In this course, students will learn to apply the methods introduced in Business Analytics I. The course builds around a case study in risk management. Faced with a real-world data set, students progress through various steps of data management and model design in order to arrive at a business decision, which is effectively supported by quantitative methods.

Topics include:

1. Basic Data Handling and Summary Statistics
2. Data Processing and Visualization
3. Hypothesis Testing
4. Regression Models
5. Exploratory Factor Analysis
6. Optimization

After completion of the course, students will be able to understand and apply the principles, methods and tools of business analytics to problems in the field of risk management. This includes knowledge on:

• Handling, visualizing and summarizing big data files in R
• Formulating and testing hypothesis, and interpreting their results in a business context
• Dealing with unbalanced data
• Design and application of appropriate regression models for the purpose of risk management
• Modern optimization techniques

• Home assignments 30 points
• In class assignments 30 points
• Final Exam 40 points

If you fullfill the attendance requirements, the following grading scale will be applied

• Excellent (1): 87.5% - 100.0%
• Good (2): 75.0% - <87.5%
• Satisfactory (3): 62.5% - <75.0%
• Sufficient (4): 50.0% - <62.5%
• Fail (5): <50.0%