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.

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, 2018 Jan 31, 2019
I semestre - 3° anno Oct 29, 2018 Jan 31, 2019
II semestre Mar 4, 2019 Jun 14, 2019
Exam sessions
Session From To
Sessione invernale d'esame Feb 1, 2019 Feb 28, 2019
Sessione estiva d'esame Jun 17, 2019 Jul 31, 2019
Sessione autunnale d'esame Sep 2, 2019 Sep 30, 2019
Degree sessions
Session From To
Sessione di laurea estiva Jul 19, 2019 Jul 19, 2019
Sessione di laurea autunnale Nov 22, 2019 Nov 22, 2019
Sessione di laurea invernale Mar 20, 2020 Mar 20, 2020
Holidays
Period From To
Sospensione dell'attività didattica Nov 2, 2018 Nov 3, 2018
Vacanze di Natale Dec 24, 2018 Jan 6, 2019
Vacanze di Pasqua Apr 19, 2019 Apr 28, 2019
Vacanze estive Aug 5, 2019 Aug 18, 2019

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 F G L M P S T U V Z

Bazzani Claudia

claudia.bazzani@univr.it 0458028734

Begalli Diego

diego.begalli@univr.it +39 045 8028491

Bolzonella David

david.bolzonella@univr.it 045 802 7965

Boschi Federico

federico.boschi@univr.it +39 045 802 7816 - 7272 (lab.)
Foto Maurizio Boselli,  October 19, 2013

Boselli Maurizio

maurizio.boselli@univr.it 045 6835628

Capitello Roberta

roberta.capitello@univr.it 045 802 8488

Favati Fabio

fabio.favati@univr.it +39 045 802 7919

Felis Giovanna

giovanna.felis@univr.it +390456835627

Gaeta Davide Nicola Vincenzo

davide.gaeta@univr.it 045 683 5632

Guzzo Flavia

flavia.guzzo@univr.it 045 802 7923

Lisanti Maria Tiziana

mariatiziana.lisanti@univr.it 0812532609

Meneghini Lorenzo

lorenzo.meneghini@univr.it

Pandolfini Tiziana

tiziana.pandolfini@univr.it 045 802 7918

Pezzotti Mario

mario.pezzotti@univr.it +39045 802 7951

Piccinelli Fabio

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

Polverari Annalisa

annalisa.polverari@univr.it 045 6835629

Sidali Katia Laura

katialaura sidali@univr it 045 802 8592

Slaghenaufi Davide

davide.slaghenaufi@univr.it +39 045 683 5615

Speghini Adolfo

adolfo.speghini@univr.it +39 045 8027900

Tornielli Giovanni Battista

giovannibattista.tornielli@univr.it 045 6835623

Torriani Sandra

sandra.torriani@univr.it 045 802 7921

Ugliano Maurizio

maurizio.ugliano@univr.it 045 683 5626

Vandelle Elodie Genevieve Germaine

elodiegenevieve.vandelle@univr.it 0458027826

Varanini Zeno

zeno.varanini@univr.it 0458027830

Zamboni Anita

anita.zamboni@univr.it +39 045 8027901

Zenoni Sara

sara.zenoni@univr.it 045 802 7941

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
9
A/C
MAT/05 ,SECS-S/01
English B1
6
E
-
ModulesCreditsTAFSSD
15
B/C
AGR/15
12
B
AGR/03
Training
6
F
-

1° Year

ModulesCreditsTAFSSD
9
A/C
MAT/05 ,SECS-S/01
English B1
6
E
-

2° Year

ModulesCreditsTAFSSD
15
B/C
AGR/15
12
B
AGR/03
Training
6
F
-

3° Year

ModulesCreditsTAFSSD
12
B/C
AGR/11 ,AGR/12
12
B/C
AGR/15
Final exam
3
E
-

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

4S02690

Credits

9

The teaching is organized as follows:

Matematica

Credits

6

Period

I semestre

Academic staff

Lorenzo Meneghini

Statistica

Credits

3

Period

II semestre

Academic staff

Marco Sandri

Learning outcomes

Aim of this course of Statistic is to present, both from a theoretical and an empirical point of view, the main methods of univariate and bivariate descriptive statistics for the analysis of qualitative and quantitative data in the context of viticulture and oenology. The educational objectives have been developed with reference to Dublin descriptors, they are consistent with those characterizing the 1st cycle degree program in which the course is inserted and have been defined in coordination with those of Mathematics module, with which it forms a unique course. More specifically, students who successfully complete this course will be able to: - collect, analyze and interpret statistical data, both qualitative and quantitative, and organize results in order to draw conclusions and decide in uncertain situations; - communicate, to experts and non-experts, statistical information and evaluations, also with the help of graphical devices. By means of a gradual learning process, linking the contents of this course with the educational objectives characterizing the 1st cycle degree programs in which the course is inserted, students will acquire the methodological and applied knowledge about the basic concepts of descriptive statistics (statistical ratios, means, variability, inequality/concentration, association, correlation and regression) necessary for the professional training. Aim of the course of Mathematic is to present, both from a theoretical and an empirical point of view, the main methods of calculus and linear algebra. The educational objectives have been developed with reference to Dublin descriptors, they are consistent with those characterizing the 1st cycle degree program in which the course is inserted and have been defined in coordination with those of Statistics module, with which it forms a unique course. More specifically, students who successfully complete this course will be able to: - determine the main characteristics of a function and sketch its graph; - differentiate a function and solve simple geometrical problems; - integrate a function and solve simple geometrical problems; - solve simple differential equations; - calculate matrix determinants and inverse matrix; - solve a linear system. By means of a gradual learning process, linking the contents of this course with the educational objectives characterizing the 1st cycle degree programs in which the course is inserted, students will acquire the methodological and applied knowledge about the basic concepts of Mathematics necessary to prosecute their studies.

Program

------------------------
MM: MATEMATICA
------------------------
(PREREQUISITES: Algebraic, exponential and logarithmic equalities and inequalities.)

1) Functions. Limits. Continuity.
2) Derivation and differentiation of functions. Rolle's, Lagrange's and de l'Hospital's theorems and their consequences. Applications and examples.
3) Functions and their graphs. Function's graph and linear transformations. Applications to natural sciences.
4) Integration of functions of a single real variable. Applications and examples.
5) Simple examples of differential equations.
6) Linear systems and matrices: determinants, inverse matrix, Applications to natural sciences.
Each topic is discussed both from a theoretical and an empirical point of view, with special focus on applications.

(notes and slides available at link https://app.box.com/s/t2jamq852r8j93qhhxomjy4rmckmh5vy )

------------------------
MM: STATISTICA
------------------------
1) Introduction to statistical data analysis: approaches and main topics 2) Univariate descriptive statistics: - Dynamic analysis by means of ratios - Frequency distributions - Location indices: Mode, median, percentiles, algebraic means - Heterogeneity and variability and indices: Gini Index, Shannon entropy, range, absolute deviations, standard deviation, variance. 3) Bivariate descriptive statistics: - Joint frequency distributions - Analysis of association - Analysis of mean dependence - Analysis of linear correlation - Simple linear regression Each topic is discussed both from a theoretical and an empirical point of view, with special focus on case studies dealing with problems arising in the context of viticulture and oenology.

Bibliography

Reference texts
Author Title Publishing house Year ISBN Notes
S. Bernstein and R. Bernstein Elements of Statistics - Descriptive Statistics and Probability - Schaum’s Outline Series. McGraw-Hill 1999 0-07-005023-6

Examination Methods

------------------------
MM: MATEMATICA
------------------------
Students are evaluated by means of a written comprehensive examination, composed of exercises and questions. A time of 2 hours is scheduled. The grades are on a scale of 30. Students who attend lessons can decide to divide the exam in two parts, to be done before the class ends. A time of 2 hours is scheduled for each part and the grades are on a scale of 30. In that case, the mark of Mathematics will be calculated as the average of the scores obtained in the two different parts; in the case of a non-integer result, the mark is rounded upward. Rules for defining the final grade of the Mathematics and Statistics course, which summarizes the tests carried out in the two modules: (1) A module is successfully completed if the student achieves a score of at least 15/30. (2) The examination of Mathematics and Statistics shall be passed only if both modules are successfully completed, provided that the average of the two scores, calculated as shown in (3), is not less than 18/30. (3) The final mark is calculated as the average of the scores obtained in the two modules weighted by the number of credits; in the computation of the average, at 30 cum laude obtained in a module is assigned a score of 31; in the case of a non-integer result, the mark is rounded upward; in the case of an average of at least 30, the final mark will be 30 cum laude.

------------------------
MM: STATISTICA
------------------------
Students (regardless whether or not they attended lessons) are evaluated by means of a written comprehensive examination, composed of exercises and questions. A time of 2 hours is scheduled. The grades are on a scale of 30. Rules for defining the final grade of the Mathematics and Statistics course, which summarizes the tests carried out in the two modules: (1) A module is successfully completed if the student achieves a score of at least 15/30. (2) The examination of Mathematics and Statistics shall be passed only if both modules are successfully completed, provided that the average of the two scores, calculated as shown in (3), is not less than 18/30. (3) The final mark is calculated as the average of the scores obtained in the two modules weighted by the number of credits; in the computation of the average, at 30 cum laude obtained in a module is assigned a score of 31; in the case of a non-integer result, the mark is rounded upward; in the case of an average of at least 30, the final mark will be 30 cum laude.


The exam can be verbalized only after passing the exams related to both modules.

Type D and Type F activities

Modules not yet included

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.

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.


Graduation


Attendance

As stated in point 25 of the Teaching Regulations for the A.Y. 2021/2022, attendance is mandatory for practical and laboratory activities, unless otherwise determined by the Teaching Committee.
Please refer to the Crisis Unit's latest updates for the mode of teaching.

Career management


Area riservata studenti