Master of Science in Healthcare Data Science

Program Director (Data Science): Yolanda Gil, PhD
Program Co-Director (Biomedical Engineering): Brent J. Liu, PhD

The Master of Science in Healthcare Data Science is an interdisciplinary joint degree program offered by the Viterbi School of Engineering and the Keck School of Medicine. Students will complete a core set of courses to provide a foundation in data science and health and choose electives to optimize their preparation for their preferred career path and unique professional opportunities.

Data science for addressing healthcare needs is an increasingly important area for creating novel devices, advancing biomedical research, and developing innovative processes that require integrative approaches linking data systems, analytics, business processes, and decision support.  Large amounts of health data are becoming available across all components of the healthcare ecosystem, and data science skills are increasingly necessary. Advances in data acquisition, storage, processing and interpretation are blurring the boundaries between traditional healthcare provision and into mobile devices and the Internet of Things.  As more personal health data are collected, there are more opportunities for disease prevention and monitoring. As life expectancy grows, there is increasing demand for devices and services that support independence and quality of life. From health organizations to startups, there is unprecedented demand for healthcare data science professionals with clinically-grounded experience relevant to current health system needs. The program will provide students with the knowledge and skills to:

  • Understand the requirements and techniques to manage health and healthcare process data collected by healthcare providers and organizations, use it to improve patient care, and analyze it to improve the business processes in and between hospitals, insurance companies, public health agencies, and other components of the healthcare ecosystem
  • Understand the use of data science in clinical research and translational medicine
  • Understand the design and development of personal devices and mobile apps to collect health data and to monitor health-related variables
  • Understand the use of emerging technologies in data science and their application to health and healthcare delivery processes
  • Gain direct experiences in finding and articulating challenges in healthcare settings that can be met through integrative data science solutions

The curriculum is designed to be accessible to students with any background, including students with a biomedical background and no computer science knowledge as well as students with a computer science background and no biomedical knowledge.  Students with undergraduate degrees in computer science, engineering, science or mathematics will acquire the necessary knowledge to analyze health data with diverse sources and purposes, and can request to replace introductory data science courses with more advanced ones.  Students with undergraduate degrees in biology, biochemistry, and other sciences will acquire formal and practical data science skills, and can request to substitute introductory courses in health with more advanced ones.  There is no requirement of prior knowledge of programming or computer science, as the curriculum is designed with special introductory courses that are accessible to students with diverse backgrounds.

Students will learn a range of data science skills such as developing scalable data systems, using state-of-the-art software and infrastructure for data science, designing data analyses with statistical methods, applying machine learning and data mining techniques, designing effective visualizations, and working in multi-disciplinary data science teams. On the health side, students will be integrated into teams working with medical students in healthcare settings as well as courses on clinical workflow and medical technology systems such as image acquisition systems and other healthcare informatics systems.