Syllabus
Registration via LPIS
Day | Date | Time | Room |
---|---|---|---|
Friday | 10/10/25 | 03:00 PM - 05:00 PM | TC.4.18 |
Friday | 10/17/25 | 03:00 PM - 05:00 PM | TC.4.18 |
Friday | 10/24/25 | 03:00 PM - 05:00 PM | TC.4.18 |
Friday | 10/31/25 | 03:00 PM - 05:00 PM | TC.4.18 |
Friday | 11/07/25 | 03:00 PM - 05:00 PM | TC.4.18 |
Friday | 11/14/25 | 03:00 PM - 05:00 PM | TC.4.18 |
Friday | 11/28/25 | 03:00 PM - 05:00 PM | TC.4.18 |
Friday | 12/05/25 | 03:00 PM - 05:00 PM | TC.4.18 |
Friday | 12/12/25 | 03:00 PM - 05:00 PM | TC.4.18 |
Friday | 01/09/26 | 03:00 PM - 05:00 PM | TC.4.18 |
Friday | 01/16/26 | 03:00 PM - 05:00 PM | TC.4.18 |
Friday | 01/23/26 | 03:00 PM - 05:00 PM | TC.4.18 |
This course offers master’s students of economics a practical insight into modern methods of data processing and machine learning. Using concrete examples such as the analysis of big data in company and transaction data, the forecasting of key economic figures or the automated evaluation of text data, students acquire application-oriented knowledge in the field of data science. Practical implementation is mainly carried out using the programming language R.
Topics covered in data science
- Data preparation and transformation
- Explorative data analysis
- Interactive data visualization
- Processing text data (natural language processing)
- Working with big data
- Synthetic data generation
Methods covered in machine learning
- Linear and logistic regression
- Decision trees and random forests
- Support Vector Machines
- Cluster analysis and principal component analysis
- Optimization methods
After completing the course, students will be able to prepare, analyse and visualize large data sets. They will be able to apply methods of data science and machine learning, develop models and interpret their results. They will also be able to apply methods to big data and text data and prepare data-based analyses for economic decisions.
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.
The assessment is made up as follows:
- Presentation of the group project: 60%
- Active participation in the courses: 40%
Grading Scheme
- 85 - 100 %: Excellent
- 70 - 85%: Good
- 60 - 70%: Satisfactory
- 50 - 60 %: Sufficient
- 0 - 50%: Not sufficient
Basic knowledge of the R programming language is required for participation.
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