Data Economy

Department
  • Master's Program Management, Communication & IT
Course unit code
  • MCI-M-3-DAE-DAE-ILV
Level of course unit
  • Master
Semester when the course unit is delivered
  • 3
Number of ECTS credits allocated
  • 5.0
Name of lecturer(s)
  • Janßen Hilko, M.Sc.
Learning outcomes of the course unit
  • Students know the basics of digital business models and ecosystems. They know the importance and value of data in these areas. They are able to design data-driven services and integrate them into (digital) business models. They know the central technologies and tools for using data as a value-added component.
Mode of delivery
  • face-to-face
Prerequisites and co-requisites
  • Please note: Prerequisite knowledge from IT-relevant studies required!
Course contents
  • - Fundamentals of digital ecosystems and digital business models
    - Fundamentals and applications of Artificial Intelligence
    - Data science pipeline
    - Data value streams
    - Data monetization & data markets
    - Legal aspects
Recommended or required reading
  • - Laney, D. B. (2017). Infonomics: How to Monetize, Manage, and Measure Information as an Asset for Competitive Advantage (1st ed.). Milton: Taylor and Francis: Taylor and Francis.
    - Schmarzo, B., & Borne, K. (2020). The the economics of data, analytics, and digital transformation: The theorems, laws, and empowerments to guide your organization's digital transformation. Birmingham England: Packt Publishing Limited: Packt Publishing Limited.
    - Kelleher, J. D., & Tierney, B. (2018). Data science. The MIT Press essential knowledge series. Cambridge, Massachusetts, London, England: The MIT Press: The MIT Press.
    - Russell, S. J., & Norvig, P. (2018). Artificial intelligence: A modern approach (4th edition). Boston: Pearson: Pearson.
    - Harvard Business Review Press (2018). Hbr guide to data analytics basics for managers. Harvard Business Review guides. Boston, Massachusetts: Harvard Business Review Press: Harvard Business Review Press.
Planned learning activities and teaching methods
  • The course comprises an interactive mix of lectures, discussions and individual and group work.
Assessment methods and criteria
  • To monitor the students’ learning this course will provide ongoing assignments as a basis for feedback and grading (formative assessment) and/or will evaluate the students learning at the end of the course or an instructional unit via exams, final project reports, essays or seminar papers (summative assessment).
Language of instruction
  • English
    • AACSB/FIBAA