Health Data Science (MS)

Program Description

Health Data Science is a rapidly evolving field that integrates informatics, machine learning, artificial intelligence (AI) and statistics to enable innovative approaches to analytics and health research. It is more important than ever to focus on what data science training in the health space can do for the growing AI industry, especially within precision medicine and predictive analytics.

To rise to this challenge, our program prepares learners to: 

• Master the machine learning algorithms that power AI systems
• Integrate and wrangle large and disparate real world data sources including registries and electronic health records
• Build statistical and predictive models using SAS, R and Python
• Create effective data visualization tools and reports to power research and business decision-making
• Communicate statistical and data-driven findings to technical and non-technical audiences
• Provide data analytics leadership to support value-based care in health organizations

Our Health Data Science program includes quantitative training in statistics, informatics, machine learning and GenAI to launch learners' careers across the health space. Learners are prepared for jobs where there is a growing need for professionals who can learn from data and analytics to address critical healthcare questions. 

This program is differentiated from other graduate programs as its faculty come from all segments of industry and academia and integrate real-world experience into the classroom environment, with a special focus on electronic health record data. In addition, there is a strong focus on statistical and mathematical competence to enable learners to go on to careers in both data science and applied statistics consulting.

Learning Goals/Program Outcomes

The HDS program prepares graduates to be successful in the ever-changing healthcare environment that is driven by data and analytics by preparing them to:

Graduate Certificate

• Explores the vital roles of data, information, and information systems in the implementation and evaluation of healthcare and value-based care initiatives
• Provides a comprehensive overview of data science, the practice of obtaining, modeling and interpreting data
• Adopt data visualization techniques that contribute to effective presentations and dashboards
• Provides a foundation for population health beginning with a working definition, incorporating public health science and policy.

Master’s Degree (Above Plus)

• Evaluate and apply multivariate statistical methodologies for various study designs of efficiency and effectiveness in healthcare
• Learn key programming techniques for data wrangling, statistical modeling and predictive analytics
• Learn advanced data science methods including supervised and unsupervised learning algorithms
• Conduct HDS research in real-world healthcare settings

Curriculum: Master of Science is a total of 33 credits

The Master of Science culminates in a Capstone Project which incorporates knowledge and skills gained through the master's program education. The Capstone should advance knowledge which can be applied to the student's discipline and/or organization.