In this course, students will learn to apply theoretical methods, such as those introduced in Quantitative Methods 1 & 2 and in Business Analytics, 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 case studies. The focus of the course will be on statistical methods and, to see how these methods can be applied in practice, the course builds around hands-on analysis of real datasets. Faced with a real-world data set, students progress through various steps of data management and model design in order to derive data-driven insights.

Topics include:

• Data summarization and visualization
• Quantifying uncertainty: sampling distributions and hypothesis testing
• Simple and multiple linear regression: fitting, interpretation, and diagnosis
• Qualitative predictors and model selection (adjusted R², AIC)
• Logistic regression for binary outcomes
• Distinguishing description, prediction, and causal inference

The emphasis is on selecting appropriate methods based on the data and research question, implementing them in R, and correctly interpreting results in business contexts.

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

• Handling, visualizing and summarizing datasets in R
• Formulating and testing hypotheses, and interpreting their results in a business context
• Design, application, diagnosis, and validation of linear and logistic regression models
• Distinguishing between descriptive, predictive, and causal claims

Attendance requirement is met if a student is present for at least 80% of the lectures.

The course is taught using a combination of lectures, class discussions, assignments and practical applications of the tools and methods applied in business analytics contexts. R is continuously used both in class and in the home assignments.

• Home assignments: 50 points (5 assignments, 10 points each)
• In-class quizzes: 30 points (to be solved in class, 5 assignments, 7 points each, 5 points can be lost for free)
• Final exam: 20 points (oral exam, random topic with 15 minutes preparation time, pre-announced list of topics)

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%

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