This course offers master’s students of economics a practical insight into modern methods of data processing and machine learning. The focus lies on the acquisition of application-oriented knowledge in the field of data science and machine learning.
Students will learn to handle and interpret massive amounts of data, utilizing concrete examples such as the analysis of big data in company and transaction data or the forecasting of key economic figures. Practical implementation is mainly carried out using the programming language R.

Topics covered in Data Science

• Data preparation, transformation, and explorative analysis
• Interactive data visualization
• Processing text data (Natural Language Processing - NLP)
• Working with Big Data
• Synthetic data generation

Methods covered in Machine Learning:

Supervised Learning:

• Linear and logistic regression
• Decision Trees and Random Forests
• XGBoost
• Support Vector Machines (SVMs)

Unsupervised Learning:

• Cluster analysis
• Principal Component Analysis (PCA)

Optimization:

• Optimization methods

After completing the course, students will be able to prepare, analyse, and visualize large data sets. They will possess a solid toolkit of data science methods and be able to apply fundamental machine learning algorithms to economic problems.

Attendance at the course is compulsory. Students may miss a maximum of three units to successfully complete the course.

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.

40% Project Work: Developing methodological skills.

30% Practical Exercises: Ongoing application of learned concepts.

30% Active Participation: Engagement during the course.

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 is required for participation.

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