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.

• 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

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. 1^st 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. 1^st 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

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

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