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.

Syllabus

	Date Lecture	Topic Lecture	Date Exercise	Topic Exercise	Homework
1	18.10.2018	What is data visualization, and why should we do it?	22.10.2018	Reading and Discussing the article: "A nested process model for visualization design and validation."
2	25.10.2018	What: Data Abstraction	29.10.2018	Introducing Term Project	Reading task description for the term project
3	01.11.2018	Why: Task Abstraction	05.11.2018	Introducing Basics of Web Development	Present project idea
4	08.11.2018	Analysis: Four Levels of Validation	12.11.2018	Introducing Visualizations with D3	Work on your term project
5	15.11.2018	Marks and Channels	19.11.2018	Term Project Idea Presentation (describe algorithm and type of intended visualization approach)	Morphological analysis
6	22.11.2018	Rules of Thumb	26.11.2018	Comparing existing visualization approaches based on selected dimensions	Work on your term project
7	29.11.2018	How: Arrange Tabular Data	03.12.2018	Evaluating existing approaches and user testing	Work on your term project
8	06.12.2018	How: Arrange Spatial Data	10.12.2018	Basics of paper writing	Work on your term project
9	12.12.2018	How: Arrange Networks + Tree	17.12.2018	How to conduct a peer review?	Carry out peer review!
10	20.12.2018	How: Map Color and Other Channels	07.01.2019	Analysis of Visualisation Case Studies	Work on your term project
11	10.01.2019	How: Manipulate View	14.01.2019	Design Feedback	Work on your term project
12	17.01.2019	How: Facet into Multiple Views	21.01.2019	Technical Feedback	Work on your term project
13	24.01.2019	How: Reduce Items and Attributes	28.01.2019	How to present your project?	Work on your term project
14	31.01.2019	How: Embed: Focus + Context	04.02.2019	Open Review
15	07.02.2019	Summary and Case Studies	11.02.2019	Class project presentation
16	14.02.2019	Exam/Project Presentation	01.03.2019	Submission of Project Report