MinneMUDAC 2018: Student Data Science Challenge

MinneAnalytics is a 501c6 nonprofit organization of professionals in Data Science and Emerging Tech. Co-founders: Dan Atkins, Sean Larson, Glenn Trygstad, and Board of Directors: John Hogue, Graeme Thickins

MinneAnalytics

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.
The Challenge
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.
<A HREF="https://drive.google.com/file/d/19f69BQzDrRvwdTY_y_vKrZjWNwq09XTf/view?usp=sharing" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">View the Challenge Question→</A>
The Data
The 2018 MinneMUDAC Challenge requires participants to obtain their own data. The data sources listed <A HREF="http://minneanalytics.org/minnemudac/2018-data/" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">here</A> 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.
<A HREF="http://minneanalytics.org/minnemudac/2018-data/" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">View Potential Data Sources→</A>
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 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.
The Analytic Acumen and Serendipitous Discovery winners will be chosen at the main event on Nov. 3. Finalists for the Overall Prediction award will be chosen on Nov. 3, and the winners will be announced following the Nov. 6 election.
<A HREF="https://drive.google.com/file/d/1xlRfD_P1vdzDalFtLYVaryveLss3sc9S/view?usp=sharing" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">View Proposed Scoring Rubric→</A>
 
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<H4>Registration</H4>
Who is invited?
<UL>
<LI>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.</LI>
<LI>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.</LI>
<LI>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.</LI>
</UL>
Team Guidelines
<UL>
<LI>Each team requires a faculty or staff advisor to register as well as provide support throughout the competition.</LI>
<LI>Teams are limited to five students and one faculty or staff advisor.</LI>
<LI>Colleges and universities outside of Minnesota are encouraged to participate.</LI>
<LI>MinneAnalytics covers room accommodations for all teams and their advisors traveling from outside the Minneapolis/St Paul seven county metropolitan area.</LI>
<LI>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.</LI>
<LI>Blended teams of students with different majors and skill-sets are encouraged.</LI>
</UL>
Competition Divisions
<UL>
<LI>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.</LI>
<LI>Graduate Division: For teams with advanced data management, data programming, and statistical/analytic skills to support predictive modeling, including at least one graduate student.</LI>
<LI>Undergraduate Division: For undergraduate teams with advanced data management, data programming, and statistical/analytic skills to support predictive modeling.</LI>
<LI>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.</LI>
</UL>
Division level is chosen by the team’s faculty or staff advisor during registration.
 
Presented by <A HREF="http://minneanalytics.org/" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">MinneAnalytics </A>and Midwest Undergraduate Data Analytics Competition (<A HREF="http://mudac.org/" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">MUDAC</A>). For more information, visit the event website: <A HREF="http://minneanalytics.org/minnemudac" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">minneanalytics.org/minnemudac</A>
 
<H4>PLEASE NOTE: </H4>
<H4>MinneAnalytics now requires a Driver's license or government issued photo ID for entrance at all events.</H4>

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MinneMUDAC 2018: Student Data Science Challenge

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MinneMUDAC 2018: Student Data Science Challenge

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