Program Requirements

General Program Requirements:
Number of Credits Required to Earn the Degree: 36

Required Courses:

College Core Courses
EPBI 5201Epidemiological Research Methods I3
HRPR 5001Current and Emerging Issues in Public Health and Health Professions0
Public Health Data Science Core Courses
EPBI 5002Biostatistics3
EPBI 8012Multivariable Biostatistics3
EPBI 8208Data Management and Analysis3
STAT 8001Probability and Statistics Theory I3
STAT 8002Probability and Statistics Theory II3
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 9187Biostat Cnslt Practicum3
Total Credit Hours36

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.