The Research & Policy Seminar focuses on the advanced application of acquired methods to complex economic scenarios. The goal is to prepare data-based analyses for strategic economic decisions.
Students will refine their methodological skills and apply them to real-world problems. The course emphasizes the transition from theoretical models to full-scale implementation.

Topics covered in Data Science:

• Advanced Data preparation and Big Data handling
• Interpretation of results for strategic decision making
• Visualization of complex results for policy advice

Methods covered in Machine Learning (Applied):

Supervised Learning:

• Advanced application of Linear and logistic regression
• Decision Trees and Random Forests (Model tuning and interpretation)
• XGBoost (Advanced Gradient Boosting)
• Support Vector Machines (SVMs)

Unsupervised Learning:

• Cluster analysis for segmentation
• Principal Component Analysis (PCA) for dimensionality reduction

Optimization:

• Optimization methods in economic modeling

Upon completion, students will be able to develop sophisticated models and interpret results for data-driven economic decisions. They will have mastered the transition from theoretical models to practical implementation in Big Data contexts.

The course combines lectures and practical exercises. This includes the joint programming of practical examples and a group project, which is concluded with a presentation of the results.

60% Project Work: Developing methodological skills
20% Practical Exercises: Ongoing application of learned concepts
20% Active Participation: Engagement and contribution to discussions.

Grading Scheme
Excellent: 85 - 100%
Good: 70 - 85%
Satisfactory: 60 - 70%
Sufficient: 50 - 60%
Not sufficient: 0 - 50%

Basic knowledge of the R programming language. Enrollment in the accompanying Field Course (or completion in a previous semester) is required.

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