Visual Analytics (Current Topics in Visual Computing)

Christian Tominski and Heidrun Schumann. “Interactive Visual Data Analysis”. AK Peters Visualization Series, CRC Press, 2020.

Course Description

The goal of this course is to explore the synthesis of visualization, interaction, and computation to facilitate insight generation from large and complex data. With enormous amounts of data being collected, being able to turn these data into informed and actionable decisions plays a key role for success. To understand complex phenomena, however, it is not enough to let machines analyze data computationally or let humans analyze data manually. Instead, visual analytics is a technology that couples human and machine analysis, taking advantage of both their strengths to understand the behavior of a system, explore phenomena and trends, discover dependencies, and finally make an informed decision. For this purpose, visual analytics combines advanced visual representations, interaction techniques, and computational methods. Through lecture sessions and a guided programming tutorial, we will dive deeper into these components.

Prerequisites

There are no enforced prerequisites for this course. However, a general interest in data visualization and computational approaches as well as basic programming experience are recommended.

Learning Outcome

Students will gain an overview of visual, interactive, and analytical methods to make sense of large and complex data. They will deepen their understanding of these methods through the implementation of selected techniques in a guided programming tutorial. At the end of this course, students will be able to

  • explain why and when computation, visualization, and interaction should work together to simplify data analysis.
  • describe, compare, and contrast different techniques for each of these building blocks and understand their interplay with respect to real-world analysis problems.
  • implement a complete data transformation process as an interactive web-based application using HTML5, CSS, JavaScript, Canvas2D, and WebGL.
  • analyze the improvement potential of the implementation and develop alternative solutions.

Course Mechanics

The course combines lecture sessions with exercise sessions that augment the lecture contents in the form of a guided programming tutorial. The tutorial centers around a code template for a web application with programming assignments that gradually extend the application’s functionality and thereby put the lecture contents into practice. The assignments incrementally work through the stages of transforming raw data into an interactive visual analytics application. For each stage, we will provide an intermediate solution that serves as a common starting point for the next assignment. The course will close with an oral exam.