Module WS 2017/2018

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 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

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)

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

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

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 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

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)

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

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

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