Fall Student Data Science Challenge
Main Event: Nov. 3, 2018 at Optum in Eden Prairie
Thank you to all the students, advisors, and judges who participated in MinneMUDAC 2018! We are proud to announce the top teams in each division:
First Place Overall: South Dakota School of Mines and Technology – SD Mines Data
Faculty Advisor: Beth Riley
Analytical Acumen Award: University of Wisconsin-Eau Claire – Datavengers
Faculty Advisor: Abra Brisbin
Serendipitous Discovery Award: University of Minnesota Duluth – We R SASsy Dogs – The SQL
Faculty Advisor: Tracy Bibelnieks
First Place Overall: St. Olaf College Mathematics – Junior Varsity Stats
Faculty Advisor: Paul Roback
Analytical Acumen: University of Iowa Management Science – TippieAnalytics Team 1
Faculty Advisor: Michael Altemeier
Serendipitous Discovery: University of Minnesota College of Liberal Arts Economics, Statistics – Team Korea
Faculty Advisor: Glen Meeden
Best Prediction: University of Minnesota Duluth Department of Management – Duluth’s Data Dragons
Faculty Advisor: Nik Hassan
Honorable Mention Prediction: St Olaf College Mathematics, Statistics, and Computer Science – Model Citizens
Faculty Advisor: Paul Roback and Matthew Richey
First Place Overall: University of Minnesota Twin Cities Carlson School of Management Masters of Science in Business Analytics – Phi-Data-Kapa
Faculty Advisor: Yicheng Song
Analytical Acumen: University of Minnesota College of Science and Engineering Mathematics – Women in Math
Faculty Advisor: Gilad Lerman
Serendipitous Discovery: University of Minnesota Duluth College of Science and Engineering – Hotdog
Faculty Advisor: Aaron Shepanik
Best Prediction: University of Minnesota, Twin Cities Biostatistics – Gophers
Faculty Advisor: Mark Fiecas
Honorable Mention Prediction: Metropolitan State University Management – Data Chasers
Faculty Advisor: Firasat Khan
Results are based on the latest numbers from the Minnesota Secretary of State
Note: Undergraduate teams predicted total number of votes for each congressional district. Graduate teams predicted total number of votes for each congressional district, but broken down by Democrat, Republican, and Third Party. Graduate predictions also include two senate races and the governor’s race.
About The Challenge
MinneAnalytics is proud to present this third-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.
This year’s competition will focus on predicting voter turnout. Student teams have several weeks to analyze data before presenting their findings to judges from the analytics community at the main event on Nov. 3. Teams with the highest scores move on to the finals round in the auditorium. Cash prizes are awarded to top teams in each division.
The 2018 MinneMUDAC Challenge requires participants to obtain their own data. The data sources listed here are a reasonable place to start for the collection of your data. This list is by no means a definitive list and we encourage participants to use other relevant data throughout this challenge, e.g. polling data, social media data, etc. Data sources must be referenced when presenting outcomes to competition judges.
Presenting Your Findings
Student teams will present their findings on Saturday, Nov. 3. 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. The competition should conclude by 4 pm.
Analytic Acumen: Awarded to the team in each division with the most technically appropriate and accomplished team presentation.
Serendipitous Discovery: Awarded to the team in each division providing the most interesting, if unrelated, findings or insights.
Overall Prediction: Awarded to the teams in each division (excluding Novice Division) with the most accurate prediction.
The Serendipitous Discovery winner will be chosen at the main event on Nov. 3. Finalists for the Analytic Acumen and Overall Prediction awards will be chosen on Nov. 3, and the winners will be announced following the Nov. 6 election.
Who is invited?
- 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.
- 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 covers room accommodations for all teams and their advisors traveling from outside the Minneapolis/St Paul seven county metropolitan area.
- 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.
- Open Division: For accomplished career professionals that may be 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.
- 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.
Division level is chosen by the team’s faculty or staff advisor during registration.