Period: First Semester

Course unit contents: 

Number representation, algorithmic complexity, data and memory management
- The python programming language, from the bases to the advance programming for scientific computing; review of the modern libraries for the data management and analysis (numpy, scipy, pandas, sciiti-learn, etc.)
- Monte Carlo methods for the simulation of physics phenomena
- Techniques to assess and extract the statistical features of a physics datasets and comparison with model predictions
- Visualisation and graphical representation of datasets and their properties

Planned learning activities and teaching methods: The course will consist of lectures (30%) and lab sessions (70%) in a dedicated room equipped with terminals. The lab session will focus on delve into and practice the data analysis techniques thought during the lectures. Students will be exposed to several programming and analysis exercises to be performed profiting from the computing resources (could computing and HPC) made available by the Department of Physics and Astronomy; furthermore students will gather in groups to tackle small research projects.

Last modified: Wednesday, 1 June 2022, 4:22 PM