Master of Science in Health Data Science | The George Washington University

Program Director: Marcos Pérez Losada

The master of science in health data science program positions graduates to be leaders and practitioners in public health and medicine. The program equips students with the advanced analytical and computational skills necessary to harness data to improve health outcomes. It emphasizes the integration of data science with health sciences, enabling students to apply cutting-edge methodologies and software architectures to complex health-related challenges. In addition to fostering critical and innovative thinking, the program cultivates a commitment to advancing public health and healthcare systems through data-driven insights and solutions, qualities essential for future leaders in health data science. The program prepares students for work as data analysts, informaticians, or software developers with opportunities in governmental, academic, and private sector settings. Graduates are primed to pursue independent doctoral-level research at leading programs in bioinformatics and computational biology. Through this research, students gain an understanding of the risk factors for chronic diseases, such as cancer, and how infectious diseases, such as Zika, Ebola, HIV/AIDS, and COVID-19 spread and evolve.

Visit the program website for additional information.

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

The following requirements must be fulfilled: 36 credits, including 18 credits in required courses, 16 credits in elective courses, and a minimum of 2 credits in research and thesis coursework.

Course List
Code	Title	Credits
Required
18 credits in required courses.
PUBH 6080	Pathways to Public Health ^*
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 6854	Applied Computing in Health Data Science
PUBH 6860	Principles of Bioinformatics
PUBH 6868	Quantitative Methods
PUBH 6884	Bioinformatics Algorithms and Data Structures
PUBH 6886	Statistical and Machine Learning for Public Health Research
Electives
16 credits in pre-approved courses selected from the following:
PUBH 6859	High Performance and Cloud Computing
PUBH 6861	Public Health Genomics
PUBH 6867	Health Data Visualization
PUBH 6894	Research Analytics
PUBH 8878	Statistical Genetics
PUBH 8885	Computational Biology
Required research/thesis courses
A minimum of 1 credit must be taken in each PUBH 6897 and PUBH 689. Any additional credits count as elective credits.
PUBH 6897	Research in Biostatistics and Bioinformatics
PUBH 6898	Master of Science Thesis

^*PUBH 6080 is waived for students who have a prior degree from a Council on Education for Public Health accredited school or program in public health.

SPH MS graduation requirements

Graduate credit requirement: Successful completion of 36 graduate credits.
Curriculum: Successful completion of required courses.
Grade-point requirement: A minimum program grade-point average of 3.0 (B average).
Time limit requirement: The degree must be completed within five years.
Culminating experience: Successful completion of a culminating experience (e.g., thesis, final project, internship, and comprehensive exam).
CITI training requirement: All students are required to complete training regarding human subject protection regulation and the Health Insurance Portability and Accountability Act of 1996 (HIPAA).
Integrity quiz and plagiarism requirement: All students are required to review the George Washington University Code of Academic Integrity and take the SPH Academic Integrity Quiz within their first semester of study.
Professional enhancement requirement: Students must participate in eight hours of public health-related lectures, seminars, and symposia, related to their field of study.