Virtual Meetup: Explainable Workflows using Python

• 5:45pm: Doors open
• 6:05pm: Welcome, announcements
• 6:15pm: Main talk + Q&A
• 7pm: Stick around for socializing
• 7:30pm: Doors close

Main talk
This talk approaches the typical data science workflow with a focus on explainability. Simply put, it focuses on skills and tactics used to help data scientists articulate their findings to end-users, stake-holders, and other data scientists. From data ingestion, cleaning and feature selection, and ultimately model selection, explainability can be incorporated into a data scientists workflow. Using a combination of semi-automated and open source software, this talk walks you through an explainable workflow.

About the presenter
Austin is a Data Scientist on the Technical Marketing and Evangelism team in San Francisco, California. As a recent graduate student of Florida State University, Austin is focused on the balance of bleeding-edge research produced by academia and the tools used in applied data science. His Masters thesis was on White Collar Crime using Time-aware Joint-Topic-Sentiment Analysis (TTS), and his areas of interests are NLP, applied data science, and Explainable AI. Austin currently resides in San Francisco, with his fiancé, dog, and two cats.