Computer Science

CIS Undergraduates Compete at 2016 DataFest

"DataFest Oregon is a celebration of data in which teams of undergraduates work around the clock to find and share meaning in a large, rich, and complex data set.

The American Statistical Association’s Datafest is a nationally coordinated data analysis competition that brings together the data science community. It was held at Oregon State University on April 15-17, 2016, and featured nine teams of 37 students and faculty from OSU, University of Oregon and Reed College.

The competitions kicked off at 7:30 pm Friday, April 15 in Weniger Hall and continued through Sunday, April 17 at 4 pm. Students came prepared with laptops locked and loaded with data analysis software and tools.

Undergraduate and graduate students did the work, working under pressure as part of a team and examined their own critical thinking processes, with assistance from a cadre of roving consultants, including graduate students, faculty, and industry professionals.

After two days of intense data wrangling, analysis, and presentation design, each team developed a mere two presentation slides in just a few minutes in hopes of impressing the panel of judges." To read more, visit DataFest Oregon Delivers Mind-Numbing Fun.

Students from the UO Computer and Information Science Department were represented among the competitors at this year's DataFest. Team Segfault 11 was comprised of four first-year CIS students: Edward Szczepanski, Joseph Livni, Howard Lin, and Joseph Yaconelli.

"Going into to the DataFest competition we didn’t know what to expect", write Szczepanski, "but we were eager to leverage our growing knowledge of Computer Science." The teams were given multiple pieces of real-word data that were each around a gigabyte large. The weekend was spent parsing through the data using various tools, hypothesizing what information was important, and creating data visualizations.

One example of a challenge set was D3.js and the Google Maps API were used to visualize data in the form of a heat map of the United States. Students then took this information and cross-referenced it with statistics from Google Trends to get a sense of the magnitude of certain events. Szczepanski added, "We wanted to delve deeper, so we utilized a custom Twitter scraper and IBM’s AlchemyAPI to gain further insight. Afterwards we drew conclusions and assembled the data in a way that other’s would understand."