Data Science Education and Research at The University of Michigan

By on

um_sealThe University of Michigan’s Data Science education program provides a wide range of training options, which can be tailored to the individual goals of the student. They offer an individualized, interactive, data-centered training experience. The U of M Graduate Data Science Certificate Program’s primary mission is to develop a corps of skilled Data Scientists with a variety of multidisciplinary experiences and strong analytical abilities (diverse backgrounds are encouraged).

While Data Science is well-known for its uses in marketing research, it is also being used to resolve a variety of problems in astronomy, engineering, medicine, social sciences, and transportation. The University of Michigan, located in Ann Arbor, is working to train a community of skilled Data Scientists for the near future.

University of Michigan’s research, and subsequent inventions, often finds their way into the business world. Last year, U of M researchers announced the creation of 428 inventions. This was the fourth year in a row with more than 400 new inventions, and includes 135 new patents. U of M’s Office of Technology Transfer signed 173 option/license agreements with businesses wanting to market U of M discoveries. The University of Michigan’s Tech Transfer Venture Center has helped to initiate twelve startups.

“Big Data can provide dramatic insights into the nature of disease, climate change, social behavior, business and economics, engineering, and the basic biological and physical sciences,” Mark Schlissel. U of M’s president, stated. “With our widely recognized strengths across all of these areas, and our longstanding culture of collaboration across disciplines, U-M is in a unique position to leverage this investment in Data Science for the good of society.”

Advanced Research Computing (ARC)

In 2008, a group of IT experts and U of M senior faculty members delivered a report to the University of Michigan, advising that to maintain its research competitiveness, U of M needed to form an Office of Research Cyberinfrastructure. The report suggested this would give U of M’s academic community a competitive edge. This program eventually evolved into ARC, with the mission of providing support and cutting-edge computing resources to U of M’s research community.

In 2015, U of M invested $100 million in a Data Sciences Initiative, to be guided by ARC. This initiative emphasizes the use of modern technology in handling and processing massive amounts of unconnected data. The goal of Data Science Initiative is to advance the “potential” of Big Data. The Michigan Institute for Data Science (MIDAS) was created to coordinate U of M’s data education programs and research projects. ARC has become the governing body of the university’s data research community.

The Michigan Institute for Data Science (MIDAS)

MIDAS is the center for a multidisciplinary program of Data Science education at the University of Michigan. It covers a broad range of scientific pursuits (the development of ideas, procedures, and technology) for collecting, managing, and analyzing problems in business, science, and other fields. The institute emphasizes modeling and the use of modern technology in handling and processing Big Data.

The University of Michigan has put a focus on applying Big Data research to transportation issues, such as driverless cars and streamlining traffic flows. One of its major accomplishments in the last year was Mcity, a “mini-city” designed for the rigorous and efficient testing of automated and driverless vehicles. To this end, researchers have gathered a steady stream of data from nearly 3,000 private buses, cars, and trucks driving the streets of Ann Arbor. The Data Science Initiative will help to collect, save, and analyze the massive amounts of data being processed as researchers increase the vehicle count to over 20,000, and spread out over Southeastern Michigan.

U of M researchers are attempting to use Big Data to increase the effectiveness of health and medical research, with the goal of turning basic research into patient care, more quickly and efficiently. By filtering through the massive amounts of data taken from DNA sequencing and medical histories, ways of getting a more precise diagnosis, or to tailor more precise therapies for individual’s with cancer, are being developed..

Another new field of research at U of M is the use of Big Data in researching learning and teaching, with the goal of providing an education tailored to individual students and their specific needs. This research will require collecting and analyzing data from several thousand student experiences and activities.

Enrolling In MIDAS

 This program uses a rolling basis (quick response time) admissions process and U of M graduate students from other fields are eligible to enroll. Minority students are encouraged to enter the program. Nine credits are required from courses outside the program, and another three credits are needed from experiential training, part of which includes a Data Analytics report. The faculty of MIDAS come from a variety of disciplines and is available for mentorship to the students. Scholarships are available for grad students who are enrolled in the certification program and students are expected to complete the program within four semesters.

The Graduate Data Science Education Certificate Program provides core experiences in:

  • Modeling: The understanding of basic Data Science principles, applications, and assumptions
  • Technology: Knowledge of computation, Data Management, Analytics, and information extraction
  • Practice: Provides hands-on experience with real data using current technology and modeling tools.

Current Projects

Reinventing Public Urban Transportation and Mobility is one project currently being worked on. The goal of this project is to organize a fleet of driverless and automated vehicles that coordinate with bus and train systems. The streets of the University of Michigan, Ann Arbor, and Detroit are being used to test these new cutting-edge transportation models. During these tests, real-time data is collected on driver behaviors. The data is then used to design and operate on-demand transportation systems, providing a solution to the “first-mile/last-mile” problem (getting people to the bus or train and then to their destination). This strategy incorporates shuttles, cars, and bicycles, and is designed to use automated and driverless cars, when they become available.

The University of Michigan has started a new research program, called ESSI (The Exercise & Sport Science Initiative). It is aimed at optimizing the health and physical performance of exercisers and athletes. This Big Data project draws on professionals from a broad range of faculty from across the university. The ESSI includes over 100 faculty members and student researchers with diverse backgrounds. People with experience in bio-engineering, kinesiology, medicine, nutrition, psychology, social sciences, and Data Science will join with industry experts to work on projects addressing human performance and physical health.

The research focuses on:

  • Performance optimization for individuals and teams: Strength training, psychology, nutrition, rest, recovery, regeneration
  • Understanding exercise: How it improves mental performance, health, and quality of life.
  • Data Science and Analytics in sports and exercise apps: Research of Big Data includes individual and team Analytics.
  • Sports technologies: Focused on designing intelligent apparel, helmets, and equipment.


“With developments in areas ranging from improved helmet designs to the analysis of sports data, science and technology are opening up a host of new opportunities to transform sports and exercise,” S. Jack Hu, Vice-President for Research, said. “With the help of industry partners, we seek not only to explore the science underlying new advances, but also to translate new ideas and insights into practice on our campus and beyond.”

The Building a Transportation Data Ecosystem project allows researchers to study Big Data taken from transportation systems, while using a high-performance computing system. The goal of this project is to create a common long-term archive of transportation data (accidents, traffic, weather, and road conditions) to be used in developing guidelines and regulations for automated and driverless vehicles. Researchers from the College of Engineering, School of Public Health, U-M Dearborn, UMTRI, Science and the Arts, College of Literature, and the Institute for Social Research are involved in this project.

“We’re trying to revolutionize mobility for entire population segments with poor access to transportation,” Pascal Van Hentenryck, of the College of Engineering, said. “On-call, affordable public transportation that can get you to and from work or the doctor’s office efficiently would increase employment opportunities and result in better health care outcomes. The potential for improved quality of life is huge.”

The Brain Trust

The University of Michigan is also part of a “Big Data Brain Trust,” organized by the National Science Foundation (NSF), with the goal of accelerating research into some of the culture’s toughest problems. The NSF has set up four hubs across the nation, with U of M taking a leading role in the Midwest Big Data Innovation Hub and Data Science education.

The Midwest Hub focuses on three fields of interest:

  • Society: With an emphasis on smart cities and smart communities.
  • Studying the natural and human-made worlds: water, food, and energy; and transportation, advanced manufacturing, and digital agriculture.
  • Medicine: Especially biomedical research and health care

The hubs attempt to find and develop relationships, using Big Data to address problems specific to a region.


photo credit: University of Michigan

We use technologies such as cookies to understand how you use our site and to provide a better user experience. This includes personalizing content, using analytics and improving site operations. We may share your information about your use of our site with third parties in accordance with our Privacy Policy. You can change your cookie settings as described here at any time, but parts of our site may not function correctly without them. By continuing to use our site, you agree that we can save cookies on your device, unless you have disabled cookies.
I Accept