Studying at the University of Verona

Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.

A.A. 2019/2020

Academic calendar

The academic calendar shows the deadlines and scheduled events that are relevant to students, teaching and technical-administrative staff of the University. Public holidays and University closures are also indicated. The academic year normally begins on 1 October each year and ends on 30 September of the following year.

Academic calendar

Course calendar

The Academic Calendar sets out the degree programme lecture and exam timetables, as well as the relevant university closure dates..

Definition of lesson periods
Period From To
I semestre Oct 1, 2019 Jan 31, 2020
II semestre Mar 2, 2020 Jun 12, 2020
Exam sessions
Session From To
Sessione invernale d'esame Feb 3, 2020 Feb 28, 2020
Sessione estiva d'esame Jun 15, 2020 Jul 31, 2020
Sessione autunnale d'esame Sep 1, 2020 Sep 30, 2020
Degree sessions
Session From To
Sessione Estiva Jul 15, 2020 Jul 15, 2020
Sessione Autunnale Oct 16, 2020 Oct 16, 2020
Sessione Autunnale Dicembre Dec 11, 2020 Dec 11, 2020
Sessione Invernale Mar 17, 2021 Mar 17, 2021
Holidays
Period From To
Festa di Ognissanti Nov 1, 2019 Nov 1, 2019
Festa dell'Immacolata Dec 8, 2019 Dec 8, 2019
Vacanze di Natale Dec 23, 2019 Jan 6, 2020
Vacanze di Pasqua Apr 10, 2020 Apr 14, 2020
Festa della Liberazione Apr 25, 2020 Apr 25, 2020
Festa del Lavoro May 1, 2020 May 1, 2020
Festa del Santo Patrono May 21, 2020 May 21, 2020
Festa della Repubblica Jun 2, 2020 Jun 2, 2020
Vacanze estive Aug 10, 2020 Aug 23, 2020

Exam calendar

Exam dates and rounds are managed by the relevant Science and Engineering Teaching and Student Services Unit.
To view all the exam sessions available, please use the Exam dashboard on ESSE3.
If you forgot your login details or have problems logging in, please contact the relevant IT HelpDesk, or check the login details recovery web page.

Exam calendar

Should you have any doubts or questions, please check the Enrolment FAQs

Academic staff

B C D F G M O P Q R S T V Z

Ballottari Matteo

matteo.ballottari@univr.it 045 802 7098

Baruffi Maria Caterina

mariacaterina.baruffi@univr.it

Bicego Manuele

manuele.bicego@univr.it +39 045 802 7072

Boscaini Maurizio

maurizio.boscaini@univr.it

Busato Federico

federico.busato@univr.it

Calanca Andrea

andrea.calanca@univr.it +39 045 802 7847

Canevari Giacomo

giacomo.canevari@univr.it +39 045 8027979

Capaldi Stefano

stefano.capaldi@univr.it +39 045 802 7907

Cicalese Ferdinando

ferdinando.cicalese@univr.it +39 045 802 7969

Combi Carlo

carlo.combi@univr.it 045 802 7985

Cristani Matteo

matteo.cristani@univr.it 045 802 7983

Daducci Alessandro

alessandro.daducci@univr.it +39 045 8027025

Dall'Alba Diego

diego.dallalba@univr.it +39 045 802 7074

Della Libera Chiara

chiara.dellalibera@univr.it +39 0458027219

Delledonne Massimo

massimo.delledonne@univr.it 045 802 7962; Lab: 045 802 7058

Dell'Orco Daniele

daniele.dellorco@univr.it +39 045 802 7637

Dominici Paola

paola.dominici@univr.it 045 802 7966; Lab: 045 802 7956-7086

D'Onofrio Mariapina

mariapina.donofrio@univr.it 045 802 7801

Drago Nicola

nicola.drago@univr.it 045 802 7081

Farinelli Alessandro

alessandro.farinelli@univr.it +39 045 802 7842

Giachetti Andrea

andrea.giachetti@univr.it +39 045 8027998

Giacobazzi Roberto

roberto.giacobazzi@univr.it +39 045 802 7995

Giorgetti Alejandro

alejandro.giorgetti@univr.it 045 802 7982

Gregorio Enrico

Enrico.Gregorio@univr.it 045 802 7937

Mariotto Gino

gino.mariotto@univr.it +39 045 8027031

Maris Bogdan Mihai

bogdan.maris@univr.it +39 045 802 7074

Menegaz Gloria

gloria.menegaz@univr.it +39 045 802 7024

Migliorini Sara

sara.migliorini@univr.it +39 045 802 7908

Oliboni Barbara

barbara.oliboni@univr.it +39 045 802 7077

Paci Federica Maria Francesca

federicamariafrancesca.paci@univr.it +39 045 802 7909

Piccinelli Fabio

fabio.piccinelli@univr.it +39 045 802 7097

Posenato Roberto

roberto.posenato@univr.it +39 045 802 7967

Quaglia Davide

davide.quaglia@univr.it +39 045 802 7811

Romeo Alessandro

alessandro.romeo@univr.it +39 045 802 7974-7936; Lab: +39 045 802 7808

Spoto Nicola Fausto

fausto.spoto@univr.it +39 045 8027940

Storti Silvia Francesca

silviafrancesca.storti@univr.it +39 045 802 7908

Trabetti Elisabetta

elisabetta.trabetti@univr.it 045/8027209

Villa Tiziano

tiziano.villa@univr.it +39 045 802 7034

Zivcovich Franco

franco.zivcovich@univr.it

Study Plan

The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University. Please select your Study Plan based on your enrolment year.

ModulesCreditsTAFSSD
6
A
(MAT/02)
6
C
(BIO/13)
12
C
(CHIM/03 ,CHIM/06)
6
A
(FIS/01)
English B1
6
E
-
ModulesCreditsTAFSSD
12
B
(INF/01)
6
C
(BIO/18)
1 module among the following
6
C
(FIS/07)

1° Year

ModulesCreditsTAFSSD
6
A
(MAT/02)
6
C
(BIO/13)
12
C
(CHIM/03 ,CHIM/06)
6
A
(FIS/01)
English B1
6
E
-

2° Year

ModulesCreditsTAFSSD
12
B
(INF/01)
6
C
(BIO/18)
1 module among the following
6
C
(FIS/07)

Legend | Type of training activity (TTA)

TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.




SPlacements in companies, public or private institutions and professional associations

Teaching code

4S00021

Credits

6

Scientific Disciplinary Sector (SSD)

MAT/06 - PROBABILITY AND STATISTICS

Language

Italian

The teaching is organized as follows:

Teoria

Credits

4

Period

II semestre

Academic staff

Silvia Francesca Storti

Laboratorio

Credits

2

Period

II semestre

Academic staff

Silvia Francesca Storti

Learning outcomes

The course aims at providing the fundamental concepts of descriptive statistics and probability, with the task of modeling real problems by means of probability methods and applying to real problems statistic techniques. At the end of the course the student will have to demonstrate to understand the main statistical techniques for describing problems; to be able to interpret results of statistical analyses; to be able to develop know-how necessary to continue the study autonomously in the context of statistical analysis.

Program

------------------------
MM: Teoria
------------------------
(1) Descriptive Statistics. Describing data sets (frequency tables and graphs). Summarizing data sets (sample mean, median, and mode, sample variance and standard deviation, percentiles and box plots). Normal data sets. Sample correlation coefficient.

(2) Probability theory. Elements of probability: sample space and events, Venn diagrams and the algebra of events, axioms of probability, sample spaces having equally likely outcomes, conditional probability, Bayes’ formula, independent events. Random variables and expectation: types of random variables, expected value and properties, variance, covariance and variance of sums of random variables. Moment generating functions. Weak law of large numbers. Special random variables: special random variables and distributions arising from the normal (chi-square, t, F).

(3) Statistical inference. Distributions of sampling statistics. Parameter estimation (maximum likelihood estimators, interval estimates). Hypothesis testing and significance levels.

(4) Regression. Least squares estimators of the regression parameters. Distribution of the estimators. Statistical inferences about the regression parameters. The coefficient of determination and the sample correlation coefficient. Analysis of residuals: assessing the model. Transforming to linearity. Weighted least squares.

------------------------
MM: Laboratorio
------------------------
The course includes a series of laboratories in the computer lab with exercises in MATLAB environment. After an introduction to MATLAB and to the main functions and tools useful for statistics, some exercises will be proposed on descriptive statistics and probability; for computing the probability density function (pdf) and cumulative distribution function (cdf) of special random variables, for generating random data and estimating parameters; on hypothesis testing for distributions and linear regression. The laboratories complement lectures by consolidating learning and developing problem-solving and hands-on practical skills.

Teaching methods. Regular lectures with power point presentation and blackboard and laboratory exercises. Educational material will be available to students enrolled in the course on the Moodle platform. This material includes lecture presentations in PDF format and material related to laboratory activities. For further details and supplementary materials, please refer to the reference books.

Examination Methods

Written exam consisting of theoretical questions (test with multiple choice), problems, and laboratory questions (open questions).
To pass the exam, the students must show that:
- they have understood the basic concepts of probability theory and statistics;
- they are able to use the knowledge acquired during the course to solve the assigned problem;
- they are able to program in MATLAB environment in the statistical and probabilistic context.

Bibliografia

Reference texts
Activity Author Title Publishing house Year ISBN Notes
Teoria Sheldon M. Ross Probabilità e Statistica per l'ingegneria e le scienze, Apogeo Education, terza edizione, 2015, ISBN: 978-88-916-0994-6 (Edizione 3) Apogeo Education 2015 978-88-916-0994-6

Type D and Type F activities

1° periodo di lezioni From 9/30/19 To 12/14/19
years Modules TAF Teacher
The fashion lab (1 ECTS) D Maria Caterina Baruffi (Coordinatore)
I semestre From 10/1/19 To 1/31/20
years Modules TAF Teacher
Python programming language D Maurizio Boscaini (Coordinatore)
II semestre From 3/2/20 To 6/12/20
years Modules TAF Teacher
CyberPhysical Laboratory D Andrea Calanca (Coordinatore)
C++ Programming Language D Federico Busato (Coordinatore)
LaTeX Language D Enrico Gregorio (Coordinatore)
Matlab-Simulink programming D Bogdan Mihai Maris (Coordinatore)
List of courses with unassigned period
years Modules TAF Teacher
Corso Europrogettazione D Not yet assigned
The course provides an introduction to blockchain technology. It focuses on the technology behind Bitcoin, Ethereum, Tendermint and Hotmoka. D Matteo Cristani

Career prospects


Module/Programme news

News for students

There you will find information, resources and services useful during your time at the University (Student’s exam record, your study plan on ESSE3, Distance Learning courses, university email account, office forms, administrative procedures, etc.). You can log into MyUnivr with your GIA login details.

Attendance

As stated in point 25 of the Teaching Regulations for the A.Y. 2021/2022, attendance at the course of study is not mandatory.
Please refer to the Crisis Unit's latest updates for the mode of teaching.

Graduation

List of theses and work experience proposals

Stage Research area
Correlated mutations Various topics

Gestione carriere


Further services

I servizi e le attività di orientamento sono pensati per fornire alle future matricole gli strumenti e le informazioni che consentano loro di compiere una scelta consapevole del corso di studi universitario.