FH Potsdam · Summer 2024
Hands-on tutorials for basic visualization techniques and the necessary data processing
Data visualization transforms datasets into visual and interactive representations. As we encounter growing datasets in various sectors we need to develop effective methods for making sense of data. Data 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 data visualizations. One of the best ways to achieve this is to create them yourself!
The following 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.
You can view the tutorials as webpages, run them on Deepnote, or download the Jupyter notebook files to edit and run them locally in your own environment. The first four tutorials lay the groundwork, after which five common data structures are covered:
The tutorials make frequent use of the data analysis library Pandas, the visualization library Altair, and a range of other packages that you can find in the respective requirements.txt
files in each tutorial. Deepnote automatically installs the respective packages. If you work with these notebooks locally, you might want to run pip3 install -r requirements.txt
in your terminal first.
The tutorials were written by Marian Dörk for data visualization courses in information science, interface design, and urban futures at FH Potsdam. Since their initial creation during summer semester of 2020, the tutorials have been gradually updated over time. Many thanks to Fidel Thomet, Jonas Parnow, Viktoria Brüggemann, Ilias Kyriazis et al. of the UCLAB for frequent feedback and to the many students who worked through the pencil exercises. Special thanks also to the many generous creators of the various open source software packages used throughout the tutorials.
Cite this resource as:
Dörk, M. (2021-2024). Data Visualization: Hands-on tutorials for basic visualization techniques and the necessary data processing. Retrieved from https://infovis.fh-potsdam.de/tutorials/
The notebooks are released under the Creative Commons Attribution license (CC BY 4.0). Feel free to reuse, adapt, and translate! If you encounter any errors or have suggestions for improvement, please, let me know.