Module WS 2023/2024

Advanced Machine Learning covers several advanced topics in the field of machine learning and is concerned with requirements engineering in particular. Students learn to analyse certain types and large amounts of data. The course covers seven primary topics:

• Requirements engineering for machine learning and business intelligence projects
• Frequent patterns and association rules
• Explaining decisions of machine learning models
• Time series analysis
• Anomaly detection
• Fundamentals of computational efficiency and distributed and parallel computing
• Hadoop ecosystems, with a focus on Spark and MLlib

Autonomous design toolas are fundamentally changing how designers work across various industries. Autonomous design tools make independent design decisions and in, some cases, execute entire design processes. They employ technologies typically associated with artificial intelligence, including machine learning, pattern recognition, meta-heuristics, and evolutionary algorithms.

Autonomous design tools allow for the generation of a variety of diverse design artifacts, including next-generation computer chips, software for specific domains, three-dimensional virtual worlds, and large amounts of content for video games and feature films. The applications for such autonomous design tools are also expanding to other industries, such as mechanical engineering, aerospace, and architecture.

Instead of creating artifacts by directly manipulating their representations, designers select tools, decide on design parameters, set values for these parameters, and evaluate and learn from the analysis of the results the tools produce. Design work in such situations involves intense interaction with autonomous tools. Designers need to be mindful of the logic, capabilities, and limitations of the tools, and the algorithms these tools employ, and find ways to make sense of and deal with the often unanticipated outputs of such tools.

The course addresses this increasingly important role of autonomous design tools by

• discussing the conceptual foundations of autonomous design tools;
• discussing how autonomous design tools change the nature of work and the role of human designers;
• analyzing examples of using autonomous tools in design practice;
• providing hands-on experience in agent-based modelling for students to simulate the behavior of these tools; and
• providing hands-on experience in using autonomous design tools for the design of virtual worlds.

Business Process Analysis focuses on process analysis, covering approaches and methods for designing, analysing, and simulating processes in organisations. The course covers four primary topics:

• Introduction to process analysis
• Process modelling and design
• Process flow analysis
• Process simulation

Business Process Management provides an introduction to fundamental concepts, frameworks, models, theories, and methods in process management and covers the operation, improvement, and innovation of business processes. Business Process Management (BPM) 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 eight primary topics:

• Business process operations
• Business process change
• Strategic alignment
• Business process governance
• Quality management
• Six Sigma
• BPM skills
• Organizational culture

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

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)

Data Visualisation covers techniques for creating effective data visualisations based on principles from statistics, cognitive science, and graphic design to help analysts and decision-makers understand and explore big data. The course covers eight primary topics:

• Visualising univariate and multivariate numerical data
• Visualising time series data
• Visualising geospatial data
• Visualising networked data
• Visualising high-dimensional data
• Visualising textual data
• Interactive dashboards
• Animations

Digital Entrepreneurship covers the intersection between digital technology and new venture creation, i.e., com-pany start-up activity. It addresses venture creation of digital artefacts as the core market offering (e.g., software, hardware, smart devices), digital technology as enablers of new venture creation (e.g., 3D printing, crowdfunding, platforms such as appStore), and venture creation in technology-intensive contexts (e.g., BioTech, IT Healthcare, FinTech). The course covers six primary topics:

• Forms and processes of entrepreneurship
• Business planning for new ventures
• Digital technologies as enablers and triggers for entrepreneurial activity
• Digital technologies as market offerings of emergent ventures
• Start-up activity in technology-intensive sectors

Emerging IT Topics addresses recent technological trends and developments in research and business, so its content can be adapted quickly to the job market’s emerging needs. Accordingly, the course content changes from
semester to semester.

Human-Centred Design is an approach that places people at the core of every decision point throughout the design process. Identifying, understanding and fulfilling people’s needs, desires, wishes, and goals are imperative in human-centred design. The approach is relevant to any design endeavour that aims to deliver useful products, services, and combinations of both to people as the end-users. The same applies to the design of software, mobile applications, collaboration platforms, and other information systems.

This course is designed with Information Systems students’ needs and goals in mind. Students are guided through their journey in understanding the basics of human cognition and human behaviour that are relevant to the design of information systems. They also learn several methods of human-centred design that are applicable in their projects.

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

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 Intrusion Detection and Mitigation covers the essential techniques for detection and mitigation of attacks against information systems. The course covers twelve primary topics:

• Taxonomy of Intrusion-detection methods
• Implementation of intrusion detection systems
• Malware functionality and operation
• Static and dynamic malware analysis
• Malware detection and classification
• Security incident response

Management Information Systems focuses on large-scale application software packages that support end-to-end processes, information and document flow, reporting, and data analytics in organizational settings. The course covers eight primary topics:

• Enterprise applications
• E-business
• Managing knowledge
• Enhancing decision-making
• Building management information systems (MIS)
• Managing projects and global systems
• MIS-related integration, transformation, innovation, and change
• Case studies on current MIS topics

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 four subject areas that constitute the core of the curriculum (i.e., Business Process Management, Data and Application Security, Data Science, and Digital Innovation).

Network and System Security covers advanced security mechanisms in computer networks and systems and attacks against information systems. The course focuses on eight primary topics:

• Essential network-security protocols
• Attacks against common network protocols
• Security issues in web applications
• Security mechanisms in operating systems
• Advanced exploitation techniques

Process Mining covers conceptual foundations, methods, and technologies for analysing business processes with the help of digital trace data that stems from information technology. In particular, students learn how to mine digital trace data. The course focuses on three primary topics:

• Petri-net foundations of process analysis
• Process mining algorithms
• Process mining tools and applications

In Project Seminar, students analyse a real-world case from a specific industry. Students divide into groups according to their preferences and work on one of four cases through the lens of process management, data and application security, data science, or digital innovation. The course topics change from semester to semester.

In Research Methods, students learn to identify pertinent research questions, conduct systematic literature reviews, apply appropriate research methods, and report on their results. The course covers nine primary topics:

• Introduction to scientific research
• Scientific writing
• Ethical standards
• Literature reviews
• Qualitative research
• Quantitative research
• Mixed-methods research
• Design science research
• Theories used in Information Systems research

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

Opportunity Recognition & Business Models

• Gelegenheiten systematisch erkennen und nutzen.
• Market-Pull, Technology-Push und Blue Ocean.
• Opportunity Recognition als Prozess.
• Systematisierung von Geschäftsmodellen und den Bestandteilen.
• Analyse und Bewertung von Geschäftsmodellen.
• Anwendung von Big Data Algorithmen zu Identifikation neuer Märkte und Technologien.

Extracurriculare Activities comprise of various activities that are not linked to the Curriculum of the MSc in Information Systems, which are optional and further support the studying of the Master programme.