Program Requirements
General Program Requirements:
Number of Credits Required to Earn the Degree: 36
Required Courses:
| Code | Title | Credit Hours |
|---|---|---|
| College Core Courses | ||
| HRPR 5001 | Current and Emerging Issues in Public Health and Health Professions | 0 |
| Public Health Data Science Core Courses 1 | ||
| EPBI 5002 | Biostatistics | 3 |
| EPBI 5201 | Epidemiological Research Methods I | 3 |
| EPBI 5208 | Data Management and Analysis | 3 |
| EPBI 8012 | Multivariable Biostatistics | 3 |
| EPBI 8305 | Big Data Analytics for Health Research | 3 |
| EPBI 8306 | Statistical Inference with Applications in Health | 3 |
| Required Programming Course 1 | ||
| HIM 5102 | Applications of Computer Programming in Health Informatics | 3 |
| Electives 2 | ||
| Select four (4) from the following: | 12 | |
EPBI 5003 | Spatial Analysis in Public Health | |
EPBI 8201 | Structural Equation Modeling | |
EPBI 8204 | Multilevel Modeling in Interdisciplinary Research | |
EPBI 8301 | Clinical Research Methods in Public Health | |
EPBI 8302 | Behavioral Measurement | |
EPBI 8304 | Applied Statistical Methods for Incomplete Data Analysis | |
EPBI 8403 | Applied Concepts and Methods in Health Research | |
HIM 5299 | Introduction to Language Processing and Text Mining for Health Professionals | |
HIM 8216 | Applications of Machine Learning for Health Informatics | |
| Consulting Practicum | ||
| EPBI 9187 | Biostat Cnslt Practicum | 3 |
| Total Credit Hours | 36 | |
- 1
Core courses may be substituted with other courses with approval.
- 2
Other electives may be selected with approval of advisor.
Minimum Grade to be Earned for All Required Courses: B-
Culminating Event:
Biostatistics Practicum:
Biostatistics is a field concerned with research subjects motivated by real data and problems in public health, biology and medicine. Through our Biostatistics Core, students gain critical hands-on experience in collaborative projects. EPBI 9187 Biostat Cnslt Practicum is a project-based course that prepares students to collaborate effectively as biostatisticians in the workforce. Emphasis is on providing hands-on experience using statistical techniques on real-life applications and developing communication and problem-solving skills. This course is designed for graduate students to achieve fluency in widely used statistical software, such as R and SAS, for the analyses of data from observational and/or interventional research studies.