Computational and data Science Synergy Track
Data science and computational methods play an increasingly important role in modern science, industry, and society. We offer a synergy track Computational and Data Science in the master Physics and Astronomy and use the synergy of different disciplines in the science faculty that are engaged in computational modelling and data science.
This track intersects with the existing specialisations in the physics master (particle and astrophysics, physics of molecules and materials, neurophysics), physical chemistry and mathematics.
The common base consists of 3 courses, that cover fundamental aspects of computing and data science independent of chosen specialisation direction:
| Quarter 1 | Quarter 2 |
| NWI-NM048D CDS: Machine Learning (3 EC) | NWI-NM015C CDS: Advanced Programming (3 EC) |
| Second Semester | |
|---|---|
| Quarter 3 | Quarter 4 |
| NWI-NM066D CDS: Numerical Methods (3 EC) | |
Depending on the chosen specialisation, students can extend this programme based on their interests:
| 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-NM067B Data analysis (3 EC) |
| Quarter 3-4 |
| NWI-NM121 Astronomical Instrumentation & Data Analysis (6 EC) NWI-NM116B Machine Learning in Particle Physics and Astronomy (6 EC) |
| Neurophysics |
| Quarter 2 |
| NWI-NM047D Computational Neuroscience (3 EC) |
| Quarter 2-3 |
| NWI-NM048B Advanced Machine Learning (6EC) |
| Quarter 3-4 |
| NWI-IMC030 Machine Learning in Practice (6 EC) |
| NWI-NM127 Modelling of Complex Real-world Systems (6 EC) |