5587 Applications of Data Science
ao.Univ.Prof. Dr. Andreas Mild, Univ.Prof. Dr. Thomas Reutterer
Weekly hours
Language of instruction
02/04/19 to 02/12/19
Registration via LPIS
Notes to the course
Day Date Time Room
Wednesday 03/06/19 03:00 PM - 07:00 PM D5.1.002
Wednesday 03/13/19 03:00 PM - 07:00 PM D5.1.002
Wednesday 03/20/19 03:00 PM - 07:00 PM D5.1.002
Wednesday 03/27/19 03:00 PM - 07:00 PM D5.1.002
Wednesday 04/03/19 03:00 PM - 07:00 PM D5.1.002
Wednesday 04/10/19 03:00 PM - 05:30 PM D5.1.002

The course gives an introduction into applications of data science in the fields of marketing and supply chain management. In this semester, we will focus on marketing science methods.

The course is organized in two modules: Module 1 will cover concepts and methods to develop models for customer-base analysis and for deriving critical components of "Customer Lifetime Value" (CLB). Module 2 will focus on tools for evaluating targeting strategies in online advertising and for evaluating the effect of online display ads' viewability on advertising effectiveness.

In both modules, conceptual foundations and their applications will be covered. All analyses will be done using R. 

Learning outcomes
After completing this course students will have knowledge about different areas of application for data science. Students will have a basic understanding of area specific challenges and algorithms. Students will learn about recent applications in marketing like customer-base analysis and digital marketing. Besides 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. Absence in one unit is tolerated if a proper reason is given.

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 and the content will be used; on the other hand, students will have to solve hands-on problems in class and as homework.

The final grade of the course will depend on

  • In-class participation (10%)
  • Module 1 project work & project presentations (45%)
  • Module 2 project work & project presentations (45%)
Prerequisites for participation and waiting lists

Successful conclusion of the course 1 of SBWL Data Science.

Please be aware that, for all courses in this SBWL, registration is only possibly for students who successfully have completed the entry course (Einstieg in die SBWL: Data Science).

Note that for courses within the SBWL "Data Science" we can only accept students enrolled in one of WU's bachelor programmes who qualify for starting an SBWL; particularly, we cannot accept students from other courses and programmes enrolled at WU as 'Mitbeleger' only.
Last edited: 2019-03-06