Student Competitions

Northwestern U Holds Big Data Analytics Hackathon

Northwestern University and Teradata held their second annual data mining hackathon on April 29.

Thirty-six students in Northwestern's Master of Science in Analytics (MSiA) program competed in teams of two to resolve complex hypothetical problems using the Teradata Aster Discovery Platform. The platform is "an analytic engine designed for big data sets," according to information from the company. Participating students used techniques such as pattern recognition, text analysis, graph analytics and predictive computation to solve problems in the domains of social network analysis, fraud detection, supply chain management, text analysis and money laundering.

Judging criteria for the competition included:

  • Creativity pertaining to data sets and analytic theory proposed;
  • Use case concept;
  • Use case approach in execution and multi-genre analytic techniques; and
  • Ability to demonstrate and tell the story.

Teams were allowed to choose from eight publicly accessible data sets, including NFL statistics, MLB statistics, Amazon reviews, State of the Union addresses, airline flight data, and United States consumer bank complaints, and they were free to choose how they analyzed the sets and what insights to search for. Participating students received three hours of training on the Teradata Aster Discovery Platform the day before the event, and Teradata employees were on hand during the event to answer software-related questions.

"It's beneficial for our students to receive a data set without having a clear question to solve," said Diego Klabjan, director of the MSiA program, in a prepared statement. "So then they have to be creative about finding the problem and look for a strategy to solve it."

The winning team analyzed every State of the Union address since 1790, using the software to label each sentence as positive or negative, and then comparing the ratios. They found that the addresses throughout history have typically been positive, regardless of the political climate of the time. The team compared the address data to approval ratings from Gallup polls taken directly before and after the addresses and found that more positive addresses correlated to higher subsequent Presidential approval ratings.

The second place team analyzed customer reviews on Amazon.com to identify those submitted by bots. Both the first and second place teams won passes to the annual Teradata Partners Conference and Expo, a data analytics event being held in Anaheim, CA in October.

Northwestern University and Teradata began collaborating more than three years ago, when they developed new business and technology curricula designed to help students learn the science and practice of business analytics, including data warehousing techniques and platforms, according to information from Teradata.

About the Author

Leila Meyer is a technology writer based in British Columbia. She can be reached at leilameyer@gmail.com.

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