Course Description
The current rapid technological development requires the processing of large amounts of data of various kinds to make them usable by humans. This challenge affects many areas of life today, such as research, business, and politics. In these contexts, decision-makers use data visualizations to explain information and its relationships through graphical representations. This course aims to familiarize students with the principles, techniques, and methods in data visualization, and to provide practical skills for designing and implementing data visualizations.
This course gives students a solid introduction to the fundamentals of data visualization, including current insights from research and practice. By the end of the course, students will
- be able to select and apply methods for designing visualizations based on a problem,
- know the essential theoretical basics of visualization for graphical perception and cognition,
- know and be able to select visualization approaches and their advantages and disadvantages,
- be able to evaluate visualization solutions critically, and
- have acquired practical skills for implementing visualizations.
This course is intended for students interested in using data visualization in their work as well as students who want to develop visualization software. Basic knowledge of programming (HTML, CSS, Javascript, Python) and data analysis (e.g., R) is helpful.
In addition to participating in class discussions, students will complete several programming and data analysis assignments. In a mini-project, students will work on a given problem. Finally, we expect students to document and present their assignments and mini-project in a reproducible manner.
Please note that the course will focus on how data is visually coded and presented for analysis after the data structure and its content are known. We do not cover exploratory analysis methods for discovering insights in data are not the focus of the course.
Lecture ScheduleEd
|
# |
Date |
Lecture |
|---|---|---|
|
1 |
14.10.2025 | Data Visualization – Introducing the Course |
|
2 |
21.10.2025 | The Process of Visualizing Data |
|
3 |
28.10.2025 |
Visualization Techniques: Distributions |
|
4 |
04.11.2025 |
Visualization Techniques: Associations, Amounts, Proportions |
|
5 |
11.11.2025 |
Effectiveness of Data Visualizations |
|
6 |
18.11.2025 |
Evaluating Data Visualizations |
|
7 |
25.11.2025 |
Visualization Techniques: Maps |
|
8 |
02.12.2025 |
Techniques: Networks & Trees |
|
9 |
09.12.2025 |
Techniques: Time Series |
|
10 |
16.12.2025 | Advanced: Visual Encoding |
|
11 |
06.01.2026 |
Advanced: Dashboard Design |
|
12 |
13.01.2026 |
Advanced: Storytelling |
| 13 | 20.01.2026 |
Advanced: Uncertainty in Data Visualizations |
| 14 | 27.01.2026 | Advanced: Privacy-aware Data Visualizations |
|
15 |
03.02.2026 | Advanced: AI-based Data Visualizations |
Textbook
Munzner, Tamara. Visualization analysis and design. AK Peters/CRC Press, 2014.
Additional Literature
Kirk, Andy: Data visualisation: A handbook for data driven design. Sage. 2016.
Yau, Nathan: Visualize This: The FlowingData Guide to Design, Visualization, and Statistics. Wiley Publishing, Inc. 2011.
Spence, Robert: Information Visualization: Design for Interaction. Pearson. 2007.
Zusätzliche Informationen
Link zum Kurs auf der HCC-Webseite: https://www.mi.fu-berlin.de/en/inf/groups/hcc/teaching/winter_term_2025_26/course_data_visualization.html