Virtual Meetup: Explainable Workflows using Python

When:  Jul 9, 2020 from 6:00 PM to 7:30 PM (PT)
Our virtual meetups aim to bring you great data science and AI content while also providing you with the opportunity to meet other members of the community and make new connections.

Agenda

  • 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, explainab
ility 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.
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Location

Contact

Christina Howell

chowell@us.ibm.com