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Modeling and Analyzing Consumer Behavior with R

Modeling and Analyzing Consumer Behavior with R
type: Vorlesung (V)
semester: SS 2020
time: 2020-04-21
14:00 - 15:30 wöchentlich
11.40 Raum -116
11.40 Kollegiengebäude am Ehrenhof


2020-04-28
14:00 - 15:30 wöchentlich
11.40 Raum -116
11.40 Kollegiengebäude am Ehrenhof

2020-05-05
14:00 - 15:30 wöchentlich
11.40 Raum -116
11.40 Kollegiengebäude am Ehrenhof

2020-05-12
14:00 - 15:30 wöchentlich
11.40 Raum -116
11.40 Kollegiengebäude am Ehrenhof

2020-05-19
14:00 - 15:30 wöchentlich
11.40 Raum -116
11.40 Kollegiengebäude am Ehrenhof

2020-05-26
14:00 - 15:30 wöchentlich
11.40 Raum -116
11.40 Kollegiengebäude am Ehrenhof

2020-06-02
14:00 - 15:30 wöchentlich
11.40 Raum -116
11.40 Kollegiengebäude am Ehrenhof

2020-06-09
14:00 - 15:30 wöchentlich
11.40 Raum -116
11.40 Kollegiengebäude am Ehrenhof

2020-06-16
14:00 - 15:30 wöchentlich
11.40 Raum -116
11.40 Kollegiengebäude am Ehrenhof

2020-06-23
14:00 - 15:30 wöchentlich
11.40 Raum -116
11.40 Kollegiengebäude am Ehrenhof

2020-06-30
14:00 - 15:30 wöchentlich
11.40 Raum -116
11.40 Kollegiengebäude am Ehrenhof

2020-07-07
14:00 - 15:30 wöchentlich
11.40 Raum -116
11.40 Kollegiengebäude am Ehrenhof

2020-07-14
14:00 - 15:30 wöchentlich
11.40 Raum -116
11.40 Kollegiengebäude am Ehrenhof

2020-07-21
14:00 - 15:30 wöchentlich
11.40 Raum -116
11.40 Kollegiengebäude am Ehrenhof


lecturer: Michael Knierim
Dr. Verena Dorner
Anke Greif-Winzrieth
sws: 2
lv-no.: <a target="lvn" href="https://campus.studium.kit.edu/events/KKGkDrWgRla1CcyCjVkFDg">2540470</a>
Bibliography

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), http://cran.r-project.org/doc/manuals/R-intro.pdf

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

Content of teaching

Students learn the fundamental methods, algorithms and concepts for analysing consumer data. The students deepen their knowledge in working on a case study and computer exercises, especially in the areas of e-commerce and behavioural economics. In addition, students learn to write applications in R and to organize and execute larger data mining and general data analytics projects. Furthermore, students learn methods for evaluating and visualizing data.

The event will focus on the following topics:

1. basic programming concepts in R

2. data mining with R using established process models such as CRISP-DM

3. text mining and analysis of online data with R

4. working on a case study from the area of Consumer and User Analytics

5. data visualization and evaluation with R

Annotation

The course has been added summer term 2015.

Workload

The total workload for this course is approximately 135.0 hours. For further information see German version.

Aim

The students

  • have advanced knowledge in handling the statistics software R
  • understands the approach of modelling and analysis consumer data
  • masters methods for evaluation, analysis and visualization of data
Exam description

The assessment consists of a written exam (60 min) (according to §4(2), 1 of the examination regulation). By successful completion of the exercises (according to §4(2), 3 of the examination regulation) a bonus can be obtained. If the grade of the written exam is at least 4.0 and at most 1.3, the bonus will improve it by one grade level (i.e. by 0.3 or 0.4). The bonus only applies to the first and second exam of the semester in which it was obtained.