First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Science
PHYSICS OF DATA
Course unit
LABORATORY OF COMPUTATIONAL PHYSICS (C.I.)
SCP8082524, 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
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Number of ECTS credits allocated
Type of assessment Mark
Course unit English denomination LABORATORY OF COMPUTATIONAL PHYSICS (C.I.)
Website of the academic structure http://physicsofdata.scienze.unipd.it/2018/laurea_magistrale
Department of reference Department of Physics and Astronomy
Mandatory attendance
Language of instruction English
Branch PADOVA
Single Course unit The Course unit can be attended under the option Single Course unit attendance
Optional Course unit The Course unit can be chosen as Optional Course unit

Lecturers
Teacher in charge MARCO ZANETTI FIS/01

Modules of the integrated course unit
Course unit code Course unit name Teacher in charge
SCP8082525 LABORATORY OF COMPUTATIONAL PHYSICS (MOD. A) MARCO ZANETTI
SCP8082526 LABORATORY OF COMPUTATIONAL PHYSICS (MOD. B) MARCO BAIESI

Course unit organization
Period  
Year  
Teaching method frontal

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

Examination board
Examination board not defined

Syllabus
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

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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