Study programme

The master's programme Computing Science is a two year (120 EC) programme leading to a MSc degree in Informatica. The programme focuses on the application of the latest techniques in the design of modern computer-based systems and offers a variety of courses and individual projects. Computing Science draws upon our expertise in software technology, network and graph algorithms, planning and scheduling, optimization algorithms, data and pattern mining and machine learning.

In addition to a common basis, you specialize in one of three tracks:

  • Algorithm design and analysis
    To automate life, we build software to solve problems such as how to reach one place from another and how to schedule busses and trains. In essence, a piece of software is an algorithm to solve a problem. This track focuses on the design and analysis of advanced and scalable algorithms to solve complex problems.


  • Operations Research
    Operations Research (OR) deals with the application of advanced analytical methods to help make better decisions. Applications occur in e.g. (public) transportation, logistics, energy networks, healthcare, computer networks and education. Being prescriptive analysis it is part of data science. In this track you learn advanced algorithms as well as data mining and machine learning techniques to solve real-life decision and optimization problems.


  • Programming technology
    Programming languages that we use have direct consequence on our productivity as well as the reliability of the software we produce. The provided abstraction matters a lot as well as the degree of the correctness control that a language imposes. This track focuses on advanced techniques related to programming languages, transformation and analysis of programs, and the verification of their correctness.

Students who started in or before February 2023 can also select the track Algorithmic Data Analysis.

Algorithmic data analysis
Nowadays, people collect massive amounts of data about various aspects of their lives. Many useful and interesting things can be learned by systematically analysing such data; this track focuses on advanced and state-of-the-art techniques to do this.