The digital home of the Michigan Data Science Team. Inquire below.
Competitive Data Science at Michigan
NFL Free Agent Prediction Challenge
We kick off the event November 10, 2017 6-7pm 10th Floor Weiser Hall.
Fill out this signup form to register for the NFL Challenge
In this competition, student teams at the University of Michigan will use historical free agent data to predict the value of new contracts signed in the 2018 free agency period. These predictions will be evaluated against the actual contracts as they are signed. This competition is organized by the Michigan Data Science Team (MDST), in collaboration with the Baltimore Ravens and the Michigan Sports Analytics Society (MSAS).
MDST Quicken Loans Challenge
Sign up here to get started on the Quicken Loans Prediction Challenge
Meeting Times for Fall 2017
- Project Team Meetings: Thursdays 5:30pm to 6:30 in BBB 3725
- MDST Tutorials: Tuesdays 5:30pm to 6:30 in Dow 3150
Check the calendar to confirm there is a meeting!
Prospective Members should sign up here to join MDST.
The Michigan Data Science Team (MDST) is a competitive collegiate data science team at the University of Michigan, Ann Arbor. Together, we compete against professional and amateur data scientists from around the world in online prediction challenges.
Competitive data science has become increasingly prominent in the past decade with the immense popularity of high-profile competitions such as The Netflix Prize. Now, online venues such as Kaggle, DrivenData, and Quantopian, among others, provide platforms for data scientists around the world to make impactful contributions to a huge variety of prediction problems while competing for cash prizes. Previous competitions have explored prediction problems in healthcare, particle physics, finance, and countless other domains, and have involved many types of structured and unstructured data.
We an organization at the University of Michigan in MIDAS that is looking for dedicated students who are interested in taking part in data science competitions. Ideally, students will have a strong background in computer science, mathematics, and statistics, but all students are welcome to participate. We will be meeting on a weekly basis throughout the year, where students can share strategies and give tutorials. Anyone is welcome to attend these meetings! For your hard work and dedication, we will be offering internal prizes to the students who achieve the highest performance on our prediction challenges.