Pre-master for MSc Data Science for Decision Making

For students that don't meet all entry requirements for the master's programme in Data Science for Decision Making, the Board of Admissions might offer a tailor-made pre-master's programme based on your previous knowledge and skills.

Who is the pre-master's programme for?

If you just fall short of meeting the admission requirements for the master's programme Data Science for Decision Making, our pre-master's programme offers you the opportunity to obtain your starting credentials. The pre-master's programme is tailor-made for each individual applicant and the courses you will follow depend on the knowledge and skills you've already obtained. The pre-master's programme has a duration of one semester which is equivalent to +/- 6 months.

To determine whether you are eligible for a pre-master's programme, our admissions board will carefully evaluate your diplomas, academic portfolio and proof of proficiency in English. The pre-master's programme should be successfully completed within one semester and entails 30 ECTS credits. After successful completion of the pre-master's programme, you will then be able to continue with the MSc Data Science for Decision Making.

Curious about your options? Don't hesitate to contact our admissions office!

Graduates from a Dutch HBO in a related field

The pre-master's programme is also a suitable option for students who have a degree from a Dutch University of Applied Sciences (HBO) in a related field, like ICT or (Technical) Informatics. You will follow a one-semester programme at our department, and gain access to the master's programme upon successful completion of the pre-master's programme. Again, our admissions office is your first point of contact.

Our admissions office is happy to look at your options. Don't hesitate to contact us for more information! 

Miranda Vermeer
Céline Duijsens-Rondagh

Admissions Officers

 dacs-admissions[at]maastrichtuniversity[dot]nl

General requirements

1. Submit your curriculum vitae and an academic transcript of your grades

All candidates must provide a cv and an academic transcript of their grades. Please list your education, relevant extracurricular activities and internship/professional experience.

2. Write an essay of motivation

All candidates should write an essay of motivation, 2 pages in A4 format. Give good arguments about why you want to do this master's programme at Maastricht University, and why you expect that you can successfully complete the programme.

3. Demonstrate proficiency in English

As English is the language of instruction in this study programme, it is essential that your English language skills are good enough for you to undertake intensive and challenging academic courses that are taught and examined in English.

Application deadlines

The regular application deadlines for the master's programme in Data Science for Decision Making apply. You can find them here. 

Study fee

The statutory as well as the institutional study fees for academic year 2023/2024 will be published on this webpage in November.

About MSc Data Science for Decision Making

IIn today’s world, many companies and organisations collect all sorts of data. They aim to extract useful information from it, to recognise patterns and anomalies. Data Science for Decision Making provides the mathematical tools to model and handle these datasets. It has widespread applications in business and engineering, ranging from scheduling customer service agents and optimising supply chains, to modelling biological processes and extracting meaningful components from brain signals. Upon graduation, you'll therefore have excellent qualifications to pursue a career as a data scientist, researcher or manager in many different industries.

Find out more about the programme here. 

Fast facts 

  • Learn to extract valuable information from large datasets

  • Analyst, researcher, or manager; career opportunities are diverse

  • 2-year, full time master's programme, taught in English
  • Starts in September and February
  • Weekly: 8 hrs classes, 32 hrs group work/individual study