Doctor of Philosophy in the Field of Health Data Science, Applied Bioinformatics Concentration

Program Director: K Crandall

The doctor of philosophy in health data science develops data science leaders for applications in public health and medicine. The program advances the field by:

• Providing rigorous training in the fundamentals of health and biomedical data science.
• Fostering innovative thinking for the design, conduct, analysis, and reporting of public health research studies.
• Providing practical training through real-world research opportunities at research centers and institutes directed by departmental faculty.

Students choose one of two concentrations: applied biostatistics or applied bioinformatics.

The program offers a unique blend of the two disciplines, which helps practitioners become successful collaborators in interdisciplinary research. Each concentration focuses on the foundations of the respective discipline to acquire fundamental knowledge and experience in the subject area while gaining core knowledge in the foundations of the other concentration. 

Visit the Milken Institute School of Public Health website for additional information about academic programs and information about GWSPH. Graduate admissions information, including application requirements and deadlines, can be found on the GWSPH Graduate Admissions website.

The following requirements must be fulfilled: 72 credits, including 14 credits in core courses, 15 credits in required concentration courses, 18 minimum credits in elective courses, a 1 credit in practicum, and 12 to 24 credits in dissertation research. Additional requirements include, but are not limited to, completion of a graduate teaching assistantship program certificate.

Course List

Code	Title	Credits
Required
Core courses
PUBH 6080	Pathways to Public Health
PUBH 6421	Responsible Conduct of Research
PUBH 6850	Introduction to SAS for Public Health Research
PUBH 6851	Introduction to R for Public Health Research
PUBH 6852	Introduction to Python for Public Health Research
PUBH 6860	Principles of Bioinformatics
PUBH 6886	Statistical and Machine Learning for Public Health Research
PUBH 8099	Doctoral Topics (Cross Cutting Concepts in Public Health topic only)
PUBH 8870	Statistical Inference for Public Health Research I
Concentration-specific courses
15 credits in applied bioinformatics courses.
PUBH 6854	Applied Computing in Health Data Science
PUBH 6859	High Performance and Cloud Computing
PUBH 6861	Public Health Genomics
PUBH 6884	Bioinformatics Algorithms and Data Structures
PUBH 8885	Computational Biology
Electives
A minimum of 18 credits in elective courses. Students must take at least 3 credits in biostatistics courses and at least 3 credits in cognate area courses.
Practicum (teaching/research)
PUBH 8413	Research Leadership
Dissertation research
PUBH 8999	Dissertation Research (taken for 12 to 24 credits)
Additional requirements
Additional program requirements include but are not limited to completion of the University's Graduate Teaching Assistantship Program Certificate, which includes enrollment in UNIV 0250. *

 *Visit the GTAP website for additional information.