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 a cross-disciplinary 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.
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.
Throughout their course work, students will assemble a digital portfolio that will help them demonstrate their capabilities and skills for the job market. Students will also have ample opportunities to be involved in practical healthcare data science projects on campus. Upon graduation, students will have both foundational and practical skills for a successful career in healthcare data science.
Upon graduation, students will be uniquely qualified to join and lead data science teams at health companies and healthcare provider organizations, perform data analytics in health-related startups and tech companies, and develop emerging technologies revolving around health data. Large amounts of health data are becoming available across all components of the healthcare ecosystem, and data science skills are increasingly necessary. As more personal health data are collected, there are more opportunities for disease prevention and monitoring. From health organizations to startups, there is unprecedented demand for healthcare data science professionals with clinically-grounded experience relevant to current health system needs.
Applications will be reviewed by the Viterbi School of Engineering.
Applicants should have an undergraduate degree in science, technology, engineering, math or healthcare from a regionally accredited university, programming experience or strong math background, and a satisfactory cumulative undergraduate GPA (grade point average).
Prospective students will need to complete and submit:
English Proficiency Requirement (International Students Only)
Financial Documentation (On-Campus International Students Only)
As of September 7, 2021, the GRE exam is not required.
The most up to date description of the program is in the USC Catalogue. Course descriptions are available here.
Total Units: 32
DSCI 510 Principles of Programming for Data Science (4 units)
DSCI 549 Introduction to Computational Thinking and Data Science (4 units)
DSCI 550 Data Science at Scale (4 units)
BME 501 Advanced Topics in Biomedical Systems (4 units)
BME 566a Topics in Health, Technology and Engineering (2 units)
BME 566b Topics in Health, Technology and Engineering (2 units)
Data Science Elective - Choose One Course (4 units):
CSCI 530 Security Systems (4 units)
CSCI 548 Information Integration on the Web (4 units)
CSCI 570 Analysis of Algorithms (4 units)
CSCI 571 Web Technologies (4 units)
DSCI 529 Security and Privacy (4 units)
DSCI 551 Foundations of Data Management (4 units)
DSCI 552 Machine Learning for Data Science (4 units)
DSCI 553 Foundations and Applications of Data Mining (4 units)
DSCI 554 Data Visualization (4 units)
DSCI 555 Interaction Design and Usability Testing (4 units)
DSCI 556 User Experience Design and Strategy (4 units)
DSCI 558 Building Knowledge Graphs (4 units)
Health Science Elective - Choose One Course (4 units):
BME 525 Advanced Biomedical Imaging (4 units)
BME 527 Integration of Medical Imaging Systems (4 units)
BME 528 Medical Diagnostics, Therapeutics and Informatics Applications (4 units)
BME 566c Topics in Health, Technology and Engineering (4 units)
BME 566d Topics in Health, Technology and Engineering (4 units)
PM 504 Quality in Health Care (4 units)
PM 508 Health Service Delivery in the U.S. (4 units)
PM 512 Principles of Epidemiology (4 units)
PM 538 Introduction to Biomedical Informatics (4 units)
Expanded List of Approved Electives:
DSCI 531 Fairness in Artificial Intelligence (4 units)
DSCI 534 Data Privacy Issues and Solutions (4 units)
DSCI 564 Probability and Statistics for Data Science (4 units)
DSCI 560 Data Science Professional Practicum (4 units)
DSCI 599 Special Topics Units: 1, 2, 3, 4, 5, 6, 7, 8 (4 units of DSCI 599 required if chosen)
BME 423 Statistical Methods in Biomedical Engineering (4 units)
BME 514 Physiological Signals and Data Analytics (4 units)
BME 515 Data Analytics in Biomedical Engineering (4 units)
BME 530 Introduction to Systems Biology (3 units)
PM 511abc Data Analysis (4 units)
The fastest you may finish this program is in 4 semesters (2 academic years). Students may only take 2 courses per semester.
Students with a computer science background will have the option of replacing DSCI 510, DSCI 549, and DSCI 550 with DSCI 551, DSCI 552, and DSCI 553. As a result, they will be able to take additional data science elective courses.
BME 527 Integration of Medical Imaging Systems is available to replace BME 501.
BME 528 Medical Diagnostics, Therapeutics and Informatics Applications is available to replace BME 566ab
IMPORTANT NOTE: Students can consult the USC Catalog for the degree requirements, but these requirements are changing and are undergoing approval processes by the university. All students in this degree program are pre-approved by the program directors to follow the curriculum on this page.
Current students follow degree requirements in effect for the academic year they began their studies at USC. If you are a current student, please refer to your STARS report or the appropriate USC Catalogue for your year. Students seeking to advance their catalogue year to follow updated curricula may contact their department advisor.