skip to content

Selected Issues in Marketing I


Marketing Data Analysis and Visualisation in R

 Lecture (14266.0018)

            Instructor: Prof. Dr. Hernán A. Bruno
            Date: Oct, 26th, 2016 - Nov, 30th, 2016 & Jan, 11th, 2017
            Time: Wednesday, 9:00am - 12:00am
            Location:  Room 1.31 (Building 810)



The examination consists of a term paper and a presentation.
Please keep in mind that you need to register for the examination within the corresponding registration period. It is NOT possible to register after the registration period. Written exams can be answered either in German or in English. Note that no responsibility is taken for the correctness of the information. Please refer to the  Examination Office for all examination-related questions.

Zum Bestehen der Veranstaltung muss eine Seminararbeit und eine Präsentation angefertigt werden.
Bitte beachten Sie, dass Sie sich nur innerhalb der vorgegebenen Fristen für die Prüfungen anmelden können. Es ist NICHT möglich, sich nach Ablauf der Frist nachträglich anzumelden. Schriftliche Prüfungen können auf Englisch oder auf Deutsch abgelegt werden. Es wird keine Verantwortung für die Richtigkeit der Informationen übernommen. Bitte informieren Sie sich beim  Prüfungsamt über alle klausurbedingten Fragen.



Marketing is more data intensive than ever before and the modern manager needs to have a working knowledge of how marketing data looks like, how it can be handled, analysed and presented. This class is an introduction to statistical analysis using a programming environment. It requires no existing knowledge of R or any other statistical software. I will follow a hands-on approach. Students are encouraged to come with a laptop to the seminar and we’ll go through the steps of loading, transforming, cleaning, exploring, visualising, analysing and reporting data. We will use R, which is the most popular statistical environment today, and it is free. The student will never have to use Stata or SPSS again in their life. As part of the workload in this course, the student will receive a messy dataset that they will have to tease apart and explore in order to make a final presentation and summarize the results in a short term paper at the end of the course.