Graduate programs
The School of Public Health graduate programs are designed to prepare students for professional work in the fields of community health, health promotion, health management, and health policy in a wide variety of settings. Students may also complete a plan of study that prepares them to pursue a doctoral degree in a health-related area.
The School of Public Health offers eight graduate degrees.
1. A Master of Public Health (M.P.H.) degree in Health Promotion.
2. A Master of Public Health (M.P.H.) degree in Health Management and Policy.
3. A Master of Public Health (M.P.H.) degree in Biostatistics
4. A Master of Public Health (M.P.H.) degree in Environmental Systems and Human Health
5. A Master of Public Health (M.P.H.) degree in Public Health Practice
6. A Master of Public Health (M.P.H.) degree in Epidemiology
7. A Master of Science (M.S.) degree in Biostatistics.
Students with a wide variety of undergraduate degrees and professional experience are admitted to the School of Public Health.
Biostatistics MS
The Master of Science in Biostatistics degree is designed to provide graduate level training in the application and theory of biostatistics. The program is primarily aimed at those wishing to pursue careers as intermediate level biostatisticians or apply for doctoral programs in Biostatistics. The program is also appropriate for individuals who have earned a Graduate Certificate in Biostatistics and wish to pursue further training.
Target audiences for this program include individuals who desire careers as collaborative biostatisticians in the basic, clinical, translational or population sciences. The program will also be appropriate for some clinical and translational researchers (e.g. K awardees or postdoctoral trainees), students in other Oregon graduate programs, as well as working professionals throughout the state and region (e.g. public health practitioners, laboratory scientists, data managers, database programmers, other research professionals).
All faculty members in the Department’s Division of Biostatistics are actively involved with externally funded projects. Students will have opportunities to work with real world applications under the supervision of faculty.
Program Competencies
Students graduating from this program will be able to:
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Apply intermediate to advanced biostatistical theory and techniques to design, plan, and manage data collection to conduct analysis for own research projects or support collaborative research teams
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Translate broad research goals into specifications and procedures for statistical analysis and interpretation of results in basic, clinical, translational and public health research studies
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Select and use appropriate statistical analysis software for assessment, decision-making and information-sharing (e.g., Stata, SAS, R or other special programs)
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Communicate statistical methods and findings clearly and unambiguously to specialists and non-specialist audiences
Required Coursework (40 credits)
| BSTA 511 | Estimation and Hypothesis Testing for Applied Biostatistics | 4 |
| BSTA 512 | Linear Models | 4 |
| BSTA 513 | Categorical Data Analysis | 4 |
| BSTA 514 | Statistical Analysis of Time-to-Event Data | 3 |
| BSTA 517 | Statistical Methods in Clinical Trials | 3 |
| BSTA 519 | Applied Longitudinal Data Analysis | 3 |
| BSTA 530 | Biostatistics Lab | 3 |
| BSTA 550 | Intro to Probability | 3 |
| BSTA 551 | Statistical Inference I | 3 |
| BSTA 552 | Statistical Inference II | 3 |
| Epi 512 | Epidemiology I | 4 |
| PHE 513 | Introduction to Public Health | 3 |
| | Comprehensive Examination: Written section | 0; Pass |
| | Comprehensive Examination: Lab section | 0; Pass |
Elective Coursework (14 credits)
| BMI 550 | Computational Biology I | 4 |
| BMI 551 | Computational Biology II | 4 |
| BSTA 500 | Reading and Research in Biostatistics | 1-3 |
| BSTA 515 | Data Management & Analysis in SAS | 3 |
| BSTA 516 | Design and Analysis of Surveys | 3 |
| BSTA 521 | Bayesian Methods for Data Analysis | 3 |
| BSTA 522 | Statistical Learning and Data Science | 3 |
| BSTA 523 | Design and Analysis of Experimental Designs | 3 |
| BSTA 524 | Statistical Methods for Next Gen Sequencing | 3 |
| BSTA 526 | R programming for Health Data Science | 3 |
| Epi 513 | Epidemiology II | 4 |
| Stat 567 | Applied Probability I | 3 |
| Stat 568 | Applied Probability II | 3 |
| Stat 580 | Nonparametric Methods | 3 |
Other courses may be may be used as electives with approval from the Program Director.
Total Credit Hours: 54
For more information and instruction on how to apply visit the MS in Biostatistics web page.