Presenting Predictive Modeling Results in the Real World
There often seems to be a disconnect between data scientists and business users “in the real world”. Data Scientists - especially those transitioning into the industry from academia or software engineering backgrounds - mention having difficulty receiving buy-in from stakeholders, and being surprised when the recipients of the models aren’t as impressed with their results as other developers and data scientists are. Stakeholders on the business side express difficulty either explaining to data scientists what they want to get out of a predictive modeling project or understanding what they can do with a model once it’s built.
In this presentation, I’ll provide tips for bridging that divide, based on my experience as a Data Scientist at HelioCampus working with clients in University Administration and Institutional Research.
There often seems to be a disconnect between data scientists and business users “in the real world”. Data Scientists – especially those transitioning into the industry from academia or software engineering backgrounds – mention having difficulty receiving buy-in from stakeholders, and being surprised when the recipients of the models aren’t as impressed with their results as other developers and data scientists are. Stakeholders on the business side express difficulty either explaining to data scientists what they want to get out of a predictive modeling project or understanding what they can do with a model once it’s built.
In this presentation, I’ll provide tips for bridging that divide, based on my experience as a Data Scientist at HelioCampus working with clients in University Administration and Institutional Research.
