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.
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.
The Academic Calendar sets out the degree programme lecture and exam timetables, as well as the relevant university closure dates..
Definition of lesson periods
Oct 1, 2020
Jan 29, 2021
Mar 1, 2021
Jun 11, 2021
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
Dec 8, 2020
Dec 8, 2020
Dec 24, 2020
Jan 3, 2021
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
Aug 9, 2021
Aug 15, 2021
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.
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.
Between the years: 1°- 2°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)
Between the years: 1°- 2°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)
Between the years: 1°- 2°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)
Between the years: 1°- 2°2 courses among the following (1st year: Data Visualisation, Data Security & Privacy, Statistical learning, Mining Massive Dataset, 2nd year: Machine Learning for Data Science)
SPlacements in companies, public or private institutions and professional associations
Statistical methods for business intelligence (2020/2021)
Scientific Disciplinary Sector (SSD)
SECS-S/01 - STATISTICA
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.
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.
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)
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.
I servizi e le attività di orientamento sono pensati per fornire alle future matricole gli strumenti e le informazioni che consentano loro di compiere una scelta consapevole del corso di studi universitario.