I teach an elective course on Visual Analytics. Visual analytics is the use of interactive visual interfaces to facilitate analytical reasoning. In essence, visual analytics is based on the–not uncontroversial–idea that humans and computers working alone are insufficient for the data challenges of today and tomorrow, and that effective synthesis of both humans and computational algorithms are needed to create human-in-the-loop systems. Thus, visual analytics bridges human-centered disciplines such as visualization and human-computer interaction with computation-centered disciplines such as machine learning, probabilistic methods, and knowledge discovery.

In this course, intended for iSchool graduate students (but open to any masters or Ph.D. student at UMD), you will learn how to apply these exciting techniques to practical problems and work on state-of-the-art research projects that could lead to a publication in one of the prestigious IEEE VIS conferences!

After taking this course, students will be able to:

  • Understand human aspects such as perception, cognition, sensemaking, critical thinking, and the analytical process.
  • Understand computational aspects such as data management, data transformations, knowledge representation, probabilistic methods, and text analytics, etc.
  • Synthesize knowledge from fields such as visualization, human-computer interaction, machine learning, knowledge discovery, and text analytics towards helping people understand data.
  • Use existing visual analytics tools to analyze basic datasets.

Here is a tentative syllabus for the course. Feedback on this is welcome!

Note! Check back here often for more information!