In this course, students will learn to apply theoretical methods, such as those introduced in Business Analytics Foundations, to real data. The focus of the course will be on statistical/computational (data science) methods and, to see how these methods can be applied in practice, the course builds around a case study. 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 (linear and logistic)

The focus is on being able to choose an appropriate method based on the type of data and on the underlying research question, to apply the methods in R and to accordingly interpret their results.

• Home assignments 30 points (6 assignments, 5 points each - group assignments)
• In-class assignments 30 points (to be solved in class, 6 assignments, 6 points each, 5 best scores are counted towards the grade)
• Final Exam 40 points. To pass the course, it is necessary to attain at least 40% (16 points) on the final exam. 

More detailed information about the assessment will be provided in the first session.

If you fullfill the attendance requirements and attain at least 40% on the final exam, 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%