Welcome to the IBM Data Science Community! Our monthly virtual meetups are structured to combine great data science and AI content with the opportunity for community members to meet each other and make new connections in an informal setting.

Agenda

• 5:45pm (all times are Pacific Time): Doors open
• 6:05pm: Welcome, announcements
• 6:15pm: Main talk + Q&A
• 7pm: Stick around for socializing
• 7:30pm: Doors close

Explainable Workflows using Python

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 Eovito 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.