MinneMUDAC 2019

Fall Student Data Science Challenge

Nov. 9, 2019 at Optum in Eden Prairie

Register Now

UPDATE: The 2019 challenge questions and data have been released! Access 2019 Challenge & Data→


MinneAnalytics is proud to present this fourth-annual analytics event inviting teams of graduate and undergraduate students to explore real-world data while enhancing and showcasing their skills. Join us for this unique collaboration between students, their academic advisors, and analytics professionals from the community.

Student teams have several weeks to analyze data before presenting their findings to judges from the analytics community at the main event on Nov. 9. Teams with the highest scores move on to the finals round in the auditorium. Cash prizes are awarded to top teams in each division. This year’s competition will focus on food, agriculture, and sustainability data. Stay tuned – the data is scheduled to be released the week of Oct. 1.

Student teams will present their findings on Saturday, Nov. 9. During the first round (9 am-noon), teams have five minutes to present their model to a series of judging teams. Judges will also have the opportunity to ask questions of each team. Student teams should expect to pitch 4-6 times with each interaction lasting 7-12 minutes. After breaking for lunch, the finalists will present to all the judges in the auditorium.

Who is invited?

MinneMUDAC 2019 is open to:

  • Students: Undergraduate and graduate students welcome. Please note that you must enter the team name and name/email of a faculty or staff advisor to register. See team guidelines below.
  • Faculty/Staff Advisors: Each team requires a faculty or staff advisor to provide guidance throughout the challenge. One advisor may advise up to three student teams. Advisors assisting more than one team must register for each team.
  • Judges/Mentors: Share your experience with the next generation of analytics professionals. Industry professionals who would like to judge and provide mentorship may register by selecting the “Judge/Mentor” ticket option.
Team Guidelines

Student teams must meet the following guidelines:

  • Each team requires a faculty or staff advisor to register as well as provide support throughout the competition.
  • Teams are limited to five students and one faculty or staff advisor.
  • Colleges and universities outside of Minnesota are encouraged to participate.
  • MinneAnalytics is able to provide Friday night accommodations for teams traveling two hours or more; however, the number of rooms available is limited. Teams must request a hotel room at least two weeks before the event.
  • More than one team from the same college or university may participate. Individual students may only join one team.
  • There is a limit to three teams from the same college department.
  • Blended teams of students with different majors and skill-sets are encouraged.
Competition Divisions

Each student team competes in one of four divisions:

  • Open Division: For accomplished career professionals that are adding a second advanced degree or enhancing their skill set, or elite teams that want to compete against the best.
  • Graduate Division: For teams with advanced data management, data programming, and statistical/analytic skills to support predictive modeling, including at least one graduate student. Any team with one or more Graduate students will automatically be in the Graduate division.
  • Undergraduate Division: For undergraduate teams with advanced data management, data programming, and statistical/analytic skills to support predictive modeling.
  • Novice Division: For students early in their studies who have limited experience and have novice to intermediate data management, data programming and statistical/analytic skills. Any team with Freshmen and Sophomores will be in the Novice division. Any team whose school’s data science program is less than two years old will be in the Novice division.

Division level is chosen by the team’s faculty or staff advisor during registration.

Challenge & Data

This year’s competition challenges teams to use data curated by Farm Femmes to predict the price of soybeans. Access 2019 Challenge & Data→

2019 Data Sponsor

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