After successful completion of the course, students are able to

• understand the relation between the data analysis process and visual tools.
• 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 will start with a theoretical introduction to visualisation, visual analytics, and their application in programming environments and software solutions. This also includes a review on the differences between current software applications for data science.
The lab part is based on the different stages of a data analysis pipeline (Discover, Wrangle, Profile, Model & Report). Practical examples will be used to try out the application of visual tools in each step of the pipeline. At the end, the results will be presented live to colleagues.

Lecture: lecture with slides (live & pre-recorded), solving tasks in TUWEL.
Lab: programming examples, creating reports and live presentation.

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

Scoring scheme

Registration can be done via TISS or TUWEL.

• Data Science basics (statistical Analysis, regression, modeling)
• Programming skills (e.g., Python)
• Preferrable knowledge about visualisation