English Term

Managing Data-Driven Business Models

Prüfungsnummer(n)

2845985

Compulsory Course/Required Elective

Compulsory Course

Type of Course

Lecture

Language

English

Lecturer

Prof. Dr. Frank Danzinger & Prof. Dr. Jianing Zhang

Faculty/Faculties
Offered in

Summer Term

Credits

12 ECTS

Credit Hours

8

Type of Course Assessment

Written Examination

Duration of the Exam

60 minutes

Grading

Decimal Grade

Admission Requirements
For third year business students who have acquired at least 80 ECTS.
General interest in and openness for digital technology, data applications and their prerequisites, willingness to learn and practice programming, sound understanding of fundamental mathematical and statistical concepts.

Objectives

 

The MD²B specialization specifically prepares students to design management and development tasks in the context of data-based projects and digital business models and for use in different roles and business sectors.

Content

 
  • Module 1: Digital Technologies and Data for Digital Business
  • Module 2: Machine Learning and Artificial Intelligence: basic concepts
  • Module 3: Applied Data Science: Machine Learning, AI and use cases
  • Module 4: Implementing and Managing Data-Driven Business Models

Recommended readings

 

Module 1: Digital Technologies and Data for Digital Business

  • Gassmann, O.; Sutter, P. (2019): Digitale Transformation gestalten. Hanser. München.
  • Kosner, A. W. (2015): Google Cabs And Uber Bots Will Challenge Jobs 'Below the API'. Forbes.
  • Ponsard, Christophe; Touzani, Mounir; Majchrowski Annick (2017): Combining Process Guidance and Industrial Feedback for Successfully Deploying Big Data Projects. In: Open Journal of Big Data (OJBD) 3 (1), S. 26–41.
  • Porter, M. E.; Heppelmann, J. E. (2014): The Internet of Everything. Spotlight on Managing the Internet of Things. In: Harvard Business Review, November 2014, S. 1–23.
  • Rogers, David L. (2017): Digitale Transformation. Das Playbook. Wie Sie Ihr Unternehmen erfolgreich in das digitale Zeitalter führen und die digitale Disruption meistern. MITP.
  • Shearer, C. (2000): “The CRISP-DM Model: The New Blueprint for Data Mining,” Journal of Data Warehousing, vol. 5 (4).
  • Teece, D. J. (2010): Business Models, Business Strategy and Innovation. In: Long Range Planning 43 (2-3).
  • Presentations by lecturers

Module 2: Machine Learning and Artificial Intelligence: basic concepts

  • Brown, R.D. (2018). Business Cases Analysis with R. 1st Berkeley, Apress.
  • Hull, J. C. (2020). Machine learning in business an introduction to the world of data science. 2nd Edition, Toronto, Independently published.
  • Milani, F. (2019). Digital Business Analysis. 1st Springer, Cham.
  • Otola, I., Grabowska, M. (2020). Business Models: Innovation, Digital Transformation, and Analytics. Boca Raton: CRC Press.
  • Presentations by lecturer

 

Module 3: Applied Data Science: Machine Learning, AI and use cases

  • Bamberg, G., Baur, F., Krapp, M. (2011). Statistik. 16. Aufl. München: Oldenbourg Verlag.
  • Crawley, M.J. (2012). The R Book. 2. Wiley.
  • Hull, J. C. (2020). Machine learning in business an introduction to the world of data science. 2nd Edition, Toronto, Independently published.
  • James, G., Witten, D., Hastie, T., Tibshirani, R. (2013). An Introduction to Statistical Learning: with Applications in R. New York, Springer.
  • Ligges, U. (2004). Programmieren mit R. New York, Springer.
  • Wickham, H., Grolemund, G. (2016). R for Data Science. 1st Edition, O’Reilly UK Ltd.
  • Xie, Y. (2015). Dynamic Documents with R and Knitr. 2nd Revised Edition. Chapman & Hall/CRC: the R Series.
  • Presentations by lecturer

 

Module 4: Digital Technologies and Data for Digital Business

  • Baines, T. S.; Lightfoot, H. W.; Evans, S.; Neely, A.; Greenough, R.; Peppard, J. et al. (2007): State-of-the-art in product-service systems. In: Proceedings of the Institution of Mechanical Engineers: Journal of Engineering Manufacture 221 (10), S. 1543–1552.
  • Gassmann, O.; Sutter, P. (2019): Digitale Transformation gestalten. Hanser. München.
  • Klötzer, C., Pflaum, A. (2015): Cyber-Physical Systems (CPS) in Supply Chain Management: A definitional approach.
  • Krause, S.; Pellens, B. (2018): Betriebswirtschaftliche Implikationen der digitalen Transformation. Wiesbaden: Springer Gabler.
  • Lusch, R.; Vargo, S. (2016): Service-dominant logic. Reactions, reflections and refinements. In: Marketing Theory 6 (3), S. 281–288.
  • Neely, A. (2011): Exploring the service paradox: How servitization impacts performance of manufacturers.
  • Piller, F. T.; Möslein, K.; Ihl, C. C.; Reichwald, R. (2017): Interaktive Wertschöpfung kompakt. Open Innovation, Individualisierung und neue Formen der Arbeitsteilung. Wiesbaden: Springer Gabler.
  • Ries, E. (2011): The Lean Startup. Penguin Group. London.
  • Verhoef, P. C.; Broekhuizen, T.; Bart, Y.; Bhattacharya, A.; Qi D., J.; Fabian, N.; Haenlein, M. (2019): Digital transformation. A multidisciplinary reflection and research agenda. In: Journal of Business Research.
  • Presentations by lecturer

Workload and Breakdown of Credits

 

12 ECTS x 30 hours = 360 hours,  combined out of the following:

  • Course attendance: 120 hours
  • Preparation / homework / self-study : 120 hours
  • Time for exercises and group work: 20 hours
  • Semester project/presentation preparation: 80 hours
  • Exam preparation: 20 hours
  • Exam time: 60 minutes

Teaching and Learning Methods

 
  • Interactive lecture
  • Presentations by students
  • Real life cases
  • Exercises
  • Guest lectures and/or study trip

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