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 (lauree) | Sep 20, 2021 | Jan 14, 2022 |
secondo semestre (lauree) | Feb 21, 2022 | Jun 1, 2022 |
Session | From | To |
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sessione invernale | Jan 17, 2022 | Feb 18, 2022 |
sessione estiva | Jun 6, 2022 | Jul 15, 2022 |
sessione autunnale | Aug 22, 2022 | Sep 16, 2022 |
Session | From | To |
---|---|---|
sessione autunnale (validità a.a. 2020/2021) | Dec 6, 2021 | Dec 10, 2021 |
sessione invernale (validità a.a. 2020/2021) | Apr 6, 2022 | Apr 8, 2022 |
sessione estiva (validità a.a. 2021/2022) | Sep 5, 2022 | Sep 6, 2022 |
Exam calendar
Exam dates and rounds are managed by the relevant Economics 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
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 activated in the A.Y. 2022/2023
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3° Year activated in the A.Y. 2023/2024
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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.
Data Analytics and Big Data (2023/2024)
Teaching code
4S008960
Teacher
Coordinator
Credits
6
Language
Italian
Scientific Disciplinary Sector (SSD)
SECS-S/01 - STATISTICS
Period
Secondo semestre (lauree) dal Feb 26, 2024 al May 31, 2024.
Courses Single
Authorized
Learning objectives
The course aims at introducing the basics of statistical learning and the techniques for manipulating and analysing large datasets with complex structures. Particular emphasis is devoted to regression and classification methods, which are studied both from a statistical and a computational perspective. All techniques are illustrated with real-data examples using statistical software. The application-oriented approach of the course aims at developing participants' skills in analysing data and applying statistical methods and algorithms appropriately.
Prerequisites and basic notions
No specific prerequisites are required.
Program
The course aims to provide students with the theoretical and practical skills to analyze, interpret and communicate data from different sources and sectors, using data science and big data analysis tools and methods. At the end of the course, students will be able to:
• Know the principles and applications of data analytics and big data in various contexts
• Use commercial and open source software for data management, processing and data visualization
• Apply the main statistical, mathematical and computational techniques and models
• Critically evaluate the quality, relevance and ethics of data and analyzes
• Effectively communicate the results and implications of the analyzes
The course is divided into four parts:
• Introduction to data analysis and big data. Concepts, definitions, challenges and opportunities. Sources, types and characteristics of data. Data lifecycle. Data management and data governance.
• Tools and Methods. Software for data extraction, manipulation, analysis and visualization, including Socioviz, KNIME, Power BI, RAWGraphs and Datawrapper. Data cleaning, data integration and data transformation techniques. Data mining, machine learning and deep learning techniques. In-depth analysis of data visualization, dashboard and storytelling techniques.
• Data analytics across various industries and domains. Examples and case studies in areas such as: business, marketing, finance, economics, healthcare, social sciences, natural sciences, sports, etc.
• Evaluation and communication of data and analyses. Criteria and indicators for the quality, relevance and ethics of data and analyses. Principles and good practices for communicating data and analyses. Writing reports, articles, presentations. Discussion and comparison of results and implications.
Bibliography
Didactic methods
The teaching is structured in 48 hours of teaching (6 CFU), divided into 3-hour lessons based on the academic calendar. The teaching, which consists of theoretical and practical lessons, is delivered in person with video recordings. With the aim of maximizing the effectiveness of teaching and ensuring the correct balance between theory and laboratory, the typical teaching week is characterized as follows:
• Theoretical lesson with possible external intervention by sector experts, in presence or through video conferences
• Hands-on workshops for the practical application of concepts.
• Discussion and analysis of case studies.
Learning assessment procedures
The exam is written; no oral integrations are foreseen. The exam consists of a written test and an optional homework.
Evaluation criteria
The student can reject the exam grade and the homework grade separately. However, the homework vote can only be rejected once. The written test is carried out remotely, lasts one hour and thirty minutes and covers the entire course programme. During the test it is possible to use the calculator, but not notes or other teaching material. The homework is carried out individually, and consists of carrying out a data analysis on a topic chosen by the student, with related documentation and presentation report.
Criteria for the composition of the final grade
Written exam with multiple choice questions. Possible optional integration homework. There are no intermediate appeals.
Exam language
Italiano
Type D and Type F activities
Nei piani didattici di ciascun Corso di studio è previsto l’obbligo di conseguire un certo numero di crediti formativi mediante attività a scelta (chiamate anche "di tipologia D e F").
Oltre che in insegnamenti previsti nei piani didattici di altri corsi di studio e in certificazioni linguistiche o informatiche secondo quanto specificato nei regolamenti di ciascun corso, tali attività possono consistere anche in iniziative extracurriculari di contenuto vario, quali ad esempio la partecipazione a un seminario o a un ciclo di seminari, la frequenza di laboratori didattici, lo svolgimento di project work, stage aggiuntivo, eccetera.
Come per ogni altra attività a scelta, è necessario che anche queste non costituiscano un duplicato di conoscenze e competenze già acquisite dallo studente.
Quelle elencate in questa pagina sono le iniziative extracurriculari che sono state approvate dal Consiglio della Scuola di Economia e Management e quindi consentono a chi vi partecipa l'acquisizione dei CFU specificati, alle condizioni riportate nelle pagine di dettaglio di ciascuna iniziativa.
Si ricorda in proposito che:
- tutte queste iniziative richiedono, per l'acquisizione dei relativi CFU, il superamento di una prova di verifica delle competenze acquisite, secondo le indicazioni contenute nella sezione "Modalità d'esame" della singola attività;
- lo studente è tenuto a inserire nel proprio piano degli studi l'attività prescelta e a iscriversi all'appello appositamente creato per la verbalizzazione, la cui data viene stabilita dal docente di riferimento e pubblicata nella sezione "Modalità d'esame" della singola attività.
COMPETENZE TRASVERSALI
Scopri i percorsi formativi promossi dal Teaching and learning centre dell'Ateneo, destinati agli studenti iscritti ai corsi di laurea, volti alla promozione delle competenze trasversali: https://talc.univr.it/it/competenze-trasversali
ATTENZIONE: Per essere ammessi a sostenere una qualsiasi attività didattica, inlcuse 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.
years | Modules | TAF | Teacher |
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Virginia Vannucci
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years | Modules | TAF | Teacher |
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1° 2° | Internationalization and Sustainability. Friends or Enemies? | D |
Angelo Zago
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1° 2° | Internationalization and Sustainability. Friends or Enemies? | D |
Angelo Zago
(Coordinator)
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1° 2° | Internationalization and Sustainability. Friends or Enemies? | D |
Angelo Zago
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1° 2° | Data Science Laboratory with SAP | D |
Marco Minozzo
(Coordinator)
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1° 2° | Programming in Matlab | D |
Marco Minozzo
(Coordinator)
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1° 2° | Programming in SAS | D |
Marco Minozzo
(Coordinator)
|
years | Modules | TAF | Teacher |
---|---|---|---|
1° 2° | What paradigms beyond the pandemic? Individual vs. Society, Private vs. Public | D |
Federico Brunetti
(Coordinator)
|
years | Modules | TAF | Teacher |
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1° 2° | Economics, financial statement and control of Italian healthcare and social care organizations | D |
Paolo Roffia
(Coordinator)
|
years | Modules | TAF | Teacher |
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1° 2° | AN INTRODUCTION TO LATEX TYPESETTING SYSTEM | D |
Alberto Peretti
(Coordinator)
|
years | Modules | TAF | Teacher |
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1° 2° | English for Business and Economics - Bachelor's Degrees | D |
Marco Minozzo
(Coordinator)
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1° 2° | Data Analysis Laboratory with R (Verona) | D |
Marco Minozzo
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1° 2° | Data Visualization Laboratory | D |
Marco Minozzo
(Coordinator)
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1° 2° | Python Laboratory | D |
Marco Minozzo
(Coordinator)
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1° 2° | Advanced Excel Laboratory (Verona) | D |
Marco Minozzo
(Coordinator)
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1° 2° | Excel Laboratory (Verona) | D |
Marco Minozzo
(Coordinator)
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1° 2° | Samsung Innovation Camp | D |
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(Coordinator)
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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.