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.
Course calendar
The Academic Calendar sets out the degree programme lecture and exam timetables, as well as the relevant university closure dates..
| Period | From | To |
|---|---|---|
| Primo semestre | Oct 4, 2021 | Jan 28, 2022 |
| Secondo semestre | Mar 7, 2022 | Jun 10, 2022 |
| Session | From | To |
|---|---|---|
| Sessione invernale d'esame | Jan 31, 2022 | Mar 4, 2022 |
| Sessione estiva d'esame | Jun 13, 2022 | Jul 29, 2022 |
| Sessione autunnale d'esame | Sep 1, 2022 | Sep 30, 2022 |
| Session | From | To |
|---|---|---|
| Sessione di laurea estiva | Jul 20, 2022 | Jul 20, 2022 |
| Sessione di laurea autunnale | Oct 19, 2022 | Oct 19, 2022 |
| Sessione invernale | Mar 15, 2023 | Mar 15, 2023 |
| Period | From | To |
|---|---|---|
| Festa di Tutti i Santi | Nov 1, 2021 | Nov 1, 2021 |
| Festa dell'Immacolata | Dec 8, 2021 | Dec 8, 2021 |
| Festività natalizie | Dec 24, 2021 | Jan 2, 2022 |
| Vacanze Pasquali | Apr 15, 2022 | Apr 19, 2022 |
| FESTA DEL LAVORO | May 1, 2022 | May 1, 2022 |
| Santo Patrono | May 21, 2022 | May 21, 2022 |
| Festa della Repubblica | Jun 2, 2022 | Jun 2, 2022 |
| Chiusura estiva | Aug 15, 2022 | Aug 20, 2022 |
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.
Should you have any doubts or questions, please check the Enrolment FAQs
Academic staff
+39 045 802 7913
marco.carradore@univr.it
mariano.ceccato@univr.it
mila.dallapreda@univr.it
giorgia.guerra@univr.it
niccolo.marastoni@univr.it
abigail.owusu@univr.it
igor.pelgreffi@univr.it
alice.raffaele@univr.it
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 enrolment year.
| Modules | Credits | TAF | SSD |
|---|
| Modules | Credits | TAF | SSD |
|---|
1° Year
| Modules | Credits | TAF | SSD |
|---|
2° Year activated in the A.Y. 2022/2023
| Modules | Credits | TAF | SSD |
|---|
| Modules | Credits | TAF | SSD |
|---|
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.
Statistical methods for business intelligence (It will be activated in the A.Y. 2021/2022)
Teaching code
4S009074
Credits
6
Scientific Disciplinary Sector (SSD)
SECS-S/01 - STATISTICA
Learning outcomes
The course will introduce the modeling / quantitative foundations of modern Business Analytics (BA) theory, taking advantage of a rigorous mathematical approach in order to effectively deal with real case studies. Through probabilistic / statistical tools of descriptive and predictive analysis, the course will provide elements of predictive analysis, risk analysis, simulation and data mining and decision analysis. Students will acquire the fundamental theoretical tools both to develop models to manage typical BA challenges, and to communicate their results concretely, so as to provide brilliant solutions to specific problems in a synergistic and proactive way. A great emphasis will be given to real world applications, also making use of specific packages for data analysis, manipulation and prediction (e.g. Rapidminer, Orange, Knime, R-AnalyticFlow, etc.). At the end of the course the student has to show to have acquired the following skills: ● master the typical pipeline: query, reporting and online analytical processing ' ● ability to control the analytical / quantitative flow, i.e. I / O data management, forecasting and optimization ● ability to develop models in predictive analytics ● ability to develop data mining and cluster analysis models ● ability to classify within heterogeneous databases ● ability to develop pro customer retention rate, targeting marketing models, also in relation to social media, financial (portfolio, insurance, etc.) analytics.
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 libretto: richiedere 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. Periodo di stage/tirocinio
Oltre ai CFU previsti dal piano di studi (verificare attentamente quanto indicato sul Regolamento Didattico): qui informazioni su come attivare lo stage.
Insegnamenti e altre attività che si possono inserire autonomamente a libretto
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: only in this way will you be able to receive notification of all the notices from your teachers and your secretariat via email and soon 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.
Attachments
| Title | Info File |
|---|---|
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 the Teaching Regulations for the A.Y. 2022/2023, attendance at the course of study is not mandatory.
Please refer to the Crisis Unit's latest updates for the mode of teaching.
