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

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== Zeitaufwand ==
 
== Zeitaufwand ==
 
* 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 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: [will follow soon]
  
 
== Unterlagen ==
 
== Unterlagen ==

Revision as of 11:30, 16 January 2020

Daten

Lecturers Johanna Schmidt
ECTS 3
Department Visual Computing and Human-Centered Technology
When winter semester
Language Deutsch
Links tiss:186868, Homepage
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" Team invite & account creation link Mattermost-Infos

Inhalt

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

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

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.

Vortrag

noch offen

Übungen

  • 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

noch offen

Dauer der Zeugnisausstellung

noch offen


Zeitaufwand

  • 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: [will follow soon]

Unterlagen

noch offen

Tipps

noch offen

Verbesserungsvorschläge / Kritik

noch offen