First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Science
PHYSICS OF DATA
Course unit
LABORATORY OF COMPUTATIONAL PHYSICS (MOD. A)
SCP8082525, A.A. 2018/19

Information concerning the students who enrolled in A.Y. 2018/19

Information on the course unit
Degree course Second cycle degree in
PHYSICS OF DATA
SC2443, Degree course structure A.Y. 2018/19, A.Y. 2018/19
N0
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Number of ECTS credits allocated 6.0
Type of assessment Mark
Course unit English denomination LABORATORY OF COMPUTATIONAL PHYSICS (MOD. A)
Website of the academic structure http://physicsofdata.scienze.unipd.it/2018/laurea_magistrale
Department of reference Department of Physics and Astronomy
E-Learning website https://elearning.unipd.it/dfa/course/view.php?idnumber=2018-SC2443-000ZZ-2018-SCP8082525-N0
Mandatory attendance
Language of instruction English
Branch PADOVA

Lecturers
Teacher in charge MARCO ZANETTI FIS/01

Integrated course for this unit
Course unit code Course unit name Teacher in charge
SCP8082524 LABORATORY OF COMPUTATIONAL PHYSICS (C.I.) MARCO ZANETTI

Mutuated
Course unit code Course unit name Teacher in charge Degree course code
INP8084211 HIGH LEVEL PROGRAMMING MARCO ZANETTI IN2371
INP8084211 HIGH LEVEL PROGRAMMING MARCO ZANETTI IN2371
INP8084211 HIGH LEVEL PROGRAMMING MARCO ZANETTI IN2371

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Core courses FIS/01 Experimental Physics 6.0

Course unit organization
Period Annual
Year 1st Year
Teaching method frontal

Type of hours Credits Teaching
hours
Hours of
Individual study
Shifts
Laboratory 3.0 24 51.0 No turn
Lecture 3.0 24 51.0 No turn

Calendar
Start of activities 01/10/2018
End of activities 28/06/2019

Examination board
Examination board not defined

Syllabus

Common characteristics of the Integrated Course unit

Prerequisites: Even though not strictly required, the development of the class assumes the attendance of at least two physics laboratory classes during the bachelor degree
Target skills and knowledge: The didactic objective of this class is to teach main data analysis techniques and their application to solve concreate physics problems.
The lectures will review the main methods to extract information from complex physics datasets. The students will be able to gather, summarise and visualise the statistically relevant features of a dataset; furthermore they will learn how to qualitatively and critically compare theoretical predictions with the experimental data.
That knowledge will have to be exercised on practical lab tests, devoted to the analysis of datasets relevant to various scientific areas, i.e. biophysics, astronomy, high energy physics, etc.
Examination methods: To verify the proficiency of the students in the subjects covered by this course, the written reports on the lab experiences will be evaluated; such evaluation will have to be confirmed by an oral exam, during which the students will also be interviewed about what is thought during the lectures.
The oral exam will be split into two parts, each relevant to one of the two modules the class consists of.
Assessment criteria: The written reports on the lab experiences will have to respect the standards of a scientific publication. The data analysis will have to be tailored to the actual scientific problem being tackled and will have to demonstrate originality and the mastering of the established methodology. During the oral exam, in addition to the critical review of the written reports, the comprehension of the fundamental concepts will be tested

Specific characteristics of the Module

Course unit contents: - The working principles and logic schemes of a modern computer and its main components. Review of the available hardware solutions to face problems in various areas of scientific computing: parallel computing, cluster/cloud computing, distributed computing
- 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.
Additional notes about suggested reading: Notes will be made available about the python programming part of the class, together with the description of the main scientific python libraries.

Code to be used as reference for the lab experience will be made available as Jupyter notebooks, stored in public repositories on GitHub
Textbooks (and optional supplementary readings)
  • Rubin Landau, Manuel Paez, Cristian Bordeianu, Computational Physics. --: Wiley-VCH, --. Cerca nel catalogo

Innovative teaching methods: Teaching and learning strategies
  • Lecturing
  • Laboratory
  • Problem based learning
  • Case study
  • Working in group
  • Problem solving

Innovative teaching methods: Software or applications used
  • jupyter notebook

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