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Instructors: Balázs Cserpes; Dr. Dr. Rafael Milani Medeiros
Event type:
Seminar
Org-unit: Fachübergreifende Studienangebote
Displayed in timetable as:
One Square Kilometre World Culture. Stories and Perspectives on Jerusalem
Crediting for:
Hours per week:
2
Location:
Hamburg
Language of instruction:
Englisch
Min. | Max. participants:
- | 30
Registration group: Q-Studies Bachelor
Grading:
Beschreibung:
Concept and Topics:
Advances in technology have enabled us to gather, analyse and visualise data in large quantities, volumes and varieties. Though terms such as big data, data-driven studies, or digital twins have become important keywords in both academic and non-academic discussion, the term “data” remains vague and unclear in the times where software is for free and data is gold Is there such thing as raw, unprepared data? Can the data speak for itself and provide insights into complex questions? Are the questions as important as the answers we can extract from data analysis and visualization?
In this course, we will explore the tools and techniques needed to create powerful data visualisations that can help highlight complex issues, argue for solutions, or tell a compelling story. From hand-drawn sketches to software products like Q-GIS and Illustrator, AI-image generators, or even self-written and adapted (Python/R) code snippets, we will cover a range of methods and approaches that can be used to transform raw data into clear and compelling visuals.
Whether the course participants have experience with coding and quantitative analysis or not, this course will provide them with the basic knowledge and skills needed to effectively collect, analyse, and visualize data. By the end of the course, the students will be able to create beautiful and intuitive data visualizations that can help you make sense of complex data and communicate your findings in a way that is both engaging and effective.
The proposed seminar has been already held as a skills seminar in the summer term of 2023 but as data visualisation is relevant for all curricula at the university, it is also suited to be held as a Q-Study. To match the demands of this specific format, we will shift our focus away to a more open approach where discussions about the principles of visualisation will be emphasized instead of providing tutorials for software products.
Results and Format:
The aim of this course is to help students learn how to present information in simple, creative, and intuitive ways that go beyond standard bar and pie charts. We will cover a range of technologies and methods, including hand-drawn sketches, Excel, Q-GIS, and coding with R or Python.
Our approach to teaching is based on a dialogic method, where we provide input lectures on data visualisation and communication with examples and students have the opportunity to apply these concepts in class. Students will also bring in examples of beautiful data visualisations (they found in various places, also beyond the context of urban engineering, design, planning and architecture) to the sessions, where we discuss how we can replicate or build on these designs, especially related to their academic fields.
Presentation of Results:
Throughout the semester, students will develop a portfolio of their own data visualization drafts, and as a final assignment, three of these will be selected for further development into polished visualisations. These final visualizations can be used in student exhibitions at the university, as well as in students' future careers in their personal portfolios.
The course, which is designed to help students understand and analyse quantitative datasets, has applications in all three clusters, most notably science | technology | knowledge and arts | culture | media.
The approach taken by the team is most closely related to the first cluster of subjects.
In the course, students will learn how to transform raw data into valuable insights, and how to communicate those insights in a clear and meaningful way. They will also explore the potential biases and distortions that can arise when handling and presenting data, and gain a critical understanding of why there is no such thing as "raw" or "natural" data. By the end of the course, students will be able to use data to underly their decisions and arguments in a more effective and accurate way.
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