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 statistical, econometric, and machine learning techniques 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 computer science, 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 Computer Science
- 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 consumer behaviour or improve digital services?
Past seminar projects have included predicting TV viewing patterns, assessing vulnerability to contagious diseases, analysing chatbot conversations, detecting survey engagement, and modelling the impact of pricing on online shopping. You will develop models, implement them in software, and present practical recommendations to the organisation.
Study schedule
The Take-Off is the introduction programme for all new students at Erasmus School of Economics. During the Take-Off you will meet your fellow students, get acquainted with our study associations and learn all the ins and outs of your new study programme, supporting information systems and life on campus and in the city.
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Bayesian Econometrics plays an important role in quantitative economics, marketing research and finance. This course discusses the basic tools which are needed to perform Bayesian analyses. It starts with a discussion on the difference between Bayesian and frequentist statistical approach. Next, Bayesian parameter inference, forecasting and Bayesian testing is considered, where we deal with univariate models, multivariate models and panel data models (Hierarchical Bayes techniques). To perform a Bayesian analysis, knowledge of advanced simulation methods is necessary. Part of the course is devoted to Markov Chain Monte Carlo sampling methods including Gibbs sampling, data augmentation and Monte Carlo integration. The topics are illustrated using simple computer examples which are demonstrated during the lectures.
- Introduction
- Regularization
- Trees, Forests and Ensemble Methods
- Support Vector Machines
- Clustering
- Neural Networks (Deep Learning)
- Reinforcement Learning
This content will be complemented with several assignments and readings.
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The students are divided in small groups. Each group works on a research question. Usually this research question is put forward by a company. First, the relevant literature is studied. Next, the research question is translated in one or more models. To estimate the parameters of the models, (company) data is used. Much attention will be paid to the selection of the best possible model, given the research question. This model can be any model dealt with in the various courses, but it can also be a model that needs to be developed by the students themselves. The model parameters are estimated, and the model results are interpreted within the light of the research question. The final results will be presented in a scientific report and a presentation.
Proposal for the Master thesis Econometrics and Management Science. This proposal can be used as a part of the Master thesis. There is no grade for this proposal.
The thesis is an individual assignment about a subject from your Master's specialisation. More information about thesis subjects, thesis supervisors and the writing process can be found on the Master thesis website.
Disclaimer
This overview provides a general impression of the 2026-2027 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.