One of the first classes a student takes in Georgia Tech’s interdisciplinary Master of Science in Analytics (MSA) program is an introductory overview of data science and analytics modeling, taught by Joel Sokol, MSA founding executive director. The MSA student experience is bookended with a final class also overseen by Sokol: the Applied Analytics Practicum. The practicum provides students with the opportunity to solve a real-world data science/analytics problem presented by a company or organization.

In 2013, Sokol, who is also Harold E. Smalley Professor in the Institute's No. 1-ranked Stewart School of Industrial and Systems Engineering (ISyE), was perhaps best known for his LRMC model (created with Paul Kvam), which more accurately predicts NCAA March Madness winners. Seeing that data science and analytics were becoming increasingly important across a breadth of industries, Sokol began developing the curriculum for what would become one of the first master's degrees in the field.

“We started talking to colleagues in the other colleges about supplying courses for the MSA program, and we quickly realized that it made sense to do it as an interdisciplinary degree,” Sokol explains. “Georgia Tech is so highly ranked in these areas—engineering, business, and computer science—it made sense to leverage those strengths.”

Students focus on one or more study tracks—analytical tools * (which Sokol also directs), business analytics, or computational data analytics—and take a combination of classes from ISyE, the Scheller College of Business, and the College of Computing. To customize their coursework, they can also choose from over 100 electives across the Institute.

“Employers regularly tell us the interdisciplinary quality of the MSA program sets our students apart,” Sokol says. “They appreciate how the students can bring different perspectives to the work of data science and analytics, rather than a single viewpoint.”

When asked why he’s personally passionate about data analytics, Sokol—who continues working in applied analytics and operations research in sports and medicine while also directing the MSA program—says, “A lot of the ideas in data science and analytics have been around for decades, but only recently have computing power and sufficient data both been available to address some of the toughest applied questions—so between theory and application, this is an exciting time.

“I’ve always enjoyed this sort of analysis,” he concludes, “and I think most people in the field would probably say the same.”