So my university offers two programs focused on particle physics. One is simply masters in nuclear/particle physics, the other is masters in mathematical modelling with focus on particle physics. I want to go into mathematical modelling and I'm choosing what I will focus on. I'm not really interested in the former program, but I'd like to know what will I be more qualified for if I do the modelling thing. The courses I would have to take are from two groups. The first group are mathematical courses, that every modelling student has to take: Introduction to functional analysis Partial differential equations 1, 2 Analysis of matrix calculations Finite elements method Numerical methods for ODE Simulations in many-particle physics Matrix iterative methods The second group is focus-specific. For particle physics it's: Physics of elementary particles Introduction to electroweak interactions Quarks, partons and quantum chromodynamics Particles and fields Selected topics on quantum field theory Software and data processing in particle physics 1, 2 Neural networks in particle physics So I like the sound of all those courses - I believe particle physics is interesting, recently I've been really interested in machine learning so the neural networks course sounds cool as well and what I am hoping for is, that when I finish this course I will know how to work with big data. So I'm also looking for practical application, as I think that data scientist might be a suitable alternative to academia job for me. My question is: will all the mathematics be of any use for me? Where will I use all the numerical mathematics? I asked the referee of the modelling masters program how does this program compare to regular particle physics masters and he told me, that it's expected, that we will do some data processing. I'd like to hear some examples of work, for which I will be more qualified, than regular particle physicist who processed data. I can't ask any students or see what kind of thesis they had, because this program is completely new. Thank you for all your answers.