Public Health Data Management and Analytics

The online Public Health Data Management and Analytics concentration equips students with the skills to use various public health and health care data sources for applied public health practice. A data equity framework is integrated into this program to ensure fairness and equality. Students of this program will access, manage, assess, analyze, and report findings from different data sources commonly used in public health, such as vital records, surveys, and surveillance, as well as health care delivery settings, such as administrative claims and electronic medical records data.

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Foundational and Core Courses

Complete all courses (34 credits):

Course NumberCourse NameCredits
PH 801 Introduction to Public Health 3
PH 802 Biostatistics for Public Health 3
PH 803 Epidemiology for Public Health 3
PH 804 Public Health Policy and Administration 3
PH 805 Social and Behavioral Aspects of Public Health 3
PH 806 Environmental Factors of Health 3
PH 827 Principles of Public Health Leadership 1
PH 828 Community Engagement in Public Health Practice 3
PH 843 Public Health Research Methods 3
PH 854 Health Equity Framework for Public Health Practice 3
  Culminating Experience  
PH 892 Public Health Applied Practice Experience 3
PH 893 Public Health Integrative Learning Experience 3

Public Health Data Management and Analytics Curriculum

Complete three Data Management and Analytics courses (9 credits):

Course NumberCourse NameCredits
PH 826 Data Management and Public Health Practice 3
PH 878 Applied Biostatistics for Public Health 3
PH 829 Public Health and Healthcare Delivery Data 3

MSU Course Descriptions


Concentration Competencies:

  1. Create data management processes for public health practice.
  2. Construct visual representations of data using software to explore a public health data set.
  3. Develop a statistical analysis plan to describe and analyze public health data.
  4. Implement a statistical analysis plan to describe and analyze public health data.
  5. Report insights from the output of data analyses to the lay public and policymakers.
  6. Integrate concepts of a data equity framework into statistical analysis planning, presentation, and interpretation of results.