News
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.
Content
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.
Lectures
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 Applications |
| 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). |
Grading
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 |