• Describe and explain the preconditions, theories, concepts, models, and methods introduced in the course
• Apply these preconditions, theories, concepts, models, and methods to an analysis of specific issues in management as well as develop solutions to these issues
• Provide an account of the theoretical and applied (practice-oriented) interconnections between management and data analysis
• Reflectively consider (meta-theoretically and critically) the preconditions, theories, concepts, models, and methods introduced in the course, and the potential and limitations of these in theory and practice

The course combines theoretical perspectives from all areas of management with digital data analytics. It equips students with an advanced methodological skill set rooted in inferential and bayesian statistics, machine learning, and network analysis.

The point of departure in management always starts with a problem statement or research question. Typical problems include make-or-buy decisions, cost leadership or differentiation, market entry or exit, in- or outsourcing, mergers and acquisitions, joint ventures and strategic alliances. Digital data analytics provide methodological approaches to find solutions to these and other problems. Consider, for example, an airline that needs to decide on opening new flight routes. It may use digital data analytics and strategic foresight to look into and predict travel preferences in order to make an informed decision on market entry. Another example is a company that wants to leverage its foray into sustainable sourcing. It may use digital data analytics to inquire into stakeholder sentiments and build a communication strategy according to the prediction of future trends.

Digital data analytics not only require sound methodological understanding and practical programming skills but also the ability to identify and articulate ethical issues associated with data collection, analysis, and prediction. To cultivate their ethical sensibility and reflexivity, students will explore the ethical dilemmas of digital data analytics through active engagement with academic literature and relevant business cases.