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, stakeholders, 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.
Austin is a Data Scientist in IBM, who focuses on the balance of bleeding-edge research produced by academia and the tools used in applied data science.