C15 Workshop Investment Banking
C15 Workshop Investment Banking
Module Coordinator/Lecturers
Study Programmes
Masterstudiengang Finance (MSc FI 15)
(01.09.2015)
C15 Corporate Governance and Ethics
C15 Corporate Governance and Ethics
Module Coordinator/Lecturers
Study Programmes
Masterstudiengang Finance (MSc FI 15)
(01.09.2015)
C15 Investment Strategies and Asset Management
C15 Investment Strategies and Asset Management
Module Coordinator/Lecturers
Study Programmes
Masterstudiengang Finance (MSc FI 15)
(01.09.2015)
C15 Educational Journey 2017 to Hong Kong and Singapore - The Future of global Wealth Management: Connections between Europe and Asia
C15 Educational Journey 2017 to Hong Kong and Singapore - The Future of global Wealth Management: Connections between Europe and Asia
Module Coordinator/Lecturers
Study Programmes
Masterstudiengang Finance (MSc FI 15)
(01.09.2015)
Project Description
Master students have the opportunity to take part in educational journeys to the world’s most important financial centres. Taking place every two years, the educational journey adds practical perspective to the academic content of the programme.
Destinations of previous journeys include: New York (2005), Shanghai (2007), Hong Kong (2009), Singapore & Kuala Lumpur (2011), New York and Chicago (2013) and Beijing and Shanghai (2015).
Destinations of previous journeys include: New York (2005), Shanghai (2007), Hong Kong (2009), Singapore & Kuala Lumpur (2011), New York and Chicago (2013) and Beijing and Shanghai (2015).
Teaching Method
Excursion
Learning Objectives
After successfull completion of this module, students
- know the role of international enterprises and organizations including banks, asset or hedge fund management services, portfolio managers, insurance companies, chambers of foreign trade, chambers of commerce, supranational organisations, ambassadors, politicians or universities in structuring the competitiveness of a financial market;
- know the specifities of the visited financial market;
- established an international and intercultural network.
Literature
Depending on the topic of the assignment and the geographical target of the educational journey.
Assessment Methods
See lecture within the module.
C15 Master's thesis
C15 Master's thesis
Module Coordinator/Lecturers
Study Programmes
Masterstudiengang Information Systems (MSc IS 15)
(01.09.2015)
Project Description
Short description
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 the major (BPM or Data Science) chosen by the student.
Learning objectives
Methods
Entry requirements
Recommended previous knowledge
Colloquium
Submissions and deadlines
Compulsory reading
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 the major (BPM or Data Science) chosen by the student.
Learning objectives
- Students will formulate appropriate research questions.
- 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 seek answers to their research question/questions. Mere conceptual works are also possible.
Methods
- The thesis is supervised by a supervisor and a co-supervisor, both of whom should be members of the Institute of Information Systems.
- The Master’s thesis is defended in an oral exam, where students may be asked questions related to their studies that may go beyond the content of their Master’s thesis.
- The official editing time is defined on the thesis proposal and may not exceed 22 weeks. A shorter editing time is possible.
Entry requirements
- A minimum of 60 ECTS must be achieved before registration.
- The modules Business Statistics I and Research Methods must be passed successfully.
- A research proposal (exposé) signed by the first supervisor and the academic director must be submitted to the study administration in parallel to module registration.
Recommended previous knowledge
- It is highly recommended that the research proposal (exposé) is developed within the module "Research Seminar"
Colloquium
- Colloquium (mid-term presentation) is usually held about two months prior to the submission of the final master's thesis.
- In the colloquium, students are expected to report on their progress and experience in writing their master's thesis.
- The outcome of the colloquium is graded "passed" or "failed".
- The colloquia for the summer term in 2017 will be held on: April 6 - April 7, 2017, starting from 09.00. A detailed schedule will be communicated two weeks prior to these dates.
Submissions and deadlines
- A copy of signed thesis proposal (Exposé) must be submitted until July 1st. (for the winter term) and February 1st (for the summer term) to: Exposé Submission link
- The master's thesis must be submitted until November 30th (for the winter term) and June 30th (for the summer term) to the the central service desk.
- The submission of master's thesis must include: (1) a CD ROM containing thesis' digital copy (at the central service desk) and (2) direct submission of thesis' digital copy to the supervisor and co-supervisor (via e-mail).
- If any of the dates above falls on a weekend or public holiday, the deadline is automatically extended until the next working day. Please also check the opening times of the central service desk, especially during summer months.
Compulsory reading
- Creswell, J.W. (2008) Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 3rd Edition, Sage Publications
- Saunders, M.N.K.; Thornhill, A.; Lewis, P.; Leedy P.D.; Ormrod, J.E. (2007) Research Methods for Business Students: AND "Practical Research, Planning and Design", Financial Times Prentice Hall
C15 Research Seminar Data Science
C15 Research Seminar Data Science
Module Coordinator/Lecturers
Study Programmes
Masterstudiengang Information Systems (MSc IS 15)
(01.09.2015)
Project Description
Short description
The course focuses on developing research proposals in the field of data science.
Topics
Learning objectives
Methods
Recommended previous knowledge
Compulsory reading
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.
C15 Project Seminar Data Science
C15 Project Seminar Data Science
Module Coordinator/Lecturers
Study Programmes
Masterstudiengang Information Systems (MSc IS 15)
(01.09.2015)
Project Description
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
Learning objectives
Methods
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.
C15 Decision Theory
C15 Decision Theory
Module Coordinator/Lecturers
Study Programmes
Masterstudiengang Information Systems (MSc IS 15)
(01.09.2015)
Project Description
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
Learning objectives
Methods
Entry requirements
Compulsory reading
Further reading
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.
C15 Data Mining & Predictive Analytics
C15 Data Mining & Predictive Analytics
Module Coordinator/Lecturers
Study Programmes
Masterstudiengang Information Systems (MSc IS 15)
(01.09.2015)
Project Description
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
Learning objectives
Methods
Recommended previous knowledge
Compulsory reading
Further reading
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
C15 Business Intelligence
C15 Business Intelligence
Module Coordinator/Lecturers
Study Programmes
Masterstudiengang Information Systems (MSc IS 15)
(01.09.2015)
Project Description
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
Learning objectives
Methods
Compulsory reading
Further reading
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.