Marian Dörk · FH Potsdam · Summer 2020
Lectures on the fundamentals of information visualization and hands-on tutorials for basic visualization techniques and the necessary data processing
Information visualization is concerned with the visual and interactive representation of abstract and possibly complex datasets. As we encounter growing datasets in various sectors there is an increasing need to develop effective methods for making sense of data. Information visualization relies on computational means and our perceptual system to help reveal otherwise invisible patterns and gain new insights. Across various fields, there is great hope in the power of visualization to turn complex data into informative, engaging, and maybe even attractive forms. However, it typically takes several steps of data preparation and processing before a given dataset can be meaningfully visualized. While visualizations can indeed provide novel and useful perspectives on data, they can also obscure or misrepresent certain aspects of a phenomenon. Thus it is essential to develop a critical literacy towards the rhetoric of information visualization. One of the best ways to develop this literacy is to learn how to create visualizations!
The main aim of this course has been to familiarize students with the principles and methods of information visualization and to enable them to design, implement and deploy visualizations for data analysis and application scenarios in the information sciences and beyond. The course material consists of two main parts: lectures and tutorials. While the lectures are meant as a basic introduction to information visualization, the tutorials offer a practical approach to working with data and to create interactive visualizations yourself.
The lectures cover the fundamentals of information visualization including some of its history, mapping data to visual variables, the role of visual perception and interactivity, techniques for visualizing various data structures, and evaluation methods.
The tutorials require basic familiarity with statistics and programming. They come as Jupyter notebooks containing both human-readable explanations as well as computable code. The code blocks in the tutorials are written in Python, which you should either have already some experience with or a keen curiosity for. The tutorials make frequent use of the data analysis library Pandas, the visualization library Altair, and a range of other packages. You can view the tutorials as webpages, open and run them on Google Colab, or download the Jupyter notebook files to edit and run them locally.
The first three tutorials lay the groundwork, after which five common data structures are covered:
The lectures and tutorials were created for the Information Visualization course at Fachhochschule Potsdam during the summer semester 2020. Many thanks to Fidel Thomet, Jonas Parnow et al. at UCLAB for frequent feedback on the tutorials, and to the many generous creators of the various open source software packages used throughout the tutorials. Special thanks to the FH Potsdam students enrolled in the course who took on the challenge to learn information visualization under very special circumstances.
If you encounter any errors or have any suggestions for improvement, feel free to send an email.