Module WS 2016/2017

Short description
The course covers conceptual foundations, implementation, and operations of Business Intelligence solutions. Students will learn how to design and operate data warehouses, reports and dashboards, based on SAP BW, SAP BusinessObjects, as well as SAP HANA.

Topics

• Conceptual foundations of data warehouses and on-line analytical processing (OLAP)
• Conceptual foundations of in-memory column-based databases
• SAP BW Data Modeling & ETL
• SAP Business Explorer
• SAP BusinessObjects Cloud and Enterprise
• In-Memory Computing with SAP HANA

Learning objectives

• Students know and understand foundational concepts and methods of data warehousing and on-line analytical processing (OLAP)
• Students know and understand foundational concepts and methods of in-memory column-based databases
• Students extract, transform, and load data from transactional systems into business intelligence solutions
• Students design and develop business intelligence reports and dashboards

Methods

• The module integrates theoretical knowledge and practical skills in an interactive seminar.
• The e-learning platform Moodle will be used throughout the course for the dissemination of course material and discussions.

Compulsory reading

• Egger et al. (2007). SAP Business Intelligence, SAP Press (ISBN: 978-1-59229-082-6)
• Plattner, H. (2009). A Common Database Approach for OLTP and OLAP Using an In-Memory Column Database, Hasso Plattner Institute for IT Systems Engineering University of Potsdam.
• Inmon, W. H. (2005). Building the Data Warehouse, (4th ed) John Wiley.
• http://help.sap.com/pcat_analytics

Further reading

• Egger et al. (2009). Reporting und Analyse mit SAP BusinessObjects, Galileo Press. (ISBN: 978-3-8362-1380-6).
• Codd, E.F. et al. (1993). Providing OLAP (Online Analytical Processing) to User-Analysts: An IT Mandate, White Paper im Auftrag von Arbor Software (jetzt Hyperion Solutions). Siehe auch: http://www.fpm.com/refer/codd.html.
• Egger N., Fiechter J.-M. & Rohlf J. (2005). SAP BW - Data Modeling, SAP Press (ISBN: 1-59229-043-4)
• Plattner, H. & Zeier, A. (2011). In-Memory Data Management. An Inflection Point for Enterprise Applications, Springer, ISBN-13: 9783642193620
• Hichert, R. & Moritz, M. (1995). Management-Informationssysteme. Praktische Anwendungen, (2nd ed.) Springer Verlag.
• Kaiser, B.-U. (1999). Unternehmensinformation mit SAP-EIS. Hrsg. von Stephen Fedtke, Vieweg.
• Keen, P. G. W. (1978). Decision support systems: an organizational perspective. Reading, Mass., Addison-Wesley Pub. Co. ISBN 0-201-03667-3
• Salmeron, Jose L. (2003). EIS Success: Keys and difficulties in major companies. Technovation, 23 (1), 35–38.

Short description
This course covers some statistical methods that can help to take decisions in business using data. These basic concepts of the statistical testing and estimating theory should – to a large extent - be known from an introductory course on probability theory and statistics in any bachelor program.

Topics

• Graphical and numerical characterizations of random variables and their distributions
• Framework and basic applications of testing hypotheses and estimating parameters
• Ordinary least squares method and its properties
• Simple linear regression including parameter estimation, diagnostic plots, hypothesis testing, predictions and model specifications using log-transformations
• Introduction to the software package R

Learning objectives

• Students present the distributions of random variables graphically, calculate and interpret their moments.
• Students can explain the framework of testing hypotheses and estimating parameters and apply basic procedures.
• Students criticize the assumptions of basic testing and estimating procedures and generalize the conclusions correctly.
• Students derive the minimal sample size for basic testing and estimating procedures.
• Students apply the ordinary least squares method to derive estimators and compare the statistical properties of different estimators.
• Students explain the classical linear model assumptions, run simple linear regressions, check the diagnostics plots, use log-transformations to specify models and interpret the results correctly.

Methods

• The e-learning platform Moodle will be used throughout the course for the dissemination of course material and discussions.
• Students are usually asked to read corresponding parts of the lecture notes or of the textbook in order to prepare for the upcoming lectures in advance.
• In the interactive lectures, statistical concepts will be introduced and motivated by discussing examples in detail. Assignments are offered to train these skills.
• During office hours, individual problems may be discussed with the lecturer.
• In order to analyse realistic data, the software package R will be used.

Entry requirements
We require basic knowledge of probability theory and statistics, which is usually presented in a basic course on these topics in any bachelor program. The module ''Statistik'' in the bachelor program at University of Liechtenstein serves as a guideline or benchmark for this previous knowledge.

This module is prerequisite for taking the Master’s thesis Module and writing the Master’s thesis


Compulsory reading

• Wooldridge, J.M. (2013). Introductory Econometrics. (International Student Edition, 5th edition). Mason: South Western Cengage Learning.

Further reading

• Sweeney, D.J., Williams, T.A., David R. Anderson, D.R. (2009). Fundamentals of Business Statistics (International Student Edition, 5th edition). Manson: South-Western Cengange Learning.
• Berensen, M.L., Levine, D.M., Krehbiel, T.C. (2012). Basic Business Statistics (Global Edition, 12th edition), Essex: Pearson Education Limited.

Short description
The course focuses on virtual collaboration, collaborative work, and modern collaboration tools in a business environment. Students will apply their knowledge in a hands-on collaboration project with partners.

Topics

• Understand the concepts of virtual collaboration and collaborative work
• Learn how IT can be used in order to support collaboration in a virtual environment
• Learn about the potentials and limits of collaboration technology
• Experience collaboration with team members from other countries

Learning objectives

• Students will repeat the fundamental concepts of collaboration and collaboration systems.
• Students will understand the benefits of collaboration and collaboration systems for sustainable competitive advantage.
• Students will solve assignments in the field of collaboration, especially collaborative research projects in the areas of current topics in IS.
• Students will identify relationships between different types of virtual collaboration systems. They compare solutions with regard to their value contribution.

Methods

• The module integrates theoretical knowledge and practical skills based on an interactive seminar that includes a hands-on collaboration project. The e-learning platform Moodle will be used throughout the course for the dissemination of course material and discussions.

Compulsory reading

• Davenport, T. H. (2005). Thinking for a living: how to get better performances and results from knowledge workers. Harvard Business Press.

Short description
The course covers the complete modern data management cycle, with a focus on collecting data from diverse sources and preparing it to enable data-driven applications. Students will learn how to handle various data formats, assess and eventually improve data quality, and store as well as process data using SQL, NoSQL, and Hadoop technologies. The course will also look into the basics of mining (big) data sets.

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)

Learning objectives

• Students will acquire and understand foundational concepts and methods of modern data management
• Students will collect and prepare data in order enable data-driven applications
• Students will select and apply appropriate technologies for building data-driven applications

Methods

• The module integrates theoretical knowledge and practical skills in an interactive lecture.
• The e-learning platform Moodle will be used throughout the course for the dissemination of course material and discussions.

Recommended previous knowledge

• Module “Process and Data Management”

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

Short description
The course covers various statistical techniques for making sense of the vast and complex data sets that have emerged in business in the past twenty years. Students will learn to detect patterns in large data sets of various formats (quantitative and qualitative) and translate them into actionable insights.

Topics

• Supervised learning techniques for regression (e.g. linear regression, SVM)
• Supervised learning techniques for classification (e.g. logistic regression, KNN)
• Unsupervised learning techniques (e.g. clustering, dimensionality reduction)
• Text mining (e.g. sentiment analysis)
• Hands-on labs with R

Learning objectives

• Students will know and understand the basic concepts and methods of data mining and predictive analytics
• Students will assess the assumptions and quality of statistical models
• Students will select and apply the right statistical models for a given task or data set
• Students will derive actionable insights from statistical results

Methods

• The module integrates theoretical knowledge and practical skills in an interactive lecture.
• The e-learning platform Moodle will be used throughout the course for the dissemination of course material and discussions.

Recommended previous knowledge

• Module “Business Statistics I”
• Module “Business Statistics II”
• Basic knowledge of statistical software R - online course available: tryr.codeschool.com

Compulsory reading

• James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning. With Applications in R. New York: Springer (a free online version is available at http://www-bcf.usc.edu/~gareth/ISL/)

Further reading

• Provost, F. & Fawcett, T. (2013). Data Science for Business. Sebastopol: O'Reilly Media

Short description
The course focuses on judgment and decision-making, with emphasis on how decisions deviate from rational and/or ethical standards, with applications in human-computer interaction.

Topics

• Introduction to decision making under certainty and risk
• Measuring and modeling individual risk preferences
• Heuristics in decision-making
• Biases in decision making
• Emotions in decision making
• Designing decisions on websites

Learning objectives

• Students will know how decisions can be influenced by various human biases and how to improve individual decisions.
• Students will know basic methods of decision making in order to overcome human biases.
• Students will use methods of decision-making in order to improve business decisions in organizations.

Methods

• The module integrates theoretical knowledge and practical skills in an interactive lecture.
• The e-learning platform Moodle will be used throughout the course for the dissemination of course material and discussions.

Entry requirements

• none

Compulsory reading

• Hastie, R. & Dawes R. M. (2010). Rational Choice in an Uncertain World. Sage: London.

Further reading

• Baron, J. (2008). Thinking and Deciding. Cambridge University Press: Cambridge.
• Bazerman, M. H. & Moore, D. A. (2013). Judgment in Managerial Decision Making. John Wiley & Sons, Inc: New York.
• Hammond, J. S., Keeney, R. L., & Raiffa, H. (1999). Smart Choices. A Practical Guide to Making Better Decisions. Havard Business Review Press: Harvard.
• Johnson, J. (2014). Designing with the Mind in Mind. Elsevier: Burlington.
• Kahneman, D. (2011). Thinking, Fast and Slow. Penguin Books: London.

Short description
In the first Innovation Lab, students collaboratively develop innovative solutions for real-life business problems in product and process design.

Topics

• Creativity
• Innovation
• Problem-solving
• Project management
• Teamwork
• Presentation

Learning objectives

• Students will demonstrate their ability to work in a team to solve contemporary business problems.
• Students will show they can plan and organize projects under time pressure and competition.
• Students will use common creativity techniques and problem-solving tools and methodologies and demonstate they can think creatively to create innovative business solutions.
• Students will understand there are different ways of looking at new problems as they will develop alternative approaches to solving the problems they are assigned with.
• Students will deliver professional quality presentations to a demanding audience.

Methods

• The module involves interactive seminars with workshops and regular presentations.
• Together with the faculty, a jury of representatives from regional companies evaluates their solutions against innovativeness and usefulness and provides them with feedback and advice.
• The e-learning platform Moodle will be used for the dissemination of course material and discussions.

Compulsory reading

• The students will be provided with a reader and all lecture slides and supporting materials.

Short description
The course focuses on management information systems, which are large-scale application software packages that support end-to-end processes, information and document flow, reporting, and data analytics in different organizational settings.

Topics

• Enterprise Applications
• E-Commerce
• Managing Knowledge
• Enhancing Decision Making
• Building Information Systems
• Managing Projects and Global Systems
• Case study: Enterprise processes in SAP

Learning objectives

• Students will know the fundamental concepts and definitions in the area of enterprise systems and application systems like ERP, CRM, and SCM systems.
• Students will understand the benefits of management information systems for sustainable competitive advantage and describe their relevance for process integration along the value chain.
• Students will assess the applicability of software solutions in different business scenarios using comprehensive evaluation schemes.
• In a case setting, students will identify business problems that typically emerge in the design and use of enterprise systems and develop solutions.

Methods

• The module integrates theoretical knowledge and practical skills in interactive lectures and seminars focusing on hands-on experience with SAP software.
• The e-learning platform Moodle will be used throughout the course for the dissemination of course material and discussions.

Recommended previous knowledge
Motiwalla L., Thompson J. (2011). Enterprise Systems for Management: International Version (2nd ed.). Harlow: Pearson Education.

Laudon K.C., Laudon J.P. (2015). Management Information Systems: Managing the Digital Firm. Global Edition (14th ed.). Harlow: Pearson Education.

Compulsory reading
Papers and case study material will be provided in class.

Further Reading:
Giachetti, R. E. (2010). Design of Enterprise Systems: Theory, Architecture, and Methods. Boca Raton: CRC Press.

Snabe J.H., Rosenberg A., Moller C., Scavillo M. (2009). Business Process Management: The SAP Roadmap. Bonn, Boston: Galileo Press.

Short description
The course focuses on data management and process management, which are complementary approaches for developing and implementing information systems in organizations.

Topics

• Introduction to process and data management
• Information management, data management, and IS strategy
• Process modeling
• Data modeling
• Reference models

Learning objectives

• Students will know how information systems can be described from different, complementary perspectives.
• Students will know basic methods of data and process modeling in order to analyze, design, and implement information systems in organizations.
• In exercises, students will use methods of data and process modeling in order to analyze, design, and implement information systems in organizations.

Methods

• The module integrates theoretical knowledge and practical skills in an interactive lecture.
• The e-learning platform Moodle will be used throughout the course for the dissemination of course material and discussions.

Compulsory reading

• Becker, J., Kugeler, M., & Rosemann, M. (Eds.). (2003). Process Management: a guide for the design of business processes: with 83 figures and 34 tables. Springer.
• Dumas, M., La Rosa, M., Mendling, J., & Reijers, H. A. (2013). Fundamentals of business process management (pp. I-XXVII). Heidelberg: Springer.
• Watson, R. T. (2008). Data management, databases and organizations. John Wiley & Sons.

Short description
The course focuses on process analysis, including approaches and methods for designing, analyzing and simulating processes in organizations.

Topics

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

Learning objectives

• Students will know how processes can be modeled, analyzed, and simulated.
• Students will know basic methods of process modeling in order to analyze, design, and implement information systems in organizations.
• Students will use methods of process flow analysis and simulation in order to analyze, design, and improve business processes in organizations

Methods

• The module integrates theoretical knowledge and practical skills in an interactive lecture.
• The e-learning platform Moodle will be used throughout the course for the dissemination of course material and discussions.

Entry requirements

• none

Compulsory reading

• Laguna, M. & Marklund, J. (2013): Business Process Modeling, Simulation and Design, CRC Press: Boca Raton.

Further reading

• vom Brocke, J. & Rosemann, M. (Eds.) (2014). Handbook of Business Process Management. Springer: New York.

Short description
The course covers conceptual foundations, methods, and technologies for implementing and managing business processes with the help of IT. In particular, students will learn how to automate and monitor software-based business processes and mine process execution logs.

Topics

• Foundations of process automation
• Workflow management systems (e.g., YAWL)
• Process mining (e.g., ProM)

Learning objectives

• Students know and understand foundational concepts and methods of process automation (workflow management)
• Students know and understand foundational concepts and methods of process mining
• Students design and develop executable business processes
• Students apply analytical techniques to mine business process logs

Methods
The module integrates theoretical knowledge and practical skills based on an interactive lecture. The e-learning platform Moodle will be used throughout the course for the dissemination of course material and discussions.

Compulsory reading

• Dumas, M., La Rosa, M., Mendling, J., Reijers, H. (2013). Fundamentals of Business Process Management. Berlin: Springer.
• Russell, N., Van Der Aalst, W., Ter Hofstede, A. (2016): Workflow Patterns: The Definitive Guide. Cambridge, MIT Press.

Further reading

• Ter Hofstede, A., Van der Aalst, W., Adams, M., Russell, N. (2010). Modern Business Process Automation. Berlin: Springer.
• Van der Aalst, W. (2011). Process Mining: Discovery, Conformance and Enhancement of Business Processes. Berlin: Springer.

Short description
Process management refers to the operation, improvement and innovation of business processes. The course covers fundamental frameworks, models, and methods in process management.

Topics

• Business process operation
• Business process change
• Strategic alignment
• Governance
• Quality management
• Six sigma
• Process management skills
• Organizational culture

Learning objectives

• Students will know the foundations and the emergence of process management (e.g. business process re-engineering, total quality management).
• Students will understand the goals of process management (e.g. time, cost, quality, sustainability).
• Students will be able to apply the core elements of process management (strategic alignment, governance, methods, technologies, people, culture).
• Students will integrate knowledge to understand the benefits and competitive advantages of a holistic process management approach.
• Students will know key principles of good process management.

Methods

• The module integrates theoretical knowledge and practical skills based on an interactive lecture.
• The e-learning platform Moodle will be used throughout the course for the dissemination of course material and discussions.

Compulsory reading

• The students will be provided with the lecture slides and supporting materials throughout the course.

Further reading

• vom Brocke, J., & Rosemann, M. (Eds.). (2010). Handbook on business process management. Berlin: Springer.

Short description
Process management refers to the operation, improvement and innovation of business processes. In the course, students apply fundamental frameworks, models, and methods in process management.

Topics

• Business process operation
• Business process change
• Strategic alignment
• Governance
• Quality management
• Six sigma
• Process management skills
• Organizational culture

Learning objectives

• Students will analyze a real-world case of a specific industry through the lens of process management knowledge.
• Students will integrate knowledge to identify areas of improvement or innovation.
• Students will use appropriate methods to develop recommendations for a case company.
• Students will identify and define meaningful skill sets for seminar groups.

Methods

• The module integrates theoretical knowledge and practical skills in a seminar focusing on a real-world case.
• The e-learning platform Moodle will be used throughout the course for the dissemination of course material and discussions.

Short description
In this course, students apply acquired data science knowledge and skills to solve a real-world business problem from the area of marketing, finance, or operations.

Topics may include

• Supervised learning (regression, classification)
• Unsupervised learning
• Text mining
• Social network analysis
• Assessing model quality

Learning objectives

• Students will analyze a real-world case through the data science lens
• Students will collect and prepare data for later analysis
• Students will build and evaluate statistical models
• Students will translate statistical models into actionable results

Methods

• The module integrates theoretical knowledge and practical skills in a seminar focusing on a real-world case.
• The e-learning platform Moodle will be used throughout the course for the dissemination of course material and discussions.

Short description
The module provides an introduction to research methods.

Topics

• Introduction to scientific research
• Literature reviews
• Qualitative research
• Quantitative research
• Design science research
• Theories used in IS research

Learning objectives

• Students will know and understand the historical development of scientific research.
• Students will know and understand the concept of scientific research.
• Students will identify appropriate theories to explain empirical phenomena.
• Students will identify suitable research methods in order to seek answers to specific research questions.
• Students will use appropriate qualitative, quantitative, and design-oriented approaches to scientific research.

Methods

• The module integrates theoretical knowledge and practical skills in an interactive lecture.
• The e-learning platform Moodle will be used throughout the course for the dissemination of course material and discussions.

This module is prerequisite for taking the Master’s thesis Module and writing the Master’s thesis

Compulsory reading

• Kumar, R. (2014). Research methodology: A step-by-step guide for beginners. London, UK: Sage Publications.
• Recker, J. (2012). Scientific Research in Information Systems: A Beginner’s Guide. Springer, Heidelberg, Germany.

Further reading

• Bryman, A. & Bell, E. (2011) Business research methods. Oxford, UK: Oxford University Press.
• Creswell, J. W. (2003). Research design: Qualitative, quantitative, and mixed methods approaches. London, UK: Sage Publications.
• Miles, M. B. & Huberman, A. M. (1994). Qualitative data analysis. An expanded sourcebook. London, UK: Sage Publications.
• Oates, B. J. (2006). Researching information systems and computing. London, UK: Sage Publications.
• Provost, F. & Fawcett, T. (2013). Data Science for Business. Sebastopol: O'Reilly Media

Short description
The course focuses on developing research proposals in the field of business process management.

Topics

• Conducting literature reviews
• Developing research questions
• Designing qualitative, quantitative, and design oriented research
• Writing research proposals
• Ethical issues in business process management research

Learning objectives

• Students will know the professional code of conduct of the academic IS discipline.
• Students will effectively communicate academic research designs.
• Students will produce rigorous research proposals in the area of business process management.
• Students will recognize and analyze ethical problems of designing and conducting research in the field of business process management.

Methods

• The module integrates theoretical knowledge and practical skills in an interactive seminar.
• The e-learning platform Moodle will be used throughout the course for the dissemination of course material and discussions.

Recommended previous knowledge

• Research Methods

Compulsory reading

• Creswell, J. W. (2013). Research design: Qualitative, quantitative, and mixed methods approaches. Sage.

Further reading

• Kumar, R. (2014). Research methodology: A step-by-step guide for beginners. London, UK: Sage Publications.
• Recker, J. (2012). Scientific Research in Information Systems: A Beginner’s Guide. Springer, Heidelberg, Germany.

Short description
The course focuses on developing research proposals in the field of data science.

Topics

• Conducting literature reviews
• Developing research questions
• Designing qualitative, quantitative, and design oriented research
• Writing research proposals
• Ethical issues in data science

Learning objectives

• Students will know the professional code of conduct of the academic IS discipline.
• Students will effectively communicate academic research designs.
• Students will produce rigorous research proposals in the area of data science.
• Students will recognize and analyze ethical problems of designing and conducting research in the field of data science.

Methods

• The module integrates theoretical knowledge and practical skills in an interactive seminar.
• The e-learning platform Moodle will be used throughout the course for the dissemination of course material and discussions.

Recommended previous knowledge

• Research Methods

Compulsory reading

• Creswell, J. W. (2013). Research design: Qualitative, quantitative, and mixed methods approaches. Sage.