Today quantitative and symbolic data are easily collected in computer for-
mat, from databases, websites, smartdevices, and anything that has intercon-
nect capabilities. When such large amounts of data are put in spreadsheets
or tabular reports, it becomes dicult to see the patterns, structure, trends,
or relationships inherent in the data. Eective data visualization exposes
these inherent relationships, consolidating and illustrating them in graphics.
A visualization organizes data in a way that the structure and relationships
in the data that may not be so easily understood becomes easily understood
and interpreted with the visualization. Visualizations of a data set give the
reader a narrative that tells the story of the data.
The purpose of data visualization is to convey information contained in
data to clearly and eciently communicate an accurate picture of what the
data says through understandable and context appropriate visualizations.
To do a visualization can be just exploratory or entails using Machine
Learning techniques that determine the structure of the data. The visualiza-
tions are then matched to the data structure.
The course will explore how principles of information graphics and design
and how principles of visual perception, can be used with machine learning
techniques to make eective data visualizations.
Each student will make a presentation of some principles of data visual-
izations or do a visualization project.
The course is open to PhD students in all programs. Non-computer sci-
ence students will be paired with computer science students for the visual-
The topic list may include but is not limited to:
-Pie and Donut Charts
-Graphs and Networks
-Polar Area Diagram
-Parallel Coordinate Displays
-Cartograms and Choropleths
-Dot Distribution Maps
- Be able to describe the key design guidelines and techniques used for the visual display of information
- Understand how to best use the capabilities of visual perception in a graphic display
- Understand the principles of interactive visualizations
- Understand how Machine Learning techniques can determine data struc- ture and pattern
- Explore and critically evaluate a wide range of visualization techniques and applications
Every student will do a project involving a presentation of the project at the
end of the course.