We’re pretty open-minded about where talented students can come from and have admitted students from a number of non-stat or math undergraduate majors (biology, psychology, journalism, etc.). What matters is that they have the appropriate mathematical and statistical preparation from taking the prerequisites.

In a typical year, the Division admits 25 to 30 students (20 percent of the total PhD applications received) to achieve an incoming PhD class of 8 to12 students.

The admissions committee reviews applicants according to their record of academic achievement, demonstrated academic potential, letters of recommendation, background and experience, and other factors. GPAs provide competitive points of reference for admission but are not alone decisive in the admissions review.

If your goal is to obtain a PhD in Biostatistics, we encourage you to apply directly to the PhD program, even if you don’t already hold a master’s degree. If you are not admitted to the PhD program and do not hold a previous master’s degree in statistics or biostatistics, you will be considered for admission to the master’s program if you indicate your interest in your SOPHAS application.

We are unable to pre-review applications for admission. All applications reviewed must be received from the centralized application company SOPHAS.

View more Information about the Biostatistics programs.

If you are only applying to the PhD program in Biostatistics at the University of Minnesota, you can send copies of your international transcript(s) and degree conferral directly to the University of Minnesota School of Public Health via e-mail to sph-ask@umn.edu.  Students who are admitted to the Biostatistics PhD program will, upon matriculation, be required to provide a WES ICAP document-by-document evaluation of academic credentials directly to the University of Minnesota School of Public Health.

If you wish to be considered for the MS in Biostatistics (or if your application is not accepted into the PhD program and you wish to be considered for MS admission), a WES evaluation is required at the time you submit your application to SOPHAS to consider your application complete.

Applicants are not required to secure an advisor before they are admitted to the PhD program. In terms of admission, admission for our PhD program is handled centrally, rather than by individual faculty. Thus all PhD applications are reviewed by a single admissions committee and admission is offered to the PhD program overall, rather than to individual faculty’s research groups. Typically, once enrolled in the PhD program, our students will identify dissertation research advisors beginning in the summer after their first year and over the course of their second year in the program.

Currently, our PhD program is a full-time program. All of our PhD students do receive full funding (in the form of an RA or TA position). Funding includes a stipend, full tuition coverage, and a portion of health insurance premiums. It is difficult to work part-time and complete all degree requirements within the required 8 years of completion.

Our faculty are involved in many areas of methodological and collaborative research in areas including, but not limited to, clinical trials, statistical genetics, machine learning, and biomedical imaging. PhD student research is in close collaboration with our faculty and potentially other collaborators, and thus the potential research areas are very broad.

Get comfortable with taking derivatives and evaluating integrals, especially if you took calculus a long time ago. Same for linear/matrix algebra, which will be important for the second-semester and second-year PhD coursework.

Most PhD students use R, so you could prepare by learning the basics of R and Rmarkdown (Datacamp has a free Intro to R class), but this is not necessary. Many PhD students take an introductory R class their first semester.

If you have no exposure to LaTeX, you can save yourself some time by learning the basic mechanics before classes start, for example through tutorials from Overleaf.

If you have not completed the prerequisites at the time you are applying, tell us your plan for completing the prerequisites in your Statement of Purpose and Objectives. This can be a sentence describing where you’ve enrolled (e.g. university, community college, Coursera) and when they will be complete.

Admission into the Biostatistics program is competitive.  Depending on the applicant pool, it is in an applicant’s best interest to have completed (or be in the process of completing) prerequisites at the time they submit their application for review to the committee. If an applicant has completed all of the other course prerequisites and only has one course in progress remaining, the committee usually will have an idea how the student will do based on their previous coursework.

The Division of Biostatistics & Health Data Science is unable to review applications for admission until we have received the complete application (including all required transcripts, letters of recommendation, statement of purpose, etc.).

The admissions committee is familiar with the calculus curriculum at various institutions and is aware that it may not always be a 3-semester sequence.

In terms of prerequisite coursework knowledge, the admissions committee wants to ensure an applicant has a working knowledge of single-variable and partial differentiation/integration, and matrix/vector algebra.

Several of the required courses for the PhD program require writing mathematical proofs. So, we strongly recommend that students complete a course in real analysis. PhD students who have not previously taken real analysis generally take real analysis their first semester in the program, and the cost is covered by their tuition benefit.

All PhD students are offered full funding, which includes a stipend (based on 20 hours of work per week), tuition, and health benefits. Funding comes as part of graduate research and teaching assistantships, and fellowships. Section 7.8 of our Biostatistics Student Guidebook explains the benefits and costs of Graduate Assistantships in the Division of Biostatistics & Health Data Science at the University of Minnesota.

A degree in biostatistics provides broad technical training that is marketable to a broad range of employers, leading to a wide range of potential jobs. Of our PhD students who graduated between 2018 and 2020, for those who were employed in the private sector or are in a government setting, job titles include Principal Biostatistician, Principal Data Scientist, Associate Scientist, and Mathematical Statistician; the academic positions our recent PhD students have moved to include postdoctoral scholar positions and tenure-track positions at major universities.

Careers are available in academic, governmental, non-governmental, and private industry settings. Recent graduates have been hired at publicly funded research institutions including: Fred Hutchinson Cancer Research Center, University of Texas, Stanford University, the Food and Drug Administration, and also at private companies including St. Jude Medical, Eli Lilly, Boston Scientific, Medtronic, Merck, 3M, and Affymetrix. Of our PhD students who graduated between 2018 and 2020, 40% were employed in the private sector, 55% moved to an academic position, and 5% moved to a government setting.