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 sem. Oct 2, 2017 Jan 31, 2018
I sem - 3° anno Oct 30, 2017 Jan 31, 2018
II sem. Mar 1, 2018 Jun 15, 2018
Exam sessions
Session From To
Sessione invernale d'esame Feb 1, 2018 Feb 28, 2018
Sessione estiva d'esame Jun 18, 2018 Jul 31, 2018
Sessione autunnale d'esame Sep 3, 2018 Sep 28, 2018
Degree sessions
Session From To
Sessione estiva di laurea Jul 20, 2018 Jul 20, 2018
Sessione autunnale di laurea Nov 27, 2018 Nov 27, 2018
Sessione invernale di laurea Mar 27, 2019 Mar 27, 2019
Holidays
Period From To
Christmas break Dec 22, 2017 Jan 7, 2018
Easter break Mar 30, 2018 Apr 3, 2018
Patron Saint Day May 21, 2018 May 21, 2018
VACANZE ESTIVE Aug 6, 2018 Aug 19, 2018

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 M P S T U V Z

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 7272
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

Meneghini Lorenzo

lorenzo.meneghini@univr.it

Mori Nicola

nicola.mori@univr.it 045 683 5628

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

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

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

Zuccolotto Paola

zuk@eco.unibs.it ++39-030-2988634 (presso Università degli Studi di Brescia)

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

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.




S Placements in companies, public or private institutions and professional associations

Teaching code

4S02690

Credits

9

Coordinatore

Lorenzo Meneghini

The teaching is organized as follows:

MATEMATICA

Credits

6

Period

I sem.

Academic staff

Lorenzo Meneghini

STATISTICA

Credits

3

Period

II sem.

Academic staff

Paola Zuccolotto

Learning outcomes

------------------------
MM: STATISTICA
------------------------
Aim of this course 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.
------------------------
MM: MATEMATICA
------------------------
Aim of this course 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: 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.
------------------------
MM: MATEMATICA
------------------------
(PREREQUISITES: Algebraic, exponential and logarithmic 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. 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, 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 )

Bibliography

Reference texts
Author Title Publishing house Year ISBN Notes
MENEGHINI LORENZO APPUNTI DI MATEMATICA - DISPENSE PER IL CORSO 2015

Examination Methods

------------------------
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.
------------------------
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.

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.

Graduation

For schedules, administrative requirements and notices on graduation sessions, please refer to the Graduation Sessions - Science and Engineering service.

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