From an engineering perspective, building a minimum viable product is an opportunity to quickly test an idea and see if it works. It doesn't need to be the best solution, it should just be a usable solution. If it is, then we can move on to the development stage and …

The OSEMN (Obtain, Scrub, Explore, Model, Interpret) framework for data science has been widely adopted since Hilary Mason introduced the concept in 2010. This is by no means the only workflow out there -- I particularly like Ciara Byrne's Development Workflows for Data Scientists -- but they're all variations of the same …

Data Science MVP is a template to jumpstart a minimum viable product (MVP), or rough draft of a data science project.

It is implemented as a cookiecutter template that generates a directory tree containing subdirectories that hold data, notebooks, reports, source code, etc., with code stubs that present a logical …

Pardon the extremely click-baity blog title. There's a good reason for that, I promise. In the world of data science, MVP stands for Minimum Viable Product. It's a first draft of a data science project in which we've put together enough of our workflow to read in some data, put …