VU, 186.868, 2022W

Johanna Schmidt

This course page describes the lecture of Wintersemester 2022/2023. See list of other semesters.


All information about the lecture (Zoom links, slides, etc) can be found on TUWEL.
Please post your questions about the lecture in the TUWEL forum.


The VU Visual Data Science will discuss how techniques from visualisation and visual analytics can be applied in data science. The lecture will start with a theoretical introduction to different concepts of visualisation and visual analytics. The second part of the lecture will deal with the practical application of Visual Data Science, namely the selection of charts, and trust in visualisations. An overview of current applications and libraries will be given. The lecture part then concludes with the late-breaking research topics of trust in visual interpretation and how AI methods are used in visualisation.


Important information about the lectures:

  • All lectures are hybrid and will be recorded.
  • Lectures will be only 60min. The remaining 30min minutes will be dedicated to solving practical questions in TUWEL. This can be done either directly in the lecture room afterwards, or later on.
  • The first slot (Oct 5th) is only an introduction to the administrative structure of the lecture (no lecture unit).
  • One lecture (Oct 19th) will be presented as a pre-recorded video in TUWEL, since Johanna Schmidt is at the conference on that day.
  • Hybrid lectures usually take place in FAV Hörsaal 3 Zemanek, except for the lecture number 5 (Nov. 23), which will take place in Seminarraum Argentinierstraße (HS 3 is taken on that day).
# Date Time Type Title
  05.10.2022 11:00 Online Meeting (Zoom) Introductory lecture ("Vorbesprechung")
General information on the lecture - no lecture unit
L1 12.10.2022 11:00 - 12:00 Hybrid Lecture
(FAV Hörsaal 3 Zemanek & Zoom)
Information Visualisation
Basics for the visual display of information 
L2 19.10.2022 available from 11:00 on Pre-recorded video Human Perception
How humans perceive data visualisations
  26.10.2022 no lexture (lecture free day)
  02.11.2022 no lexture (lecture free day)
L3 09.11.2022 11:00 - 12:00 Hybrid Lecture
(FAV Hörsaal 3 Zemanek & Zoom)
Data Science Workflow
How to structure the data science workflow
L4 16.11.2022 11:00 - 12:00 Virtual Lecture
(Zoom only)
BI Tools
Getting to know Tableau, MS Power BI, and OmniSci
L5 23.11.2022 11:00 - 12:00 Hybrid Lecture
(Seminarraum Argentinierstraße & Zoom)
Applications & Libraries I
L6 30.11.2022 11:00 - 12:00 Hybrid Lecture
(FAV Hörsaal 3 Zemanek & Zoom)
Applications & Libraries II
Charting Libraries
L7 07.12.2022 11:00 - 12:00 Virtual Lecture
(Zoom only)
Usage of Charts and Plots in Data Science
How to select the right chart based on the data?
L8 14.12.2022 11:00 - 12:00 Hybrid Lecture
(FAV Hörsaal 3 Zemanek & Zoom)
Trust in Visualisation
How to correctly interpret what we see?
  21.12.2022 no lecture (lecture free day)
  28.12.2022 no lecture (lecture free day)
  04.01.2023 no lecture (lecture free day)
L9 11.01.2023 available from 11:00 on Pre-recorded video AI in Visualisation
Visualisation for understanding AI & Using AI for better visualisations


Lab Part

Lab organisation and submissions are done via TUWEL.

The lab part of the VU outlines the different stages of a data science workflow. Every data science workflow consists of five stages: Discover, Wrangle, Profile, Model & Report. We will work on all stages and see how visualization can be used in every stage (except Discover). The lab part has the following deadlines:

# Date & Time Description
D1 09.11.2022 23:59 Selection of topic & stage Discover finished - submission of short report (1 A4 page).
D2 14.12.2022 23:59 Stages Wrangle and Profile finished - submission of report (2-4 A4 pages).
D3 11.01.2023 23:59 Stages Model finished - submission of report (at least 1-2 A4 pages).
D4 16.01.2023 - 20.01.2023 Final presentations (with dashboard from stage Report).



The following points can be achieved in the lecture:

Interactive lecture part (TUWEL) 30 points
Discover 5 points
Wrangle 10 points
Profile 15 points
Model 10 points
Report 20 points
Final presentation 10 points

The points define the final grade:

Sehr Gut (1) > 85 points
Gut (2) > 75 points
Befriedigend (3) > 62 points
Genügend (4) > 50 points
Nicht Genügend (5) <= 50 points