Wooster sends two teams to virtual Datafest

Students develop data plan that places third in regional competition

April 29, 2021   /  

Two teams of College of Wooster students competed in this year’s DataFest competition, applying skills in research, data analysis, and presentation to a set of data that could be used by the client. Judges representing the industry placed the “Fighting Scots” team third out of 17 teams from universities in the region, and while the competition only names the top three competitors, all the students who competed took away skills they’ll be able to apply as data-analysts, researchers, and critical thinkers in their chosen fields.

DataFest is a program of the American Statistical Association that takes place at sites across the country in April and early May, in which “teams of undergraduates work around the clock to find and share meaning in a large, rich, and complex data set.” This was the third time that Wooster participated in the event through Miami University. Jillian Morrison, assistant professor of statistical and data sciences at Wooster acted as a faculty mentor for the two teams, the Fighting Scots and ZEMY. “Students had the chance to work with a real dataset under lots of pressure to come up with a solution to a very complicated, unsolved problem,” said Morrison. “They also got the opportunity to chat with experts in the industry to pitch their ideas and hear critiques from people who use data to solve pressing problems daily.” The students (listed by team below) presented their solutions to a panel of judges, including representatives from the client organization.

Fighting-scots-datafest21
Two teams of College of Wooster students competed in this year’s DataFest competition.

Fighting Scots (pictured above)

  • Brett Stern ’21, mathematics and business economics major from Livonia, Michigan (bottom corner, team leader)
  • Demetris Papaloucas ’23, statistical and data sciences major from Nicosia, Cyprus (bottom left)
  • Dillon Wheeler ’22, statistical and data sciences major from Chicago, Illinois (top left)
  • Quan Nguyen Hien ’22, statistical and data sciences major from Hanoi, Vietnam (top right)
  • Vinh Nghiem To ’22, statistical and data sciences major from Ho Chi Minh, Vietnam (bottom right)
ZEMY-datafest21
Two teams of College of Wooster students competed in the 2021 DataFest competition

ZEMY (pictured above, left to right)

  • Matt Ulishney ’23, mathematics major from Greensburg, Pennsylvania
  • El Yazid Chalabi ’22, statistical and data sciences and business economics major from Rabat, Morocco
  • Zhen Guo ’21, computer science and sociology major from Beijing, China
  • Merlin Li ’21, biochemistry and molecular biology major from Zhengzhou, China

With the competition being held online, the teams adapted to the virtual format by maintaining social distance when meeting or arranging their schedules to accommodate team members who were studying and participating remotely from different time zones. As a mentor for the competition, Morrison met with both teams remotely as they analyzed and discussed the data. “They worked with their strengths to come up with their solutions,” she said. “After having many of them in data science courses, it was very fun to see them think through the problem and dataset using skills they learned in my courses.”

For the students, participating in the event offered an opportunity to work together on a real-world solution. “Attempting to identify a unique research question and insights with my team under the time constraint made Datafest so enjoyable,” said Stern who led the Fighting Scots team. In addition to time management, he sees “learning to find insights that are actionable” and “being able to conduct analysis that leads to actions” as skills he’ll carry forward in a career in analytics. For Chalabi, who served as leader for team ZEMY, participating in the competition allowed him to see different perspectives. He explained that working with the team, they divided the data in a way he hadn’t thought about before that he can apply down the road. “We divided the data into categories and focused on each one separately, then we analyzed what impacted the trend in that group,” he said. “That is for me, revolutionary.”