The MPH in Health Data Science provides the professional training that is common to all MPH Programs of Study in the College of Public Health (the Core MPH requirements) as well as substantive and meaningful training in Health Data Science.  This degree is designed to train public health professionals who can provide leadership in the analysis of public health data and the design of studies for public health investigations. Individuals with an interest in public health and with quantitative ability may find this an interesting career track.

Graduates of the MPH in Health Data Science will be able to:

• Determine and implement appropriate descriptive statistical methods for summarizing public health data.
• Produce graphical displays of data that effectively summarize descriptive and analytical findings.
• Use appropriate regression models to study the relationships among variables to answer public health research questions.
• Design a computer program for implementation of data management using R.
• Propose and defend good statistical design as a collaborator on a public health project.

Examples of careers include:

• Biostatistician
• Data Scientist
• Data Analyst
• SAS Programmer
• Informatics Analyst

Prerequisites

• Although no specific major is required, previous coursework or experience in statistical methods or data analysis is preferred.
• Familiarity with the mathematics of single variable calculus and matrix algebra are required. These requirements can be satisfied by a one-semester college course in calculus equivalent to AP Calculus AB and a high school algebra course involving matrices.
• Knowledge of elementary computer programming is required. Programming in any commonly used modern programming language (e.g., Python, Java, C++) is acceptable.

A typical student completes the MPH in two years. The following are two sample plans of study based on a full-time student starting in the Fall. Please work with your advisor to choose a plan that works best for you.

*Denotes MPH Core Course that must be completed prior to enrolling in CPH:7800.
** BIOS:6720 Statistical Machine Learning for Biomedical and Public Health Data may be approved with prior approval.

Electives (10 s.h.)

Students must complete at least 10 s.h. of elective courses. At least 3 s.h. must come from the set of data science electives and at least 3 s.h. must come from the biostatistics electives. Electives must be approved by the student’s academic advisor and the Director of Graduate Studies

Data Science Electives: