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BWL XI: Courses in Summer Semester 2020

In summer semester 2020, we offer the course "Data Science for Management" for bachelor's students. Master's students are welcome to apply for a spot in the "Data Science Seminar." Seminar participants are welcome to propose seminar topics based on their personal interests. The first deadline for seminar applications is 27th January 2020 (via e-mail).

Course: Data Science for Management (B. Sc.)


Prior to the start of the Information Age, companies were forced to collect data from non-automated sources manually. As a result, company decisions were frequently based on gut feeling and intuition. With the emergence of ubiquitous computing technology, company decisions nowadays rely strongly on data science methods and machine learning.

The course “Data Science for Management” provides an overview of the multi-disciplinary field of data science for management students. Topics include (but are not limited to) data collection, integration, management, modeling, analysis, visualization, prediction and data-driven decision making. The course includes practical sessions focusing on data analysis and programming in R.

The main objectives of this course are:

  1. Understand the basic concepts and business relevance of data science and data-driven decision making
  2. Gain an overview of different methods, algorithms and software tools for data science applications
  3. Understand the pitfalls and myths of data science


  • Module codes: 02-Meth:BSc-B11-Extra1 & 02-Meth:BSc-Extra6CP
  • Lecturer: Prof. Dr. Nicolas Pröllochs (BWL XI)
  • Term: Summer semester 19 (next course: summer semester 20)
  • Language: German
  • Course format: Lecture (6 CP)
  • Grading: Final exam

Seminar (M. Sc.) in Data Science


Data science is the field of study that combines domain expertise, programming skills, and knowledge of maths and statistics to extract meaningful insights from data. In this seminar, we will focus on Data Science methods and tools. Examples include machine learning models, data visualization, model selection, clustering, and forecasting. We will also review best practices in scientific writing. Students are welcome to propose seminar topics based on their personal interests.  Basic experience in R programming is desirable but not mandatory. Please indicate your level of programming expertise. Individual assignments will consist of a specific problem from data science. Grading will be based on a seminar paper and an oral presentation.

Exemplary topics include, but are not limited to:

  • Applying a data science method to a dataset (e.g. predicting movie ratings based on movie reviews)
  • Presenting an R package
  • Presenting a data science / machine learning method 


  • Lecturer: Prof. Dr. Nicolas Pröllochs (BWL XI)
  • Course format: Seminar / Proseminar
  • Term: summer semester 20 (Seminar)
  • Language: German
  • Grading: Seminar paper & presentation