The Data Science for Econometrics specialisation focuses on the creative and technical side of data science. You will learn how to develop and apply advanced techniques from econometrics, statistics, machine learning, and artificial intelligence to support decision-making. The programme prepares you to turn complex data into actionable insights and to contribute to the development of new analytical methods.
Programme structure
The programme consists of seven core courses, a seminar, and a master’s thesis, spread across five blocks of eight weeks.
- Core courses introduce key methodologies from statistics, econometrics, machine learning, and artificial intelligence, each focusing on a specific set of techniques.
- Seminar is a team-based project in collaboration with companies or other organisations, where you solve a real-world problem from start to finish.
- Master thesis is written individually in the final blocks, based on your own research and under close supervision.
Curriculum overview
- 20% Statistics
- 30% Econometrics
- 20% Machine Learning and Artificial Intelligence
- 30% Seminar
The curriculum has a strong technical focus, with applications in business and broader data science contexts.
In class
You will work on real-world problems provided by participating companies or other organisations. For example:
How can we predict individual behaviour or improve digital services?
Past seminar projects have included assessing vulnerability to contagious diseases, analysing chatbot conversations, detecting survey engagement, modelling the impact of pricing on online shopping, and predicting TV viewing patterns. You will develop models, implement them in software, and present practical recommendations to the organisation.
Study schedule
| period | course name | EC |
|---|---|---|
| block1 | Take-Off Master | 0 ec |
| block1 | Statistical Learning | 4 ec |
| block1 | Bayesian Econometrics | 4 ec |
| block1 | Machine Learning in Econometrics | 4 ec |
| block2 | Robust Methods | 4 ec |
| block2 | Probalistic Modeling | 4 ec |
| block2 | Text Mining and Recommender Systems | 4 ec |
| block3 | Casual Inference | 4 ec |
| block3+4 | Seminar Case Studies in Data Science for Econometrics | 12 ec |
| block4 | Master's Thesis Proposal E&MS | 4 ec |
| block4+5 | Master's Thesis Data Science for Econometrics | 16 ec |
| Total | 60 ec | |
Disclaimer
This overview provides a general impression of the 2027-2028 curriculum. It is not the current study schedule. Enrolled students can find the most up-to-date version on MyEUR. Please note that minor changes may occur in future academic years.