Skip to main content

Health Data Science, M.S.

  1. Graduates will be able to identify and define an analytic/operational question.
  2. Graduates will be able to apply appropriate statistical methods.
  3. Graduates will be able to apply appropriate data-management strategies.
  4. Graduates will be able to critically evaluate methodological designs.
  5. Graduates will be able to understand the organization and financing of health care and resulting data sets.
  6. Graduates will be able to effectively communicate the results of analyses.
 
Applied Statistics Courses
HDS 5310Analytics, Statistics & Visualization Methods in Health Data Science3
HDS 5330Predictive Modeling and Health Machine Learning3
ORES 5160Data Management and Programming in Healthcare3
Practical Computing Courses
HDS 5230 High-Performance Computing and Health Artificial Intelligence3
HDS 5430Health Image Processing and Deep Learning3
HDS 5530Natural Language Processing and Large Language Models in Healthcare3
Health Science Applications Courses
HDS 5000Foundations in Health Data Science3
HDS 5130Healthcare Organization, Management, and Policy3
ORES 5300Foundations of Health Outcomes Research3
Capstone Experience
HDS 5960Health Data Science Capstone Experience3
Total Credits30

Continuation Standards

- Every student must maintain a 3.00 cumulative grade point average to remain in good standing in the program.
- Any course with a letter grade of C+ or below will have to be retaken. Furthermore, students should require at most two attempts to successfully complete any HCOR courses required for the degree.
- Students who fail to achieve a 3.00 GPA after completing 3 academic probation courses are reviewed by the Academic Affairs Committee for dismissal from the program.
- Students earning a grade of F may be subject to immediate dismissal upon the recommendation of the Academic Affairs Committee.

 

Roadmaps are recommended semester-by-semester plans of study for programs and assume full-time enrollment unless otherwise noted.  

Courses and milestones designated as critical (marked with !) must be completed in the semester listed to ensure a timely graduation. Transfer credit may change the roadmap.

This roadmap should not be used in the place of regular academic advising appointments. All students are encouraged to meet with their advisor/mentor each semester. Requirements, course availability and sequencing are subject to change.

Plan of Study Grid
Year One
FallCredits
HDS 5000 Foundations in Health Data Science 3
ORES 5300 Foundations of Health Outcomes Research 3
ORES 5160 Data Management and Programming in Healthcare 3
 Credits9
Spring
HDS 5310 Analytics, Statistics & Visualization Methods in Health Data Science 3
HDS 5130 Healthcare Organization, Management, and Policy 3
HDS 5330 Predictive Modeling and Health Machine Learning 3
 Credits9
Year Two
Fall
HDS 5430 Health Image Processing and Deep Learning 3
HDS 5230 High-Performance Computing and Health Artificial Intelligence 3
 Credits6
Spring
HDS 5530 3
HDS 5960 Capstone Experience 3
 Credits6
 Total Credits30