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
Written exam in conjunction with assignments (2 partial exams)
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 class will follow a flipped classroom approach and involve self-studying based on provided reading, audio and video material. Excersises in which small solution are implemented will help the participants to explore available tools and help to gain practical skills for data visualization.

Learning Material Provided by Lecturer

  • lecture slides and videos
  • exercises

Literature