Syllabus

Title
4890 Marketing and Retail (1)
Instructors
ao.Univ.Prof. Dr. Andreas Mild, Dr. Martin Waitz
Contact details
Type
PI
Weekly hours
2
Language of instruction
Englisch
Registration
02/04/19 to 05/03/19
Registration via LPIS
Notes to the course
This class is only offered in summer semesters.
Subject(s) Master Programs
Dates
Day Date Time Room
Wednesday 05/08/19 04:00 PM - 07:00 PM D2.0.030
Wednesday 05/15/19 04:00 PM - 07:00 PM D2.0.030
Wednesday 05/22/19 04:00 PM - 07:00 PM D2.0.030
Wednesday 05/29/19 04:00 PM - 07:00 PM D2.0.030
Wednesday 06/05/19 04:00 PM - 07:00 PM D2.0.030
Wednesday 06/12/19 04:00 PM - 07:00 PM D2.0.030
Wednesday 06/19/19 02:30 PM - 07:00 PM D2.0.030
Contents

The course gives an introduction into Marketing Research and Analytics with an emphasis on problems in Retailing. All analyses will be done using R.  

Unit:

1: Course overview & Introduction to R

2: Describing data

3: Relationships between continuous variables

4: Linear models

5: Segmentation

6: Mapping & Positioning

7: Final Exam

Learning outcomes
After completing this course students will have a basic knowledge of fundamentals of Data Analysis for Marketing problems. They are able to describe and visualize data with R and apply models for advanced marketing applications.  Students will learn about decision problems in retailing. Beside an understanding of the problem structure, students will learn to apply mathematical and statistical tools to support decision making. Apart from that, completing this course will contribute to the students’ ability to efficiently work and communicate in a team, work on solutions for complex practical problems by using modern statistical software.
Attendance requirements

According to the examination regulation full attendance is intended for a PI.

Teaching/learning method(s)
The course will combine alternative ways to deliver the different topics to the students. On the one hand, a classical lecture style approach where the instructor presents the software will be used; on the other hand, students will have to solve hand on problems in class and as homework.
Assessment

Homework (individual)  (20%)

Seminar paper (group work) (20%)

Final exam (oral) (60%)


Readings
1 Author: Chapman/MCDonnell Feit
Title: R for Marketing Research and Analytics

Publisher: Springer
Year: 2015
Content relevant for class examination: Yes
Recommendation: Strongly recommended (but no absolute necessity for purchase)
Type: Book
Availability of lecturer(s)

martin.waitz@wu.ac.at

Last edited: 2018-11-29



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