Spring 2014 - Graduate Course Descriptions
Description of Course:
Understanding and doing Information Design and Visualization are now essential parts of our literacy and skill-set. Relevant areas related to ATEC/EMAC range from industry and academia to broad audience newspaper consumption and dashboards in computer games. Wherever we look, we are confronted with ever-increasing amounts of data that cannot be understood without more-or-less sophisticated aesthetic representations.
In this course we will understand and do visualization as a cognitive feedback process, where we have to learn how to read, do, re-read, and re-do in order to reach an optimum of insight from given sets of data. We will both look at and discuss striking examples of information design. We will also do and discuss our own visualizations.
The key learning objective of this course is both theoretical and practical "visual literacy".
For our discussion of striking examples in information design, we will mostly rely on the two books mentioned below.
1. Isabel Meirelles: Design for Information: An introduction to the histories, theories, and best practices behind effective information visualizations. (Beverly/MA: Rockport Publishers, 2013).
2. Julie Steele and Noah Iliinsky (eds.): Beautiful Visualization. Looking at Data Through the Eyes of Experts. (Sebastopol/CA: O'Reilly 2010).
Please feel free to contact the instructor for specific readings regarding specific tools. Particularly useful tools include pencil & paper, applications such as Excel, Cytoscape, Gephi, and Illustrator, as well as coding tools such as D3, Processing, Python, and R.
Further selected readings will be provided by the instructor.
Course Requirements/Evaluation Criteria:
The typical assignment in this course is a semester-long visualization project, which can either be done as an individual or in a group. Project suggestions to make progress in a given area of enthusiasm are welcome. As we do not focus on a specific method, you can choose and work with your favorite tools. Students are equally welcome to the course, not matter if they are qualitatively oriented yet to make their first quantification, if they aim to "misuse" their game engine skills to do information visualization, or if they are data science wizards that aim to use High Performance Computing to solve their project. Our goal is a productive multidisciplinary conversation. Project results will be summarized in a scientific poster.