MS in Population Health Research: Public Health Data Science

The 34-credit 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 cr)
  • SPH PH 700 Foundations of Public Health (0 cr)
  • SPH PH 750 Essentials of Population Health Research (4 cr)
  • SPH PH 760 Accelerated Training in Statistical Computing (2 cr)
  • SPH PH 870 Research Skills Seminar (2 cr)
  • SPH PH 880 Research Dissemination Seminar (2 cr)
  • SPH PH 890 Mentored Research Experience (0 cr)
  • 8–12 credits in quantitative methods:
    • GMS MS 650 Machine Learning (4 cr)*
    • SPH BS 728 Public Health Surveillance (2 cr)
    • SPH BS 821 Categorical Data Analysis (4 cr)
    • SPH BS 835 Applied Intermediate Biostatistics (4 cr)
    • SPH BS 849 Bayesian Modeling (2 cr)
    • SPH BS 852 Statistical Methods in Epidemiology (4 cr)
    • SPH BS 853 Generalized Linear Models with Applications (4 cr)
  • 6–8 credits in computing courses:
    • CAS CS 505 Introduction to Natural Language Processing (4 cr)*
    • CAS CS 506 Computational Tools for Data Science (4 cr)*
    • CDS DS 543 Algorithmic Techniques for Taming Big Data (4 cr)*
    • ENG BF 768 Biological Database Systems (4 cr)*
    • GRS CS 640 Artificial Intelligence (4 cr)*
    • SPH BS 750 Essentials of Quantitative Data Management (2 cr)
    • SPH BS 803 Statistical Programming for Biostatisticians (2 cr)
    • SPH BS 805 Intermediate Statistical Computing (4 cr) or BS 806 Multivariable Analysis for Biostatisticians (4 cr)
      • SPH BS 845 Applied Statistical Modeling and Programming in R (4 cr)
    • 2–4 credits of electives:
      • GMS IM 600 Bioimaging Foundations (4 cr)*
      • SPH BS 825 Advanced Methods in Infectious Disease Epidemiology (2 cr)
      • SPH BS 831 Genomics Data Mining and Statistics (2 cr)
      • SPH BS 858 Statistical Genetics I (4 cr)
      • SPH GH 811 Applied Research Methods in Global Health (4 cr)

    *Students must apply for preapproved transfer credit, 8 credits 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.