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

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

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

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).

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

• 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:

• understand the foundations and contextual roots of BPM (e.g., business process re-engineering, total quality management)
• understand the goals of BPM (e.g., time, cost, quality, sustainability)
• understand the core components of holistic BPM approaches (strategic alignment, governance, methods, technologies, people, culture)
• understand the key principles of good BPM

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

• The module combines interactive lectures with case studies and exercises to integrate theoretical knowledge with practical start-up and business planning skills.
• The e-learning platform Moodle will be used throughout the course to disseminate course material and for information and discussion.
• Case studies will be used to discuss and illustrate course contents.
• Contemporary scientific publications from Information Systems and Entrepreneurship will be discussed in class.

After successful completion of the course, students will:

• understand the fundamentals of entrepreneurship
• understand the unique challenges and opportunities of digital entrepreneurship
• understand peculiarities of digital technologies with respect to new venture emergence processes
• assess and evaluate the role of digital technologies in different phases of entrepreneurship

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.

• 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.
• Contemporary scientific publications from Information Systems, Management, and Computer Science are discussed in class.
• The NetLogo software is used to model and simulate autonomous design agents.
• Further software tools may be used throughout class.

After successful completion of the course, students will:

• understand the main concepts, theories, and methods related to autonomous design tools
• be able to analyze how autonomous design tools change work processes
• be able to develop agent-based models for simulating autonomous design tools

Complexity Management

• Unterscheiden von Komplexität und Kompliziert.
• Quellen von Komplexität und Auswirkungen in Unternehmen.
• Das Unternehmen als Komplexes System.
• VUCA (Volatile, Uncertain, Complex and Ambiguous), Wicked Problems und Unforeseeable Uncertainty.
• Komplexe Adaptive Systeme und deren Eigenschaften.
• Strategien zum Umgang mit Komplexität in Unternehmen.
• Herausforderungen beim Management komplexer Systeme.
• Implementierung eines komplexen Projektes am Beispiel eines konzernweiten Kooperationssystems.

Corporate Change

• Grundlagen des Change Managements.
• Zusammenhang zwischen Wandel im Unternehmensumfeld und Wandel im Unternehmen.
• Instrumente des Change Managements.
• Theorien der Organisation in der Unternehmensentwicklung.
• Prozesse, Phasen und kritische Erfolgsfaktoren in der Organisationsentwicklung.
• Gesamtkonzept einer lernenden Organisation.
• Inhaltliche strategische Planung eines Change Prozesses.
• Abstimmung von Kundennutzen und Entwicklung des Ökosystems.

Corporate Entrepreneurship

• Bausteine des Corporate Entrepreneurship (Definition, Allgemeine Rahmenbedingungen, Prozess und Formen des Corporate Entrepreneurship).
• Unterschiede zwischen Corporate Entrepreneurship und Startup-Entrepreneurship.
• Aufbau einer Corporate Entrepreneurship-Organisation (Human Ressource Management, die Persönlichkeit des Unternehmers, Motivation für unternehmerisches Handeln, Unternehmensstrategie und Unternehmertum, die Elemente und die Entwicklung einer Kultur des Unternehmertums).

This module supports the development of basic communicative and cultural competence in Spanish.

Interaction, study skills

>understanding and passing on simple information
>taking part in simple everyday conversation
>reporting past events
>creating simple texts

80% compulsory attendance