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Activity Number: 277
Type: Roundtables
Date/Time: Tuesday, August 2, 2016 : 7:00 AM to 8:15 AM
Sponsor: Section on Statistical Learning and Data Science
Abstract #320970
Title: Data Science: Bridging Academia and Industry
Author(s): Justin Dyer* and Donal McMahon
Companies: Google and Google
Keywords: data science ; education ; big data ; research ; technology ; finance

The last few years have seen an incredible growth in the collection and use of large-scale data sets in commercial settings and an attendant increase in interest in statistical methodology applied to these data. The term "data science" has cropped up to loosely describe this marriage of large-scale computing, Big Data, statistics, and computer science. In this roundtable, we will seek to discuss this rapidly changing area from a variety of perspectives with an emphasis on ways to enhance the (two-way) bridge between academia and industry. Topics will include some of the main challenges students experience in transitioning from academia to a data-science position in industry, differences in data-scientist roles across industries (e.g., technology, finance, etc.), the role of academia (primarily at the graduate level) in educating and preparing individuals for such roles, and strategies for strengthening research collaborations between academia and industry. Attendees are encouraged to bring to bear their experiences and opinions as professors, curriculum developers, students, researchers, and practitioners.

Authors who are presenting talks have a * after their name.

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