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TH Köln

Master Digital Sciences

Documents for Study Program Accreditation

Module »Data Visualization« (DVI)

Organizational Details

Responsible for the module
Prof. Dr. Konrad Förstner (Faculty F03)
Language
English
Offered in
Summer Semester (Duration 1 Semester)
Location
Campus Köln Süd, or remote
Number of participants
minimum 6, maximum 20
Precondition
none
Recommendation
Basic Python coding skills
ECTS
3
Effort
Total effort 90h
Total contact time
60h (30h lecture / 30h exercise)
Time for self-learning
30h (containing 30h self-organized project work)
Exam
Project (during semester)
Competences taught by the module
Implement Concepts, Deploy Products
General criteria covered by the module
Digitization

Mapping to Focus Areas

Below, you find the module's mapping to the study program's focus areas. This is done as a contribution to all relevant focus areas (in ECTS, and content-wise). This is also relevant for setting the module in relation to other modules, and tells to what extent the module might be part of other study programs.

Focus Area ECTS (prop.) Module Contribution to Focus Area
Generating and Accessing Knowledge 3

In this module, fundamental data visualization concepts as well as concrete skills to represent large data sets are taught.

Learning Outcome

In this class fundamental visualization concepts as well as concrete skills to represent large data sets are taught.

Participants will gain an basic understanding of the physiology of perception and learn to effectively encode information in figures. Futhermore, they will be introduced to widely used Python plotting libraries to create figures based on openly available data sets.

After visiting this class students are able to interprete as well as design figures and are capable to visualize large data sets.

Module Content

  1. Basics of data visualisation
  2. Physiology of perception
  3. The grammar of graphics
  4. Python based visualisation (matplotlib, seaborn, bokeh)

Forms of Teaching and Learning

The course adopts an interactive seminaristic style, fostering active engagement and collaborative learning among participants. In addition to comprehensive lectures, the seminar incorporates paper discussions, enabling students to critically analyze and debate research papers and case studies related to data visualisation. Furthermore, students will have the opportunity to showcase their understanding through presentations, where they can articulate their insights and findings on relevant topics. To reinforce practical application, the seminar culminates in a programming project, where participants implement own visualisation. This multifaceted approach ensures that students not only acquire theoretical knowledge but also gain hands-on experience and the ability to apply these concepts in practical scenarios.

Learning Material Provided by Lecturer

  • lecture slides and videos
  • exercises

Literature