As a student in the Business Analytics and Quantitative Marketing master you learn how to make use of the enormous amount of data that is available in corporations and other organisations to support decision-making in business. You will be introduced to the latest research techniques and will, after completion of the program, be able to contribute to new developments in this field.
The programme is made up of 7 core courses (each 4 ECTS), a seminar (12 ECTS) and the master's thesis (20 ECTS, including the master's thesis proposal). Each core course focuses on a particular set of techniques or a methodology. In the seminar, students form small groups and focus their complete attention to solve an actual business case. The seminar is usually organised in cooperation with a company.
The curriculum consists of:
- 20% Statistics
- 30% Econometrics
- 20% Machine learning & Computer Science
- 30% Seminar
As the curriculum shows, this programme has a technical focus. You will study the mathematical and statistical details of modern methods with applications in marketing and business in the broad sense.
In the seminar students study concrete problems that are put forward by participating companies. Some examples of problems that were tackled in the seminar in the past:
- How to predict which TV show an individual will watch and for how long?
- How to detect whether a respondent really pays attention when filling out a survey?
- How can so-called graphical models be used to summarise data sets with many variables?
- How can we use the text of conversations to understand the areas in which a chatbot can be improved?
- What impact does price have on online shopping?
For all these questions students developed appropriate models and methods, implemented these in software, and provided concrete advice to the involved company.
The overview below shows the preliminary programme curriculum for the new academic year. Current students can find their schedules in MyEUR
The Take-Off is the introduction event for all new students of Erasmus School of Economics. During this interesting introduction event, you will be provided with useful practical information and receive an introduction to your studies, meet your fellow students and our School.
This course deals with several advanced topics in Microecometrics:
- Estimation methods, like , Methods of Moments, General Methods of Moments (GMM) and simulated maximum likelihood;
- Linear and nonlinear panel data models;
- Treatment effect.
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.
After a general introduction several machine learning methods will be treated sequentially. First, the basics behind a machine learning technique will be introduced after which the students will deepen their knowledge and understanding of the methods by using selected state-of-the art academic literature and applying the methods to real data.
Companies currently have many sources of data available. In this course, we focus on multivariate relations in the data. There is much emphasis on the visualization and exploration of data. This course deals with various multivariate statistical and visualisation techniques to summarise such data sets.
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, the phenomenon of Big Data is presented, followed by the Semantic Web as a set of technologies bringing structure to Big Data. As a large amount of Big Data resides on the Web, techniques for extracting information from online sources will also be discussed. Additionally, in order to to be able to query relational databases, the SQL query language will be studied.
In applied data analysis, outliers and missing data are frequently occurring problems. Therefore, these two topics are the focus of this course.
The students are divided in small groups. Each group puts works on a research question. Usually this research question is put forward by a company.
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 the crown on your Master’s degree programme.
While you have to start in early December, the last two blocks of the programme are especially devoted to the Master’s thesis. The thesis is written individually under close supervision by one of our academic staff members.