MS in Public Health Data Science

The 34-unit program provides students with a solid foundation in quantitative methods. Students will learn how to make data-driven recommendations to improve public health research and interventions.

The MS curriculum will provide graduates with a skill set in data management and analysis, and application of these methods in a health-related focus area of choice. The program will prepare students for hands-on careers in health data analytics or further study in quantitative and applied fields in public health.

Learning Outcomes

Upon completion of the program, graduates will be able to:

• Critically evaluate quantitative data and methodology in research reports and peer-reviewed publications in the field of public health.
• Identify and select the appropriate study design, research methods, and data collection strategies for public health studies.
• Analyze and synthesize research findings to inform evidence-based policies or recommendations.
• Develop a scientific hypothesis and design a research study to test the hypothesis.
• Apply the essential elements of data science research to inform evidence-based public health policies or recommendations.

Course Requirements

• SPH EP 816 A Guided Epidemiologic Study (2 units)
• SPH PH 700 Foundations of Public Health (0 units)
• SPH PH 750 Essentials of Population Health Research (4 units)
• SPH PH 760 Accelerated Training in Statistical Computing (2 units)
• SPH PH 870 Research Skills Seminar (2 units)
• SPH PH 880 Research Dissemination Seminar (2 units)
• SPH PH 890 Mentored Research Experience (0 units)
• 8–12 units in quantitative methods:
  • GMS MS 650 Machine Learning (4 units)*
  • SPH BS 728 Public Health Surveillance, a Methods Based Approach (2 units)
  • SPH BS 821 Categorical Data Analysis (4 units)
  • SPH BS 835 Applied Biostatistical Methods for Public Health Practice (4 units)
  • SPH BS 849 Bayesian Modeling for Biomedical Research & Public Health (2 units)
  • SPH BS 852 Biostatistical Methods for Observational Studies (4 units)
  • SPH BS 853 Generalized Linear Models with Applications (4 units)
• 6–8 units in computing courses:
  • CAS CS 505 Introduction to Natural Language Processing (4 units)*
  • CAS CS 506 Computational Tools for Data Science (4 units)*
  • CAS CS 640 Artificial Intelligence (4 units)*
  • CDS BF 768 Biological Databases (4 units)*
  • CDS DS 543 Algorithmic Techniques for Taming Big Data (4 units)*
  • SPH BS 750 Essentials of Quantitative Data Management (2 units)
  • SPH BS 803 Statistical Programming for Biostatisticians (2 units)
  • SPH BS 805 Intermediate Programming in SAS for Applied Linear Models (4 units) or BS 806 Statistical Learning with Applications in R (4 units)
  • SPH BS 845 Data Science and Statistical Modeling in R (4 units)
• 2–8 units of electives:
  • GMS IM 600 Bioimaging Foundations (4 units)*
  • SPH BS 825 Advanced Methods in Infectious Disease Epidemiology (2 units)
  • SPH BS 831 Methods and Applications for Genomics (2 units)
  • SPH BS 858 Statistical Genetics (4 units)
  • SPH GH 811 Applied Research Methods in Global Health (4 units)

*Students must apply for preapproved transfer units, 8 units maximum.

Mentored Research Experience

The 400-hour mentored research experience requirement gives students the opportunity to collaborate with a BUSPH faculty member or an approved partner.