cookiecutter-data-science

Why we included this project

Cookiecutter Data Science generates a standard project skeleton from a single command, so teams stop rebuilding the same folder layout for every new analysis. The structure it produces separates raw, interim, and processed data, gives models, notebooks, and source code their own directories, and ships a Makefile with ready-made targets like make data and make train. A newcomer can find where inputs, outputs, and code live without asking, which makes handoffs smoother and keeps results reproducible from one experiment to the next. It is flexible enough that you can bend the layout when a particular workflow demands it, and that mix of consistency and adaptability is what saves time for data scientists and small analytics groups juggling many short-lived projects. If you are tired of starting every repository from a blank page, this is a low-friction way to standardize how your team shares data science work.