|Type||Lecture with Exercise|
|Number of Places||20|
|Room||Takustr. 9 Lecture: SR 046 Exercise: SR 053|
|Start||Oct 18, 2018|
|end||Feb 14, 2019|
Lecture: Thursday 10-12
Exercise: Monday 14-16
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.
Munzner, Tamara. Visualization analysis and design. AK Peters/CRC Press, 2014.
|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|
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|
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|