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
Semester 1 Oct 1, 2024 Jan 31, 2025
Semester 2 Mar 3, 2025 Jun 13, 2025
Exam sessions
Session From To
Winter exam session Feb 3, 2025 Feb 28, 2025
Summer exam session Jun 16, 2025 Jul 31, 2025
Autumn exam session Sep 1, 2025 Sep 30, 2025
Holidays
Period From To
Tutti i Santi Nov 1, 2024 Nov 1, 2024
Festa dell'Immacolata Dec 8, 2024 Dec 8, 2024
Vacanze di Natale Dec 23, 2024 Jan 6, 2025
Vacanze di Pasqua Apr 18, 2025 Apr 21, 2025
Festa della Liberazione Apr 25, 2025 Apr 25, 2025
Festa del Lavoro May 1, 2025 May 1, 2025
Festa del Santo Patrono May 21, 2025 May 21, 2025
Festa della Repubblica Jun 2, 2025 Jun 2, 2025
Vacanze estive Aug 11, 2025 Aug 16, 2025

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 Enrollment FAQs

Academic staff

A B C D G M N P Q R S T V

Albi Giacomo

symbol email giacomo.albi@univr.it symbol phone-number +39 045 802 7913

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

Bazzoni Damiano

symbol email damiano.bazzoni@univr.it

Blasi Silvia

symbol email silvia.blasi@univr.it symbol phone-number 045 8028218

Bonetti Silvia

symbol email silvia.bonetti@univr.it

Boscolo Galazzo Ilaria

symbol email ilaria.boscologalazzo@univr.it symbol phone-number +39 045 8127804

Carra Damiano

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

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

Chiurco Carlo

symbol email carlo.chiurco@univr.it symbol phone-number +390458028159

Collet Francesca

symbol email francesca.collet@univr.it symbol phone-number +39 045 802 7979

Combi Carlo

symbol email carlo.combi@univr.it symbol phone-number +39 045 802 7985

Confente Ilenia

symbol email ilenia.confente@univr.it symbol phone-number 045 802 8174

Daffara Claudia

symbol email claudia.daffara@univr.it symbol phone-number +39 045 802 7942

Dai Pra Paolo

symbol email paolo.daipra@univr.it symbol phone-number +39 045 802 7093

Dalla Preda Mila

symbol email mila.dallapreda@univr.it

D'Asaro Fabio Aurelio

symbol email fabioaurelio.dasaro@univr.it symbol phone-number 0458028431

Di Persio Luca

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

Gatti Stefano

symbol email stefano.gatti@univr.it

Gaudenzi Barbara

symbol email barbara.gaudenzi@univr.it symbol phone-number 045 802 8623

Giachetti Andrea

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

Guerra Giorgia

symbol email giorgia.guerra@univr.it

Marastoni Niccolo'

symbol email niccolo.marastoni@univr.it

Mola Lapo

symbol email lapo.mola@univr.it symbol phone-number 0458028565

Nocella Giuseppe

symbol email giuseppe.nocella@univr.it symbol phone-number 045/8028516

Paci Federica Maria Francesca

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

Pianezzi Daniela

symbol email daniela.pianezzi@univr.it

Pravadelli Graziano

symbol email graziano.pravadelli@univr.it symbol phone-number +39 045 802 7081

Quintarelli Elisa

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

Rizzi Romeo

symbol email romeo.rizzi@univr.it symbol phone-number +39 045 802 7088

Setti Francesco

symbol email francesco.setti@univr.it symbol phone-number +39 045 802 7804

Tomazzoli Claudio

symbol email claudio.tomazzoli@univr.it

Toniolo Sara

symbol email sara.toniolo@univr.it symbol phone-number 045 802 8683

Vadala' Rosa Maria

symbol email rosamaria.vadala@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 enrollment year.

2° Year  It will be activated in the A.Y. 2025/2026

ModulesCreditsTAFSSD
Final exam
21
E
-
It will be activated in the A.Y. 2025/2026
ModulesCreditsTAFSSD
Final exam
21
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°
1 module among the following
6
C
IUS/17
Between the years: 1°- 2°
1 module among the following (A.A. 2024/2025 Network science and econophysics not activated)
Between the years: 1°- 2°
1 module among the following
Between the years: 1°- 2°
2 modules among the following
Between the years: 1°- 2°
Further activities: International students (ie students who do not have an Italian bachelor's degree) must compulsorily gain 3 credits of Italian language skills level B2.
6
F
-
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

Type D and Type F activities

Le attività formative di tipologia D sono a scelta dello studente, quelle di tipologia F sono ulteriori conoscenze utili all’inserimento nel mondo del lavoro (tirocini, competenze trasversali, project works, ecc.). In base al Regolamento Didattico del Corso, alcune attività possono essere scelte e inserite autonomamente a libretto, altre devono essere approvate da apposita commissione per verificarne la coerenza con il piano di studio. Le attività formative di tipologia D o F possono essere ricoperte dalle seguenti attività.

1. Insegnamenti impartiti presso l'Università di Verona

Comprendono gli insegnamenti sotto riportati e/o nel Catalogo degli insegnamenti (che può essere filtrato anche per lingua di erogazione tramite la Ricerca avanzata).

Modalità di inserimento a libretto: se l'insegnamento è compreso tra quelli sottoelencati, lo studente può inserirlo autonomamente durante il periodo in cui il piano di studi è aperto; in caso contrario, lo studente deve fare richiesta alla Segreteria, inviando a carriere.scienze@ateneo.univr.it il modulo nel periodo indicato.

2. Attestato o equipollenza linguistica CLA

Oltre a quelle richieste dal piano di studi, per gli immatricolati dall'A.A. 2021/2022 vengono riconosciute:

  • Lingua inglese: vengono riconosciuti 3 CFU per ogni livello di competenza superiore a quello richiesto dal corso di studio (se non già riconosciuto nel ciclo di studi precedente).
  • Altre lingue e italiano per stranieri: vengono riconosciuti 3 CFU per ogni livello di competenza a partire da A2 (se non già riconosciuto nel ciclo di studi precedente).

Tali cfu saranno riconosciuti, fino ad un massimo di 6 cfu complessivi, di tipologia F se il piano didattico lo consente, oppure di tipologia D. Ulteriori crediti a scelta per conoscenze linguistiche potranno essere riconosciuti solo se coerenti con il progetto formativo dello studente e se adeguatamente motivati.

Gli immatricolati fino all'A.A. 2020/2021 devono consultare le informazioni che si trovano qui.

Modalità di inserimento a librettorichiedere l’attestato o l'equipollenza al CLA e inviarlo alla Segreteria Studenti - Carriere per l’inserimento dell’esame in carriera, tramite mail: carriere.scienze@ateneo.univr.it

3. Competenze trasversali

Scopri i percorsi formativi promossi dal TALC - Teaching and learning center dell'Ateneo, destinati agli studenti regolarmente iscritti all'anno accademico di erogazione del corso https://talc.univr.it/it/competenze-trasversali

Modalità di inserimento a libretto: non è previsto l'inserimento dell'insegnamento nel piano di studi. Solo in seguito all'ottenimento dell'Open Badge verranno automaticamente convalidati i CFU a libretto. La registrazione dei CFU in carriera non è istantanea, ma ci saranno da attendere dei tempi tecnici.  

4. Contamination lab

Il Contamination Lab Verona (CLab Verona) è un percorso esperienziale con moduli dedicati all'innovazione e alla cultura d'impresa che offre la possibilità di lavorare in team con studenti e studentesse di tutti i corsi di studio per risolvere sfide lanciate da aziende ed enti. Il percorso permette di ricevere 6 CFU in ambito D o F. Scopri le sfide: https://www.univr.it/clabverona

ATTENZIONE: Per essere ammessi a sostenere una qualsiasi attività didattica, incluse quelle a scelta, è necessario essere iscritti all'anno di corso in cui essa viene offerta. Si raccomanda, pertanto, ai laureandi delle sessioni di dicembre e aprile di NON svolgere attività extracurriculari del nuovo anno accademico, cui loro non risultano iscritti, essendo tali sessioni di laurea con validità riferita all'anno accademico precedente. Quindi, per attività svolte in un anno accademico cui non si è iscritti, non si potrà dar luogo a riconoscimento di CFU.

5. Periodo di stage/tirocinio

Oltre ai CFU previsti dal piano di studi (verificare attentamente quanto indicato sul Regolamento Didatticoqui si possono trovare le informazioni su come attivare lo stage. 

Verificare nel regolamento quali attività possono essere di tipologia D e quali di tipologia F.

Si ricorda, inoltre, che per i tirocini attivati dal 1 ottobre 2024 sarà possibile riconoscere le ore eccedenti in termini di crediti di tipologia D limitatamente alle sole esperienze di tirocinio svolte presso enti ospitanti esterni all’Ateneo.

PROCEDURA PER IL RICONOSCIMENTO DELL'ATTIVITA' LAVORATIVA COME CREDITI DI STAGE

Come previsto da delibera del collegio didattico di Matematica e Data Science n°8 -23/24, lo studente che intende farsi riconoscere ore di attività lavorativa come crediti di stage, prima dell'inizio dell'attività, è tenuto ad inviare all'indirizzo mail della segreteria studenti e in copia conoscenza alla commissione pratiche studenti (paolo.daipra@univr.it, luca.dipersio@univr.it, barbara.gaudenzi@univr.it) esplicita richiesta. Nella richiesta va specificato il tipo di attività, nome dell’azienda e sede lavorativa e ore/crediti di cui si sta chiedendo il riconoscimento.

Affinché l'attività sia riconoscibile è d'obbligo che si sia svolta durante gli anni di iscrizione al corso di studi. Una volta accertata la coerenza tra l'attività lavorativa in essere e gli obiettivi del corso, lo studente riceverà tempestiva comunicazione dalla commissione pratiche studenti con in copia conoscenza la segreteria.

Al termine del periodo lavorativo stabilito, lo studente invia alla segreteria studenti la seguente documentazione:

- relazione finale dettagliata che viene inoltrata alla commissione per l’approvazione finale (firmata dallo studente e da un referente aziendale);

- una dichiarazione del legale rappresentante dell'azienda/ente e/o documentazione atta a dimostrare la tipologia di attività professionale e l'impegno orario ad essa dedicato.

La segreteria studenti provvederà all'invio della documentazione ricevuta alla commissione pratiche studenti e alla registrazione dei CFU (taf F ed eventuali ulteriori crediti taf D) deliberati dalla commissione stessa.

Insegnamenti e altre attività che si possono inserire autonomamente a libretto valide per l'a.a. 2024/25

Semester 1  From 10/1/24 To 1/31/25
years Modules TAF Teacher
1° 2° Elements of Cosmology and General Relativity D Claudia Daffara (Coordinator)
1° 2° Introduction to quantum mechanics for quantum computing D Claudia Daffara (Coordinator)
1° 2° APP REACT PLANNING D Graziano Pravadelli (Coordinator)
Semester 2 From 3/3/25 To 6/13/25
years Modules TAF Teacher
1° 2° Python programming language D Carlo Combi (Coordinator)
1° 2° Programming Challanges D Romeo Rizzi (Coordinator)
1° 2° Protection of intangible assets (SW and invention)between industrial law and copyright D Mila Dalla Preda (Coordinator)
List of courses with unassigned period
years Modules TAF Teacher
1° 2° Mathematics mini courses Giacomo Albi (Coordinator)

Teaching code

4S009077

Coordinator

Paolo Dai Pra

Credits

9

Language

English en

Scientific Disciplinary Sector (SSD)

MAT/06 - PROBABILITY AND STATISTICS

Period

Semester 1  dal Oct 1, 2024 al Jan 31, 2025.

Courses Single

Authorized

Learning objectives

The course will provide a self-contained and mathematically rigorous introduction to modern techniques of data analysis and modeling of random phenomena, with special emphasis to the theoretical bases, typical of probability theory, necessary to develop effective solutions to the challenges characterizing heterogeneous areas, eg , finance, fault-detection, innovation forecasting, energy prediction, etc., typical of Industry 4.0, with particular reference to the challenges posed in the field of big data analytics. The presentation of concepts, problems and related theoretical / practical solutions will be oriented to the applications, also making use of specific statistical software (e.g. Matlab, R, KNIME, etc.) always maintaining a high level of mathematical rigor. The course will discuss the basics of modern Probability theory (eg: random variables, their distributions and main statistical properties, convergence theorems and applications), with particular attention to the fundamental stochastic processes (eg: Markov chains , birth and death processes, code theory with real world applications) and their applications within real world scenarios characterized by the presence of big data and related time series.

At the end of the course the student has to show to have acquired the following skills:
- knowledge of the formal basis of probability theory
- ability to use the concepts of random variables (both in a discrete and continuous environment)
- ability to develop models based on known probabilistic models, e.g., v.a. binomial, Poisson, Gaussian, Gaussian mixtures, etc.
- understanding and knowing how to use the basic theory of stochastic processes, with particular reference to Markov chain theory (discrete and continuous time), birth and death processes and related applications
- know and know how to use the basic notions in descriptive and inferential statistics

Prerequisites and basic notions

Calculus and linear algebra

Program

1. Probability, conditioning and independence.
2. Random variables and their distributions. Discrete distributions. Expectation and variance. Continuous distributions.
3. Random vectors. Independence of random variables. Covariance and correlation.
4. Limit Theorems: law of large numbers and central limit theorem. Normal approximation.
5. Normal random vectors.
6. Discrete time Markov chains. Markov Chain Monte Carlo methods.
7. Poisson processes and queuing theory. Continuous time Markov chains.

Bibliography

Visualizza la bibliografia con Leganto, strumento che il Sistema Bibliotecario mette a disposizione per recuperare i testi in programma d'esame in modo semplice e innovativo.

Didactic methods

All the topics will be illustrated in class. Additional material, as exercises, lecture notes and further references, will be available on Moodle page of the course.
The rights of students will be preserved in situations of travel limitation or confinement due to national provisions or in particular situations of fragile health. In these cases, you are invited to contact the teacher directly to organize the most appropriate remedial strategies.

Learning assessment procedures

The exam consists of a written test that involves solving several exercises. It can be completed either by passing two midterm exams during the semester or by passing a regular sitting during an exam session. A student must obtain a mark of at least 18/30 to pass the exam.

Students with disabilities or specific learning disorders (SLD), who intend to request the adaptation of the exam, must follow the instructions given HERE

Evaluation criteria

The student must demonstrate that she is familiar with the basics in Probability and in Markov chain theory, that she can apply theory to problem-solving and she is able to solve exercises of appropriate difficulty.

Exam language

English

Sustainable Development Goals - SDGs

This initiative contributes to the achievement of the Sustainable Development Goals of the UN Agenda 2030. More information on sustainability

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: only in this way will you be able to receive notification of all the notices from your teachers and your secretariat via email and also via the Univr app.

Graduation

Deadlines and administrative fulfilments

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

Need to activate a thesis internship

For thesis-related internships, it is not always necessary to activate an internship through the Internship Office. For further information, please consult the dedicated document, which can be found in the 'Documents' section of the Internships and work orientation - Science e Engineering service.

Final examination regulations

Upon completion of the Degree programme, students will need to submit and present their thesis/dissertation, which must be in English and focusing on a scientific topic covered during the programme. Alternatively, the thesis/dissertation may consist of the analysis and solution of a case study (theoretical and/or relevant to a real industrial context), experimental work, possibly developed as part of an internship, or original and independent research work that may include mathematical formalisation, computer design and a business-oriented approach.

These activities will be carried out under the guidance of a Thesis Supervisor at a University facility, or even outside the University of Verona, either in Italy or abroad, provided that they are recognised and accepted for this purpose in accordance with the teaching regulations of the Master's Degree programme in Data Science.

22 CFU credits shall be awarded for the final examination (assessment of the thesis/dissertation).

The Graduation Committee, which is in charge of the evaluation of the final examination (presentation of the dissertation in English) shall evaluate each candidate, based on their achievements throughout the entire degree programme, carefully assessing the degree of consistency between educational and professional objectives, as well as their ability for independent intellectual elaboration, critical thinking, communication skills and general cultural maturity, in relation to the objectives of the Master's Degree programme in Data Science, and in particular, in relation to the topics dealt with by the candidate in their thesis.

Students may take the final exam only after they have passed all the other modules and exams that are part of their individual study plan, and fulfil all the necessary administrative requirements, in accordance with the terms indicated in the General Study Manifesto.

The graduation exam and ceremony will be carried out by the Graduation Committee appointed by the Chair of the Teaching Committee and composed of a President and at least four other members chosen among the University's lecturers.

The thesis/dissertation will be assessed by the Dissertation Committee, which is composed of three lecturers possibly including the Thesis Supervisor, and appointed by the Chair of the Teaching Committee. The Dissertation Committee shall produce an evaluation of the dissertation, which will be submitted to the Graduation Committee, which will issue the final graduation mark. The Teaching Committee shall govern the procedures of the Dissertation Committee and the Graduation Committee, and any procedures relating to the score awarded for the final exam through specific regulations issued by the Teaching Committee.

Documents

Title Info File
File pdf Regulations for the final exame pdf, it, 326 KB, 19/03/24

Attendance modes and venues

As stated in the Teaching Regulations, attendance at the course of study is not mandatory.

Part-time enrolment is permitted. Find out more on the Part-time enrolment possibilities page.

The course's teaching activities take place in the Science and Engineering area, which consists of the buildings of Ca‘ Vignal 1, Ca’ Vignal 2, Ca' Vignal 3 and Piramide, located in the Borgo Roma campus. 
Lectures are held in the classrooms of Ca‘ Vignal 1, Ca’ Vignal 2 and Ca' Vignal 3, while practical exercises take place in the teaching laboratories dedicated to the various activities.

 


Career management


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