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)
• Supervised learning techniques for classification (e.g. classification trees)
• Unsupervised learning techniques (e.g. clustering, dimensionality reduction)
• Text mining (e.g. topic modeling)
• Hands-on labs with R

• 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.

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

Written exam (90min)

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)
• Concurrency control techniques
• NoSQL databases (e.g., MongoDB)
• Apache Hadoop (HDFS, MapReduce)

• The module integrates theoretical knowledge and practical skills in an interactive lecture.
• Selected sessions will also require preparation of the participants through videos that will be provided in advance.
• The e-learning platform Moodle will be used throughout the course for the dissemination of course material and discussions.

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

Written exam (90min)

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

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.

• 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.

Seminar thesis, mid-term and final presentation

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

• 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.

• 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.

• Short descriptionThe 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. TopicsConceptual foundations of data warehouses and on-line analytical processing (OLAP)Conceptual foundations of in-memory column-based databasesSAP BW Data Modeling & ETLSAP Business ExplorerSAP BusinessObjects Cloud and EnterpriseIn-Memory Computing with SAP HANA

• 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.

• 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 databasesStudents extract, transform, and load data from transactional systems into business intelligence solutionsStudents design and develop business intelligence reports and dashboards

Exam in first (30 min) and last (60 min) session! Check Moodle for exam content.
Grade consists of project (40%), presentation (10%), written exam (50%, 90min)

Das Modul Corporate Entrepreneurship gliedert sich in Bausteine des Corporate Entrepreneurship, in offene Innovationsprozesse, in Lead User Innovation sowie in Entrepreneurial Performance

Während die Bausteine des Corporate Entrepreneurship die Unterschiede zwischen Corporate Entrepreneurship und Startup-Entrepreneurship, die zwischen internem und externem Venture Management sowie Spin-Offs beinhalten, gehören zu den offenen Innovationsprozessen das theoretische Framework, verschiedene Anwendungen in Abhängigkeit der Unternehmensgrösse sowie Chancen und Risiken des Konzepts.

Anhand der Lead User Innovation werden Innovationserfolg und User Innovation erläutert. Hinzu kommen Motive und Charakteristika von innovativen Endanwendern bzw. Methoden zur Integration von innovativen Endanwendern.

Die Entrepreneurial Performance beschäftigt sich mit den Themengebieten Einschränkungen, Leitung, Beurteilung und Erhalt von unternehmerischer Leistung.

Präsentationen, Fallstudien, Diskussionen

Essay

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

• 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. The exposé is developed in the module Research Seminar

Submission deadlines

• Exposé: February 1st (summer term) and July 1st (winter term). Submit via Moodle
• Master's Thesis: June 30 (summer term) and November 30 (winter term)

• 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.
• The dates and deadlines given in this module description are the last possible dates for the adhering presentation and defense. Thesis submissions made after these deadlines are presented at the following date for presentation and defense.
• The official editing time is defined on the thesis proposal and may not exceed 22 weeks. A shorter editing time is possible.

Submission of master's thesis consists of:

• 4 (four) hardbound, linen-covered, and signed copies of Master's thesis.
• A CD ROM containing thesis' digital copy (to be submitted to the central service desk). Please label the CD with your name and the title of your work.
• Direct submission of thesis' digital copy to the supervisor and co-supervisor (via e-mail).

Commencement ceremony:

• The already scheduled dates of the commencement ceremony can be found in the menu bar on the left.
• The registration for the commencement ceremony is handled by the study administration. Dates of the commencement ceremony are given in the menu bar located on the left / topic commencement ceremony.

Compulsory reading:

• Bryman, A. & Bell, E. (2015) Business research methods (4th ed.). Oxford, UK: Oxford University Press.
• Creswell, J.W. (2013) Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (3rd ed.). Sage Publications
• Oates, B. J. (2006). Researching information systems and computing. London, UK: Sage Publications.
• Recker, J. (2012). Scientific Research in Information Systems: A Beginner’s Guide. Springer, Heidelberg, Germany.

Further information:

• The Study and Assessment Regulations and the Guidelines for Writing Academic Paper in Economics contains further information. >>Link

Theories of International Trade and International Finance aim at explaining underlying dynamics of economic and social developments in a global context. They also allow to investigate relations between market and industry structures and flows in goods, services and financial flows. Furthermore they serve in designing and evaluating the impact of macro-economic policies, currency fluctuations and trading regimes on economies, business strategy and operations.

The module aims at examining how national policies can, in theory, shape international trade and finance, and constrain the climate for international business. It extends this analysis to the international system discussing the role of international arrangements and institutions, e.g. the WTO, on international trade and investment.

Using a series of country-based case studies, students will have the opportunity to investigate how firms feel and master the impact of government policy and institutional set-ups in a specific nation state or economic region, for example China, India, the Arab World and South-America.

Contents of the module are:

• Sources of international comparative advantages (differences in natural resources, technology and factor endowment)
• Principles of strategic trade theory and geographic economic integration
• Goals, instruments and impact of modern international trade policy in industrial and emerging economies
• International trade agreements and organisations

Interactive lectures, exercises

The purpose of this module is to enable students to understand the challenges of international trade and investment and to analyse the opportunities they represent concerning a specific nation state or a wider economic region.

• Explain fundamentals of neo-classical and strategic trade theories
• Distinguish implications for national trade policies
• Illustrate main themes and debates in international economics
• Assess the impact of macroeconomics, political forces and international agreements on firm strategies and competition in a specific nation-state or economic region

Written term paper

Web-based online evaluation upon completion of module

This module consists of elective courses. Please register for one of the lectures offered.

In this course we discuss some statistical methods that can help to take decisions in business using data. After reviewing the basic concepts of ''Testing and Estimating'', usually known from an introductory course on probability theory and statistics in any bachelor program, we introduce and discuss some aspects of ''Multiple Linear Regression Analysis'', which can be regarded as one of the practically most relevant statistical techniques.

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
• parameter estimation in multiple linear regression
• classical linear model assumptions and model diagnostics
• inference in multiple linear regression
• model specification techniques
• model selection techniques
• introduction to the software package R

Students are usually asked in advance to read corresponding parts of the textbook (Wooldridge, 2009) in order to prepare for the upcoming lectures. In the interactive lectures, we then introduce the statistical concepts and motivate them by discussing examples in detail. Assignments are then offered to train these skills. During the office hours, individual problems may finally be discussed with the lecturer. In order to analyze realistic data, the software package R will be used.

The same teaching methods will be used in the two different lecture series ''Testing and Estimating'' and ''Multiple Linear Regression Analysis''.

The e-learning platform Moodle will be used throughout the course for the dissemination of course material and discussions.

During an intensive weekly workshop it enables students to further deepen their knowledge of an issue addressed in the project studios and/ or conduct excursions to places and sites addressed in their design project.

Intensive seminar week: excursion, exercises, experiment, research, writing, visualising, modelling, presenting, case study, peer feedback

Professional competence

• Execute complex defined and self-defined projects of research, development or investigation and identify and implement relevant outcomes.
• Communicate and articulate ideas and information fluently in English language and work comprehensively in visual, oral and written forms.
• Make formal presentations about specialist topics to informed audiences.
• Exercise autonomy and initiative in carrying out set project briefs and self-directed programmes of study.
• Demonstrate ability to manage time and physical resources in relation to set project briefs and self-directed programmes of study as an individual and a group member.
• Show confidence in analysing case studies and the ability to infer principles and motivations.

Methodological competence

• Apply a variety of design- and research methods and visualization and production techniques
• Have knowledge of scientific or artistic methods within an interdisciplinary context

Social competence

• Explain competently, discuss and critique own work through oral presentations, writing or visual communication
• Lead a team and assume responsibility
• Demonstrate the ability to work with other students for assignments, exercises, experiments, presentations etc

Personal competence

• Gain confidence in own role, and the persuasive and accountable manner in which it is expected to be performed.

Presentation, portfolio, participation, minimum 75% mandatory presence