MPS, Applied AI for Public Health Data Science (Online)

Graduates of the program will be prepared to:

1. Apply artificial intelligence and machine learning methods to public health data.
2. Analyze complex health and population datasets using modern computational approaches.
3. Integrate principles of epidemiology and biostatistics into AI-driven analyses.
4. Evaluate the accuracy, validity, bias, and limitations of AI systems used in health.
5. Apply ethical and responsible AI principles to public health research and practice.
6. Communicate AI-generated evidence to public health practitioners, policymakers, and other stakeholders.
7. Translate AI and data science findings into practical solutions for real-world public health challenges.
8. Lead diverse teams in AI, public health, and public policy.

Students complete interdisciplinary coursework spanning public health, epidemiology, biostatistics, artificial intelligence, machine learning, and responsible AI.

AT-A-GLANCE PROGRAM FACTS

• 30 CREDITS
• Professional Master's Degree
• Full-Time / Part-Time Options [if applicable]

CORE AREAS OF STUDY

• Public Health Foundations
• Epidemiology
• Biostatistics
• Data Science
• Artificial Intelligence
• Machine Learning
• Responsible and Ethical AI
• Applied Public Health Data Science
• Capstone / Applied Project

See how the program's coursework builds from foundational public health and quantitative methods to advanced AI and applied data science.

SAMPLE COURSE SEQUENCE ACCELERATED

15 months with a Summer start:

Summer Session I (6 weeks) 

• EPIB 650 Biostatistics 1  (3 credits)

Summer Session II (6 weeks)

• EPIB 651 Applied Regression Analysis  (3 credits)

Fall Semester (16 weeks) 

• EPIB 674 Statistical Foundations of Machine Learning  (3 credits)
• EPIB 695 Exploratory Data Analysis and Visualization in R  (3 credits)
• SPHL 601 Core Concepts in Public Health  (1 credit)

Winter Intersession (3 weeks) 

• EPIB 645 AI Ethics in Public Health - new  (3 credits)

Spring Semester (16 weeks)

• EPIB 675 Advanced Machine Learning Methods  (3 credits)
• Elective  (3 credits)

Summer Session I (6 weeks)

• EPIB 610 Foundations of Epidemiology  (3 credits)
• EPIB 7xx Applied AI for Public Health Data Science Research Project  (1 credit)

Summer Session II (6 weeks)

• Elective  (3 credits)
• EPIB 7xx Applied AI for Public Health Data Science Research Project  (2 credits)

Explore AI Across Public Health

AI & Machine Learning
Learn to apply modern AI and machine learning methods to health and population data.

Computational Epidemiology
Use large-scale and emerging data sources to understand patterns of disease and population health.

Responsible AI & Ethics
Examine fairness, bias, transparency, privacy, and responsible deployment of AI in health.

Public Health Data Science
Develop practical skills for collecting, analyzing, interpreting, and communicating complex health data.

Why Study Applied AI and Public Health at the University of Maryland?

The University of Maryland offers a unique environment for developing leaders at the forefront of AI and public health. Located near the Washington, DC region, students are positioned at the center of emerging changes in AI, health, and policy, gaining the interdisciplinary expertise to shape policy, lead innovation, and drive meaningful change in public health. This location also connects students to the growing technology and AI ecosystem across Maryland, Washington, DC, and Northern Virginia.

Interdisciplinary: Training across AI, data science, and public health.

Applied: Learn by working with real-world health data and problems.

Responsible: Understand not only how to use AI, but how to evaluate and deploy it responsibly.

DC Advantage: Study near federal health agencies, research organizations, technology companies, nonprofits, and policymakers.

Put AI into Practice

Students apply the skills developed throughout the program to real-world public health problems. Through applied coursework and a culminating project, students integrate AI, data science, and public health methods to address practical health challenges.

Where Can This Degree Take You?

Graduates will be prepared to work across organizations that seek professionals who understand both health and artificial intelligence.

Government & Public Health Agencies
Federal, state, and local health agencies

Health care & Health Systems
Hospitals, health systems, and clinical organizations

Technology & AI
Technology companies, AI organizations, and digital health companies

Research & Academia
Universities, research institutes, and scientific organizations

Consulting & Industry
Health analytics, biotechnology, pharmaceutical, and consulting organizations

This program is designed for professionals and emerging leaders who want to shape how AI and data are used across public health, health care, policy, and related sectors. Applicants may come from public health, health care, government, policy, technology, data science, research, or other fields and do not need to have a traditional computer science background.

Do I need a computer science background?
No. A computer science background is not required.

Do I need previous public health training?
No. The program welcomes students from diverse academic and professional backgrounds.

Can I complete the program part-time?
Yes. The program is designed to accommodate both full-time and part-time students.

Is the program in-person, online, or hybrid?
Online.

How is this program different from a traditional MS in data science or public health?
It integrates AI and data science with public health, policy, ethics, and leadership.

What types of careers does the program prepare students for?
Leadership and professional roles in AI, public health, health data, digital health, policy, research, and responsible AI.