Curriculum Information – Public Health Data Science MPH

The Public Health Data Science MPH program is administered by faculty and staff within the School of Public Health (SPH) Division of Biostatistics & Health Data Science, with core MPH courses offered in partnership with faculty and staff across the school.

Students in this program enjoy small classes and individual faculty attention, state-of-the-art computing facilities, proximity to a large academic health center, a strong record in job placement, and access to a wide variety of teaching and research assistantship experiences.

The curriculum combines a solid understanding of public health concepts with advanced data science skills and techniques. Elective credits allow students to tailor the program to their own interests and career objectives. Elective credits in methods and study design can be aimed at a range of application areas from clinical trials to epidemiologic studies to genomics to health management. Additional elective courses focus on developing further expertise in areas such as data visualization, databases, advanced programming, or geographic/spatial data analysis.

Students complete at least 43 credits as follows:

  • Public health core (16 credits)
  • Biostatistics core requirements (minimum 15 credits)
  • Elective courses in statistical methods and study design (minimum 6 credits)
  • Elective courses in programming, visualization, and informatics (minimum 6 credits)

As part of the core curriculum, all students complete the following experiences to embed their classroom learning into real-word settings:

  • Applied Practice Experience: The Applied Practice Experience can be completed in a wide variety of settings, from analyzing data with a non-governmental organization in a developing country, to learning about regulatory processes for drug approval in the pharmaceutical industry.
  • Integrated Learning Experience: The Integrated Learning Experience can involve a wide range of possible data-driven applied projects, such as creating a Shiny app or doing an enhanced analysis of an existing dataset.

Questions? Contact:

Sally Olander

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