MICHIGAN
DATA SCIENCE TEAM
WELCOME!
Click here to join MDST
MDST has ongoing projects that are looking for new members all the time. Take a look at our projects to see what we're currently working on. The best way to get involved is to reach out to project leaders and ask what you can do to help. It doesn't matter if you're new to data science, as long as you're eager to learn quickly!
See our new member FAQ for more info on joining MDST.
Data Hackathon January 18th, 2019
MDST will be co-hosting a data hackathon with the Michigan Sports Analytics Society in January! This will be an overnight event, where student teams will complete a project around a dataset in 24 hours and present their project to a panel of judges. There will be free food provided, and winning teams will win prizes! If you would like to be notified when signups open, please sign up for MDST or visit mdatahack.com.
MDST IN THE PRESS
- August 2018 - MDST wins Best Student Paper in the Applied Data Science track at KDD 2018.
- March 2017 - Michigan Allots $87 Million to Replace Flint's Tainted Water Pipes, NY Times
- January 2017 - Professors and students develop app to detect contaminated lead pipes in Flint, The Michigan Daily
- December 2016 - University research team releases app to help Flint residents assess water risk levels, NY Times
- December 2016 - Google-funded Flint water app helps residents find lead risk, resources, Technology.org
- December 2016 - Google-funded Flint water app helps residents find lead risk, resources, University of Michigan News
- December 2016 - Google-funded Flint water crisis app helps find lead risk, resources, MLive
- December 2016 - Professors and students develop app to detect contaminated lead pipes in Flint, The Michigan Daily
- October 2016 - Number of homes that need new water pipes in Flint has doubled, study says, MLive
- May 2016 - Flint gets a boost from Google to help with water crisis data, Michigan Radio
- May 2016 - Google Is Helping Flint Prioritize Which Lead Pipes Need to Be Replaced, GIZMODO
- April 2016 - Machine learning and the wisdom of the crowd, NSF