSE
├── 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
│
├── environment.yml    <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `conda env export --from-history > environment.yml`
│
├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
└── src                <- Source code for use in this project.
    ├── __init__.py    <- Makes src a Python module
    │
    ├── utils           <- Project wide utility code
    │   └── configs.py
    │
    ├── 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

---

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