Health Data Analytics, MS

The digitization of healthcare systems in clinical settings, in combination with the explosion of personal data collection devices, provides the opportunity of using data for revolutionizing approaches to care at all levels with an emphasis on precision medicine and person-centered care. The ability to take advantage of this “Big Data” opportunity, however, requires expertise at the intersection of health informatics, data science, and computational modeling. The Master of Science in Health Data Analytics is designed to prepare students to succeed in this emerging field. This program offers a strong, competency-based curriculum that addresses data analytics ranging from data acquisition from traditional and emerging data streams, data aggregation methods, data mining algorithms, predictive computational modeling, and visualization techniques. Students can expect to amass a broad and deep understanding of the various methods, software tools, and topical expertise needed to discover meaningful patterns in health-related data and effectively communicate their implications to a number of diverse stakeholders. Successful graduates of the Master of Science in Health Data Analytics will be effective practitioners and leaders in the rapidly developing domain of data analytics with a focus on health and healthcare.

The interdisciplinary Master of Science in Health Data Analytics consists of 12 courses, drawn from the College of Computer and Information Science and the Bouvé College of Health Science; a capstone project; and an ongoing series of seminars on topics in health data analytics. Two tracks will be available to matriculating students: standard and research based.

Learning Outcomes

  • Proficiency in the health and healthcare ecosystem, including stakeholder roles such as payers, providers, and government; social determinants of health; wellness promotion; acute vs.chronic care
  • Ability to acquire, store, and validate data; familiarity with common health-related data sources and formats
  • Proficiency in analyzing data using statistical, epidemiological, and data-mining methods along with appropriate software tools and programming languages
  • Ability to interpret and present analytical results to nontechnical stakeholders using visualization and accessible narrative structures

Complete all courses and requirements listed below unless otherwise indicated.

Core Requirements

DA 5020Collecting, Storing, and Retrieving Data4
DA 5030Introduction to Data Mining/Machine Learning4
HINF 6400Introduction to Health Data Analytics3
PPUA 5301Introduction to Computational Statistics4
PPUA 5302Information Design and Visual Analytics4
HINF 5102Data Management in Healthcare3
HINF 5105The American Healthcare System3
HINF Predictive Analytics and Modeling (TBA)3


Complete either Thesis or Capstone:3
HINF Health Informatics Thesis
Health Informatics Capstone Project


At least one course must be chosen from the methods list.

Complete 3–6 semester hours from the following:3-6
Intermediate Epidemiology
Applied Regression Analysis
Advanced Methods in Biostatistics
Empirical Research Methods
Intermediate Statistical Data Analysis Techniques
Advanced Research and Data Analyses 2
Other Electives
Complete 0–4 semester hours from the following:0-4
Visualization Technologies 1
Design of Information-Rich Environments
Theoretical Foundations in Personal Health Informatics
Personal Health Interface Design and Development
Project Management
Database Design, Access, Modeling, and Security
Strategic Management and Leadership in Healthcare
Evaluating Healthcare Quality
Economic Perspectives on Health Policy

Program Credit/GPA Requirements

37 total semester hours required
Minimum 3.000 GPA required