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MS in Biostatistics

at the University of Minnesota

Preparing Our Students

Our program will prepare you to:

  • Collaborate in the design of research studies
  • Address research questions through analysis or simulation
  • Draw inferences from data and communicate those inferences
  • Write programs in two or more statistical packages

Our Biostatistics MS Program

The MS-Plan B program takes two academic years for full-time students, but we also accept part-time students and will help plan a schedule for completing the MS.

The first year consists of three courses in applied biostatistics methods with extensive computing in SAS, two semesters of statistical theory, and a health science elective course. At the end of the first year, there is a written exam covering the two theory and three methods courses.

During the summer many students gain experience at an internship in industry or research.

In the second year, students take courses in clinical trials, analysis of survival data, plus three biostatistics electives.

The MS is completed with a project during the final semester.

Curriculum Sheet

When to apply to our PhD program

Students may also choose to pursue PhD studies in biostatistics after completing an MS; however, if you are relatively certain that you want to obtain a PhD we encourage you to apply now for our PhD program.

More information is available on the PhD program page.

For specific questions, please contact Sally Olander at brown198@umn.edu.

Thank you for considering our program and the University of Minnesota. We look forward to receiving an application from you.

Welcome to Biostatistics

WeiHi, thank you for visiting our website! I am Wei Pan and am currently the director of graduate studies in the Division of Biostatistics. I received my PhD in statistics from the University of Wisconsin and joined the Division of Biostatistics at the University of Minnesota in 1997.

This year I am teaching a Survival Analysis course — which is a required course for all students in the Division of Biostatistics. My research area includes survival analysis, correlated response data analysis, bioinformatics, and machine learning.

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