Master of Professional Studies in Applied AI for Public Health Data Science

The Master of Professional Studies in Applied AI for Public Health Data Science, Online (MPAA) an interdisciplinary program offered through the Department of Epidemiology and Biostatisticsin the School of Public Health. Equipping students to address pressing public health challenges through ethical, equity-centered AI and data science, the program integrates public health, epidemiology, biostatistics, machine learning, and human-centered AI.

Mentoring and advising are an essential part of the program. Students meet with faculty and the academic program director to ensure that educational goals and career learning and development goals are met. Students with specific academic questions may contact Dr. Jennifer Bachner at jbachner@umd.edu.

Overview

The Master of Professional Studies in Applied AI for Public Health Data Science is a 30-credit, 11-course online program that combines public health, epidemiology, biostatistics, machine learning, and human-centered AI to address pressing public health challenges.

• Prepares graduates to apply AI tools and data science methods to complex health issues across government, nonprofit, healthcare, and research settings. 
• Teaches students to use methods such as visualization, image and text analysis, clustering, and prediction while centering ethics and equity. 
• Successful graduates enter the field of work or service ready to translate data and AI approaches into practical responses to public health challenges. 

Program Features

Students participate in both academic and applied training.

• 27 credits (ten courses) of required coursework and three credits (one elective course) selected in consultation with an advisor.
• 16 credits (six foundational courses) in public health, epidemiology, biostatistics, regression, and R for health data analysis.
• 11 credits (four applied courses) in machine learning, text and image analysis, and AI ethics, including a three-credit research project applying AI and data science methods to a public health issue.

Registration Overview

• See the sample plan of study below. Students should use this as a guide to develop a plan with the academic program director.
• Actual course offerings are determined by the program and may vary. Students should note if a course has a prerequisite or corequisite.
• Specific class meeting information (days and time) is posted on Testudo, University of Maryland’s (UMD) interactive online course catalog. Once on that site, select “Schedule of Classes,” then choose the term/year. Courses are listed by academic unit.
• The program uses specific section codes for registration that are listed on the sample plan of study.

Sample Plan, Full-time, Fall

Sample Plan, Full-time, Summer

For a listing of program courses, see the Graduate School Catalog program requirements for MPAA. For course descriptions that include prerequisites or corequisites, see SPHL and EPIB.

Overview

• Instruction is provided by UMD faculty and professionals in the field.
• The program follows the semester academic calendar, with classes held during the 16-week fall and spring semesters, one three-week Winter Session, and two six-week Summer Session.

Online Learning

• Features 100% online instruction with engaging and interactive learning.
• Using advanced audio and video technology, UMD’s online learning environment delivers dynamic and interactive content.
• Featuring convenience and flexibility, online instruction permits asynchronous or synchronous participation.
• Lectures are recorded and archived.

Upon successful completion, graduates will have developed foundational public health and advanced data science competencies to apply AI methods to public health research and practice.

• Apply core public health, epidemiological, and biostatistical methods to analyze and interpret health data.
• Employ advanced data science and AI tools to visualize and synthesize large-scale public health datasets using best practices for reproducibility and scalability.
• Develop and evaluate machine learning models to address complex challenges in public health research and practice.
• Critically appraise public health AI applications to ensure that they are ethical, human-centered, equitable, and trustworthy.
• Communicate and translate AI-driven insights to diverse audiences to inform evidence-based public health decision-making and policy development.