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.

Written exam (60min)

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.

Written exam (60min)
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

Compulsory reading:

• Students are provided with the lecture slides and supplementary material.

Additional reading:

• vom Brocke, J., & Rosemann, M. (Eds.). (2014). Handbook on Business Process Management. Berlin: Springer.

Written exam (90min)

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

• 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.

After successful completion of the course, students will:

• know how processes can be modelled, analysed, and simulated
• know the basic methods of process modelling for analysing, designing, and implementing information systems in organisations
• be able to use the methods of process flow analysis and simulation to analyse, design, and improve business processes in organisations

• Compulsory reading:Dumas, M., La Rosa, M., Mendling, J., & Reijers, H. (2018). Fundamentals of Business Process Management (2nd edition). Berlin, Germany: Springer. - Additional reading:vom Brocke, J., & Rosemann, M. (Eds.) (2014). Handbook of Business Process Management. New York, USA: Springer.

Written exam (60min)

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

Written exam (60min)

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

• 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.
• Lab exercises and programming assignments are used to support the acquisition of practical skills.

After successful completion of the course, students will:

• understand the typical attacks against various components of information systems
• understand the main network security protocols and their implementation
• understand the main preventive security mechanisms in operating systems

Technical documentation of the attacks and protection methods presented are provided.

Voraussetzung für die Anmeldung zum Modul:

• erfolgreicher Abschluss des Moduls "Data and Application Security"

Exercise: Lab assignments
Lecture: Written exam (60min??)

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

• 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.

• After successful completion of the course, students will:have deepened their understanding in the field of machine learning and acquired a larger set of machine-learning techniquesunderstand the challenges and solutions of processing large amounts of databe able to gather requirements for projects in the field of machine learning and business intelligence

• Compulsory reading:Witten, H., Eibe, F., & Hall, M. (2016). Data Mining: Practical Machine Learning Tools and Techniques. Amsterdam, The Netherlands: Elsevier.Aggarwal, C.C. (2015). Data Mining: The Textbook. Heidelberg, Germany: Springer.

Written exam (60min)

Just as the doctoral consortium, the summer school serves multiple purposes in the students' education: Whereas the doctoral consortium aims at presenting the own work to an international audience, the summer school is supposed to deepen the students' methodological skills. In addition, working together with professors and PhD students from abroad also contributes to developing the students' social and communicative skills in an international and intercultural environment.

In preparing their proposals and applications for a summer school, students will be assisted by lecturers of the Institute of Information Systems at the University of Liechtenstein. Accepted students will take part in the summer school. A reflection of the lessons learned at the summer school together with the lecturers is also part of the module.

The primary objective of the summer school is to get further insights into the research methods applied by the PhD students and to discuss evolving questions with other young scientists.

The students will be assessed in this module through:

• competitive selection process of the summer school
• specific mechanisms of the summer school

Module availability:
On application at an internationally renown summer school, such as organised e. g. by the European Research Center for Information Systems (ERCIS).

Just as the doctoral consortium, the summer school serves multiple purposes in the educational programme of the students: Whereas the doctoral consortium aims at presenting the own work in an international frame, the summer school intends to deepen methodological skills in a specific field of choice relevant to the PhD theses of the students. In addition, working together with professors and PhD-students from abroad also contributes to social and communicative skills of the students in an international and intercultural environment.

Doctorate entrepreneurship and management students participating in an international Ph.D. summer school study contemporary issues in research design and/or methodology.

The primary objective of the summer school is to get further insights into the research methods applied by the PhD students and to discuss evolving questions with other young scientists and leading experts in the field.

The students will be assessed in this module through:

• competitive selection process of the summer school
• specific mechanisms of the summer school

Module availability:
On application at an internationally renown summer school, such as organised e. g. by the Swiss National Science Foundation or Essex Summer School in Social Science Data Analysis and Collection.

Research Methods in International Financial Services can be very different, depending on the specific research area of Banking, Finance and Taxation. This module description is developed for a student with a need for advanced methods in econometrics. For students with different needs appropriate courses will be choosen and credited.

• Principles of Estimation and Testing
• Limited Dependent Variable Methods
• Longitudinal Data Models
• Stationary Time Series Models
• Stochastic Trends and Co-Integration

Lecture and self-study; presentation and paper by students is possible.

The module "Research Methods in International Financial Services " aims at deepening the students' competences regarding knowledge in their research design.

• This course should help - based on research methods offered on the master's level - to apply advanced econometric research methods, currently used by the research community.
• This course helps the student to independently develop a research concept for specific research questions.
• This course helps students to discuss methodological issues with colleagues working in the same area.

Students will be able to:

• Have an advanced overview of econometric principles for cross-sectional, panel, and time-series data sets.
• Apply econometric techniques in the area of microeconomics, macroeconomics and finance.

Required Reading:

• Copeland, T.E., Weston, J.F., Shastri , K. (2005), Financial Theory and Corporate Policy. Boston: Pearson Addison Wesley.
• Cochrane, J. (2001). Asset Pricing. Princeton: Princeton University Press.
• Specific articles and books on Econometrics.

The students will be assessed in this module through:

• Written exam or presentation and paper (about 4000 - 5000 words)