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Selected Issues in Marketing I

 

Marketing Data Analysis and Visualisation in R

Interactive Lecture and Exercise (14266.0503)

            Instructor: Prof. Dr. Hernán A. Bruno
            Date: Oct, 11th, 2017 - January 31st 2017
            Time: Wednesday, 10:00am - 11:30am
            Location: Room 1.31 (Building 810)

 

Examinations

The examination consists of two parts:

  • Term paper (January 24th 2017)
  • Presentation (January 31st 2017)

Please keep in mind that you need to register for the examination personally at the chair for Marketing and Digital Environment within the first two sessions (till October 18th 2017).

Zum Bestehen der Veranstaltung muss eine Seminararbeit angefertig und eine Präsentation gehalten werden. Da die Veranstaltung auf einem individuellen Projekt basiert, das in jedem Termin weiterentwickelt wird, beachten Sie bitte, dass Sie sich nur innerhalb der ersten beiden Veranstaltungstermine über den Lehrstuhl für die Klausur anmelden können (Stichtag ist der 18. Oktober 2017). Es ist NICHT möglich, sich nach Ablauf der Frist nachträglich anzumelden. Es wird keine Verantwortung für die Richtigkeit der Informationen übernommen. Bitte informieren Sie sich beim Prüfungsamt über alle prüungsbedingten Fragen.

 

Content

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.