TU Wien:Visual Data Science VU (Schmidt)

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Vortragende Johanna Schmidt
Abteilung Visual Computing and Human-Centered Technology
Wann Wintersemester
Sprache Deutsch
Links tiss:186868, Homepage
Master Data Science Wahlmodul VAST/EX - Visual Analytics and Semantic Technologies - Extension

Mattermost: Channel "visual-data-science" Team invite & account creation link Mattermost-Infos


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What is nice is that often the presented material is backed up by hints to literature (e.g. that the visualization presented, and the best practices for using it, are backed up by a scientific study about how to properly use it, etc.)


Weekly lectures with well prepared slides. Material is presented rather slow (not in a bad way) and thoroughly [imho].

Different "grading packages" available: Either do:

  • two practical exercises with datasets, visualize and analyze data and submit reports + presentation in the end.
  • 1 practical exercise with dataset, as above. Then a final exam.

Attendance is checked with list, depending on grading package a certain number of attendances (5 to 7) can earn you points toward your final grade.

Benötigte/Empfehlenswerte Vorkenntnisse[Bearbeiten]

Knowledge of Python, especially Pandas, and visualization tools such as matplotlib, seaborn, etc., is necessary.

Some statistical knowledge is also requrired, and visual data analysis.


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  • Assignment ("Lab") 1: Given data-set, compare computational and visualization methods. Tasks: Cluster analysis. Check for correlations. Compare groups of variables. Written report of at least 3 pages is to be submitted.
  • Assignment ("Lab") 2: Search your own large data-set. Explore, get insights. Then either: make a Dashboard for exploration, or: write report about analysis of 6 different visualizations, i.e. a literature review/survey.

Prüfung, Benotung[Bearbeiten]

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Dauer der Zeugnisausstellung[Bearbeiten]

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Verbesserungsvorschläge / Kritik[Bearbeiten]

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