As the use of data in research, business and politics become increasingly important, well-designed data visualizations are needed to improve understanding, reduce human memory and support decision-making.

This course aims to familiarize students with the principles, techniques, and algorithms of data visualization.
This course teaches students the basics of the current state of data visualization. At the end of the course, students will have an understanding of the following:

1. essential visualization techniques and theory, including data models, graphical perception and methods for visual coding and interaction.
2. basic techniques and algorithms for visualizing data, including multivariate data, networks, and maps.
3. practical experience in the creation and evaluation of visualizations.

The course calls for students who are interested in using data visualization in their work as well as students who develop visualization tools and systems. Basic knowledge and willingness to learn of graphics/visualization tools (e.g., D3) and data analysis tools (e.g., R) are helpful.

In addition to participating in discussions in class, students must complete several short programming and data analysis tasks as well as a final project. Students are expected to submit the results of the project in the form of a conference paper.

Please note that the course does not include exploratory approaches to the discovery of data. Instead, the course focuses on how data is visually encoded and presented to an audience after the structure of the data and its content is known.

Text Book

Munzner, Tamara. Visualization analysis and design. AK Peters/CRC Press, 2014.

Additional Literature

• Interactive Data Visualization for the Web, 2nd Edition. Scott Murray, O'Reilly Press. 2017. 

• Yau, Nathan: Visualize This: The FlowingData Guide to Design, Visualization, and Statistics. Wiley Publishing, Inc. 2011.

• Spence, Robert: Information Visualization: Design for Interaction. Pearson. 2007.