graduate Business

Qualification goals:
Students will be able to use data-driven and quantitative approaches to solve business economic (decision-making) problems and combine these approaches appropriately where necessary. They are familiar with relevant software for decision support in complex business management problems. In particular, they understand how to model elements of uncertainty and how to map and process them using advanced methods of data analysis, optimization or stochastic simulation. They are aware of the challenges arising from the amount of available data (“big data”) and the runtime complexity of quantitative approaches (NP-hard problems). Students can further develop the methods used in practical applications and critically analyze the results.

Contents:
Building on the basic Operations Research and Business Intelligence modules, knowledge of approaches to advanced data analysis, business intelligence and optimization or simulation is imparted. This includes special methods of explorative, descriptive and predictive modelling (data mining), the modelling and implementation of application-oriented business intelligence methods as well as methods of dynamic or stochastic optimization (e.g. metaheuristics). The relevant methods are introduced in an application-oriented manner, implemented on the basis of general programming languages and/or software packages where appropriate, and challenges and opportunities are discussed using practical examples and case studies.