This course covers data science principles and their application to large biomedical data sets. Topics include methods in data collection and data quality assessment, data preparation, dimensionality reduction, feature extraction, machine learning, data visualization, and the use of data modeling in clinical decision making. The course reinforces these concepts through hands-on experiences with biomedical data sets across a broad range of topics: multi-omics, point-of-care diagnostic devices, biomedical imaging, cardiac and neural readouts, and electronic health records.
Gopher Grades is maintained by Social Coding with data from Summer 2017 to Fall 2025 provided by the University in response to a public records request