MS in Biostatistics
Degree Description and Learner Objectives
The M.S. program trains students in the planning and data analysis of biomedical and public health studies, and is designed to take two years to complete. The degree requirements and electives include biostatistics courses, statistics courses, and health-related courses. Program graduates successfully compete for positions in research institutions, pharmaceutical companies, government agencies, and universities.
Upon completion of the M.S. in Biostatistics, the student should be prepared to function as a statistician or statistical consultant. Therefore the student must have an extensive understanding of statistical theory and practice and should be proficient in the application of statistical methods to one or more areas in the health sciences. At the completion of the M.S. degree in Biostatistics the graduate should be able to:
- Demonstrate a broad knowledge and understanding of current statistical theory, methods, and practices in the health sciences
- Effectively collaborate on a research team
- Develop statistical designs and implement analyses for health science investigations
- Develop computer programs for the management and analysis of data sets
- Prepare reports and publications resulting from health science studies
- Effectively communicate key statistical principles to a non-statistical audience
Prerequisites
A bachelor’s degree in mathematical, biological, or physical sciences is recommended.
The applicant’s training should include three semesters of calculus, a course in linear algebra, and the ability to program in at least one computer language. Applicants will be asked to provide, as part of the application, transcript verification or a brief statement indicating whether and how the calculus and linear algebra prerequisites have been met, either through coursework at the University of Iowa (MATH:1850, MATH:1860, MATH:2850, MATH:2700) or through comparable regularly-scheduled coursework or independent study at other institutions.
Course Requirements
| Number | Course | Hours |
|---|---|---|
| Required courses | ||
| BIOS:5510 | Biostatistical Computing | 4 s.h. |
| BIOS:5710 | Biostatistical Methods I | 4 s.h. |
| BIOS:5720 | Biostatistical Methods II | 4 s.h. |
| BIOS:5730 | Biostatistical Methods in Categorical Data | 3 s.h. |
| BIOS:6610 | Statistical Methods in Clinical Trials | 3 s.h. |
| BIOS:7500 | Preceptorship in Biostatistics* | 3 s.h. |
| STAT:4100/4101 or STAT:5100/5101 | Mathematical Statistics I & II or Statistical Inference I & II | 6 s.h. |
| EPID:4400 | Epidemiology I: Principles | 3 s.h. |
| CPH:6100 | Essentials of Public Health | 2 s.h. |
| BIOS:7270 | Scholarly Integrity in Biostatistics | 1 s.h. |
*Preceptorship may be taken for only 1 s.h. if the student has sufficient experience in biostatistical collaborations, as determined by the student’s advisor and the Director of Graduate Studies.
Electives:
Complete 5-6 s.h. of elective courses, of which at least 3 s.h. must be in quantitative coursework (i.e., Statistics or Biostatistics). It is recommended that students consider a Biology/Public Health course as the other elective – particularly for those who have not had prior exposure to these areas. Electives must be approved by the advisor and the Director of Graduate Studies.
| Number | Course | Semester | Hours |
|---|---|---|---|
| BIOS:6210 | Applied Survival Analysis | (Spring) | 3 s.h. |
| BIOS:6310 | Introductory Longitudinal Data Analysis | (Fall) | 3 s.h. |
| BIOS:6420 | Survey Design and Analysis | (Spring even) | 3 s.h. |
| BIOS:6650 | Causal Inference | (Spring) | 3 s.h. |
| BIOS:6720 | Machine Learning for Biomedical Data | (Spring even) | 3 s.h. |
| BIOS:6810 | Bayesian Methods & Design | (Fall even) | 3 s.h. |
| BIOS:7110 | Likelihood Theory and Extensions | (Fall) | 4 s.h. |
| BIOS:7210 | Survival Data Analysis | (Fall odd) | 3 s.h. |
| BIOS:7230 | Advanced Clinical Trials | (Fall even) | 3 s.h. |
| BIOS:7240 | High-Dimensional Data Analysis | (Spring odd) | 3 s.h. |
| BIOS:7250 | Theory of Linear/Generalized Linear Models | (Spring) | 4 s.h. |
| BIOS:7310 | Longitudinal Data Analysis | (Spring odd) | 3 s.h. |
| BIOS:7330 | Advanced Biostatistical Computing | (Fall 0dd) | 3 s.h. |
| BIOS:7410 | Analysis of Categorical Data | (Spring even) | 3 s.h. |
| BIOS:7600 | Advanced Biostatistics Seminar | (arr) | 1-3 s.h. |
| BIOS:7700 | Problems/Special Topics in Biostatistics | (arr) | 1 s.h. |
| BME:5335 | Computational Bioinformatics | (Spring) | 3 s.h. |
| DATA:6200 | Predictive Analytics | (Spring) | 3 s.h. |
| STAT:4520 | Bayesian Statistics | (Fall) | 3 s.h. |
| STAT:4540 | Statistical Learning | (Fall) | 3 s.h. |
| STAT:4580 | Data Visualization and Data Technologies | (Spring) | 3 s.h. |
| STAT:6560 | Applied Time Series Analysis | (Spring) | 3 s.h. |
| STAT:7400 | Computer Intensive Statistics | (Spring) | 3 s.h. |
| CS:5110 | Introduction to Informatics | (Fall) | 3 s.h. |
| ISE:4172 | Big Data Analysis | (Fall) | 3 s.h. |
Possible Public Health or Biology Electives
| Number | Course | Semester | Hours |
|---|---|---|---|
| BIOL:4213 | Bioinformatics | (Fall) | 4 s.h. |
| CBH:4105 | Introduction to Health Promotion & Disease Prevention | (Spring) | 3 s.h. |
| CPH:5100 | Introduction to Public Health | (Fall) | 3 s.h. |
| GENE:7191 | Human Molecular Genetics | (Spring) | 3 s.h. |
| HMP:4000 | Introduction to U.S. Health Care System | (Spring) | 3 s.h. |
| OEH:4240 | Global Environmental Health | (Fall) | 3 s.h. |
| PATH:5270 | Pathogenesis of Major Human Diseases | (Spring) | 3 s.h. |
| PATH:8133 | Introduction to Human Pathology | (Fall) | 2-4 s.h. |
The student must complete at least 38 semester hours of coursework. The student may choose to take additional graduate-level courses in consultation with her/his advisor.
Master’s Examination
The master’s core examination is a written in-class exam focusing on the required biostatistics and statistics coursework. This exam is offered twice per year.