After successful completion of the course, students are able to

• understand the use of visual data analytics in the data science workflow,
• apply different techniques from visualization and visual analytics for exploratory data analysis,
• use different techniques from visualization and visual analytics for presentation of results,
• use techniques from human-computer interaction (HCI) and perception to enhance visualizations, and
• understand the differences between various current software libraries and applications.

The lecture part includes a theoretical introduction to visualization, visual analytics, and its application in different programming environments and applications. This includes, among other things, a presentation of current visualization solutions for different Data Science areas.
The exercise/lab part is based on the different stages of a data analysis pipeline (Discover, Wrangle, Profile, Model, and Report). Practical examples will be used to try out the application of visual tools in each step of the pipeline.

Lecture part: Lecture with slides (hybrid), solving tasks in TUWEL.
Exercise/lab part: programming examples, creating reports, presentation

ECTS-Breakdown: 3 ECTS = 75 working hours, of which
  55 working hours (73%) are meant for the exercise/lab part, and
  20 working hours (27%) are meant for the lecture part

In total, 100 points can be achieved by combining the lecture and exercise parts of the course. The students themselves decide in which areas they would like to achieve how many points. In the end, the number of points determines the grade.

Registration can be done via TISS or TUWEL.

• Data Science basics (statistical analysis, regression, modeling)
• Programming skills (e.g., Python, R)
• Basic knowledge of visualization is an advantage