Syllabus

Title
5149 Applications of Data Science
Instructors
Dr.habil. Nadine Schröder
Contact details
  • Type
    PI
  • Weekly hours
    2
  • Language of instruction
    Englisch
Registration
01/31/22 to 02/14/22
Registration via LPIS
Notes to the course
Subject(s) Bachelor Programs
Dates
Day Date Time Room
Wednesday 03/09/22 10:00 AM - 02:00 PM TC.4.12
Wednesday 03/16/22 10:00 AM - 02:00 PM TC.4.12
Wednesday 03/23/22 10:00 AM - 02:00 PM TC.4.12
Wednesday 03/30/22 10:00 AM - 02:00 PM TC.4.12
Wednesday 04/06/22 10:00 AM - 02:00 PM TC.4.12
Wednesday 04/20/22 10:00 AM - 02:00 PM TC.4.12

Contents

The course gives an introduction into applications of data science in the field of digital marketing. As such, the course will cover relevant metrics in customer analysis.

The course is organized in two modules. Module 1 will discuss models typically applied in online marketing campaigns. Module 2 will focus on customer reviews and present tools to explore customer feedback.

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 in marketing. 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

The rules on the attendance of a Continuous Assessment Course (PI) apply.

Pursuant to the general guidelines issued by the Vice-Rector for Academic Programs and Student Affairs, the attendance requirement is met if a student is present at least 80% of the time (5 of the 6 units). Students who fail to meet the attendance requirement will be de-registered from the continuous assessment course with a “fail” grade.

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.

Assessment

The final grade of the course will depend on

  • In-class participation (10%)
  • First project work & project presentations (45%)
  • Second project work & project presentations (45%)

Grades:

1: =>90%

2: =>80%

3: =>70%

4: =>60%

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: 2022-02-16



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