Modules SS 2022

Artificial Intelligence and Deep Learning covers the basics of artificial intelligence and deep learning and recent technological trends. The course covers five primary topics:

• Fundamentals of artificial intelligence
• Fundamentals of deep learning, network design, and training
• Convolutional neural networks, illustrated through image recognition
• Recurrent neural networks, illustrated through text mining
• Deep reinforcement learning – Learning to play games and beyond: Google’s AlphaGo

BPM and Organisational Practice explores Business Process Management (BPM) through an organisational-studies lens, so it is a BPM elective. Emphasizing the duality of stability and change in organisational work, the course covers the factors, mechanisms, and interventions that affect how processes behave over time. The course covers six primary topics:

• Organisation theory
• Process- and practice-based research
• Organisational routines
• Intra-organisational dynamics and endogenous change
• Organisational learning, unlearning, and forgetting
• The role of agency and intention in the execution of organisational work

Data and Application Security provides an introduction to cyber security and covers topics related to information and communication security. This is one of the core subject areas of the degree programme, and the course provides a foundation for choosing further electives in the area of cybersecurity. The course covers the following topics:

• Security goals and design principles
• Economic aspects of security and risk analysis
• Basics of cryptography
• Authentication and access control
• Key instruments of network security
• Key instruments of web security
• Software security, vulnerabilities, and attacks
• Email and mobile device security

Data Science covers statistical and exploratory techniques that are used to make sense of the vast and complex data sets that have emerged in business. Data Science is one of the core topics of the degree programme, so the course also provides a basis on which students can choose their electives. Students learn to detect patterns in large data sets in quantitative and qualitative formats to translate them into actionable insights. The course covers seven primary topics:

• Data visualisation and exploration
• Supervised learning techniques for regression (e.g. logistic regression)
• Supervised learning techniques for classification (e.g. classification trees)
• Unsupervised learning techniques (e.g. clustering, dimensionality reduction)
• Fundamentals of deep learning
• Text mining (e.g. topic modelling)
• Hands-on labs with Python

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

Digital Humanities stands at the intersection between digital technology and social action – between computing and humanities. Besides enabling digital innovation, digital technology has fundamentally changed the way we see the world, work, and socialise. We are increasingly challenged to make sense of data and information, and turn them into things we can use for different goals. On the other hand, we also need to adjust ourselves in order to collaborate with each other through digital technology – and sometimes even with digital technology itself. How far should we go? How do we find a balance? This course is primarily concerned with understanding different and sometimes contradicting views on the relationship between digital technology and social action. The course covers five primary topics:

• Introduction to digital humanities
• The computational turn
• Favourable views on digitisation and digitalisation
• Critical views on digitisation and digitalisation
• Examples of digital humanities projects

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 Educational Journey covers lectures at a foreign university, company visits, and leisure activities. Course topics change from semester to semester.

• Planning security: Even if the study trip cannot take place, there is the possibility that you can acquire the 3 ECTS through an alternative examination performance

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. In contrast to other architecture disciplines (such as, e.g., solution architecture or software architecture), EAM covers the entire business-to-IT stack, complete lifecycles of business technology, and all relevant EA components across the enterprise (or even beyond the enterprise, e.g. in business ecosystems).

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

In their Master’s Thesis, students use scientific methods and work in accordance with standards of scientific writing. The master's thesis is typically related to one of the three subject areas that constitute the core of the curriculum (i.e., Business Process Management, Data and Application Security, and Data Science).

In the Research Seminar course, students learn to apply in practice what they learned in the Research Methods course. The seminar covers issues related to identifying and formulating research questions, choosing a suitable research design to use in answering these questions, evaluating the feasibility of a planned research study, and writing research proposals. Together with faculty, students develop research proposals (so-called “exposés”) for their master’s theses

Security Management covers technical and organisational methods for the definition and implementation of security policies. The course covers five primary topics:

• People, processes, and strategic planning
• Risk management
• Regulatory compliance, aw, and ethics
• Security analysis, safeguards, and frameworks
• Maturity and performance measurement