Program Requirements
General Program Requirements:
Number of Credits Required to Earn the Degree: 36
Required Courses:
| Code | Title | Credit Hours |
|---|---|---|
| College Core Courses | ||
| EPBI 5201 | Epidemiological Research Methods I | 3 |
| HRPR 5001 | Current and Emerging Issues in Public Health and Health Professions | 0 |
| Public Health Data Science Core Courses | ||
| EPBI 5002 | Biostatistics | 3 |
| EPBI 8012 | Multivariable Biostatistics | 3 |
| EPBI 8208 | Data Management and Analysis | 3 |
| STAT 8001 | Probability and Statistics Theory I | 3 |
| STAT 8002 | Probability and Statistics Theory II | 3 |
| Select one from the following programming courses: | 3 | |
HIM 5102 | Applications of Computer Programming in Health Informatics | |
HIM 5190 | Special Topics 1 | |
HIM 5299 | Introduction to Language Processing and Text Mining for Health Professionals | |
| Public Health Data Science Electives | ||
| Select four from the following: | 12 | |
EPBI 8201 | Structural Equation Modeling | |
EPBI 8204 | Multilev Mod in Int Res | |
EPBI 8302 | Behavioral Measurement | |
EPBI 8304 | Applied Statistical Methods for Incomplete Data Analysis | |
EPBI 8305 | ||
EPBI 8306 | ||
EPBI 8403 | Applied Concepts and Methods in Health Research | |
GUS 5069 | GIS for Health Data Analysis | |
| Consulting Practicum | ||
| EPBI 9187 | Biostat Cnslt Practicum | 3 |
| Total Credit Hours | 36 | |
- 1
Students are expected to take "Applications of Computer Programming in Health Informatics" as the HIM 5190 Special Topics course.
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.