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 through training in fundamentals and innovative data analysis. The program uniquely blends the biostatistics and bioinformatics disciplines so that practitioners can become successful collaborators in interdisciplinary research. The program takes advantage of the rich biostatistical and bioinformatics resources at GW, such as the Biostatistics Center, and its location in the Nation’s Capital. It is designed to prepare students to be collaborative and independent practitioners in interdisciplinary research.
Students in this program choose one of two concentrations, applied biostatistics or applied bioinformatics. 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 track. Students will be well positioned to compete for health data science, biostatistician, or bioinformatician jobs requiring an MS level of education or to apply for study at the doctoral level in health and biomedical data science, biostatistics, bioinformatics, or another related discipline.
All applicants to the 36-credit MS program must have completed the following prerequisites (assumed at the undergraduate level), with a grade of B or better to be considered for admission:
|Applied Biostatistics||Applied Bioinformatics|
Full Pre-requisite* (in addition to above)
* May complete these classes during first term as a MS student.
MS Core Requirements
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 6860 | Principles of Bioinformatics (3 credits)
PUBH 8870 | Statistical Inference for Public Health Research I (3 credits)
SPH Course Descriptions
CORE TOTAL: 9 CREDITS
Requirements for Applied Biostatistics Concentration
PUBH 6862 | Applied Linear Regression Analysis for Public Health Research (3 credits)
PUBH 6864 | Applied Survival Analysis for Public Health Research (3 credits)
PUBH 6865 | Applied Categorical Data Analysis for Public Health Research (3 credits)
PUBH 6866 | Principles of Clinical Trials (3 credits)
PUBH 6879 | Propensity Score Methods for Causal Inference in Observational Studies (3 credits)
PUBH 6887 | Applied Longitudinal Data Analysis (3 credits)
PUBH 8870 | Statistical Inference for Public Health Research II (3 credits)
BIOSTATISTICS SPECIFIC-REQUIREMENTS TOTAL: 21 CREDITS
BIOSTATISTICS ELECTIVES: 4 CREDITS
(see pre-approved options in program guide)
Requirements for Applied Bioinformatics Concentration
PUBH 6859 | High Performance Cloud Computing (3 credits)
PUBH 6861 | Public Health Genomics (3 credits)
PUBH 6884 | Bioinformatics Algorithims and Data Structures (3 credits)
PUBH 6885 | Computational Biology (3 credits)
PUBH 6886 | Statistical and Machine Learning for Public Health and Biomedical Research (3 credits)
BIOINFORMATICS SPECIFIC REQUIREMENTS TOTAL: 15 CREDITS
BIOINFORMATICS ELECTIVES: 9 CREDITS
(see pre-approved options in program guide)
PUBH 6869 | Principles of Biostatistical Consulting (Biostatistics ONLY)- (1 credit)
PUBH 6897 | Independent Research (Bioinformatics ONLY)- (2 credits)
PUBH 6898 | DBB Master's Thesis (Both Concentrations)- (1 credit)
CONSULTING/RESEARCH/THESIS TOTAL: 2-3 CREDITS
Ethics and Professional Skills
Students in the MS, Health and Biomedical Data Science program will participate in department-led ethics and professional skills training.
Students in degree programs 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 department firstname.lastname@example.org.
Collaborative Institutional Training Initiative (CITI) Training
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 GWSPH Academic Integrity requirements can be found here.
Past Program Guides
**For graduation requirements, please download the program guide.**
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.
Co- Program Directors
Dr. Marcos Perez-Losada (Applied Bioinformatics)
Dr. Angelo Elmi (Applied Biostatistics)