After successful completion of the course, students are able to

• understand the use of visual data analytics in the data science workflow,
• employ techniques from visualisation and visual analytics for the exploratory data analysis,
• employ techniques from visualisation and visual analytics for data presentation,
• employ strategies from human-computer-interaction (HCI) and perception to improve visualizations, and
• understand the differences between current software libraries and applications for data science.

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 video

ECTS-Breakdown: 3 ECTS = 75 working hours, of which
  55 working hours (73%) are meant for the 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.

Material: Lecture slides and additional material/links

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