0829 Advanced Data Analysis with R
Dr. Marcus Wurzer
  • LV-Typ
  • Semesterstunden
  • Unterrichtssprache
23.09.2019 bis 01.10.2019
Anmeldung über LPIS
Hinweise zur LV
Planpunkt(e) Doktorat/PhD
Wochentag Datum Uhrzeit Raum
Montag 07.10.2019 15:00 - 16:30 TC.3.02
Montag 14.10.2019 15:00 - 17:00 D2.-1.019 Workstation-Raum
Montag 21.10.2019 13:00 - 15:00 D2.0.025 Workstation-Raum
Montag 04.11.2019 13:00 - 15:00 D2.0.025 Workstation-Raum
Montag 11.11.2019 15:00 - 17:00 D2.0.031 Workstation-Raum
Montag 18.11.2019 15:30 - 17:00 D2.0.031 Workstation-Raum
Montag 25.11.2019 15:30 - 17:00 D2.-1.019 Workstation-Raum
Montag 02.12.2019 15:30 - 17:00 D2.0.025 Workstation-Raum
Montag 09.12.2019 15:30 - 17:00 D2.-1.019 Workstation-Raum
Montag 16.12.2019 15:30 - 17:00 D2.0.025 Workstation-Raum
Montag 13.01.2020 15:30 - 17:00 D2.0.031 Workstation-Raum
Montag 20.01.2020 15:00 - 18:00 D2.0.025 Workstation-Raum
Montag 27.01.2020 15:00 - 18:00 D2.0.025 Workstation-Raum

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.


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
Zuletzt bearbeitet: 21.03.2019