Organizations, big and small, are making significant investments in data science. New data hires are told to model anything and everything so that the organization might find a competitive edge. The problem is that few companies are investing enough in infrastructure or hiring the number of data engineers required to support modeling efforts. As a result, data scientists arrive on the scene and quickly burn out because they can’t build the models they want to build… because the pipelines don’t exist.

In this course, Max Humber will teach you how to build resilient pipelines with industry-leading tools. Specifically, this course will introduce Airflow (the open source standard for automating data pipeline workflows), Python Fire (a library for automatically generating command line interfaces), and scikit-learn/pandas (used for data wrangling). All so that you can get back to actual data science!