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, 2020 Jan 29, 2021
II semestre Mar 1, 2021 Jun 11, 2021
Exam sessions
Session From To
Sessione invernale d'esame Feb 1, 2021 Feb 26, 2021
Sessione estiva d'esame Jun 14, 2021 Jul 30, 2021
Sessione autunnale d'esame Sep 1, 2021 Sep 30, 2021
Holidays
Period From To
Festa dell'Immacolata Dec 8, 2020 Dec 8, 2020
Vacanze Natalizie Dec 24, 2020 Jan 3, 2021
Vacanze Pasquali Apr 2, 2021 Apr 5, 2021
Festa del Santo Patrono May 21, 2021 May 21, 2021
Festa della Repubblica Jun 2, 2021 Jun 2, 2021
Vacanze estive Aug 9, 2021 Aug 15, 2021

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 H I P Q S Z

Badino Massimiliano

symbol email massimiliano.badino@univr.it symbol phone-number +39 045 802 8459

Bazzani Claudia

symbol email claudia.bazzani@univr.it symbol phone-number 0458028734
LBO,  January 31, 2017

Bullini Orlandi Ludovico

symbol email ludovico.bulliniorlandi@univr.it symbol phone-number 045 802 8095

Carra Damiano

symbol email damiano.carra@univr.it symbol phone-number +39 045 802 7059

Carradore Marco

symbol email marco.carradore@univr.it

Castellini Alberto

symbol email alberto.castellini@univr.it symbol phone-number +39 045 802 7908

Ceccato Mariano

symbol email mariano.ceccato@univr.it

Chiarini Andrea

symbol email andrea.chiarini@univr.it symbol phone-number 045 802 8223

Cordoni Francesco Giuseppe

symbol email francescogiuseppe.cordoni@univr.it

Dai Pra Paolo

symbol email paolo.daipra@univr.it symbol phone-number +39 0458027093

Dalla Preda Mila

symbol email mila.dallapreda@univr.it

Di Persio Luca

symbol email luca.dipersio@univr.it symbol phone-number +39 045 802 7968

Farinelli Alessandro

symbol email alessandro.farinelli@univr.it symbol phone-number +39 045 802 7842

Giachetti Andrea

symbol email andrea.giachetti@univr.it symbol phone-number +39 045 8027998

Paci Federica Maria Francesca

symbol email federicamariafrancesca.paci@univr.it symbol phone-number +39 045 802 7909

Quintarelli Elisa

symbol email elisa.quintarelli@univr.it symbol phone-number +39 045 802 7852

Spoto Nicola Fausto

symbol email fausto.spoto@univr.it symbol phone-number +39 045 8027940

Zardini Alessandro

symbol email alessandro.zardini@univr.it symbol phone-number 045 802 8565

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
Training
6
F
-
Final exam
22
E
-

1° Year

ModulesCreditsTAFSSD

2° Year

ModulesCreditsTAFSSD
Training
6
F
-
Final exam
22
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°1 module among the following (1st year: Big Data epistemology and Social research; 2nd year: Cybercrime, Data protection in business organizations, Comparative and Transnational Law & Technology)
6
C
IUS/17
Between the years: 1°- 2°2 courses among the following (1st year: Business analytics, Digital Marketing and market research; 2nd year: Logistics, Operations & Supply Chain, Digital transformation and IT change, Statistical methods for Business intelligence)
Between the years: 1°- 2°2 courses among the following (1st year: Complex systems and social physics, Discrete Optimization and Decision Making, 2nd year: Statistical models for Data Science, Continuous Optimization for Data Science, Network science and econophysics, Marketing research for agrifood and natural resources)
Between the years: 1°- 2°2 courses among the following (1st year: Data Visualisation, Data Security & Privacy, Statistical learning, Mining Massive Dataset, 2nd year: Machine Learning for Data Science)
Between the years: 1°- 2°

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

4S009070

Academic staff

Andrea Chiarini,

Coordinatore

Andrea Chiarini

Credits

9

Language

English en

Scientific Disciplinary Sector (SSD)

SECS-P/08 - MANAGEMENT

Period

I semestre dal Oct 1, 2020 al Jan 29, 2021.

Learning outcomes

The course aims to provide students with the knowledge related to the basic elements for understanding the analytical, strategic, operational management processes and the various structures and operating modes of an organization. Conceptual and operational tools are provided for reading and designing the organizational structures. The course also aims to develop the reading and interpretation skills of the different phenomena of the corporate management and organization.

At the end of the course the student has to show to have acquired the following skills:
● understanding of the basic concepts of management and business organization
● knowledge the main strategies, management techniques and types of organization
● ability of recognizing the opportunities arising from new technologies that facilitate the
development of new business plans and business models
● ability to translate the theoretical concepts presented in the course into concrete situations and business cases

Program

First part (Lecturer, Andrea Chiarini, 24 hours)
- Organisational charts
- Typical departments inside organisations
- Management by processes
- Operations Management and Supply Chain Management
- Different kinds of production systems
- push and pull
- Material Requirements Planning - MRP
- Lean Manufacturing and JIT - pull systems
- ERP and its modules
- Industry 4.0 Cyber-Physical Systems
- Process digitalisation

Examination Methods

Written Exam. 10 closed questions concerning Operations Management and 20 closed questions concerning organization management.

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Type D and Type F activities

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.

Attachments

Title Info File
Doc_Univr_pdf Regolamento esame finale | Final exam regulation 387 KB, 27/04/22 

List of theses and work experience proposals

theses proposals Research area
Domain Adaptation Computer Science and Informatics: Informatics and information systems, computer science, scientific computing, intelligent systems - Computer graphics, computer vision, multi media, computer games
Domain Adaptation Computer Science and Informatics: Informatics and information systems, computer science, scientific computing, intelligent systems - Machine learning, statistical data processing and applications using signal processing (e.g. speech, image, video)
Domain Adaptation Computing Methodologies - IMAGE PROCESSING AND COMPUTER VISION
Domain Adaptation Computing methodologies - Machine learning

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.

Career management


Area riservata studenti