Frequently Asked Questions
Note: New academic requirements are in effect for students who are accepted to the program after Oct., 2022. Students who have already enrolled in the program by Oct., 2022 will still follow the previous requirements.
Note: New academic requirements are in effect for students who are accepted to the program after Oct., 2022. Students who have already enrolled in the program by Oct., 2022 will still follow the previous requirements.
Yes, you must hold off on applying until your first semester grades are posted.
Each student is different. Some might have already taken, or plan to take, courses in one category through their own programs. Others might be new to courses in all three categories. We expect that students are exposed to all three categories. Therefore, you should take one course from each category, unless you have already taken, or will take, courses in a category within your own program.
We require that each student takes at least two core courses.
Yes, if you already have taken some of the courses (remember, you can only double-count one course with your core graduate program), you may be able to quickly satisfy the remaining DS Certificate requirements. Register for the program and be sure to go through the complete checklist to ensure you do receive your certificate upon graduation.
Yes, you need a total of 12 credits. Of these, 3 credits are experiential (e.g., internship or a hands-on research project) and a maximum of 3 credits of appropriate course work may be double-counted. The remaining 6 credits are other courses from the approved courses list.
This is either an internship with an external mentor/organization or a research project with a U-M faculty mentor. It must involve substantial data-handling and be equivalent to 3 credit semester course (~140 hrs of work).
The project scope and deliverables are determined on a case-by-case basis (in discussion with MIDAS DS Program Chair). The project must have a significant “data science” (modeling, analytics, visualization) aspect to it with a clearly defined end-product (e.g., software, paper, report, app, service, learning module, DB, platform, infrastructure, etc.)
Yes, most students determine their hands-on experiential requirements while they are enrolled in the program.
Include your current preliminary plan, what you would like to do for the practicum. Specifics can be determined later while enrolled in the program and after reviewing the MIDAS opportunities (http://midas.umich.edu/careers/).
Technically speaking, yes. However, you can’t double-count 2 courses and it’s generally better to have the 3 credits experimental training be hands-on data emersion. It can be a project, internship or rotation with a faculty, collaborator, or industry partner.
You may find a project by signing up through the MDP Program (https://mdp.engin.umich.edu/) and/or search the MIDAS Faculty site (http://midas.umich.edu/affiliated-faculty/) for faculty with your preferred data science focus and inquire if they have a project. Be sure to contact Prof. Ivo Dinov (dinov@umich.edu) to alert him of the data science capstone project you are working on.
No, prior experience is not a criterion for admission into the graduate Data Science certificate program. Motivation, hard work, and drive are more important.
See website: http://midas.umich.edu/seminar-series/ and calendar: http://midas.umich.edu/events
Yes, attendance at the MIDAS Seminar Series is a requirement for the Certificate and the Masters program students. Students are required to register for one-semester of attendance at the Seminar Series which is listed in the course catalogue as EECS 409.
You do not have to complete the program. If you don’t, you should let MIDAS and Rackham know and you won’t receive the certificate at the end of your study (core program).
Yes, we can consider courses (that meet all certificate criteria) retroactively.
Yes, you can take appropriate courses in any UM School/College. In general, it’s better to take courses in Mathematics, Engineering, Health Science, but some statistics courses may also be appropriate.