CHICAGO, May 18, 2017 /PRNewswire/ --  Teradata (NYSE:  TDC), the leading data and analytics company, today announced student winners from the Teradata and Northwestern University Hackathon at the school's McCormick Education Center held on May 2. The winning graduate students, Lauren Yu and Jessica Chan, will receive trip packages to the upcoming Teradata PARTNERS conference to present their findings to a global audience of data-driven professionals.

Teradata Corporation logo. (PRNewsFoto/Teradata)

For this year's Hackathon, the Northwestern University Master of Science in Analytics (MSiA) Program, one of the top data science programs, challenged 22 graduate student teams to put their data science skills to the test while solving real-world big analytics problems. With only a few hours of training, the students used Teradata Aster Analytics to compete, utilizing Teradata Multi-Genre Advanced Analytics™ techniques including pattern, text, prediction and graph analytics.

Hackathon winners Yu and Chan analyzed 500,000 Amazon reviews to pinpoint the characteristics that best predicted a review's "helpfulness." The team built a series of analytical models to break down a subset of the data consisting of all reviews that had at least five helpful notifications. They then developed a "helpfulness ratio" that allowed them to compare the impact of variables like word count and readability.  The results of their analytical exercise are detailed in the Northwestern University newsletter, included here .

"In this Hackathon, these students did an excellent job when tested with real-world challenges such as short deadlines, unfamiliar tools, and the need to apply soft skills such as creativity, collaboration, and communication.  You can complete a hackathon with coding skills, but to win the Northwestern Hackathon you need the full skill stack," said Roger Fried, senior data scientist on Teradata Aster's Advanced Strategy team.

"What I enjoyed about the winning presentations in this Hackathon was that, despite my familiarity with the datasets and the normal solutions, the students in this program put a fresh spin on the solutions and surprised me with their approaches," Fried added.

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