Data Science

Modeling & Analyzing Consumer Behavior with R

  • Type:
  • Semester: SS
  • Lecturer:

    PD Dr. Jella Pfeiffer
    Dr. Verena Dorner

  • SWS: 2/1/0

bibliographic references

Field, A., Miles, J., Field, Z., Discovering Statistics Using R, SAGE 2014

Jones, O., Maillardet, R., Robinson, A., Scientific Programming and Simulation Using R, Chapmann & Hall / CRC Press 2009

Venables, W. N., Smith, D. M. and the R Core Team,"An Introduction to R", 2012 (version 2.15.2),

Wickham, Hadley, ggplot2: Elegant Graphics for Data Analysis (Use R!), Springer 2009 (2nd edition)


teaching content

The students use the R software to work on case studies in the areas of e-commerce and decision support (DSS). At the development level, students learn to write their own functions in R, e. g., to write functions in R. Simulate enterprise data. At the user level, students learn methods for evaluating and visualizing data, e. g. for analyzing product reviews.

The event will focus on the following topics:

  •     Data types and programming concepts in R
  •     Data selection and structuring in dataframes
  •     Text Mining with R
  •     Optimization with R
  •     Visualization with R



Limited number of participants.

New lecture from summer semester 2015.


Labour input activity

Total effort with 4,5 achievement points: approx. 30*4,5 = 135 hours

Presence time: approx. 32 hours

Preparation / follow-up: approx. 52 hours

Examination and exam preparation: approx. 51 hours



The student (s)

  •     have advanced knowledge in working with the statistics software R
  •     understands the approach to modeling and simulation of decision support systems
  •     masters methods for evaluation, analysis and visualization of data



The results are checked in the form of a written examination (60 min.) (according to §4 (2), 1 SPO). By successfully participating in the training operation as a performance review of a different kind (according to §4 (2), 3 SPO) a bonus can be acquired. If the grade of the written exam is between 4.0 and 1.3, the bonus improves the grade by one grade (0.3 or 0.4). The bonus is only valid for the main and subsequent exams of the semester in which it was acquired.