Data Visualization for Biomedical Applications

2 credits - Fall Term

Data visualization is an essential component of the analysis toolkit in any data-driven research endeavor. As the primary interface through which analysts are consuming data, data visualization can facilitate new discoveries but also mislead, bias, and slow down progress if done poorly. This visualization course will focus on the role of data visualization in biomedical data analysis applications and also cover the principles of perception and cognition relevant for data visualization. It will also introduce the data visualization design process, and visualization tools and techniques used in biomedical informatics. Major topics include interaction techniques and implementations, high-dimensional data, networks, genomes, time and event sequences, and common generic visualization systems. Students are expected to complete class readings, a final project, and make a final presentation.

Prerequisites: ‚ÄčBMI 713, BMI 714, or equivalent programming experience with R or Python. Students enrolled in the course will be required to implement assignments and final projects in Python. 

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Nils Gehlenborg

Nils Gehlenborg, PhD

Associate Professor of Biomedical Informatics
Director, Master of Biomedical Informatics (MBI) Program

Gehlenborg Lab