Modulhandbuch (Module manual)

M.184.5333 Data Science for Business
(Data Science for Business)
Koordinator (coordinator): Prof. Dr. Oliver Müller
Ansprechpartner (contact): Prof. Dr. Oliver Müller (oliver.mueller[at]
Credits: 5 ECTS
Workload: 150 Std (h)
Semesterturnus (semester cycle): WS
Studiensemester (study semester): 1-4
Dauer in Semestern (duration in semesters): 1
Lehrveranstaltungen (courses):
Nummer / Name
(number / title)
(contact time)
Status (P/WP)
(group size)
a) K.184.53331 / Data Science for Business Vorlesung / Übung P
Wahlmöglichkeiten innerhalb des Moduls (Options within the module):
Empfohlene Voraussetzungen (prerequisites):

​Grundzüge der Statistik, Grundzüge der angewandten Statistik für Wirtschaftsinformatiker, Methoden der Data Science

Inhalte (short description):
The term Data Science generally describes the extraction of knowledge from large amounts of data, its goal to improve the effectiveness and efficiency of decision-making processes through the knowledge thus gained. The course covers basic and advanced concepts and methods of Data Science and their application in an economic context with a focus on supervised and unsupervised machine learning methods (e.g. linear and logistic regression, random forest, boosted decision trees, neural networks, clustering and dimensionality reduction). The course is organized according to the flipped-class-room concept (, i.e. students work out the theoretical contents supported by videos and textbooks in self-study and practising the application in classroom sessions based practical issues and case studies. The course is aimed at students of economics who want to gain first practical experience in the field of data science. Willingness to learn the R programming language is a basic requirement.​
Lernergebnisse (learning outcomes):
Fachkompetenz Wissen (professional expertise):
  • ​know common features and differences between supervised and unsupervised machine learning methods.
  • know basic linear (esp. linear and logistic regression and their respective extensions) and non-linear models (e.g. tree-based methods, neural networks) of machine learning and can explain how they work
  • know methods and metrics for assessing the quality of machine learning models
  • Fachkompetenz Fertigkeit (practical professional and academic skills):
  • ​apply various machine learning techniques to explain and predict economic phenomena
  • evaluate the quality of supervised and unsupervised machine learning models
  • Personale Kompetenz / Sozial (individual competences / social skills):
  • ​​​​solve exercise tasks and case studies together in classroom sessions
  • Personale Kompetenz / Selbstständigkeit (individual competences / ability to perform autonomously):

  • independently work out course content at home with the help of a textbook, accompanying videos and presentation slides
  • Prüfungsleistungen (examinations)
    Art der Modulprüfung (type of modul examination): Modulabschlussprüfung
    Art der Prüfung
    (type of examination)
    a) Klausur 90 minutes 100.00 %
    Studienleistung / qualifizierte Teilnahme (module participation requirements)
    Voraussetzungen für die Teilnahme an Prüfungen (formal requirements for participating in examinations)
    Voraussetzungen für die Vergabe von Credits (formal requirements for granting credit points)
    Die Vergabe der Credits erfolgt, wenn die Modulnote mindestens „ausreichend“ ist
    Gewichtung für Gesamtnote (calculation of overall grade)
    Das Modul wird mit der Anzahl seiner Credits gewichtet (Faktor: 1)
    Verwendung des Moduls in den Studiengängen (The module can be selected in the following degree programmes)
    M.Sc. International Business Studies, M.Sc. Betriebswirtschaftslehre, M.Sc. International Economics and Management, M.Sc. Management Information Systems, M.Sc. Wirtschaftsinformatik, M.Sc. Wirtschaftspädagogik, M.Ed. Wirtschaftspädagogik
    Umfang QT (participation requirements):
    Lernmaterialien, Literaturangaben (learning material, literature):
    The course is based on the textbook "Introduction to Statistical Learning" by James et al. and the associated Massive Open Online Course (
    Teilnehmerbegrenzung (participant limit):
    Sonstige Hinweise (additional information):
    ​The course language is English.

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