Twin Cities campus
Twin Cities Campus

Biostatistics M.P.H.

School of Public Health - Adm
School of Public Health
Link to a list of faculty for this program.
Contact Information
School of Public Health, MMC 819, A395 Mayo Memorial Building, 420 Delaware Street S.E., Minneapolis, MN 55455 (612-626-3500 or 1-800-774-8636)
  • Program Type: Master's
  • Requirements for this program are current for Fall 2020
  • Length of program in credits: 42
  • This program requires summer semesters for timely completion.
  • Degree: Master of Public Health
Along with the program-specific requirements listed below, please read the General Information section of this website for requirements that apply to all major fields.
A biostatistician is an important member of many research teams. Working in close partnership with researchers across a wide array of scientific disciplines, a biostatistician designs studies and develops statistical tools to extract meaning from complex data. The Biostatistics MPH program requires students to meet core competencies in public health administration, behavioral science, biostatistics, environmental health, foundations, epidemiology, and ethics, as well as complete both an applied practice and integrative learning experience.
This program is accredited by CEPH (Council on Education for Public Health).
Program Delivery
  • via classroom (the majority of instruction is face-to-face)
Prerequisites for Admission
The preferred undergraduate GPA for admittance to the program is 3.00.
Other requirements to be completed before admission:
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 and standardized test scores provide competitive points of reference for admission but are not alone decisive in the admissions review. Prospective applicants should have taken at least: - Three semesters of calculus (including multivariable calculus) - One semester of linear algebra Experience with a programming language (eg. Java, C, Python) is helpful, but not required.
Special Application Requirements:
International applicants who have attended post-secondary institutions outside of the U.S. are also required to submit the following supporting documentation to SOPHAS. World Education Services (WES) evaluation of foreign academic credentials. The University of Minnesota School of Public Health requires all applicants with foreign academic credentials to provide a WES course-by-course evaluation of those credentials. Note: Applicants with transcripts from Canadian schools are exempt from this requirement. Instead, applicants should have copies of their Canadian transcripts sent directly to SOPHAS. Through special arrangements with SOPHAS, WES will deliver its credential evaluation report directly to SOPHAS by secure electronic transmission. This expedites the delivery of the evaluation report as well as images of the applicant's verified transcripts to SOPHAS and allows SOPHAS to process the report most efficiently. Go to for more information. Note: Once WES receives the required documentation, it can complete an evaluation in seven business days or less, depending on the type of service requested. However, if additional research, correspondence, or verification is required, the evaluation will take longer. Students are recommended to start the process at least six weeks prior to the program deadline to ensure that their WES evaluation reports are complete by the deadline. Proof of English Proficiency Applicants whose native language is not English, or whose academic study was done exclusively at non-English speaking institutions, must prove English proficiency by providing either official Test of English as a Foreign Language (TOEFL) or International English Language Testing System (IELTS) scores. Official report of the scores should be sent directly to SOPHAS using designation code 5688 for the TOEFL or designation code SOPHAS for the IELTS.
Applicants must submit their test score(s) from the following:
  • GRE
    • General Test - Verbal Reasoning: 150
    • General Test - Quantitative Reasoning: 146
    • General Test - Analytical Writing: 4.0
International applicants must submit score(s) from one of the following tests:
    • Internet Based - Total Score: 100
    • Paper Based - Total Score: 600
    • Total Score: 7.0
    • Final score: 80
Key to test abbreviations (GRE, TOEFL, IELTS, MELAB).
For an online application or for more information about graduate education admissions, see the General Information section of this website.
Program Requirements
Plan C: Plan C requires 42 major credits and 0 credits outside the major. The final exam is oral. A capstone project is required.
Capstone Project: Students complete 1 credit of PUBH 7494 (Integrative Learning Experience).
This program may be completed with a minor.
Use of 4xxx courses towards program requirements is not permitted.
A minimum GPA of 3.00 is required for students to remain in good standing.
At least 2 semesters must be completed before filing a Degree Program Form.
Courses must be taken A/F unless offered only S/N. Minimum grade of B- must be earned for required courses.
Public Health Core Requirements (12 credits)
A minimum grade of B- must be earned for each of the following PUBH core courses:
PUBH 6751 - Principles of Management in Health Services Organizations (2.0 cr)
Foundations of Social and Behavioral Science
PUBH 6020 - Fundamentals of Social and Behavioral Science (2.0 cr)
Environmental Health
PUBH 6102 - Issues in Environmental Health (2.0 cr)
PUBH 6320 - Fundamentals of Epidemiology (3.0 cr)
or PUBH 6341 - Epidemiologic Methods I (3.0 cr)
Foundations of Public Health
PUBH 6250 - Foundations of Public Health (2.0 cr)
PUBH 6741 - Ethics in Public Health: Professional Practice and Policy (1.0 cr)
Biostatistics Core Requirements (22 credits)
PUBH 7405 - Biostatistics: Regression (4.0 cr)
PUBH 7406 - Advanced Regression and Design (4.0 cr)
PUBH 7420 - Clinical Trials: Design, Implementation, and Analysis (3.0 cr)
PUBH 7450 - Survival Analysis (3.0 cr)
STAT 5101 - Theory of Statistics I (4.0 cr)
STAT 5102 - Theory of Statistics II (4.0 cr)
Applied Practice Experience (1 cr)
PUBH 7496 - Applied Practice Experience: Biostatistics (1.0 cr)
Integrated Learning Experience (1 credit)
Take PUBH 7494 for one credit.
PUBH 7494 - Integrative Learning Experience: Biostatistics (1.0-3.0 cr)
Biostatistics Electives (6 credits)
PUBH 7430 - Statistical Methods for Correlated Data (3.0 cr)
PUBH 7440 - Introduction to Bayesian Analysis (3.0 cr)
PUBH 7445 - Statistics for Human Genetics and Molecular Biology (3.0 cr)
PUBH 7460 - Advanced Statistical Computing (3.0 cr)
PUBH 7461 - Exploring and Visualizing Data in R (2.0 cr)
PUBH 7462 - Advanced Programming and Data Analysis in R (2.0 cr)
PUBH 7465 - Biostatistics Consulting (3.0 cr)
PUBH 7470 - Study Designs in Biomedical Research (3.0 cr)
PUBH 7475 - Statistical Learning and Data Mining (3.0 cr)
PUBH 7485 - Methods for Causal Inference (3.0 cr)
PUBH 8422 - Modern Nonparametrics (3.0 cr)
PUBH 8472 - Spatial Biostatistics (3.0 cr)
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PUBH 6751 - Principles of Management in Health Services Organizations
Credits: 2.0 [max 2.0]
Grading Basis: A-F only
Typically offered: Every Fall, Spring & Summer
Understanding of and improvement in the competencies of managers in organizations, particularly as applied to health services and public health organizations. prereq: [Public hlth MPH or MHA or certificate] student or [environmental health MS or PhD] student or dentistry MS student or instr consent
PUBH 6020 - Fundamentals of Social and Behavioral Science
Credits: 2.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Every Fall, Spring & Summer
Four major approaches to public health problems: psychosocial, economic, community, policy. Theory, implementation. Small groups practice skills.
PUBH 6102 - Issues in Environmental Health
Credits: 2.0 [max 2.0]
Grading Basis: A-F only
Typically offered: Every Fall, Spring & Summer
Current issues, principles, and methods of environmental/occupational health practice. prereq: Public health [MPH or MHA or certificate] student or health journalism MA major or nursing MS student or instr consent
PUBH 6320 - Fundamentals of Epidemiology
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Every Fall, Spring & Summer
This course provides an understanding of basic methods and tools used by epidemiologists to study the health of populations.
PUBH 6341 - Epidemiologic Methods I
Credits: 3.0 [max 3.0]
Course Equivalencies: 02236 - PubH 6320PubH /6341
Grading Basis: A-F only
Typically offered: Every Fall
Introduction to epidemiologic concepts and methods: (1) Study design (randomized trials and observational studies); (2) Measures of exposure-disease association; (3) Casual inference and bias; (4) Confounding and effect modification.
PUBH 6250 - Foundations of Public Health
Credits: 2.0 [max 4.0]
Grading Basis: A-F only
Typically offered: Every Fall, Spring & Summer
In this course we will examine values, contexts, principles, and frameworks of public health. We will provide an introduction to public health, consider the history of public health, social/political determinants, impact of health disparities on race, class and gender, moral and legal foundations, public health structures, historical trauma and cultural competence, health and human rights, advocacy and health equity, communication and financing, and the future of public health in the 21st century. Grounded in theory and concepts, we will incorporate core competencies and skills for public health professionals and will focus on developing problem solving and decision-making skills through critical analysis, reflection, case studies, readings, and paper assignments.
PUBH 6741 - Ethics in Public Health: Professional Practice and Policy
Credits: 1.0 [max 1.0]
Grading Basis: A-F only
Typically offered: Every Fall, Spring & Summer
Introduction to ethical issues in public health practice/policy. Ethical analysis, recognizing/analyzing moral issues. prereq: Public health [MPH or MHA or certificate] student or environmental health [MS or PhD] major or instr consent
PUBH 7405 - Biostatistics: Regression
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
T-tests, confidence intervals, power, type I/II errors. Exploratory data analysis. Simple linear regression, regression in matrix notation, multiple regression, diagnostics. Ordinary least squares, violations, generalized least squares, nonlinear least squares regression. Introduction to General linear Model. SAS and S-Plus used. prereq: [[Stat 5101 or concurrent registration is required (or allowed) in Stat 5101], biostatistics major] or instr consent
PUBH 7406 - Advanced Regression and Design
Credits: 4.0 [max 4.0]
Prerequisites: [7405, [STAT 5102 or &STAT 5102], biostatistics major] or #
Typically offered: Every Spring
Topics include maximum likelihood estimation, single and multifactor analysis of variance, logistic regression, log-linear models, multinomial logit models, proportional odds models for ordinal data, gamma and inverse-Gaussian models, over-dispersion, analysis of deviance, model selection and criticism, model diagnostics, and an introduction to non-parametric regression methods. R is used. prereq: [7405, [STAT 5102 or concurrent registration is required (or allowed) in STAT 5102], biostatistics major] or instr consent
PUBH 7420 - Clinical Trials: Design, Implementation, and Analysis
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Introduction to and methodology of randomized clinical trials. Design issues, sample size, operational details, interim monitoring, data analysis issues, overviews. prereq: 6451 or concurrent registration is required (or allowed) in 6451 or 7406 or instr consent
PUBH 7450 - Survival Analysis
Credits: 3.0 [max 3.0]
Prerequisites: 7406, [STAT 5102 or STAT 8102]
Typically offered: Every Fall
Statistical methodologies in analysis of survival data. Kaplan-Meier estimator, Cox's proportional hazards multiple regression model, time-dependent covariates, analysis of residuals, multiple failure outcomes. Typical biomedical applications, including clinical trials and person-years data. prereq: 7406, [STAT 5102 or STAT 8102]
STAT 5101 - Theory of Statistics I
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Logical development of probability, basic issues in statistics. Probability spaces. Random variables, their distributions and expected values. Law of large numbers, central limit theorem, generating functions, multivariate normal distribution. prereq: (MATH 2263 or MATH 2374 or MATH 2573H), (CSCI 2033 or MATH 2373 or MATH 2243)
STAT 5102 - Theory of Statistics II
Credits: 4.0 [max 4.0]
Typically offered: Every Fall & Spring
Sampling, sufficiency, estimation, test of hypotheses, size/power. Categorical data. Contingency tables. Linear models. Decision theory. prereq: [5101 or Math 5651 or instr consent]
PUBH 7496 - Applied Practice Experience: Biostatistics
Credits: 1.0 [max 6.0]
Grading Basis: S-N only
Typically offered: Every Fall, Spring & Summer
MPH students are required to complete a supervised Applied Practice Experience (APEx). Students must address five competencies and must submit two products that demonstrate attainment of the competencies. prereq: biostatistics MPH student
PUBH 7494 - Integrative Learning Experience: Biostatistics
Credits: 1.0 -3.0 [max 3.0]
Grading Basis: S-N only
Typically offered: Every Fall, Spring & Summer
MPH students complete an integrative learning experience (ILE) that demonstrates synthesis of foundational and concentration competencies. Students in consultation with faculty select foundational and concentration-specific competencies appropriate to the student?s educational and professional goals. prereq: Biostatistics program, instr consent
PUBH 7430 - Statistical Methods for Correlated Data
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Correlated data arise in many situations, particularly when observations are made over time and space or on individuals who share certain underlying characteristics. This course covers techniques for exploring and describing correlated data, along with statistical methods for estimating population parameters (mostly means) from these data. The focus will be primarily on generalized linear models (both with and without random effects) for normally and non-normally distributed data. Wherever possible, techniques will be illustrated using real-world examples. Computing will be done using R and SAS.
PUBH 7440 - Introduction to Bayesian Analysis
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Introduction to Bayesian methods. Comparison with traditional frequentist methods. Emphasizes data analysis via modern computing methods: Gibbs sampler, WinBUGS software package. prereq: [[7401 or STAT 5101 or equiv], [public health MPH or biostatistics or statistics] grad student] or instr consent
PUBH 7445 - Statistics for Human Genetics and Molecular Biology
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Introduction to statistical problems arising in molecular biology. Problems in physical mapping (radiation hybrid mapping, DDP), genetic mapping (pedigree analysis, lod scores, TDT), biopolymer sequence analysis (alignment, motif recognition), and micro array analysis. prereq: [6450, [6451 or equiv]] or instr consent; background in molecular biology recommended
PUBH 7460 - Advanced Statistical Computing
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Statistical computing using SAS, Splus, and FORTRAN or C. Use of pseudo-random number generators, distribution functions. Matrix manipulations with applications to regression and estimation of variance. Simulation studies, minimization of functions, nonlinear regression, macro programming, numerical methods of integration. prereq: [7405, biostatistics major, [C or FORTRAN]] or instr consent
PUBH 7461 - Exploring and Visualizing Data in R
Credits: 2.0 [max 2.0]
Typically offered: Every Fall
This course is intended for students, both within and outside the School of Public Health, who want to learn how to manipulate data, perform simple statistical analyses, and prepare basic visualizations using the statistical software R. While the tools and techniques taught will be generic, many of the examples will be drawn from biomedicine and public health.
PUBH 7462 - Advanced Programming and Data Analysis in R
Credits: 2.0 [max 2.0]
Typically offered: Every Spring
This course is intended for students who are relatively proficient with R, and are looking to improve their coding and data analysis skills. The emphasis will be on learning tools and techniques which are useful to students who will be doing non-trivial programming and/or data analysis in either a research or production environment.
PUBH 7465 - Biostatistics Consulting
Credits: 3.0 [max 3.0]
Typically offered: Periodic Spring
Professional roles/responsibilities of practicing biostatistician as consultant/collaborator in health science research. Discussion, written assignments, student presentations, meeting notes, interviews, guests. prereq: [[[7405, 7406, 7407] or [STAT 8051, STAT 8052]], [[STAT 5101, STAT 5102] or [STAT 8101, STAT 8102]], biostatistics major] or instr consent
PUBH 7470 - Study Designs in Biomedical Research
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Diagnostic medicine, including methods for ROC curve. Bioassays. Early-phase clinical trials, methods including dose escalation, toxicity, and monitoring. Quality of life. prereq: [[6450, 6451] or equiv], [grad student in biostatistics or statistics or clinical research], familiarity with SAS
PUBH 7475 - Statistical Learning and Data Mining
Credits: 3.0 [max 3.0]
Typically offered: Periodic Spring
Various statistical techniques for extracting useful information (i.e., learning) from data. Linear discriminant analysis, tree-structured classifiers, feed-forward neural networks, support vector machines, other nonparametric methods, classifier ensembles, unsupervised learning. prereq: [[[6450, 6452] or equiv], programming backgroud in [FORTRAN or C/C++ or JAVA or Splus/R]] or instr consent; 2nd yr MS recommended
PUBH 7485 - Methods for Causal Inference
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Although most of statistical inference focuses on associational relationships among variables, in many biomedical and health sciences contexts the focus is on establishing the causal effect of an intervention or treatment. Drawing causal conclusions can be challenging, particularly in the context of observational data, as treatment assignment may be confounded. The first part of this course focuses on methods to establish the causal effect of a point exposure, i.e., situations in which treatment is given at a single point in time. Methods to estimate causal treatment effects will include outcome regression, propensity score methods (i.e., inverse weighting, matching), and doubly robust approaches. The second half of the course focuses on estimating the effect of a series of treatment decisions during the course of a chronic disease such as cancer, substance abuse, mental health disorders, etc. Methods to estimate these time-varying treatments include marginal structural models estimated by inverse probability weighting, structural nested models estimated by G-estimation, and the (parametric) G-computation algorithm. We will then turn our attention to estimating the optimal treatment sequence for a given subject, i.e., how to determine “the right treatment, for the right patient, at the right time,” using dynamic marginal structural models and methods derived from reinforcement learning (e.g., Q-learning, A-learning) and classification problems (outcome weighted learning, C-learning). PubH 8485 is appropriate for Ph.D students in Biostatistics and Statistics. The homework and projects will focus more on the theoretical aspects of the methods to prepare students for methodological research in this area. PubH 7485 is appropriate for Masters students in Biostatistics and PhD students in other fields who wish to learn causal methods to apply them to topics in the health sciences. This course uses the statistical software of R, a freely available statistical software package, to implement many of the methods we discuss. However, most of the methods discussed in this course can be implemented in any statistical software (e.g., SAS, Stata, SPSS, etc.) and students will be free to use any software for homework assignments.
PUBH 8422 - Modern Nonparametrics
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Classical nonparametric inference, exact tests, and confidence intervals. Robust estimates. The jackknife. Bootstrap and cross-validation. Nonparametric smoothing and classification trees. Models/applications. Formal development sufficient for understanding statistical structures/properties. Substantial computing. prereq: [7406, STAT 5102, [public health or grad student]] or instr consent
PUBH 8472 - Spatial Biostatistics
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Spatial data, spatial statistical models, and spatial inference on unknown parameters or unobserved spatial data. Nature of spatial data. Special analysis tools that help to analyze such data. Theory/applications. prereq: [[STAT 5101, STAT 5102] or [STAT 8101, STAT 8102]], some experience with S-plus; STAT 8311 recommended