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..

Academic year:
Definition of lesson periods
Period From To
Semester 1 Oct 2, 2023 Jan 26, 2024
Semester 2 Mar 4, 2024 Jun 14, 2024
Exam sessions
Session From To
Winter exam session Jan 29, 2024 Mar 1, 2024
Summer exam session Jun 17, 2024 Jul 31, 2024
Autumn exam session Sep 2, 2024 Sep 30, 2024
Degree sessions
Session From To
Summer graduation session Jul 23, 2024 Jul 23, 2024
Autumn graduation session Oct 24, 2024 Oct 24, 2024
Winter graduation session Mar 27, 2025 Mar 27, 2025
Holidays
Period From To
Festa di Ognissanti Nov 1, 2023 Nov 1, 2023
Festa dell'Immacolata Dec 8, 2023 Dec 8, 2023
Vacanze di Natale Dec 24, 2023 Jan 7, 2024
Festività pasquali Mar 29, 2024 Apr 1, 2024
Ponte della Festa della Liberazione Apr 25, 2024 Apr 26, 2024
Festa del Lavoro May 1, 2024 May 1, 2024
Festività del Santo Patrono: San Zeno May 21, 2024 May 21, 2024
Festa della Repubblica Jun 2, 2024 Jun 2, 2024
Vacanze estive Aug 12, 2024 Aug 17, 2024

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 F G M O P Q R S T V Z

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

Boscolo Galazzo Ilaria

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

Calanca Andrea

symbol email andrea.calanca@univr.it symbol phone-number +39 045 802 7847

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

Collet Francesca

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

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

Farinelli Alessandro

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

Fummi Franco

symbol email franco.fummi@univr.it symbol phone-number 045 802 7994

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

Owusu Abigail

symbol email abigail.owusu@univr.it

Paci Federica Maria Francesca

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

Pelgreffi Igor

symbol email igor.pelgreffi@univr.it

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

Sidali Katia Laura

symbol email katialaura sidali@univr it symbol phone-number 045 802 8592

Toniolo Sara

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

Vadala' Rosa Maria

symbol email rosamaria.vadala@univr.it

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 enrollment year.

1° Year

ModulesCreditsTAFSSD

2° Year  activated in the A.Y. 2024/2025

ModulesCreditsTAFSSD
Training
6
F
-
Final exam
22
E
-
activated in the A.Y. 2024/2025
ModulesCreditsTAFSSD
Training
6
F
-
Final exam
22
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°
Between the years: 1°- 2°
1 module among the following 
6
C
IUS/17
Between the years: 1°- 2°
2 courses among the following (a.a. 2023/24: Complex systems and social physics and Network science and econophysics not activated)
Between the years: 1°- 2°
2 courses among the following (a.a. 2023/24: Statistical methods for business intelligence not activated)
Between the years: 1°- 2°
2 courses among the following
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

Type D learning activities are the student's choice, type F activities are additional knowledge useful for job placement (internships, transversal skills, project works, etc.). According to the Teaching Regulations of the Course, some activities can be chosen and entered independently in the booklet, others must be approved by a special committee to verify their consistency with the study plan. Type D or F learning activities can be covered by the following activities.

1. Modules taught at the University of Verona

Include the modules listed below and/or in the Course Catalogue (which can also be filtered by language of delivery via Advanced Search).

Booklet entry mode: if the teaching is included among those listed below, the student can enter it independently during the period in which the curriculum is open; otherwise, the student must make a request to the Secretariat, sending the form to carriere.scienze@ateneo.univr.it during the period indicated.

2. CLA certificate or language equivalency

In addition to those required by the curriculum/study plan, the following are recognized for those matriculated from A.Y. 2021/2022:

  • English language: 3 CFUs are recognized for each level of proficiency above that required by the course of study (if not already recognized in the previous course of study).
  • Other languages and Italian for foreigners: 3 CFUs are recognized for each proficiency level starting from A2 (if not already recognized in the previous study cycle).

These CFUs will be recognized, up to a maximum of 6 CFUs in total, of type F if the study plan allows it, or of type D. Additional elective credits for language knowledge may be recognized only if consistent with the student's educational project and if adequately justified.

Those enrolled until A.Y. 2020/2021 should consult the information found here.

Method of inclusion in the booklet: request the certificate or equivalency from CLA and send it to the Student Secretariat - Careers for the inclusion of the exam in the career, by email: carriere.scienze@ateneo.univr.it

Warning: to students, who have achieved the B2 level of English in their three-year careers (bachelor), we emphasize the need to replace the full B2 level of English, provided by the study plan, with the C1 level of "computerized" English (prova informatizzata) or to acquire other language proficiency in a community language at least at the full B1 level.

3. Transversal skills

Discover the training paths promoted by the University's TALC - Teaching and learning center intended for students regularly enrolled in the academic year of course delivery

Mode of inclusion in the booklet: the teaching is not expected to be included in the curriculum. Only upon obtaining the Open Badge will the booklet CFUs be automatically validated. The registration of CFUs in career is not instantaneous, but there will be some technical time to wait.  

4. CONTAMINATION LAB 

The Contamination Lab Verona (CLab Verona) is an experiential course with modules on innovation and enterprise culture that offers the opportunity to work in teams with students from all areas to solve challenges set by companies and organisations.  

Upon completion of a CLab, students will be entitled to receive 6 CFU (D- or F-type credits).  

Find out more:  https://www.univr.it/clabverona 

PLEASE NOTE: In order to be admitted to any teaching activities, including those of your choice, you must be enrolled in the academic year in which the activities in question are offered. Students who are about to graduate in the December and April sessions are therefore advised NOT to undertake extracurricular activities in the new academic year in which they are not enrolled, as these graduation sessions are valid for students enrolled in the previous academic year. Therefore, students who undertake an activity in an academic year in which they are not enrolled will not be granted CFU credits.

5. Internship/internship period

In addition to the CFUs stipulated in the curriculum/study plan (check carefully what is indicated on the Teaching Regulations): here information on how to activate the internship. 

Check in the regulations which activities can be Type D and which can be Type F.

Modules and other activities that can be entered independently in the booklet

Academic year:

Teaching code

4S009064

Credits

12

Language

English en

Scientific Disciplinary Sector (SSD)

INF/01 - INFORMATICS

Courses Single

Authorized

The teaching is organized as follows:

Programming

Credits

6

Period

Semester 1

Academic staff

Niccolo' Marastoni

Database
The activity is given by Data warehouse and integration of the course: Master's degree in Computer Engineering for Robotics and Smart Industry

Credits

6

Period

Semester 2

Academic staff

Elisa Quintarelli

Learning objectives

The course is structured as follows [Programming Module] The purpose of the module is to provide skills and knowledge in programming in Python and R, giving the basic concepts of algorithm with particular reference to the use of the Python language (syntax, data structures, data import / export in Python, data visualization in Python) and its applications in data science [Database form] The course aims to provide the skills necessary for the design of data according to the requirements with reference to different application contexts and within the production process of software systems; for the management and effective and efficient use of data and for the study of a system for the management of relational databases in order to create, manage and query databases.

At the end of the course the student has to show to have acquired the following skills:
- ability to develop Python code to solve concrete examples
- ability to evaluate algorithms in terms of complexity in time and space
- knowledge of the syntax and semantics of the language used
- knowledge of the bases of: database management; architecture and functionality of a database management system; concepts of physical independence, logical independence, persistence, competition, reliability, query and updating of a database; advantages of a database management system compared to an operating system file system
- ability to conceptually design databases, e.g., conceptual models for data design; the Entity-Relationship (E-R) model; elements of the E-R model: entities, attributes, relationships, generalization hierarchies and cardinality constraints; the conceptual scheme of a database
- knowledge of the basics of the logical design of a database: data models for database management systems; the relational model; relationship definitions, integrity constraints and relationship scheme; the logical scheme of a database; rules for the translation of conceptual schemes into relationship schemes
- understanding of the mechanisms of interaction with a database: introduction to languages for the definition, modification and query of a database; relational algebra; optimization of algebra expressions; the SQL language; the selection construct (Select-From-Where), nested queries, sorting and grouping of data in SQL; the concept of sight.

Prerequisites and basic notions

Basic concepts of logic and of the notion of algorithm

Program

The course is structured in two parts:
------------------------
Programming
------------------------
1. Introduction to Software Development
- Procedural programming recap
- Jupyter Notebooks, Python
- Software product requirements analysis

2. Data analysis
- Loading structured and unstructured data
- Data manipulation with Numpy and Pandas
- Data visualization with Matplotlib

3. Scientific computing
- NumPy and overall Python open-source ecosystem
- Introduction to Sklearn

4. Object-oriented programming
- OOP fundamentals
- OOP in open-source products
- Redesign of procedural programming

5. Operationalization
- Operationalization with Notebooks
- Operationalization with Streamlit

------------------------
Database
------------------------
1. Introduction to database management systems (DBMS): architectures and functionalities of a DBMS. Physical and logical data independence. Data models. Concepts of model, schema and instance of a database. Languages for database systems. DBMS vs. file system. Information Systems and Data Science.
2. The relational theory: the model and the algebra.
3. Interacting with a database system: languages for the definition, querying and update of a database. SQL: select-from-where statement, join in SQL, the GROUP BY and ORDER BY clauses, using subqueries. Views.
4. Conceptual database design: conceptual data models. The Entity-Relationship model (ER). Elements of the ER model: entities, attributes, relationships, ISA hierarchies and cardinality constraints. Logical database design: logical data models, the relational data model. Elements of the relational data models: relations and integrity constraints. Mapping between conceptual schemas in ER model and logical schema in the relational model.
5. Decision support systems.
1. Datawarehouse systems
2. Designing Data Warehouse on integrated data (GAV and LAV approaches)
3. OLAP queries
6. Beyond the Relation Model: main differences with NO-SQL Models

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

In-person classes and practical sessions

Learning assessment procedures

To pass the Programming exam, the students must:
- complete a Python project that showcases the fundamentals of Data Science taught during the first semester
- pass an oral exam which will test the knowledge of the aforementioned project and general programming skills
- [OPTIONAL] undertake a written midterm test which will grant at most 6 points
The combination of a successful project, oral exam, and midterm will be evaluated with a grade between 18 and 33.

The Database exam consists of a written test (2 hours), possibly supplemented by an oral, containing some open questions about theory concepts, an exercise about conceptual modeling (using the E-R model or DFM schema), and the logical modeling (using the relational model) of a database, and some exercises about the specification of queries in SQL on a given database.

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

To pass the exam, the students must show that:
- they can write a Python program to analyze datasets and provide relevant data analysis, following the Data Science techniques investigated during the course lessons;
- they have understood the concepts related to the theory of relational databases and data warehouses and their design;
- they are able to describe the concepts in a clear and exhaustive way;
- they are able to apply the acquired knowledge to solve application scenarios described by means of questions and exercises.
The exam will be evaluated with at most 33 points (30 cum Laude).

Criteria for the composition of the final grade

The final mark will be the average of the marks obtained in the Programming and Database parts.

Exam language

Inglese

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 Regolamento esame finale | Final exam regulation pdf, it, 387 KB, 27/04/22

List of thesis 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

Career management


Student login and resources


Erasmus+ and other experiences abroad


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.