Graduate admission FAQ
- When is the next application cycle?
- When will I receive the results of my application?
- How do I fulfill the prerequisite requirements for the Data Science MS program?
- What computer science background is needed for this program?
- Why must I provide a separate list of courses fulfilling the prerequisites when they are already listed on my transcript?
- Are GRE scores mandatory?
- Are TOEFL scores mandatory?
- Can my letter of recommendation writers submit their letters after I submit my application?
- Can the Data Science M.S. program be completed in 2 years?
- Can I work full time while completing this program?
- Can I complete this degree as a remote student?
- I am not yet in data science but have already taken all the courses needed for a track. Can I use them as part of my data science program?
- Are there funding opportunities for the Data Science M.S. program?
- Do you accept transfer credits?
- How much does the program cost?
- What are your employment/placement statistics?
- Is the Data Science M.S. considered a STEM program?
When is the next application cycle?
Applications for the M.S. program must be completed and submitted by or before March 1 at 11:59 p.m. CST to be reviewed.
To ensure your application is processed as efficiently as possible, we strongly recommend that you submit your completed application (and all required materials) at least two weeks before the application deadline. If application materials and official test scores are received more than one week after deadlines, we can't guarantee that you will be considered for admission. Applicants who submit their application and official test scores on time will be prioritized for review.
There is no spring admission for the M.S. program.
The admissions committee will review all applications after our deadline:
- M.S. and certificate applicants for the fall semester will receive notifications between April and May.
- Certificate applicants for the spring semester will receive notifications between November and December.
Individuals must have a knowledge base in calculus (2 semesters), plus multivariable calculus, linear algebra, and statistics (at least 1 semester each). Also required is programming experience in a general-purpose programming language (e.g., C, C++, Java, Python), including basic algorithms and data structures, equivalent to the first two semesters of beginning computer science courses either as part of the undergraduate degree or subsequent work experience.
Experience with mathematical software environments such as Matlab, R,or the equivalent is a big plus. Work experience will need to be documented on an applicant’s CV and supported by letters of recommendation if an applicant plans to use it in lieu of coursework.
You should have experience equivalent to two semesters of beginning computer science. This is more than just programming. Some sample topics are listed below.
We do not expect most applicants have even seen all of these, but your experience should be at a level where you could learn these concepts fairly easily. If you have not had two semesters of formal coursework, it would greatly help your application to describe your computing experience in your personal statement/statement of purpose (4,000 character limit)—for example, by describing the most complicated computing problem you have solved. Many of our applicants have picked up some of the necessary experience through work or by taking some make-up courses (other than self-paced courses), so lacking formal experience in everything does not rule out admission. It can also help if a letter of recommendation addresses this.
For those wishing for some extra formal training before entering our program, we recommend the equivalent of our computer science classes CSCI 1933 and 4041 (with CSCI 1133 only for those with almost no computing experience with a general-purpose programming language). In addition, our CSCI 2021 and 4061 are good preparation for those interested in large-scale data applications that depend on parallel, distributed, or cloud computing. In any case, otherwise strong candidates can be admitted on condition that they take one or more of these courses in their first semester.
Here is a non-exclusive sample of topics it is helpful to know or may need to succeed in our classes (the last few topics more critical for big data applications):
- divide and conquer problem-solving paradigm
- deques in object-oriented fashion using linked lists
- symbolic algebra manipulation
- simple numerical and iterative methods
- sorting and searching: implementation & theoretical analysis of the complexity of different methods
- concepts of hash tables or dictionaries, queues, stacks, [binary] trees, heaps
- implement a discrete event simulation of a set of waiting queues
- use simple interactive graphics program interface: e.g., make graphical objects that interact with a user
- elementary graph algorithms: graph search (BFS,DFS). graph insert & delete
- insert, retrieve & delete in various data structures: lists, trees, hash tables
- dynamic programming
- memoization (what is it, when does it help)
- asynchronous processes and communication, locks and semaphores
- data and algorithm abstraction and modularity
Why must I provide a separate list of courses fulfilling the prerequisites when they are already listed on my transcript?
Our experience has shown that the greatest hindrance to success in our program has been a student's lack of previous preparation in computing and/or mathematics.
By asking applicants to separately list the relevant courses from their transcript, it is made clear to both the applicant and the program faculty how the background of the student fits with our program. Even if the preparation in one prerequisite topic area is weak, applicants who are strong in other areas may still be admitted, possibly with some conditions for make-up work. Applicants are encouraged to elaborate on their experience in the prerequisite areas in their statement of purpose.
GRE scores are not required, but are recommended particularly for those applying from international institutions for the MS degree.
If submitted, the GRE is only one of many factors considered for admission, and no score will guarantee or preclude admission. Applications without the GRE will still be considered based on the material submitted.
The GRE is not needed for the Post-Baccalaureate Certificate.
TOEFL scores are mandatory for most international applicants applying to the Data Science masters program.
Exceptions may be made for those applicants that meet the following requirements:
Applicants who have completed 24 quarter credits or 16-semester credits (within the past 24 months) at a U.S. university.
Are in residence as a full-time student at a recognized institution of higher learning in the United States (or other English-speaking countries) before entering the University of Minnesota may be exempted from this requirement.
Applicants who have been working full-time in the United States in a data science related field for at least two years.
Yes. Recommendation providers can submit their recommendations at any time, before or after an application has been submitted. Additionally, applicants can send reminder email notifications from their application status page after submission.
Yes, the program can be completed in two years. For more information on courses and curriculum, please visit the curriculum page. The Post-Baccalaureate Certificate can be completed in one year. Students may opt to enroll as part-time students, taking longer to complete their degree.
9 hours of graduate coursework equates to roughly 40 hours of work each week. It is highly recommended that students take 6 credit hours if they plan to work while attending the program.
Many course requirements for this degree can be met using courses offered over the University's instructional video system, UNITE, without coming to campus. We do not guarantee that this degree can be done completely online. Occasional visits to campus may be necessary for certain courses including the capstone project.
I am not yet in data science but have already taken all the courses needed for a track. Can I use them as part of my data science program?
There are new policies (effective as of fall 2018) regarding the application of credits prior to official admission to a program. Please review this Graduate School website regarding the application of credits. At least 20 credits minimum must be taken while enrolled as a degree-seeking student in the data science graduate program. Those 20 credits cannot be applied to any other graduate program’s degree requirements.
The only exception to this policy is for students previously enrolled in a related graduate program. Currently, approved related programs are any other College of Science & Engineering graduate program.
Required courses taken here as an undergraduate cannot be used directly, but once enrolled in the data science program, you can replace any required course already taken with a more advanced course in the same track, or a related elective, with DGS approval.
The data science program has no internal funding sources, inasmuch, students are expected to provide their own funding. There are rare cases after the student has established a record at the U of M when M.S. students do get a teaching assistantship through a participating department or a research assistantship through an individual professor. More information must be obtained from the individual departments regarding funding opportunities after being accepted to the program.
Yes, credits may be transferred into the program. The University of Minnesota requires that 20 credits of the M.S. coursework be taken at the University as an admitted, registered data science student. More information can be found in the U of M'sPolicy Library.
Tuition and fees for the current academic year are set by the University. Data science students pay tuition at the general Graduate and Professional rate. Students enrolled entirely through UNITE pay tuition at the resident rate plus a UNITE fee regardless of where they reside.
Our department doesn't track employment rates, but you may find useful statistics and information from the CSE Career Center.
Yes, this program is listed as a STEM program, with a CIP code of 11.0401.