The current rapid technological development requires the processing of large amounts of data of various kinds to make them usable by humans. This challenge affects many areas of life today, such as research, business, and politics. In these contexts, decision-makers use data visualizations to explain information and its relationships through graphical representations. This course aims at familiarizing students with the principles, techniques, and methods in data visualization, and to provide practical skills for designing and implementing data visualizations.

This course gives students a solid introduction to the fundamentals of data visualization, including current insights from research and practice. By the end of the course, students will

1. be able to select and apply methods for designing visualizations based on a problem,
2. know essential theoretical basics of visualization for graphical perception and cognition,
3. know and be able to select visualization approaches and their advantages and disadvantages,
4. be able to evaluate visualization solutions critically, and
5. have acquired practical skills for implementing visualizations.

This course is intended for students interested in using data visualization in their work as well as students who want to develop visualization software. Basic knowledge of programming (HTML, CSS, Javascript, Python) and data analysis (e.g., R) is helpful.

In addition to participating in class discussions, students will complete several programming and data analysis assignments. In a mini-project, students will work on a given problem. Finally, we expect students to document and present their assignments and mini-project in a reproducible manner.

Please note that the course will focus on how data is visually coded and presented for analysis after the data structure and its content are known. We do not cover exploratory analysis methods for discovering insights in data are not the focus of the course.

Here you can find our Code of Conduct.

Literature

Text Books

• Munzner, Tamara. Visualization analysis and design. AK Peters/CRC Press, 2014.
• Kirk, Andy: Data visualisation: A handbook for data driven design. Sage. 2016.

Further Literature

• 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.

Additional Information

https://www.mi.fu-berlin.de/en/inf/groups/hcc/teaching/Winter-Term-2022_23/course_data_visualization.html