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Health Data Science

Program

Richard Hass, PhD
  • Associate Professor
  • Program Director

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Curriculum

The Graduate Certificate is a total of 15 credits and the Master of Science is a total of 33 credits. Each course is 3 credits.

Graduate Certificate

  • AHE 502: Statistics I
  • AHE 501: Economics of Health Insurance or
    POP 500: Essentials of Population Health
  • HAI 501: Health Informatics, Analytics, and AI
  • HDS 518: Supervised Learning & Unsupervised Learning: Prediction & Classification
  • HDS 532: Data Visualization

Master's Degree

  • AHE 502: Statistics I
  • AHE 505: Statistics II
  • AHE 501: Economics of Health Insurance or
    POP 500: Essentials of Population Health
  • HDS 500: Fundamentals of Data Wrangling
  • HAI 501: Health Informatics, Analytics, and AI
  • HDS 502: Exploratory Data Analysis & Unsupervised Learning
  • HDS 518: Supervised Learning & Unsupervised Learning: Prediction & Classification
  • HDS 519: Deep Learning & AI Systems
  • HDS 532: Data Visualization
  • Elective in HDS or AHE (Program Director approval needed)
  • HDS 651: Capstone Research Project

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 studetn's discipline and/or organization.

For course descriptions, please view the Jefferson College of Population Health Course Descriptions page.

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