Healthcare Data Science (MS)

datascience.usc.edu

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

 

The USC Master of Science in Healthcare Data Science provides students with the knowledge and skills needed for applying data science methods to healthcare-related data in a variety of contexts.

The degree consists of a set of required core courses in data science and healthcare as well as a set of electives in both areas enabling students to pursue specific areas in more depth. On the data science side, students will learn about machine learning, privacy, and data visualization, data management, and semantic data models. On the healthcare side, students will be trained to work with data in healthcare settings as well as on clinical workflow and medical technology systems such as image acquisition systems and other healthcare informatics systems in order to understand where healthcare data originates and how to extract specific information from healthcare data management systems.

The five-course core of the program will provide students in the program with an understanding of:

● the requirements and techniques needed to collect, curate, and analyze health and healthcare related data collected by healthcare providers and organizations through exposure to clinical workflow and systems such as medical imaging and Electronic Medical Record (EMR) systems;

● the design and development of medical devices and systems (e.g., picture archiving and communication systems, emergency medical records, mobile health devices, etc.) used to collect, monitor, and store health-related variables and data; and

● the use of emerging technologies and methods in data science and how they can be applied to patient health and healthcare delivery processes, as well as to clinical research and translational medicine.

The program core coupled with the program electives will also provide the data analysis background needed to improve business processes in and between hospitals, insurance companies, public health agencies, and other components of the healthcare ecosystem and allow students to gain experience in finding and articulating challenges in healthcare settings that can be met through integrated engineering solutions.

Through data science electives, students may choose courses in advanced topics covering data privacy (CSCI 530 , CSCI 548 , DSCI 529 ), data mining (DSCI 552  and DSCI 553 ), user interface design (DSCI 555  and DSCI 556 ), ethics (DSCI 531 ), and knowledge technologies (DSCI 558 ). 

Through healthcare electives, students may choose to study healthcare processes (PM 504  and PM 508 ), applications of data science techniques using biostatistics data (BME 423 , BME 514 , BME 515 , PM 511aL , PM 511bL , PM 511cL ), and biomedical engineering devices and systems related to healthcare (BME 514 , BME 525 , PM 538 ).

Students may also choose to take a capstone elective course, DSCI 560 , with real-world projects that will enable them to acquire practical experience with data science.

Students with requisite programming knowledge are allowed to test out of DSCI 510 . An entrance exam will be held at three time periods: two weeks before the start of the semester, one week before the start of the semester, and during the first week of the semester. Students who pass the exam will be allowed to skip DSCI 510 , and may replace that with an elective.