• plan their case study / project
• summarize the contents of the case study they are provided with.
• discuss the case study in the light of existing models and theories.
• solve different assignments in the case study with the help of different methods.
• compare the as-is situation described in the case study and identify possible solutions for improvement.
• develop new strategies for a successful implementation of a business intelligence solution for the case study and
• do the conceptional breakdown of the case study /project with a blue print

...evaluate different solutions regarding their value contribution and sustainability.

• describe different concept of Data Warehousing and In-Memory columns
• give examples of practical application
• apply knowledge in implementing solutions based on SAP applications
• compare different solutions
• relate different concepts to develop an individual solution

Students…

• understand a real-world case from a company partner
• identify and define meaningful skill sets for seminar groups
• analyze the real-world case through the lens of process management knowledge
• identify areas of improvement or innovation
• develop recommendations for the real-world case of the company

Students…

• explain the foundations and the emergence of process management (e.g. business process re-engineering, total quality management)
• describe the goals of process management (e.g. time, cost, quality, sustainability)
• understand the core elements of process management (strategic alignment, governance, methods, technologies, people, culture)
• identify benefits and competitive advantages of a holistic process management approach
• summarize key principles of good process management

• review the literature on a specific IS-related topic
• develop the structure of an exposé
• formulate research questions
• identify suitable methods for seeking answers to research questions
• formulate a research design using those methods
• write an exposè

> understand a contemporary, relevant topic in Information Systems (IS) related research
> explain the academic and practical relevance of the topic

• identify suitable research methods in order to seek answers to specific research questions
• apply basic qualitative, quantitative, and design-oriented research methods

• describe the historical development of scientific research
• explain the concept of scientific research
• explain the role of theory in scientific research
• understand qualitative, quantitative, and design-oriented approaches for scientific research

• explain the framework of statistical reasoning.
• judge the benefits and limits of statistical methods and conclusions.
• summarize the results and conclusions of statistical analyses in a precise way.
• select statistical procedures according to given situations and questions.
• apply standard techniques in new situations and adapt the procedures.
• appraise the content and the limits of statistical analyses in publications.

Lecture Series ''Testing and Estimating''

• represent the distributions of random variables graphically.
• calculate moments of random variables and interpret them in a given context.
• explain the framework of testing hypotheses and estimating parameters.
• apply basic testing and estimating procedures and generalize the conclusions correctly.
• criticize the assumptions of basic testing and estimating procedures.
• derive the minimal sample size for basic testing and estimating procedures.

Lecture Series ''Multiple Linear Regression''

• apply the ordinary least squares method to derive estimators.
• analyze and compare the statistical properties of estimators.
• explain the classical linear model assumptions.
• run the calculations of a multiple linear regression for toy examples with small data sets by hand.
• interpret the software outputs of multiple linear regression for application examples in the given context.
• use model diagnostics to check the assumptions and to judge the quality of adapted models.
• apply inference procedures in multiple linear regression models.
• compare the advantages and disadvantages of different inference procedures.
• construct testing procedures for multiple linear constraints in multiple linear regression models.
• apply specification techniques to improve the quality of models.
• apply selection techniques to choose appropriate models.