Difference between revisions of "TU Wien:Visual Data Science VU (Schmidt)"

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| zuordnungen =
 
| zuordnungen =
 
     {{Zuordnung|E066645|VAST/EX - Visual Analytics and Semantic Technologies - Extension|wahl=1}}
 
     {{Zuordnung|E066645|VAST/EX - Visual Analytics and Semantic Technologies - Extension|wahl=1}}
 +
  {{Zuordnung|E066935|Image Processing & Visualization|wahl=1}}
 
}}
 
}}
 
{{mattermost-channel|visual-data-science}}
 
{{mattermost-channel|visual-data-science}}
Line 17: Line 18:
  
 
https://www.cg.tuwien.ac.at/courses/VisDataScience/
 
https://www.cg.tuwien.ac.at/courses/VisDataScience/
 +
 +
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.)
  
 
== Ablauf ==
 
== Ablauf ==
 +
Weekly lectures with well prepared slides. Material is presented rather slow (not in a bad way) and thoroughly [imho].
 +
 
Different "grading packages" available:
 
Different "grading packages" available:
 
Either do:
 
Either do:
 
* two practical exercises with datasets, visualize and analyze data and submit reports + presentation in the end.
 
* 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.
+
* 1 practical exercise with dataset, as above. Then a final oral exam.
  
Attendance is checked with list, depending on grading package a certain number of attendances can earn you points toward your final grade.
+
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 ==
 
== Benötigte/Empfehlenswerte Vorkenntnisse ==
 
Knowledge of Python, especially Pandas, and visualization tools such as matplotlib, seaborn, etc., is necessary.
 
Knowledge of Python, especially Pandas, and visualization tools such as matplotlib, seaborn, etc., is necessary.
 +
If assignment 2 is selected, then usage of a tool or library for interactive data visualization is also required (or needs to be picked up).
  
Some statistical knowledge is also requrired, and visual data analysis.
+
Some statistical knowledge is also required, and basics of visual data analysis.
  
 
== Vortrag ==
 
== Vortrag ==
Line 35: Line 41:
  
 
== Übungen ==
 
== Übungen ==
noch offen
+
* 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 ==
 
== Prüfung, Benotung ==
Line 45: Line 52:
  
 
== Zeitaufwand ==
 
== Zeitaufwand ==
noch offen
+
* Assignment 1: a few hours, to familiarize yourself with the data, the tasks to perform, and the tools you need. Depending on your skills in tools for visualizations (some plots have to be created and analyzed) as well as methods for clustering  (e.g. scikit-learn), your milage may vary. Then a little more effort for writing min. 3 page report (including figures where appropriate).
 +
* Assignment 2: [can't really say, I switched to taking the exam rather than doing the assignment 2, as I coulnd't afford taking the time; it seems that taking the exam is much less effort than doing the assignment, but that is only my humble opinion. Please add other experiences]
 +
* Exam preparation: 2-3 days (without attending the lecture: most of the stuff (diagrams) has been discussed in other courses (charts, perception, ...), for some other topics (AI Vis, charts/apps, ...) the referenced papers have to be read since the slides are bit sparse)
  
 
== Unterlagen ==
 
== Unterlagen ==
noch offen
+
Slides are available from the classe's web page, see link above.
  
 
== Tipps ==
 
== Tipps ==
Line 55: Line 64:
 
== Verbesserungsvorschläge / Kritik ==
 
== Verbesserungsvorschläge / Kritik ==
 
noch offen
 
noch offen
 
 
  
 
[[Kategorie:Computergraphik]]
 
[[Kategorie:Computergraphik]]

Latest revision as of 17:50, 21 January 2020

Daten[edit]

Lecturers Johanna Schmidt
ECTS 3
Department Visual Computing and Human-Centered Technology
When winter semester
Language Deutsch
Links tiss:186868, Homepage, Mattermost-Channel
Zuordnungen
Master Data Science Wahlmodul VAST/EX - Visual Analytics and Semantic Technologies - Extension
Master Media and Human-Centered Computing Wahlmodul Image Processing & Visualization

Mattermost: Channel "visual-data-science"RegisterMattermost-Infos

Inhalt[edit]

noch offen, bitte nicht von TISS/u:find oder Homepage kopieren, sondern aus Studierendensicht beschreiben.

https://www.cg.tuwien.ac.at/courses/VisDataScience/

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.)

Ablauf[edit]

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 oral 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[edit]

Knowledge of Python, especially Pandas, and visualization tools such as matplotlib, seaborn, etc., is necessary. If assignment 2 is selected, then usage of a tool or library for interactive data visualization is also required (or needs to be picked up).

Some statistical knowledge is also required, and basics of visual data analysis.

Vortrag[edit]

noch offen

Übungen[edit]

  • 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[edit]

noch offen

Dauer der Zeugnisausstellung[edit]

noch offen


Zeitaufwand[edit]

  • Assignment 1: a few hours, to familiarize yourself with the data, the tasks to perform, and the tools you need. Depending on your skills in tools for visualizations (some plots have to be created and analyzed) as well as methods for clustering (e.g. scikit-learn), your milage may vary. Then a little more effort for writing min. 3 page report (including figures where appropriate).
  • Assignment 2: [can't really say, I switched to taking the exam rather than doing the assignment 2, as I coulnd't afford taking the time; it seems that taking the exam is much less effort than doing the assignment, but that is only my humble opinion. Please add other experiences]
  • Exam preparation: 2-3 days (without attending the lecture: most of the stuff (diagrams) has been discussed in other courses (charts, perception, ...), for some other topics (AI Vis, charts/apps, ...) the referenced papers have to be read since the slides are bit sparse)

Unterlagen[edit]

Slides are available from the classe's web page, see link above.

Tipps[edit]

noch offen

Verbesserungsvorschläge / Kritik[edit]

noch offen