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Programme of Computational and Data Science

Dear student,

We are working on solutions for hybrid education that will take place online and on campus. Therefore the instructional modes, number of exams, the form of the exams and/or assignments may change. You will be informed through Brightspace in case of changes. The course information in the Course guide provides an indication of what you can expect in the course.

In the case of not being able to attend one or more practical courses/lab days due to corona measures, the course coordinator will decide if the student is obligated to re-take the missed meeting and how this will take place.
Data science and computational methods play an increasing important role in modern science as well as in industry and society. We offer a synergy track Computational and Data Science (15 EC) in the master Physics and Astronomy and use the synergy of different disciplines in the science faculty that are engaged in computational modeling and data science. This track intersects with the existing specialisations in the physics master (particle physics and astronomy, physics of molecules and materials, neuroscience), physical chemistry and mathematics.

The common base consists of 3 courses (3 EC), that cover fundamental aspects of computing and data science independent of chosen specialisation direction:

Depending on the chosen specialisation, student can extend this programme based on their interests:
Physics of Molecules and Materials
Quarter 1
NWI-SM297 Molecular Modelling (3 EC)
Quarter 2
NWI-MOL406 Quantum Chemistry (3 EC)
NWI-SM299 Pattern Recognition for Natural science (3 EC)
Quarter 4
NWI-SM295 Quantum Dynamics (3 EC)
Particle and Astrophysics
Quarter 1
NWI-NM042B Monte Carlo Techniques (6 EC)
Quarter 2
NWI-NM116B Machine Learning in Particle Physics and Astronomy (6 EC)
Quarter 3
NWI-NM121 Astronomical Instrumentation & Data Analysis (6 EC)
Quarter 4
NWI-NM067B Data analysis (3 EC)
Quarter 2
NWI-NM048B Advanced Machine Learning (6 EC)
NWI-NM047D Computational Neuroscience (3 EC)
Quarter 3
NWI-IMC030 Machine Learning in Practice (6 EC)
NWI-NM127 Modelling of Complex Real-world Systems (6 EC)