The workshop will take place on July 15th, 10:00 AM CEST on Zoom
Data visualization is one of the most powerful tools to explore, understand and communicate patterns in quantitative information. At the same time, good data visualization is a surprisingly difficult task and demands three quite different skills: substantive knowledge, statistical skill, and artistic sense. The course is intended to introduce participants to a) key principles of analytic design, b) useful visualization techniques for the exploration and presentation of various data types and c) new developments of data visualization, such as visual inference. This course is highly applied in nature and emphasizes the practical aspects of data visualization in social data science. Students will learn how to evaluate data visualizations based on principles of analytic design, how to construct compelling visualizations using the free statistics software R, and how to explore and present their data and models with visual methods. In short, students will get hands-on experience producing modern visualizations for their practical social data science problems.
Prof. Richard Traunmüller
Richard Traunmüller is a professor of political science at the University of Mannheim and has held positions in Konstanz, Berne, Essex, and Frankfurt. His online course on data visualization is part of the regular curriculum of the Mannheim Master in Applied Data Science and Measurement of the Mannheim Business School and the International Program in Survey and Data Science of the University of Maryland, College Park. His book project on Data Visualization for the Social Sciences is under contract with Cambridge University Press.
Data Visualization with R (Part I)
Data Visualization with R (Part II)
All workshop materials and recording are under Creative Commons Attribution-NonCommercial-ShareAlike 2.0 license. You are free to share — copy and redistribute the material in any medium or format, and adapt — remix, transform, and build upon the material. However, you must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. You may not use the material for commercial purposes. If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.