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 2018
  • Length of program in credits: 30 to 33
  • 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.
Biostatistics combines statistics, biomedical science, and computing to advance health research. Biostatisticians design, direct, and analyze clinical trials; develop new statistical methods; and analyze data from observational studies, laboratory experiments, and health surveys. This is an ideal field for students who have strong mathematical backgrounds and who enjoy working with computers, collaborating with investigators, and participating in health research. Students take courses in biostatistical methods, theory of statistics, clinical trials, statistical computing, categorical data, survival analysis, and health sciences.
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 M.S., 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 14 major credits, 6 credits outside the major, and 10 thesis credits. The final exam is oral.
Plan B: Plan B requires 22 major credits and 9 to 11 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 to be taken after finals of spring semester in year 1. The Plan B project demonstrates the student's familiarity with the tools of research or scholarship in the major, the capacity to work independently, and the ability to present the results of the investigation effectively. The master's project should involve a combined total of 120 hours of work. The Plan A project requires students to complete a thesis project. The required courses for the M.S. Degree Plan B do not prepare a student to write a thesis, that is, to do original research in biostatistics methodology. That is why nearly all students choose to do a written project (Plan B) rather than a thesis. Only students with an advanced background in mathematics or theoretical statistics should consider Plan A.
Biostatistics M.S. Coursework (Plan B)
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 - Culminating 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)
Biostatistics Electives
3 Biostatistics elective courses (at least 8 credits)
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 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 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)
Health Science Elective
Students must complete at least 3 credits of a health science elective.
CSCI 5481 - Computational Techniques for Genomics (3.0 cr)
or PMB 5301 {Inactive} (3.0 cr)
or PSY 5137 - Introduction to Behavioral Genetics (3.0 cr)
or PUBH 6020 - Fundamentals of Social and Behavioral Science (3.0 cr)
or PUBH 6101 - Environmental Health (2.0 cr)
or PUBH 6102 - Issues in Environmental Health (2.0 cr)
or PUBH 6320 - Fundamentals of Epidemiology (3.0 cr)
or PUBH 6341 - Epidemiologic Methods I (3.0 cr)
or PUBH 6381 - Genetics in Public Health in the Age of Precision Medicine (2.0 cr)
or PUBH 6751 - Principles of Management in Health Services Organizations (2.0 cr)
 
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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 - Culminating Experience: Biostatistics
Credits: 1.0 -3.0 [max 3.0]
Grading Basis: S-N only
Typically offered: Every Fall, Spring & Summer
Directed research toward completion of master's or Plan B project in biostatistics. prereq: Biostatistics major, 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
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 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 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
CSCI 5481 - Computational Techniques for Genomics
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Techniques to analyze biological data generated by genome sequencing, proteomics, cell-wide measurements of gene expression changes. Algorithms for single/multiple sequence alignments/assembly. Search algorithms for sequence databases, phylogenetic tree construction algorithms. Algorithms for gene/promoter and protein structure prediction. Data mining for micro array expression analysis. Reverse engineering of regulatory networks. prereq: 4041 or instr consent
PSY 5137 - Introduction to Behavioral Genetics
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Genetic methods for studying human/animal behavior. Emphasizes nature/origin of individual differences in behavior. Twin and adoption methods. Cytogenetics, molecular genetics, linkage/association studies. prereq: 3001W or equiv or instr consent
PUBH 6020 - Fundamentals of Social and Behavioral Science
Credits: 3.0 [max 6.0]
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. prereq: Public health [MPH or MHA or certificate] student or health journalism MA major or instr consent
PUBH 6101 - Environmental Health
Credits: 2.0 [max 2.0]
Grading Basis: A-F only
Typically offered: Every Fall & Spring
Principles of environmental health relating to macro-/micro-environments and to products consumed or used by people. prereq: Public health [MPH or MHA or certificate] student or instr consent
PUBH 6102 - Issues in Environmental Health
Credits: 2.0 [max 2.0]
Course Equivalencies: 01063 - PubH 3102/PubH 6102
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 6381 - Genetics in Public Health in the Age of Precision Medicine
Credits: 2.0 [max 2.0]
Typically offered: Every Fall
Mechanisms of molecular genetics. Issues related to medical/public health genetics, including basis of human diversity, Human Genome Project, novel genetic mechanisms underlying diseases, ethical/legal issues. prereq: Grad student or professional school student or instr consent
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