This course is open for applications from May 1 – May 31

Data visualization is the science and practice of creating interactive graphical representations from data to support the data’s exploration and presentation. In data analysis, visualization is the method of choice when the analysis objectives cannot be crisply and formally specified – i.e., when looking for regularities (patterns, trends,…) or irregularities (outliers, anomalies,…) in the data in an “I-know-it-when-I-see-it” manner. 
 
In this course, students will get a broad introduction to the field of Data Visualization. Course topics include core concepts of data visualization, data and task taxonomies, data pre-processing, visual encoding and perception, visualization techniques and layout algorithms, interaction techniques and software design patterns for interactive visualization. Students will learn how to design a visualization that is expressive of the data to be shown, effective for the analysis task to be carried out with it, and appropriate for the technical context (display size, screen vs. print, etc.) 

In the course project, students will implement a visualization from start to finish – either for a dataset of their own choice, or for one of the provided datasets. They will learn how to clean and reduce their data, how to establish visualization requirements, how to iterate through visualization prototypes, how to realize them in software using state-of-the-art visualization tools and libraries, and how to eventually evaluate them to determine the best visualization solution for a given problem. By means of review sessions, participants will not only learn to develop their own visualization, but also to analyze and critique the visualizations of others and to suggest improvements based on the principles and models introduced in the course.