Health Data Science - MS
The Master of Science program in Health Data Science, administered by the Department of Biostatistics and Bioinformatics in the Milken Institute School of Public Health, develops leaders and practitioners in public health and medicine. Students in the program develop practical skills for innovative data analysis and will be trained in becoming excellent communicators of scientific findings in public health and biomedical research. The program takes advantage of the rich bioinformatics and biostatistical resources at GW and in the nation’s capital and is designed to prepare students to be independent practitioners and collaborators in interdisciplinary research. The MS in Health Data Science program requires the completion of 36 credits. Coursework can generally be completed in 2 years as a full-time student or 4 years as a part-time student, though we offer flexible pathways to degree completion.
Upon completion of the MS program in Health Data Science, students will possess the following competencies.
- Programming: Develop skills in programming, data structures, algorithms, machine learning, high-performance computing and apply these skills to create approaches that facilitate biological data analysis.
- Biology: Develop a basis of knowledge in biology and evaluate biological data generation technologies.
- Statistics: Apply statistical research methods in the context of molecular biology, genomics, medical, and population genetics research.
- Foundational Knowledge: Interpret and synthesize the various foundational concepts of bioinformatics, including genomics, algorithms, and other key tools used in bioinformatics.
- Conceptual Integration: Integrate concepts and data across fields of computer science, statistics, data science, biology, and health sciences through bioinformatics.
Program Director: Dr. Marcos Pérez Losada
All applicants to the MS in Health Data Science program must hold an undergraduate degree from an accredited institution of higher learning and should have a strong background in mathematics, statistics, biology, bioengineering, and/or computer science. Applicants must have completed the following prerequisite courses (assumed at the undergraduate level) to be considered for admission:
- One course in statistics
- One course in biology
- One course in computer science
Course descriptions are available in the GW Bulletin.
See the program guide and the SPH Graduate Student Handbook for additional information.
- Core Courses
PUBH 6080 | Pathways to Public Health (0 credits)*
PUBH 6850 | Introduction to SAS for Public Health Research (1 credit)
PUBH 6851 | Introduction to R for Public Health Research (1 credit)
PUBH 6852 | Introduction to Python for Public Health Research (1 credit)
PUBH 6854 | Applied Computing (3 credits)
PUBH 6860 | Principles of Bioinformatics (3 credits)
PUBH 6868 | Quantitative Methods (3 credits)
PUBH 6884 | Bioinformatics Algorithms and Data Structures (3 credits)
PUBH 6886 | Statistical and Machine Learning for Public Health Research (3 credits)* See the Graduate Advising page for more information.
CORE TOTAL: 18 CREDITS
- Electives
Students may choose any graduate-level course (6000 level or higher) at SPH, in consultation with their advisor. Pre-approved options are listed on the program guide.
ELECTIVES: 16 CREDITS
- Research and Thesis
PUBH 6897 | Research in Biostatistics and Bioinformatics (1 credit minimum)
PUBH 6898 | Master of Science Thesis (1 credit minimum)Students are required to complete a minimum of 2 credits of research and Master's thesis work. Any additional research and thesis credits taken may count towards elective credits.
Students must successfully defend their Master's thesis or a presentation of a research report. This defense is in addition to PUBH 6898.
RESEARCH/THESIS TOTAL: 2 CREDITS
- Non-Academic Requirements
Professional Enhancement
Students must participate in eight hours of Professional Enhancement. These activities may be public health-related lectures, seminars, or symposia related to your field of study.
Professional Enhancement activities supplement the rigorous academic curriculum of the SPH degree programs and help prepare students to participate actively in the professional community. You can learn more about opportunities for Professional Enhancement via the Milken Institute School of Public Health Listserv, through departmental communications, or by speaking with your advisor.
Students must submit a completed Professional Enhancement Form to the student records office.
Complete Human Subjects Research Training Requirements
All students are required to complete the Basic CITI training module in Social and Behavioral Research prior to beginning the practicum. This online training module for Social and Behavioral Researchers will help new students demonstrate and maintain sufficient knowledge of the ethical principles and regulatory requirements for protecting human subjects - key for any public health research.
Academic Integrity Quiz
All Milken Institute School of Public Health students are required to review the University’s Code of Academic Integrity and complete the GW Academic Integrity Activity. This activity must be completed within 2 weeks of matriculation. Information on SPH Academic Integrity requirements can be found here.
- Program Guides
Students in the MS in Health Data Science should refer to the guide from the year in which they matriculated into the program. For the current program guide, click the "PROGRAM GUIDE" button at the top of the page.
In the past this program had different names. Students who entered those programs should follow the program guide from the academic year in which they applied and entered the program.
Students pursuing a MS in Health Data Science have access to a world-class faculty with relevant expertise and diverse experience in all sectors of public health and medical research. Areas of interest and research experience for professors and lecturers in the program include: clinical trials, statistical modeling, machine learning, computing and software development, survival analysis, and finite population sampling, with applications in infectious diseases (including COVID-19, HIV, and bacterial superbug infections), mental health, diabetes, maternal-fetal medicine, and cardiovascular disease. Learn about the Department of Biostatistics and Bioinformatics faculty here.