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鈥檚 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.
This course deals with several (teoretical and applied) advanced topics in Microecometrics such as:
- methods of moments, general methods of moments;
- linear, dynamic, and nonlinear panel data models;
- heterogeneity and cross-section dependence in panel data;
- duration models;
- treatment effect evaluation.
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.
Companies currently have many sources of data available. In this course, we focus on multivariate relations in the data. This course deals with various multivariate statistical techniques to analyze such data sets. Examples are:
- Discriminant analysis/Classification
- Canonical correlation analysis
- Factor analysis
The course also contains the introduction to robust statistics and its interplay with the multivariate statistical methods.
In each week a different modeling technique is discussed. Examples are models for sales, models for market shares, Hidden Markov Models, Conjoint analysis, modeling heterogeneity, and modeling dynamics. For all topics we will discuss the technical details of the techniques as well as how to apply the techniques and how to interpret the model results.
The course starts by introducing various Computer Science topics relevant for Business Analytics. After that, in order to be able to query relational databases, the SQL query language will be studied. As means to process Big Data we will look at parallel computing models, focusing on the Map-Reduce parallel computing style.
Also, with the purpose of reducing the number of computations for finding similar items (a fundamental data mining problem), we will describe Locality-Sensitive Hashing.
In order to better assimilate the topics covered during lectures, the students will work in teams on several assignments on the discussed topics. The students will also write a report describing the proposed solutions. In addition a larger individual assignment that involves programming will result in a short paper.
In this course we will focus on nonparametric statistics and kernel methods.
We are familiar with methods such as maximum likelihood or linear regression. These are called parametric analysis as the data are assumed to come from a certain distribution (e.g., normal distribution) or have a certain relationship (e.g., linear relationship). Nonparametric analysis intends to uncover patterns in data without such assumptions. These methods are much more flexible and robust, but also come at a different cost.
Kernel is an essential concept in nonparametric statistics and helps facilitate many nonparametric methods. It also has many applications in machine learning and allows to extend many basic methods into more flexible versions, such as kernel PCA and kernel SVM.
Other possible topics for this class include a review of cross validation, bootstrapping, reproducing kernel Hilbert space and semiparametric statistics, depending on the level of the class. (To be decided later by the instructor)
The assessment will be based on the combination of two types of assignments. There will be a mathematical written assignment every week following the lecture, and there will be 3-4 practical assignments where you need to programme and apply the methods on data.
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 . Please note that minor changes may occur in future academic years.