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 |
|---|---|---|
| I semestre | Oct 1, 2020 | Jan 29, 2021 |
| II semestre | Mar 1, 2021 | Jun 11, 2021 |
| Session | From | To |
|---|---|---|
| Sessione invernale d'esame | Feb 1, 2021 | Feb 26, 2021 |
| Sessione estiva d'esame | Jun 14, 2021 | Jul 30, 2021 |
| Sessione autunnale d'esame | Sep 1, 2021 | Sep 30, 2021 |
| Period | From | To |
|---|---|---|
| Festa dell'Immacolata | Dec 8, 2020 | Dec 8, 2020 |
| Vacanze Natalizie | Dec 24, 2020 | Jan 3, 2021 |
| Vacanze Pasquali | Apr 2, 2021 | Apr 5, 2021 |
| Festa del Santo Patrono | May 21, 2021 | May 21, 2021 |
| Festa della Repubblica | Jun 2, 2021 | Jun 2, 2021 |
| Vacanze estive | Aug 9, 2021 | Aug 15, 2021 |
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 Enrollment 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
Raffaele Alice
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 enrollment year.
1° Year
| Modules | Credits | TAF | SSD |
|---|
2° Year activated in the A.Y. 2021/2022
| Modules | Credits | TAF | SSD |
|---|
| Modules | Credits | TAF | SSD |
|---|
| Modules | Credits | TAF | SSD |
|---|
| Modules | Credits | TAF | SSD |
|---|
1 module among the following (1st year: Big Data epistemology and Social research; 2nd year: Cybercrime, Data protection in business organizations, Comparative and Transnational Law & Technology)2 courses among the following (1st year: Business analytics, Digital Marketing and market research; 2nd year: Logistics, Operations & Supply Chain, Digital transformation and IT change, Statistical methods for Business intelligence)2 courses among the following (1st year: Complex systems and social physics, Discrete Optimization and Decision Making, 2nd year: Statistical models for Data Science, Continuous Optimization for Data Science, Network science and econophysics, Marketing research for agrifood and natural resources)2 courses among the following (1st year: Data Visualisation, Data Security & Privacy, Statistical learning, Mining Massive Dataset, 2nd year: Machine Learning for Data Science)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 (2021/2022)
Teaching code
4S009079
Academic staff
Coordinator
Credits
6
Language
English
Scientific Disciplinary Sector (SSD)
MAT/06 - PROBABILITY AND STATISTICS
Period
Primo semestre dal Oct 4, 2021 al Jan 28, 2022.
Learning outcomes
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.
At the end of the course the student has to show to have acquired the following skills:
● know and know how to use the basic tools for the treatment of time series and their indicators, e.g.,
● know and know how to develop forecasting solutions based on statistical inferential models, eg, AR, MA, ARMA, ARIMA, ARIMAX: Box-Jenkins, partial self-variance and autocorrelation, seasonality (SARIMA), analysis in variance (ANOVA, MANOVA), etc .
● knowing how to identify the parameters that characterize a certain population via methods such as error minimization, maximum likelihood, etc.
● know how to estimate / identify / reconstruct characteristics related to first-order analysis, smoothing techniques, spectral decomposition, polynomial fitting, etc.
Program
The course program is divided into the following macro-topics
Time domain analysis
Frequency domain analysis
Data analysis and cleaning tools (e.g. identification of outliers)
Maximum likelihood methods, likelihood metrics, probability density fitting
Principal Component Analysis (PCA) [PCA-based regressors / predictors]
AR, MA, ARMA, ARIMA, Box-Jenkins, ARCH, GARCH models and generalizations
Time series decomposition
ACF / PACF and related "views"
Hypothesis test
Gaussian / jump / compound processes
Decomposition of "white noise" processes
Bayesian statistics and applications
Forecast evaluations via consideration of inferential statistical models, based, e.g.,
on autocovariance and partial autocorrelation, seasonality (SARIMA), variance analysis (ANOVA, MANOVA), etc.
Smoothing techniques, spectral decomposition, polynomial fitting, etc.
Creation of the models referred to in the previous points for the resolution of concrete case studies.
The latter aspect will mainly, but not exclusively, concern Python coding as well as using statistical/probabilistic libraries and software such as, e.g., Knime, ElasticSearch, Kibana, R, TensorFlow, Prophet, AnalyticFlow, Orange, etc.
Bibliography
Examination Methods
The final exam consists of two parts: theoretical and practical/coding.
Consequently, the first is functional to the learning verification of theoretical concepts characterizing statistical methods and associated models/algorithms, then exploited to solve a project chosen by the student in agreement with the course's lecturers.
Such a "case study", together with the discussion of the coding parts realized to complete it, will be the subject of the second and final part of the exam.
Type D and Type F activities
Documents and news
-
Regolamento didattico 2020/2021
(pdf, it, 470 KB, 12/04/21)
| years | Modules | TAF | Teacher |
|---|---|---|---|
| 1° | The course provides an introduction to blockchain technology. It focuses on the technology behind Bitcoin, Ethereum, Tendermint and Hotmoka. | D |
Nicola Fausto Spoto
(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 |
|---|---|
|
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.
