In Digital Business, students collaborate with small and medium-sized companies to develop new business models, open new markets, and innovate with existing products and services, so students learn to recognise, understand, develop, and exploit digital innovations. The course topics change from semester to semester, but the course usually addresses seven grand themes:

• Designing digital business strategy
• Digital entrepreneurship and intrapreneurship
• Opportunity recognition
• Business model innovation
• Value creation and cocreation
• Digital transformation
• Project management

• The course involves interactive seminars with workshops and regular presentations.
• The faculty and a jury of representatives from regional companies evaluate the students’ solutions in terms of innovativeness and usefulness and provide them with feedback and advice.
• The e-learning platform Moodle will be used throughout the course for the dissemination of course material and for information and discussion.

After successful completion of the course, students will:

• understand the complex nature of digitalisation in small and medium-sized enterprises as well as start-up ventures
• understand the entrepreneurial aspects in digital business: from opportunity recognition to designing digital strategy and business model and convincing potential stakeholders
• demonstrate readiness to innovate and to view an idea, a problem, or a solution from several different angles
• be able to articulate their ideas clearly in an elevator pitch, in order to persuade potential collaborators and sponsors of the values of their ideas
• be able to outline a project plan to implement their ideas and complete the project under time pressure
• be able to collaborate in teams and with external partners
• be able to develop feasible solutions to their identified issues and evaluate them using appropriate methods

Today, virtually all large organizations have to cope with growing complexity in their enterprise architectures (EA), which often comprise several hundreds or even thousands of IT applications that support an increasing variety of business processes. The underlying software components run on several generations of IT infrastructure, and digitization leads to increased intensity in inter-organizational interfaces and customer-centric solutions. As a consequence, EA comprises not only the fundamental structure and dependencies of business processes, IT applications, software components, IT infrastructure, and data in an enterprise, but also connected components of business ecosystem partners and customers. Changing only one of these EA components can impact a potentially large number of related components. Simultaneously changing several of these components in a number of change projects or transformation programs leads to potentially redundant (i.e. inefficient) and/or inconsistent processes, software systems, and/or IT infrastructure components. The short-term consequence is a waste of resources, and the longer-term consequences are increased effort and difficulty in maintaining existing information systems (because of excessive complexity) and shortage of resources that can be used for innovation.

EA management (EAM) is a management discipline that guides EA’s design and evolution. The goals of EAM are to control complexity, reduce inconsistencies, and leverage synergies in EA. EAM also supports the implementation of business innovation from a holistic perspective.
This course covers EA and EAM, incorporating both research findings and current examples from business practice. The course covers four primary topics:

• Core concepts and the necessity of EAM
• EAM use cases
• EA modelling and analysis
• Continuous improvement and maturity of EAM

• The course involves interactive lectures, class room exercises, and practitioner presentations to integrate theoretical knowledge with practical design and analysis skills.
• The e-learning platform Moodle is used throughout the course to disseminate course materials and for information and discussion.
• Case studies are used to integrate the aspects of EA/EAM covered in the course.
• Students complete homework assignments between lectures.

After successful completion of the course, students will:

• understand the fundamentals of EAM
• understand EA’s complexity and know how to align the components in an EA and how to align EA with organisational strategies and structures
• be able to use methods for modelling, analysing, and improving EA
• be able to evaluate the consistency, fit, and effectiveness of EAM initiatives in organizations

Optional reading:

• Ross, J. W., Weill, P., & Robertson, D. C. (2006). Enterprise Architecture as Strategy: Creating a Foundation for Business Execution. Boston, MA: Harvard Business School Press.
• Winter, R., & Fischer, R. (2007). Essential Layers, Artifacts, and Dependencies of Enterprise Architecture. Journal of Enterprise Architecture 3(2), 7-18.
• Bucher, T., Fischer, R., Kurpjuweit, S., & Winter, R. (2007). Enterprise Architecture Analysis and Application – An Exploratory Study. Journal of Enterprise Architecture 3(3), 33-43.
• Aier, S., Gleichauf, B., & Winter, R. (2011). Understanding Enterprise Architecture Management Design – An Empirical Analysis. Proceedings of the 10th International Conference on Wirtschaftsinformatik (WI 2011). Zurich, Switzerland.
• Brosius, M., Aier, S., Haki, K., & Winter, R. (2018). Enterprise Architecture Assimilation: An Institutional Perspective. Proceedings of 39th International Conference on Information Systems (ICIS 2018). San Francisco, CA, USA.
• Hanschke, I. (2009). Strategic IT Management: A Toolkit for Enterprise Architecture Management. Berlin, Heidelberg, Germany: Springer.

Digital Innovation covers the fundamentals of digital innovation and the development and implementation of novel and original solutions in which the innovation process, its outcomes, or the ensuing organisational and social transformation is embodied in or enabled by digital technologies. Digital Innovation is one of the core topics of
the degree programme, so the course also provides a basis on which students can choose their electives. The course covers six primary topics:

• Fundamental properties of digital technologies and digital innovation
• Organising for digital innovation
• Digital platforms and ecosystems
• Digital innovation and capital creation
• Digital business models
• Digital entrepreneurship

• The module involves interactive lectures with exercises to integrate theoretical knowledge with practical design and analysis skills.
• The e-learning platform Moodle is used throughout the course to disseminate course material and for information and discussion.
• Case studies are used to discuss the course contents. Contemporary scientific publications from Information Systems and Management are discussed in class.

After successful completion of the course, students will:

• understand the main concepts, theories, and methods related to digital innovation
• be able to analyse the role of digital technologies in existing business models
• be able to develop business models that consider options created through digital technologies

• Compulsory reading:Papers and case-study material are provided.

Information Systems Development provides an introduction to programming including web frameworks that can be used in online environments such as e-commerce platforms or blog systems. The course covers six primary topics:

• Introduction to scripting / programming
• Software / programme development
• Web technologies and web development
• Web applications and their frameworks
• Programming using existing frameworks
• Project: Web platform

• The course involves interactive lectures with exercises to integrate theoretical knowledge with practical design and analysis skills.
• The e-learning platform Moodle is used throughout the course to disseminate course material and for information and discussion.
• Case studies are used to discuss the course contents and to train students in using the methods covered.

After successful completion of the course, students will:

• memorize programming concepts and web technologies for information systems development
• develop introductory software programs (or scripts)
• assess the advantages and disadvantages of various web frameworks
• produce an online platform (e.g., website, shop, blog) using an existing web application framework

Compulsory reading:

• Holovaty, A., & Moss, J.K. (2009). The Definitive Guide to Django: Web Development Done Right. New York, NY: Springer (www.djangobook.com).

Additional reading:

• Gries, P., Campell, J., & Montojo, J. (2013). Practical Programming: An Introduction to Computer Science Using Python 3 (2nd ed.). Newton, MA: O’Reilly.

Information Systems Modelling focuses on systems analysis and design. In particular, the course covers methods of and approaches to modelling information systems in organisations. The course covers five primary topics:

• Introduction to object-oriented systems
• Project planning and initiation
• Requirements analysis (i.e. requirements gathering and structuring)
• Information systems modelling (i.e. UML modelling languages)
• Information systems documentation

• The course involves interactive lectures with exercises to integrate theoretical knowledge with practical design and analysis skills.
• The e-learning platform Moodle is used throughout the course to disseminate course material and for information and discussion.
• Case studies are used to show how the course contents are related.

After successful completion of the course, students will:

• know how information systems can be modelled
• know and apply basic methods of systems modelling and design (i.e. UML modelling languages)
• use systems-modelling methods to analyse, design, and implement information systems

Compulsory reading:

• Rosenberg, D. & Stephens, M. (2007). Use Case Driven Object Modeling with UML. New York, NY: Apress.

Additional reading:

• Booch, G., Rumbaugh, J., & Jacobson, I. (2005). Unified Modeling Language User Guide. Boston, MA: Addison-Wesley.
• Kölling, M. (2009). Introduction to Programming with Greenfoot: Object-Oriented Programming in Java with Games and Simulations. Upper Saddle River, NJ: Prentice Hall.

In the Innovation Lab seminar, students work in small groups to solve practical IT problems in cooperation with multiple regional companies. Representatives of these companies regularly provide students with feedback at the university and take part in networking events. The seminar thus also supports dialogue between regional industry and the university, helping students to interact with world-renowned companies right from the start of their studies. Students learn to work independently, to work in a team, to take responsibility, and to present project results effectively. In addition to creative thinking, the use of skills related to problem-solving, organizing and planning, communication, and project management is encouraged. Course topics change from semester to semester.

• The course involves interactive seminars with workshops and regular presentations.
• The faculty and a jury of representatives from regional companies evaluate the students' solutions in terms of their innovativeness and usefulness and provide them with feedback and advice.
• The e-learning platform Moodle is used throughout the course to disseminate course material and for information and discussion.

After successful completion of the course, students will be able to:

• work in a team to solve contemporary IT problems
• plan and organize IT projects under time pressure in a competitive environment
• use creativity techniques and problem-solving tools to work on the IT projects
• think creatively to create innovative business and IT solutions
• look at IT problems from multiple perspectives to develop alternative approaches to solving these problems
• deliver professional presentations to a demanding audience
• work independently and reliably

Compulsory reading:

• The students are provided with all lecture slides and supporting materials.

Students must attend at least 80% of all dates (i.e., class meetings, presentations, workshops) to pass the course.

Business Statistics covers statistical methods that are used to support decision-making in business contexts, so it also provides a methodological foundation for the students' master's thesis projects. The course builds on the basic concepts of statistical testing and estimation theory that are usually taught in bachelor’s programmes. The course covers five primary topics:

• Graphic and numeric characterizations of random variables and their distributions
• Framework and basic applications for testing hypotheses and estimating parameters
• The ordinary least squares (OLS) method
• Simple linear regression, including parameter estimation, diagnostic plots, hypothesis testing, predictions, and model specifications using log-transformations
• Introduction to the software package R

• The module involves interactive lectures with exercises to integrate theoretical knowledge with practical design and analysis skills.
• Students complete homework assignments after each lecture.
• The e-learning platform Moodle is used throughout the course to disseminate course material and for information and discussion.

After successful completion of the course, students will:

• be able to present the distributions of random variables graphically and to calculate and interpret their moments
• understand the framework of testing hypotheses and estimating parameters
• know the assumptions made in basic testing and estimating procedures when drawing general conclusions
• be able to derive the minimum sample size for basic testing and estimation procedures
• be able to apply the ordinary least squares method to derive estimators and compare their statistical properties
• be able to explain the classic linear model assumptions, run simple linear regressions, check the diagnostics plots, use log-transformations to specify models, and interpret the results correctly

Compulsory reading:

• Berensen, M.L., Levine, D., & Szabat, K.A. (2014). Basic Business Statistics (13th edition). Essex: Pearson Education Limited.

To successfully pass the module, students must collect at least 50 percent of points in a final exam (60 minutes; 30 points in total). During the exam, students may use a self-created “cheat sheet” (DIN A4, double-sided, machine-written or handwritten, any contents) and a calculator of their choice (including programmable calculators).

Data Management covers the modern data-management cycle, from the collection of data from diverse sources to the preparation of data for data-driven applications. Students learn how to handle various data formats, how to assess and improve data quality, and how to store and process data using SQL, NoSQL, and Hadoop technologies. The course covers eight primary topics:

• Modern data-management requirements
• Database system architecture
• Diagnosing and handling data quality problems
• Relational databases (SQL)
• Hands-on labs with MySQL
• Concurrency control techniques
• NoSQL databases (e.g., MongoDB)
• Apache Hadoop (HDFS, MapReduce)

• The module involves interactive lectures with exercises to integrate theoretical knowledge with practical design and analysis skills.
• The e-learning platform Moodle is used throughout the course to disseminate course material and for information and discussion.

After successful completion of the course, students will:

• understand the basic concepts and methods of modern data management
• be able to collect and prepare data for data-driven applications
• be able to select and apply appropriate technologies for building data-driven applications

Compulsory reading:

• Elmasri, R., & Navathe, S.B. (2016). Fundamentals of Database Systems, 7th edition. New York: Pearson Education
• Harrison, G. (2015). Next Generation Databases – NoSQL, NewSQL, and Big Data. California: Apress Media.

Abstraktes Modul zur Aufnahme ins Learning Agreement für Bachelor-Incomings.

Abstraktes Modul zur Aufnahme ins Learning Agreement für Bachelor-Incomings.