1044 Advanced Data Analysis with R
Dr. Marcus Wurzer
Contact details
Weekly hours
Language of instruction
09/02/16 to 09/28/16
Registration via LPIS
Notes to the course
Day Date Time Room
Monday 10/17/16 03:00 PM - 04:30 PM D2.0.031 Workstation-Raum
Monday 11/07/16 03:00 PM - 06:00 PM TC.-1.61
Monday 11/21/16 03:00 PM - 06:00 PM TC.-1.61
Monday 11/28/16 03:00 PM - 06:00 PM TC.-1.61
Monday 12/05/16 03:00 PM - 06:00 PM LC.-1.038
Monday 01/09/17 03:00 PM - 06:00 PM LC.-1.038
Monday 01/16/17 03:00 PM - 06:00 PM LC.-1.038
Monday 01/23/17 03:00 PM - 06:00 PM LC.-1.038

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:

  • Linear Models (Standard Linear Regression and Analysis of Variance)
  • Binary and Multinomial Logistic Regression
  • Decision Trees
  • Naive Bayes Classifiers
  • Association Rules
  • Cluster Analysis
  • Correspondence Analysis 
  • Principal Components Analysis and Factor Analysis
  • Social Network Analysis
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
Teaching/learning method(s)
Lectures, Practicals
  • 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 %)
Availability of lecturer(s)
Last edited: 2016-04-06