Program Requirements

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

Required Courses:

College Core Courses
±á¸é±Ê¸éÌý5001Current and Emerging Issues in Public Health and Health Professions0
·¡±Êµþ±õÌý5201Epidemiological Research Methods I3
Public Health Data Science Core Courses
·¡±Êµþ±õÌý5002Biostatistics3
·¡±Êµþ±õÌý5208Data Management and Analysis3
·¡±Êµþ±õÌý8012Multivariable Biostatistics3
³§°Õ´¡°ÕÌý8001Probability and Statistics Theory I 13
³§°Õ´¡°ÕÌý8002Probability and Statistics Theory II 13
Required Programming Course 2
Select one of the following:3
±á±õ²ÑÌý5102
Applications of Computer Programming in Health Informatics
±á±õ²ÑÌý5299
Introduction to Language Processing and Text Mining for Health Professionals
Electives 3
Select four from the following:12
·¡±Êµþ±õÌý5003
Spatial Analysis in Public Health
·¡±Êµþ±õÌý8201
Structural Equation Modeling
·¡±Êµþ±õÌý8204
Multilevel Modeling in Interdisciplinary Research
·¡±Êµþ±õÌý8301
Clinical Research Methods in Public Health
·¡±Êµþ±õÌý8302
Behavioral Measurement
·¡±Êµþ±õÌý8304
Applied Statistical Methods for Incomplete Data Analysis
·¡±Êµþ±õÌý8305
·¡±Êµþ±õÌý8306
·¡±Êµþ±õÌý8403
Applied Concepts and Methods in Health Research
Consulting Practicum
·¡±Êµþ±õÌý9187Biostat Cnslt Practicum3
Total Credit Hours36
1

Equivalent course may be taken with approval of advisor.

2

May be selected as a general elective if not taken as a programming course.

3

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. ·¡±Êµþ±õÌý9187 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.