First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Science
PHYSICS OF DATA
Course unit
MANAGEMENT AND ANALYSIS OF PHYSICS DATASET (C.I)
SCP8082533, A.A. 2019/20

Information concerning the students who enrolled in A.Y. 2019/20

Information on the course unit
Degree course Second cycle degree in
PHYSICS OF DATA
SC2443, Degree course structure A.Y. 2018/19, A.Y. 2019/20
N0
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Number of ECTS credits allocated
Type of assessment Mark
Course unit English denomination MANAGEMENT AND ANALYSIS OF PHYSICS DATASET (C.I)
Website of the academic structure http://physicsofdata.scienze.unipd.it/2019/laurea_magistrale
Department of reference Department of Physics and Astronomy
Mandatory attendance No
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 DONATELLA LUCCHESI FIS/01

Modules of the integrated course unit
Course unit code Course unit name Teacher in charge
SCP8082534 MANAGEMENT AND ANALYSIS OF PHYSICS DATASET (MOD. A) GIANMARIA COLLAZUOL
SCP8082535 MANAGEMENT AND ANALYSIS OF PHYSICS DATASET (MOD. B) DONATELLA LUCCHESI

Course unit organization
Period  
Year  
Teaching method frontal

Calendar
Start of activities 30/09/2019
End of activities 20/06/2020
Show course schedule 2019/20 Reg.2018 course timetable

Examination board
Board From To Members of the board
1 Commissione Management and Analysis of Physics Datasets 2018/2019 01/10/2018 30/11/2019 LUCCHESI DONATELLA (Presidente)
COLLAZUOL GIANMARIA (Membro Effettivo)
ZANETTI MARCO (Supplente)

Syllabus
Prerequisites: Elements of analysis and algebra.
General physics.
Statistics.
Basic programming elements.
Target skills and knowledge: Fundamental knowledge of Unix operating systems
Knowledge of distributed computing.
Knowledge of the management of big data on distributed architectures.
Ability to build a cluster with the available hardware.
Data management on the distributed cluster.
Analysis of data on distributed clusters.
Examination methods: Development of a project assigned at the end of the course. Presentation and discussion of the project, questions on the material presented in class.
Assessment criteria: Evaluation of the project delivered: accuracy, completeness and correctness of the work.
Presentation of the assignment: ability to synthesize information, completeness, correctness and accuracy in the presentation.
Evaluation of the answers: correctness, completeness and accuracy.

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

Innovative teaching methods: Software or applications used
  • Moodle (files, quizzes, workshops, ...)

Sustainable Development Goals (SDGs)
Quality Education Industry, Innovation and Infrastructure