There are a lot of good resources out there for getting started with data science and machine learning, where you can walk through starting with a dataset and ending up with a model and set of predictions.  Think something like the homework for your favorite machine learning class, or your most recent online machine learning competition.  However, if you've ever tried to maintain a machine learning workflow (as opposed to building it from scratch), you know that taking a simple modeling script and turning it into clean, well-structured and maintainable software is way harder than most people give it credit for.  That said, if you're a professional data scientist (or want to be one), this is one of the most important skills you can develop.

In this episode, we'll walk through a workshop Katie is giving at the Open Data Science Conference in San Francisco in November 2017, which covers building a machine learning workflow that's more maintainable than a simple script.  If you'll be at ODSC, come say hi, and if you're not, here's a sneak preview!

Improving Upon a First-Draft Data Science Analysis

Linear Digressions