MinneMUDAC 2019

Fall Student Data Science Challenge

Nov. 9, 2019 at Optum in Eden Prairie

Thank you to all who participated in the MinneMUDAC 2019 Student Data Science Challenge! The top teams from each division are listed below.

2019 Top Teams

Novice Division

  • First Prize: Cornell College (team N02 – Better than Last Year)
    Bailey Barnard, Jack Bressett, Dan Heinsch, Parker Linscott
    Faculty Advisor: Ann Cannon
  • Analytic Acument Award: University of Wisconsin-Platteville (team N21 – The Platte Villains A-Team)
    Nicholas Buchert, Emma Dums, Joel Egelhoff, Kristin Sheyko, Daniel Zellmer
    Faculty Advisor: Michael Black
  • Serendipitous Discovery: South Dakota School of Mines and Technology (team N16 – Algebros)
    Timotheo Ford, Katelyn Raposa, Tiati Thelen
    Faculty Advisor: Kyle Caudle
  • Honorable Mention: Purdue University (team N03 – Big Data Energy)
    Thrishna Bhandari, Kruthi Krishnappa
    Faculty Advisor: Mark Ward

Undergraduate Division

  • First Prize: South Dakota School of Mines and Technology (team U27 – SDMinesData)
    Marc Mascarenhas, Manasi Paste, Shashwati Shradha
    Faculty Advisor: Kyle Caudle
  • Analytic Acument Award: Hamline University (team U14-  Hamline Team 1)
    Andrew Argeros, Lyndsey Hawk, Lindsay Steiger
    Faculty Advisor: John Lochner
  • Serendipitous Discovery: University of Minnesota Duluth (team U05 – Data Doggos)
    Kaylee Andersen, Daniel Crist, Christopher Kuehn, Noah Lahr, Brian Paulsen
    Faculty Advisor: Tracy Bibelnieks
  • Honorable Mention: University of Minnesota Duluth (team U29 – Skippy Bois)
    Michael Gorbatenko, Tucker Hazzard, Joey Kmiec, Sam Ott, Austin Steinmetz
    Faculty Advisor: Pushkar Raj
  • Honorable Mention: St. Olaf College (team U31 – St. Olaf Model Behavior)
    Eleanor Hastings, Victoria Knutson, Henry Miller, Nicole Sanford, Leon Zhou
    Faculty Advisor: Paul Roback
  • Honorable Mention: Winona State University (team U42 – WSU Team 2)
    Jenny Ackerman, Bryce Banton, Samuel Broberg, Sawyer Fratzke, Benjamin Winters
    Faculty Advisor: Todd Iverson

Graduate Division

  • First Prize: University of Minnesota (team G28 – Song Birds)
    Piyush Gupta, Hamed Khoojinian, Yassine Manane, Harsh Seksaria, Pushkar Vengurlekar
    Faculty Advisor: Yicheng Song
  • Analytic Acument Award: University of Minnesota (team G34 – Woman in Math and Stats)
    Somyi Baek, Cora Brown, Sarah Milstein, Yu Yang
    Faculty Advisor: Gilad Lerman
  • Serendipitous Discovery: University of Minnesota (team G09 – Data Vaders)
    Vijay Ranjan Dhulipala, Sai Akhil Kodali, Anisha Mula, Mainak Roy, Sampada Sathe
    Faculty Advisor: Edward McFowland III
  • Honorable Mention: University of St. Thomas (team G23 – perceptron)
    John Affolter, Kiel Auer, Himanshu Gamit, Shantanu Hadap, Shubha Shubha
    Faculty Advisor: Michael Dorin
Overview

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 & Awards

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.

The Awards

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.

Challenge & Data

This year’s competition challenges teams to use data curated by Farm Femmes to predict the price of soybeans.

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