Case Studies: X Never Marks the Spot/Feedback

Recorded On: 10/06/2020

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'X’ Never, Ever Marks the Spot: The Evolution of a Data Science Project

James Rogol, Assistant Director of Advancement Business Intelligence, University of Virginia

In many ways, the use of Machine Learning and AI are like quests for mythical riches. Commonly romanticized to depict a series of basic tasks promising knowledge or fortunes beyond one’s wildest aspirations, Data Science projects are rarely (if ever) straightforward endeavours. To quote a certain fictional archaeologist, “We do not follow maps to buried treasure.”

Previous work, shifting business priorities and technical limitations will inevitably transform the questions and goals underlying a data-driven investigation. More importantly, navigating such different challenges requires unique Data Science skillsets. Rooted in experiences diving into the University of Virginia’s data, learn how the interplay between data analysis, engineering, and statistical rigor can help Data Science projects succeed.

The Importance of Feedback in Data Science

Natalie Spring, Director, Prospect Research, Management, & Analytics, Duke University

This session is  a case study that demonstrates how creating an implementation strategy and iterating analytic tools creates stronger client-focused products. Natalie discusses methods of feedback, communication, and stages in implementation.

This session is part of the Data Science Now 2020 Bundle.

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Case Studies: X Never Marks the Spot/Feedback
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Open to view video. 'X’ Never, Ever Marks the Spot: The Evolution of a Data Science Project James Rogol, Assistant Director of Advancement Business Intelligence, University of Virginia In many ways, the use of Machine Learning and AI are like quests for mythical riches. Commonly romanticized to depict a series of basic tasks promising knowledge or fortunes beyond one’s wildest aspirations, Data Science projects are rarely (if ever) straightforward endeavours. To quote a certain fictional archaeologist, “We do not follow maps to buried treasure.” Previous work, shifting business priorities and technical limitations will inevitably transform the questions and goals underlying a data-driven investigation. More importantly, navigating such different challenges requires unique Data Science skillsets. Rooted in experiences diving into the University of Virginia’s data, learn how the interplay between data analysis, engineering, and statistical rigor can help Data Science projects succeed. The Importance of Feedback in Data Science Natalie Spring, Director, Prospect Research, Management, & Analytics, Duke University This session is a case study that demonstrates how creating an implementation strategy and iterating analytic tools creates stronger client-focused products. Natalie discusses methods of feedback, communication, and stages in implementation.
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James Rogol

Data Consultant

University of Virginia

James Rogol joined the University of Virginia’s Central Advancement Business Intelligence group in 2018. In partnership with the University’s multitude of schools, units and foundations, he tackles data science problems to better inform strategic decision making. A “Double Hoo,” he holds a bachelor of arts in Russian literature and language and a master of science in data science from UVA. Prior to joining the philanthropic sector, James worked as a videographer, video coordinator and performance analyst for teams at the University of California, University of Virginia and the United States Soccer Federation. A veteran of national and regional broadcasts of athletic events, he continues to freelance for the ACC Network.

Natalie Spring

Director, Prospect Research, Management, & Analytics

Duke University

Natalie Spring is the Director of Prospect Research, Management, and Analytics at Duke University. She is a trained quantitative social scientist who loves ethnography and checking other people’s numbers. She is a data storyteller who provides clears insights through beautifully accurate data.