At the end of the course the student will have better knowledge and understanding as she/he will: master the basics in probability theory; master the most basic foundations of statistical techniques like mean values and errors estimates, validation of hypotheses.
The student will be able to apply knowledge and understanding and in particular she/he will be able to: compute mean values and errors of a given data set; validate a simple hypothesis (which boils down to YES/NO alternatives) within a given confidence level; pin down the basic steps in setting up a simulation (singling out the relevant degrees of freedom, choosing a representation of the latter as data, choosing and implementing an algorithm for the simulation dynamic).
The student will be able to make judgements and in particular she/he will be able to: distinguish cases in which a problem can be directly simulated and cases in which a modeling phase is compelling, capturing the relevant degrees of freedom; understand whether the relevant degrees of freedom are to be looked for in the form of macrostates.
The student will also have acquired communication skills as she/he will be able to: present her/his results in a clean, precise and concise way; present her/his results both synthetically as for the overall picture and analytically as for the most delicate points; argue her/his thesis in public, in particular acting in a team.
Finally, the student will have acquired learning skills as she/he will be able to: understand whether numerical simulation solutions are due in the context of problems she/he will be facing in the context of future studies or work; progress in the study of solutions (e.g. algorithmic solutions) beyond what she/he has learnt in this course.