Syllabus

Titel
0731 Advanced Data Analysis with R
LV-Leiter/innen
Dr. Marcus Wurzer
Kontakt
  • LV-Typ
    PI
  • Semesterstunden
    2
  • Unterrichtssprache
    Englisch
Anmeldung
08.09.2020 bis 28.09.2020
Anmeldung über LPIS
Hinweise zur LV
Planpunkt(e) Doktorat/PhD
Termine
Wochentag Datum Uhrzeit Raum
Montag 05.10.2020 14:30 - 15:30 Online-Einheit
Montag 12.10.2020 15:00 - 17:00 Online-Einheit
Montag 19.10.2020 15:30 - 17:30 Online-Einheit
Montag 09.11.2020 16:00 - 18:00 Online-Einheit
Montag 23.11.2020 14:30 - 17:00 Online-Einheit
Montag 30.11.2020 14:30 - 17:00 Online-Einheit
Montag 14.12.2020 14:30 - 16:30 Online-Einheit
Montag 21.12.2020 14:30 - 16:30 Online-Einheit
Montag 11.01.2021 14:30 - 16:30 Online-Einheit
Montag 18.01.2021 14:30 - 16:30 Online-Einheit
Montag 25.01.2021 14:30 - 16:30 Online-Einheit

Ablauf der LV bei eingeschränktem Campusbetrieb

  In case of limited activity on campus the course will take place via Distance Mode.

Inhalte der LV

R is a high-level language and an environment for data analysis and data visualization. While R can be used as a calculator and all important basic statistical methods are included as well, the main benefit is its open-source philosophy which makes R highly extensible and renders possible the availability of new, cutting edge applications in many different fields. The popularity of R increased constantly during the last years and by now, it is arguably the most popular software for data analysis in the statistical community. The course starts with an introduction to R, covers some elementary statistical techniques and then continues with more advanced methods. In particular, the course will focus on the following:

  • Linear Models (Standard Linear Regression and Analysis of Variance)
  • Generalized Linear Models (Binary, Multinomial and Proportional-Odds Logistic Regression, Poisson and Negative-Binomial Regression)
  • Decision Trees
  • Naive Bayes Classifiers
  • k-NN Classifiers
  • Cluster Analysis (hierarchical and non-hierarchical)
  • Correspondence Analysis

Additional methods may be covered as well, depending on student's interest, e.g.,

  • Principal Components Analysis
  • Social Network Analysis
  • Mixed-Effects Models
  • ...

Lernergebnisse (Learning Outcomes)

On completion of the course students are able to:

  • manipulate and visualize data in R
  • understand the theory and functionality of the methods employed in the course
  • apply the adequate statistical methods to a given problem and perform the statistical calculations using R
  • interpret the results of such analyses
  • communicate and discuss the results of the statistical analysis of data

Regelung zur Anwesenheit

  • Attendence is compulsory. Students have to be attend classes for at least 80% of the total time, i.e., 18 of 22.5 hours.
  • It is not possible to compensate for absences, e.g., if someone misses their project presentation.

Lehr-/Lerndesign

Lectures, Practicals

Leistung(en) für eine Beurteilung

  • development of a project concept (10 %)
  • written report on the analysis of a dataset using advanced statistical methods (50 %)
  • oral presentation of analysis results (40 %)

Erreichbarkeit des/der Vortragenden

marcus.wurzer@wu.ac.at
Zuletzt bearbeitet: 15.09.2020



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