Module WS 2018/2019

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

Short description
The course provides an introduction to cyber security and covers selected issues related to computer and information security.

Topics

• Security goals and design principles
• Economic aspects of security and risk analysis
• Basics of cryptography
• Authentication and access control
• Security mechanisms of common operating systems
• Key instruments of network security
• Key instruments of web security
• Software security, vulnerabilities, and attacks
• Security management and standards

Short description
The course covers the important aspects of modern data management, from design and querying to transaction processing and from traditional to present-day data-driven applications. Students will learn how to handle various data formats, assess and eventually improve data quality, and handle 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
• Database design using the ER model
• Relational databases (SQL)
• Concurrency control techniques
• NoSQL databases (e.g., MongoDB)
• Apache Hadoop (HDFS, MapReduce)

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

• Data Visualization and Exploration
• 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)
• Deep Learning Fundamentals
• Text mining (e.g. topic modeling)
• Hands-on labs with Python

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

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

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

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.

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

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

• Petri-net foundations of process automation
• Business process management systems
• Process mining

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

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

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 learningText mining
• Social network analysis
• Assessing model quality
• Assessing technologies

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

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

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