M.S. Biomedical Data Science Curriculum

The M.S. Biomedical Data Science curriculum is designed to develop leaders who will improve health care, science and public health through data science. Our professional degree program includes the latest analytical methods to draw insights from real-world data and communicate your findings to influence decisions.

The curriculum includes specialized tracks in precision medicine and population health. Your studies will conclude with a comprehensive real-world, industry-type capstone, oriented toward your domain of interest.

The curriculum includes every aspect of biomedical data science: 

  • Programming languages (Python, R, SAS, SQL)
  • Computational software engineering
  • GIS algorithms for health care analytics
  • Precision medicine informatics
  • Population health informatics
  • Biostatistics
  • Big data management and analytics
  • Artificial Intelligence and Computational Machine Learning
  • NLP and Text Analytics
  • Predictive modeling and analytics
  • Visualization and unstructured data analysis
  • Data conscientiousness
  • Ethics of biomedical data science
  • Cloud computing – to provide access to databases, frameworks, programming languages, and operational tools

Degree Requirements

14 courses, 42 graduate credits

Students will take ten core courses that provide a common background in biomedical data science. You can also select either a Precision Medicine Informatics or Population Health Informatics Concentration Track. (Schedule permitting and with the approval of the program director, students may also choose their three emphasis courses from either of the concentration track areas.) The degree culminates with a comprehensive real-life, industry-type capstone oriented toward the student’s domain of interest.