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
Primo semestre Oct 3, 2022 Jan 27, 2023
Secondo semestre Mar 6, 2023 Jun 16, 2023

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


Albi Giacomo +39 045 802 7913

Badino Massimiliano +39 045 802 8459

Bazzani Claudia 0458028734

Begalli Diego +39 045 8028491

Blasi Silvia 045 8028218

Boscolo Galazzo Ilaria +39 045 8127804

Carra Damiano +39 045 802 7059

Carradore Marco

Castellini Alberto +39 045 802 7908

Ceccato Mariano

Chiarini Andrea 045 802 8223

Collet Francesca

Confente Ilenia 045 802 8174

Dai Pra Paolo +39 0458027093

Dalla Preda Mila

D'Asaro Fabio Aurelio 0458028431

Di Persio Luca +39 045 802 7968

Gatti Stefano

Gaudenzi Barbara 045 802 8623

Giachetti Andrea +39 045 8027998

Guerra Giorgia

Marastoni Niccolo'

Mola Lapo 045/8028565

Owusu Abigail

Paci Federica Maria Francesca +39 045 802 7909

Pelgreffi Igor

Pianezzi Daniela

Quintarelli Elisa +39 045 802 7852

Rizzi Romeo +39 045 8027088

Setti Francesco +39 045 802 7804

Toniolo Sara 045 802 8683

Vadala' Rosa Maria

Zardini Alessandro 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.


1° Year


2° Year

Modules Credits TAF SSD
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°

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



Luca Di Persio




English en

Scientific Disciplinary Sector (SSD)



Primo semestre dal Oct 3, 2022 al Jan 27, 2023.

Learning objectives

The course will be devoted to the mathematical background necessary to describe, analyze and derive value from datasets, possibly Big Data and unstructured, and to master the main probabilistic models used in the data science field. Starting from basic models, for example regressions, PCA-based predictors, Bayesian statistics, filters, etc., particular emphasis will be placed on mathematically rigorous quantitative approaches aimed at optimizing the data collection, cleaning and organization phases (e.g. series historical data, unstructured data generated in social media, semantic elements, etc.). The mathematical tools necessary to deal with the description of the time series, their analysis and forecasts will also be introduced. The contents of the entire course will be structured in interaction with the study of real problems relating to industrial, economic, social, etc., heterogeneous sectors, using software oriented to probabilistic modeling, for example, Knime, ElasticSearch, Kibana, R AnalyticFlow, Orange , etc.

Prerequisites and basic notions

Regarding both component modules of the entire course: basic notions of Probability Theory, knowledge of the main models of notable discrete and continuous random variables (eg: binomial, Poisson, Gaussian) and their main statistical properties; convergence theorems (eg: law of large numbers, central limit theorem), basic notions of discrete and continuous time stochastic processes (eg: Markov chains, birth and death processes), rudiments of statistical analysis and data (eg : frequency, average, mode, square deviation). Basics of programming in Python, relating in particular to general syntax, data structures, import / export, main graphics for data visualization. Rudiments of the main libraries such as Numpy, Pandas and Matplotlib.


The course program is divided into the following macro-topics

Part 1 [module 1]
1. Time domain analysis
2. Frequency domain analysis
3. Tools for data analysis and cleaning (eg identification of outliers)
4. Methods of maximum verseimilitude, likelihood metrics, fitting density Probability
5. Principal Component Analysis (PCA) [PCA-based regressors / predictors]
6. AR, MA, ARMA, ARIMA, Box-Jenkins, ARCH, GARCH models and their generalizations
7. TIme series decomposition ACF / PACF and connected visualizations
8. Hypothesis tests Gaussian and jump processes / compound processes
9. Decomposition of white noise type processes
10. Bayesian statistics and applications
11. Forecast evaluations via consideration of inferential statistical models, based, eg, on autocovariance and partial autocorrelation, seasonality (SARIMA), variance analysis (ANOVA, MANOVA) , etc.
12. Smoothing techniques, spectral decomposition, polynomial fitting, etc.

Part 2 [module 2]
1. Recalls to programming in Python
2. Manage and view time series
3. Descriptive statistics
4. Analysis in the frequency domain
5. Linear regression for time series
6. Analyze and decompose the principal components of the time series (trend, cycle, seasonality)
7. Forecasting methods: Exponential Smoothing (simple, double, triple)
8. Forecasting methods: AR, MA, ARMA, ARIMA, SARIMA
9. Forecasting methods: ARCH, GARCH and generalizations
10. How to evaluate the different forecasting models

All the above points will be deepened through practical exercises that will require their implementation by appropriate Python codes.
Moreover, the main forecasting methods will be further investigated thanks to the treatment and resolution of real case studies of various types.

Didactic methods

The course will be divided into lectures, with slides as well as notes sharing, and computer simulations / exercises.

Learning assessment procedures

The final exam consists of two parts: one theoretical, the next practical / implementative. Consequently, the first part of the exam is functional to the verification of the learning of the theoretical concepts characterizing the statistical methods and the connected models and algorithms, at the basis of the IT-computational implementations used to donduct a project that the student will agree with the course teachers.
Latter "case study", together with the discussion of the coding parts created to complete it, will be the subject of the second and final part of the exam.

Evaluation criteria

The evaluation of the exam will be carried out by combining the results obtained from the two modules of the course, therefore giving equal importance to the correctness and effectiveness of the solutions adopted in the phase of solving concrete problems due to computer implementations, as well as to understanding of the probabilistic / statistical models underlying them.

Criteria for the composition of the final grade

The final grade will be the result of the joint evaluation of the two theoretical tests and the resolution of the "case study" agreed by the student with the teachers., in accordance with what is expressed in the sections "Examination procedures" and "Evaluation criteria".

Exam language

Inglese / English

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 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:

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

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. 

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

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.


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


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


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