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.

A.A. 2017/2018

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..

Academic year:
Definition of lesson periods
Period From To
Primo Semestre Magistrali Oct 2, 2017 Dec 22, 2017
Secondo Semestre Magistrali Feb 26, 2018 May 25, 2018
Exam sessions
Session From To
Esami sessione invernale Magistrali Jan 8, 2018 Feb 23, 2018
Esami sessione estiva Magistrali May 28, 2018 Jul 6, 2018
Esami sessione autunnale 2018 Aug 27, 2018 Sep 14, 2018
Degree sessions
Session From To
Lauree sessione autunnale (validità a.a. 2016/17) Nov 27, 2017 Nov 28, 2017
Lauree sessione invernale (validità a.a. 2016/17) Apr 4, 2018 Apr 6, 2018
Lauree sessione estiva (validità a.a. 2017/18) Sep 10, 2018 Sep 11, 2018
Holidays
Period From To
All Saints Day Nov 1, 2017 Nov 1, 2017
Festa Immacolata Concezione Dec 8, 2017 Dec 8, 2017
attività sospese (Natale) Dec 23, 2017 Jan 7, 2018
Easter break Mar 30, 2018 Apr 3, 2018
Liberation Day Apr 25, 2018 Apr 25, 2018
attività sospese (Festa dei lavoratori) Apr 30, 2018 Apr 30, 2018
Labour Day May 1, 2018 May 1, 2018
Festa Patronale May 21, 2018 May 21, 2018
attività sospese estive Aug 6, 2018 Aug 24, 2018

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.

Exam calendar

Should you have any doubts or questions, please check the Enrolment FAQs

Academic staff

B C D G L M N P R S V Z

Bucciol Alessandro

alessandro.bucciol@univr.it 045 802 8278

Campolmi Alessia

alessia.campolmi@univr.it 045 802 8071

Chesini Giuseppina

giusy.chesini@univr.it 045 802 8495 (VR) -- 0444/393938 (VI)

Ciampi Annalisa

annalisa.ciampi@univr.it 045 802 8061

Cipriani Giam Pietro

giampietro.cipriani@univr.it 045 802 8271

De Sinopoli Francesco

francesco.desinopoli@univr.it 045 842 5450

Gaudenzi Barbara

barbara.gaudenzi@univr.it 045 802 8623

Levati Maria Vittoria

vittoria.levati@univr.it 045 802 8640

Menon Martina

martina.menon@univr.it 045 802 8420

Minozzo Marco

marco.minozzo@univr.it 045 802 8234

Noto Sergio

elefante@univr.it 045 802 8008

Pellegrini Letizia

letizia.pellegrini@univr.it 045 802 8345

Perali Federico

federico.perali@univr.it 045 802 8486

Pertile Paolo

paolo.pertile@univr.it 045 802 8438

Roffia Paolo

paolo.roffia@univr.it 045 802 8012

Sommacal Alessandro

alessandro.sommacal@univr.it 045 802 8716

Veronesi Marcella

marcella.veronesi@univr.it 045 802 8025

Zoli Claudio

claudio.zoli@univr.it 045 802 8479

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.

ModulesCreditsTAFSSD
2 courses to be chosen among the following
2 courses to be chosen among the following
6
B
(SECS-P/11)
6
B
(SECS-P/08)
Final exam
15
E
-

1° Year

ModulesCreditsTAFSSD
9
B
(SECS-P/05)
3
F
(-)

2° Year

ModulesCreditsTAFSSD
2 courses to be chosen among the following
2 courses to be chosen among the following
6
B
(SECS-P/11)
6
B
(SECS-P/08)
Final exam
15
E
-
Modules Credits TAF SSD
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.




SPlacements in companies, public or private institutions and professional associations

Type D and Type F activities

Academic year:
Primo Semestre Magistrali From 10/2/17 To 12/22/17
years Modules TAF Teacher
Business plan D Paolo Roffia (Coordinatore)
List of courses with unassigned period
years Modules TAF Teacher
English for business and economics D Claudio Zoli (Coordinatore)
An Introduction to the History of Economics and Business Economics D Sergio Noto (Coordinatore)
Introduction to dynamic optimization with economic applications D Claudio Zoli (Coordinatore)
Programming in Matlab D Not yet assigned
Programming in SAS D Not yet assigned
Programming Stata (3 cfu) D Not yet assigned
1° 2° Python Laboratory D Marco Minozzo (Coordinatore)
1° 2° Advanced Excel Laboratory (Verona) D Marco Minozzo (Coordinatore)
1° 2° Excel Laboratory (Verona) D Marco Minozzo (Coordinatore)

Teaching code

4S007120

Coordinatore

Marco Minozzo

Credits

3

Scientific Disciplinary Sector (SSD)

- - -

Language

Italian

Period

Not yet assigned

Learning outcomes

The course "Python Laboratory" is an optional "type f" activity, which allows to students to obtain 3 CFU, once a final examination is passed. Participation to the course does not require any particular background knowledge of the software Python. In particular:

- The course is open to all students, but priority is given to the students of the Master’s degree in Economics and of the Master’s degree in Banking and Finance.

- The frequency to the classes is compulsory. Students are required to attend at least 2/3 of the exercise lessons and tutorial activities in order to be admitted to the final evaluation.

The course consists of 18 hours of exercise lessons and tutorial activities (plus 2 hours of final examination) in computer laboratory (50 seats) at the University site of Santa Marta (Verona), to be delivered during the first semester. The course will start if a minimum number of requests will be collected. The calendar of the activities will be available as soon as possible.

Tutor: dott. Jacopo Morabito

Requests for participation will be considered following the registration order. The registration is possible from the 15th of October 2017 to the 3rd of November 2017. Please, register through the elearning platform.

Program

Python is a widely used high-level programming language for general-purpose programming. It is an interpreted language, it has a design philosophy that emphasizes code readability and it has a syntax that allows programmers to express concepts in fewer lines of code than might be used in other languages, allowing new users to learn it in a few days. Python features a dynamic type system and automatic memory management and supports multiple programming paradigms, including object-oriented, imperative, functional programming, and procedural styles. It has a large and comprehensive standard library and it can easily be integrated with other programming languages, in particular with R. Python interpreters are available for many operating systems, allowing Python code to run on a wide variety of systems.

Python has gained wide popularity mainly for its use in the management and analysis of large data sets (data science). Today, R and Python are the two most widely used programming languages among data scientists. Both of them have rapidly advanced over the past few years. For these languages there exist many libraries for collecting, handling, visualizing and analyzing large data volumes and for implementing advanced machine learning models. Python is used in many organizations like NASA, Yahoo and Google. Python is open source and free software and has a community-based development model. Other information can be found at https://www.python.it/ and https://www.python.org/

The program of the course will start with an introduction to the software Python and its main functions. Then, some of the topics encountered in mathematical and statistic courses will be considered, as for example, matrix algebra, optimization and interpolation. Arguments will be presented mainly through examples. The course aims at improving the computational and programming skills of the students and at providing instruments that might be useful for the subsequent thesis work. The activity will allow students to improve the knowledge of a programming language that is highly requested in some sectors of the job market.

Bibliografia

Reference texts
Author Title Publishing house Year ISBN Notes
Dmitry Zinoviev Data Science con Python: dalle stringhe al machine learning, le tecniche essenziali per lavorare sui dati (Edizione 1) APOGEO 2017 9788850334148
Joel Grus Data Science from Scratch: First Principles with Python (Edizione 1) O'Reilly Media, Inc. 2015 9781491901410
Sarah Guido, Andreas C. Müller Introduction to Machine Learning with Python (Edizione 1) O'Reilly Media, Inc. 2016 9781449369880
Tony Gaddis Introduzione a Python (Edizione 1) Pearson Italia, Milano-Torino 2016 9788891900999
Samir Madhavan Mastering Python for Data Science (Edizione 1) Packt Publishing 2015 9781784390150
Ahmed Sherif Practical Business Intelligence (Edizione 1) Packt Publishing 2016 9781785885433
Toby Segaran Programming Collective Intelligence (Edizione 1) O'Reilly Media, Inc. 2007 9780596529321
Jake VanderPlas Python Data Science Handbook: Essential Tools for Working with Data (Edizione 1) O'Reilly Media, Inc. 2016 9781491912126
William Wesley McKinney Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython (Edizione 2) O'Reilly Media, Inc. 2017 9781491957653
Vahid Mirjalili, Sebastian Raschka Python Machine Learning (Edizione 2) Packt Publishing 2017 9781787125933
Chris Albon Python Machine Learning Cookbook (Edizione 1) O'Reilly Media, Inc. 2018 9781491989371
Allen B. Downey Think Stats: Exploratory Data Analysis (Edizione 2) O'Reilly Media, Inc. 2014 9781491907344
Richard Lawson Web Scraping with Python (Edizione 1) Packt Publishing 2015 9781782164364

Examination Methods

The final examination will consist in a written exam, with an oral examination if necessary, on the use of the software Python.

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.

Area riservata studenti


Gestione carriere


Graduation

List of theses and work experience proposals

theses proposals Research area
La (cattiva) gestione dei fondi comunitari in Italia ECONOMICS - ECONOMICS
Analisi dell'Impatto della Regolamentazione: potenziale e applicazioni concrete Various topics
Costs and benefits of the new Turin-Lyon railway line Various topics
Costs and benefits of new systems for speed control on italian motorways Various topics
Contingent valuation for the quality of hospital characteristics Various topics
Evaluating occupational impacts of large investment projects Various topics

Internships


Linguistic training CLA


Admission policy

ADMISSION POLICY

The admission procedure for international students is explained in details at:
www.magecverona.it/admission-benefits/
For further information please contact magec@dse.univr.it


Additional information

 

Additional information

For further information visit the program website, http://magec.dse.univr.it, or send an email at magec@dse.univr.it.

 


Further services

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.