Twin Cities campus
 
Twin Cities Campus

Biostatistics M.S.

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, Minneapolis, MN 55455 (612-626-3500 OR 1-800-774-8636, Fax: 612-624-4498)
  • Program Type: Master's
  • Requirements for this program are current for Spring 2019
  • Length of program in credits: 30 to 31
  • This program does not require summer semesters for timely completion.
  • Degree: Master of Science
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. With an MS in biostatistics, you’ll collaborate in the design of biomedical studies, analyze data, and put the results in context for researchers. You’ll need mathematical, computational, and communication skills, as well as a curiosity about science.
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 3.10.
Other requirements to be completed before admission:
For the MS, prospective applicants should have taken at least three semesters of calculus (including multivariable calculus) and one semester of linear algebra. A year (two semesters) of coursework in undergraduate-level probability and mathematical statistics is recommended. Experience with a programming language (e.g., R, Java, C, Python) and exposure to applied statistics is helpful, but not required.
Special Application Requirements:
Students should apply for admission during fall semester only. New students are not admitted in spring semester.
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: 4.5
International applicants must submit score(s) from one of the following tests:
  • TOEFL
    • Internet Based - Total Score: 100
    • Paper Based - Total Score: 600
  • IELTS
    • Total Score: 7.0
  • MELAB
    • 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 A: Plan A requires 20 major credits, 0 credits outside the major, and 10 thesis credits. The final exam is oral.
Plan B: Plan B requires 31 major credits and 0 credits outside the major. The final exam is oral.
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.
A comprehensive written exam is taken after year 1 spring semester final examinations. An advanced background in mathematics or theoretical statistics is necessary for the Plan A (thesis) option. Students considering the Plan A must first consult with the advisor and director of graduate studies.
Coursework (31 Credits)
Plan B students take the following courses for at least 31 credits. Plan A students select at least 20 credits from the following in consultation with the advisor and director of graduate studies.
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)
PUBH 7494 - Integrative Learning Experience: Biostatistics (1.0-3.0 cr)
STAT 5101 - Theory of Statistics I (4.0 cr)
or STAT 8101 - Theory of Statistics 1 (3.0 cr)
STAT 5102 - Theory of Statistics II (4.0 cr)
or STAT 8102 - Theory of Statistics 2 (3.0 cr)
Public Health Foundations
PUBH 6250 - Foundations of Public Health (2.0 cr)
Biostatistics Electives
Take at least 8 Biostatistics elective credits from the following:
GEOG 5561 - Principles of Geographic Information Science (4.0 cr)
or GIS 5571 - ArcGIS I (3.0 cr)
or MATH 5615H - Honors: Introduction to Analysis I (4.0 cr)
or MATH 5616H - Honors: Introduction to Analysis II (4.0 cr)
or PUBH 7430 - Statistical Methods for Correlated Data (3.0 cr)
or PUBH 7435 - Latent Variable Measurement Models and Path Analysis (3.0 cr)
or PUBH 7440 - Introduction to Bayesian Analysis (3.0 cr)
or PUBH 7445 - Statistics for Human Genetics and Molecular Biology (3.0 cr)
or PUBH 7460 - Advanced Statistical Computing (3.0 cr)
or PUBH 7461 - Exploring and Visualizing Data in R (2.0 cr)
or PUBH 7462 - Advanced Programming and Data Analysis in R (2.0 cr)
or PUBH 7465 - Biostatistics Consulting (3.0 cr)
or PUBH 7470 - Statistics for Translational and Clinical Research (3.0 cr)
or PUBH 7475 - Statistical Learning and Data Mining (3.0 cr)
or PUBH 7485 - Methods for Causal Inference (3.0 cr)
or PUBH 8422 - Modern Nonparametrics (3.0 cr)
or PUBH 8435 - Latent Variable Measurement Models and Path Analysis (3.0 cr)
or PUBH 8472 - Spatial Biostatistics (3.0 cr)
or PUBH 8475 - Statistical Learning and Data Mining (3.0 cr)
or STAT 5401 - Applied Multivariate Methods (3.0 cr)
or STAT 5601 - Nonparametric Methods (3.0 cr)
or WRIT 5051 - Graduate Research Writing for International Students (3.0 cr)
or WRIT 5052 - Graduate Research Presentations and Conference Writing for Non-Native Speakers of English (3.0 cr)
 
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· Biostatistics MS Sample Plan
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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]
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
STAT 5101 - Theory of Statistics I
Credits: 4.0 [max 4.0]
Course Equivalencies: 00259 - Math 5651/Stat 5101
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 8101 - Theory of Statistics 1
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Review of linear algebra. Introduction to probability theory. Random variables, their transformations/expectations. Standard distributions, including multivariate Normal distribution. Probability inequalities. Convergence concepts, including laws of large numbers, Central Limit Theorem. delta method. Sampling distributions. prereq: Statistics grad major or instr consent
STAT 5102 - Theory of Statistics II
Credits: 4.0 [max 4.0]
Course Equivalencies: Stat 4102/5102
Typically offered: Every Spring
Sampling, sufficiency, estimation, test of hypotheses, size/power. Categorical data. Contingency tables. Linear models. Decision theory. prereq: 5101 or Math 5651
STAT 8102 - Theory of Statistics 2
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Statistical inference. Sufficiency. Likelihood-based methods. Point estimation. Confidence intervals. Neyman Pearson hypothesis testing theory. Introduction to theory of linear models. prereq: 8101, Statistics graduate major or instr consent
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.
GEOG 5561 - Principles of Geographic Information Science
Credits: 4.0 [max 4.0]
Course Equivalencies: 02490
Typically offered: Every Fall & Spring
Introduction to the study of geographic information systems (GIS) for geography and non-geography students. Topics include GIS application domains, data models and sources, analysis methods and output techniques. Lectures, reading, and hands-on experience with GIS software. prereq: grad
GIS 5571 - ArcGIS I
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
First of a two-course series focusing on ArcGIS Desktop. Overview of ArcGIS system and its use for spatial data processing. Data capture, editing, geometric transformations, map projections, topology, Python scripting, and map production. prereq: [GEOG 5561 or equiv, status in MGIS program, familiarity with computer operating systems] or instr consent
MATH 5615H - Honors: Introduction to Analysis I
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Axiomatic treatment of real/complex number systems. Introduction to metric spaces: convergence, connectedness, compactness. Convergence of sequences/series of real/complex numbers, Cauchy criterion, root/ratio tests. Continuity in metric spaces. Rigorous treatment of differentiation of single-variable functions, Taylor's Theorem. prereq: [[2243 or 2373], [2263 or 2374], [2283 or 3283]] or 2574
MATH 5616H - Honors: Introduction to Analysis II
Credits: 4.0 [max 4.0]
Typically offered: Every Spring
Rigorous treatment of Riemann-Stieltjes integration. Sequences/series of functions, uniform convergence, equicontinuous families, Stone-Weierstrass Theorem, power series. Rigorous treatment of differentiation/integration of multivariable functions, Implicit Function Theorem, Stokes' Theorem. Additional topics as time permits. prereq: 5615
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 7435 - Latent Variable Measurement Models and Path Analysis
Credits: 3.0 [max 3.0]
Course Equivalencies: 01262 - PubH 7435/PubH 8435
Typically offered: Every Fall
Introduction to use of latent variable models. Exploratory/confirmatory factor analysis, path analysis, structural equation modeling, latent trait models, latent class models. Uses SAS/AMOS software. prereq: [6414, 6415] or [6450, 6451] or biostatistics major or instr consent
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 - Statistics for Translational and Clinical Research
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Diagonostic medicine, including methods for ROC curve. Bioassays. Early-phase clinical trials, methods including dose escalation, toxicity, and monitoring. Qualitiy 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 8435 - Latent Variable Measurement Models and Path Analysis
Credits: 3.0 [max 3.0]
Course Equivalencies: 01262
Typically offered: Every Fall
Introduction to use of statistical techniques known collectively as latent variable models. Exploratory/confirmatory factor analysis, path analysis, structural equation modeling, latent trait models, latent class models. SAS/AMOS software are used. prereq: Biostatistics PhD 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
PUBH 8475 - Statistical Learning and Data Mining
Credits: 3.0 [max 3.0]
Course Equivalencies: 01358
Typically offered: Periodic Spring
Statistical techniques for extracting useful information from data. Linear discriminant analysis, tree-structured classifiers, feed-forward neural networks, support vector machines, other nonparametric methods, classifier ensembles (such as bagging/boosting), unsupervised learning. prereq: [[[6450, 6451, 6452] or STAT 5303 or equiv], [biostatistics or statistics PhD student]] or instr consent
STAT 5401 - Applied Multivariate Methods
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall
Bivariate and multivariate distributions. Multivariate normal distributions. Analysis of multivariate linear models. Repeated measures, growth curve and profile analysis. Canonical correlation analysis. Principal components and factor analysis. Discrimination, classification, and clustering.
STAT 5601 - Nonparametric Methods
Credits: 3.0 [max 3.0]
Typically offered: Every Fall & Spring
Order statistics. Classical rank-based procedures (e.g., Wilcoxon, Kruskal-Wallis). Goodness of fit. Topics may include smoothing, bootstrap, and generalized linear models. prereq: 3022 or 4102 or 5021 or 5102 or instr consent
WRIT 5051 - Graduate Research Writing for International Students
Credits: 3.0 [max 3.0]
Typically offered: Every Fall, Spring & Summer
Graduate-level writing techniques/formats for summaries, critiques, research, and abstracts. Persuasion, documentation, structure, grammar, vocabulary, field-specific requirements. Writing through several drafts, using mentor in specific field of study. Revising/editing to meet graduate standards. Discussions. prereq: Grad student
WRIT 5052 - Graduate Research Presentations and Conference Writing for Non-Native Speakers of English
Credits: 3.0 [max 3.0]
Typically offered: Every Fall & Spring
Practice in writing/presenting graduate-level research for conferences or professional seminars. Delivery of professional academic presentations to U.S. audiences. Conference abstract, paper, and poster presentation. Communication in research process. Students select topics from their own research/studies. Format, style, transitions, topic narrowing, non-verbal presentation skills. prereq: [Grad student, non-native speaker of English] or instr consent