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 |
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Semester 1 | Oct 1, 2024 | Jan 31, 2025 |
Semester 2 | Mar 3, 2025 | Jun 13, 2025 |
Session | From | To |
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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 |
Period | From | To |
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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.
Academic staff
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
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2° Year It will be activated in the A.Y. 2025/2026
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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 models for Data Science (2024/2025)
Teaching code
4S009079
Academic staff
Coordinator
Credits
6
Also offered in courses:
- Statistical Models of the course Master's degree in Artificial intelligence
- Statistical methods for business intelligence of the course Master's degree in Data Science
- Statistical models for Data Science of the course Master's degree in Data Science
Language
English
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 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.
Program
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.
Bibliography
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 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. 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 Didattico) qui 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.
years | Modules | TAF | Teacher |
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1° 2° | Mathematics mini courses |
Giacomo Albi
(Coordinator)
|
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 |
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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.