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What will I learn?

Data Science for Decision Making will familiarise you with methods, techniques and algorithms that can be used to address major issues in mathematical modelling and decision making. You will also get hands-on experience in applying this knowledge through computer classes, group research projects and the thesis research. The unique blend of courses will equip you with all the knowledge and skills you’ll need to have a successful career.

Data Science for Decision Making links data science with making informed decisions. It has widespread applications in business and engineering, such as scheduling customer service agents, optimising supply chains, discovering patterns in time series and data, controlling dynamical systems, modelling biological processes, finding optimal strategies in negotiation and extracting meaningful components from brain signals. This means you'll be able to pursue a career in many different industries after you graduate.

Data Science for Decision Making covers the following topics:

  • production planning, scheduling and supply chain optimisation
  • modelling and decision making under randomness, for instance in queuing theory and simulation
  • signal and image processing with emphasis on wavelet analysis and applications in biology
  • algorithms for big data
  • estimation and identification of mathematical models, and fitting models to data
  • dynamic game theory, non-cooperative games and strategic decision making with applications in evolutionary game theory and biology
  • feedback control design and optimal control, for stabilisation and for tracking a desired behaviour
  • symbolic computation and exact numerical computation, with attention to speed, efficiency and memory usage
  • optimisation of continuous functions and of problems of a combinatorial nature

Data science and big data are very important to companies nowadays, and this programme will provide you with all the training you’ll need be active in these areas. The comprehensive education, practical skills and international orientation of the programme will open the world to you. When applying for positions, graduates from Data Science for Decision Making are often successful because of their problem-solving attitude, their modern scientific skills, their flexibility and their ability to model and analyse complex problems from a variety of domains.

Graduates have found positions as:

  • Manager Automotive Research Center at Johnson Electric
  • Creative Director at Goal043 | Serious Games
  • Assistant Professor at Department of Data Science and Knowledge Engineering, Maastricht University
  • BI strategy and solutions manager at Vodafone Germany
  • Scientist at TNO
  • Digital Analytics Services Coordinator at PFSweb Europe
  • Software Developer at
  • Data Scientist at BigAlgo
  • Researcher at Thales Nederland

Which department am I in?

Faculty of Humanities and Sciences

Study options

Full Time (2 years)

Tuition fees
€16,800.00 (13,57,027) per year
Statutory fee for this programme is € 2,083
Start date

Expected September 2022


Maastricht University

Minderbroedersberg 4-6,



6211, Netherlands

Entry requirements

For students from United States

Applicants must have eligible for admission if you have a bachelor's degree from a relevant academic field, for example, philosophy or sociology. English language requirements: IELTS (6.5); TOEFL Paper-based test (575); TOEFL Internet test (90); TOEIC listening and reading (720) and speaking and writing (310); Cambridge [Advanced (CAE) Grade C (scale 180-184), First Certificate in English (FCE) Grade A (scale 180-184), First Certificate in English (FCE) Grade B (scale 176-179)].

For international students

Graduates from a university bachelor's programme in a related field

You will be considered eligible for admission by the Board of Admission if you have a BSc or an equivalent diploma in a programme whose content is a sufficient preparation for the Data Science for Decision Making master.

The relevant competences are:

  • Mathematics: A strong background, including, but not limited to, calculus, linear algebra, probability and statistics.
  • Computer Science: Basic knowledge of data structures and algorithms, with some programming experience.

Bachelor's programmes in Mathematics, Computer Science, Operations Research, Artificial Intelligence, Econometrics, Engineering and Physics typically provide a sufficient preparation.

IELTS: 6.5

TOEFL: 90 (internet-based), 232 (computer-based), 575 (paper-based)

Cambridge CAE: Grade C


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