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.
Type D and Type F activities
This information is intended exclusively for students already enrolled in this course.If you are a new student interested in enrolling, you can find information about the course of study on the course page:
Laurea in Economia e commercio - Enrollment from 2025/2026| years | Modules | TAF | Teacher |
|---|---|---|---|
| 1° | Future matters | D |
Alessandro Bucciol
(Coordinator)
|
| 1° | Future matters | D |
Alessandro Bucciol
(Coordinator)
|
| years | Modules | TAF | Teacher |
|---|---|---|---|
| 1° | Design and Evaluation of Economic and Social Policies | D |
Federico Perali
(Coordinator)
|
| 1° | Public debate and scientific writing - 2020/2021 | D |
Martina Menon
(Coordinator)
|
| 1° | Wake up Italia - 2020/2021 | D |
Sergio Noto
(Coordinator)
|
| years | Modules | TAF | Teacher | |
|---|---|---|---|---|
| 1° | Ciclo di video conferenze: "L’economia del Covid, Verona e l’Italia. Una pandemia che viene da lontano?" - 2020/21 | D |
Sergio Noto
(Coordinator)
|
|
| 1° | Ciclo tematico di conferenze (on-line): “Come saremo? Ripensare il mondo dopo il 2020” - 2020/21 | D |
Federico Brunetti
(Coordinator)
|
|
| 1° | Marketing plan - 2020/21 | D |
Virginia Vannucci
(Coordinator)
|
|
| 1° 2° 3° | Data Analysis Laboratory with R (Verona) | D |
Marco Minozzo
(Coordinator)
|
|
| 1° 2° 3° | Data Visualization Laboratory | D |
Marco Minozzo
(Coordinator)
|
|
| 1° 2° 3° | Python Laboratory | D |
Marco Minozzo
(Coordinator)
|
|
| 1° 2° 3° | Data Science Laboratory with SAP | D |
Marco Minozzo
(Coordinator)
|
|
| 1° 2° 3° | Advanced Excel Laboratory (Verona) | D |
Marco Minozzo
(Coordinator)
|
|
| 1° 2° 3° | Excel Laboratory (Verona) | D |
Marco Minozzo
(Coordinator)
|
|
| 1° 2° 3° | Programming in Matlab | D |
Marco Minozzo
(Coordinator)
|
|
| 1° 2° 3° | Programming in SAS | D |
Marco Minozzo
(Coordinator)
|
|
Data Analysis Laboratory with R (Verona) (2020/2021)
Teaching code
4S009610
Teacher
Coordinator
Credits
3
Also offered in courses:
- Data Analysis Laboratory with R (Verona) of the course Bachelors' degree in Business Administration and Management
- Data Analysis Laboratory with R (Verona) of the course Master’s degree in Banking and Finance
- Data Analysis Laboratory with R (Verona) of the course Master’s degree in Economics and Data Analysis
- Data Analysis Laboratory with R (Verona) of the course Master’s degree in Business Administration and Corporate Law
- Data Analysis Laboratory with R (Verona) of the course Master’s degree in Marketing and Corporate Communication
- Data Analysis Laboratory with R (Verona) of the course Master’s degree in Economics
- Data Analysis Laboratory with R (Verona) of the course Bachelor's degree in Business Administration (Verona)
- Data Analysis Laboratory with R (Verona) of the course Bachelor's degree in Economics and Business (Verona)
Language
Italian
Scientific Disciplinary Sector (SSD)
NN - -
Period
Not yet assigned
Learning outcomes
The course "Data Analysis Laboratory with R" is an optional "type f" activity, which allows to students to obtain 3 CFU, once a final examination is passed. In particular:
- The course is open to all CdL and CdLM students of the Verona campus of the School of Economics and Management.
- Due to the COVID-19 emergency, this year lessons will be delivered online through Zoom meetings. Each course has, approximately, 50 places.
- There will be two courses, one in the first semester and another one in the second semester.
- Requests for participation will be considered following the registration order, considering that priority will be given to CdLM students, in particular to students of the Master’s degree in Economics, of the Master’s degree in Economics and Data Analysis, and of the Master’s degree in Banking and Finance. Students are required to be present at the first lesson, or to send an email to the tutor to communicate their absence.
- Participation to the course does not require any particular background knowledge of the software R.
- 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).
The calendar of the course of the first semester will be available as soon as possible.
The calendar of the course of the second semester will be available as soon as possible.
Tutor (first semester): dott. Luca Bisognin
Tutor (second semester): dott. Alessandro Cipolla
For the course of the first semester, it is possible to register from the 13th of October 2020 to the 18th of October 2020.
For the courses of the second semester, it is possible to register from the 13th of October 2020 to the 28th of February 2021.
Please, register through the elearning platform (Moodle). Students without a university IT account can ask to be registered by writing an email to the coordinator of the course. All other students must use the procedure on Moodle.
Program
R è un software open-source per l’analisi statistica. Nato alla fine degli anni Novanta dal software S, R è un linguaggio multi-paradigma che nell’arco di due decenni ha acquisito un ruolo centrale tra i software che si occupano di analisi statistica, grazie anche al forte sviluppo che ha ricevuto lo sviluppo di pacchetti (attualmente più di 16000) che implementano tecniche e metodi provenienti dai campi più diversi della statistica metodologica e applicata. Negli anni più recenti, grazie allo sviluppo di un’intera famiglia di pacchetti volti a semplificare ed organizzare su nuove basi le modalità di interazione e programmazione con R, il software ha trovato nuovi sbocchi per esprimere al meglio le proprie potenzialità. Il linguaggio R, assieme a Python, è oggi considerato il linguaggio di riferimento nell’ambito della moderna data science e, in particolare, nel machine learning, e si interfacciano agevolmente con molti altri software quali Excel, Tableau, Microsoft Power BI ecc.
Il corso si propone di fornire le basi della programmazione e della logica di funzionamento del software R introducendo al contempo i partecipanti ad alcune delle innovazioni più recenti che lo stanno interessando. Oltre ad un’introduzione al linguaggio R e a R Studio, il corso verterà sui seguenti argomenti: tecniche di trattamento e manipolazione dati (data management), strumenti di grafica avanzata per l’analisi statistica, cenni sulla rappresentazione grafica di informazioni geo-referenziate, analisi di regressione, simulazioni Monte Carlo, cenni sulla reportistica automatica e sulla produzione di documenti interattivi.
| Author | Title | Publishing house | Year | ISBN | Notes |
|---|---|---|---|---|---|
| Hadley Wickham | Advanced R (Edizione 1) | CRC Press, Taylor & Francis Group | 2015 | 9781466586970 | |
| Espa G., Micciolo R. | Analisi esplorativa dei dati con R | Apogeo | 2012 | 978-88-503-3031-7 | |
| Ronald K. Pearson | Exploratory Data Analysis Using R (Edizione 1) | CRC Press, Taylor & Francis Group | 2018 | 9781138480605 | |
| Marco Bee, Flavio Santi | Finanza quantitativa con R (Edizione 1) | Apogeo Education | 2013 | 9788838787041 | |
| Hadley Wickham | ggplot2: Elegant Graphics for Data Analysis (Edizione 1) | Springer | 2009 | 9780387981413 | |
| Francesca Ieva, Chiara Masci, Anna Maria Paganoni | Laboratorio di Statistica con R (Edizione 2) | Pearson | 2016 | 9788891901521 | |
| Christopher P. Adams | Learning Microeconometrics with R (Edizione 1) | Chapman and Hall/CRC | 2021 | 9780367255381 | |
| Benjamin S. Baumer, Daniel T. Kaplan, Nicholas J. Horton | Modern Data Science with R (Edizione 1) | CRC Press, Taylor & Francis Group | 2017 | 978-1-4987-2448-7 | |
| Giuseppe Espa, Rocco Micciolo | Problemi ed esperimenti di statistica con R (Edizione 1) | Apogeo Education | 2013 | 9788838786105 | |
| Hadley Wickham, Garrett Grolemund | R for Data Science (Edizione 1) | O'Reilly | 2016 | 9781491910399 | |
| Ngai Hang Chan, Hoi Ying Wong | Simulation Techniques in Financial Risk Management (Edizione 1) | Wiley | 2015 | 9781118735817 | |
| M. Bécue-Bertaut | Textual Data Science with R (Edizione 1) | CRC Press, Taylor & Francis Group | 2018 | 9781138626911 | |
| Graham J. Williams | The Essentials of Data Science: Knowledge Discovery Using R (Edizione 1) | CRC Press, Taylor & Francis Group | 2017 | 9781138088634 |
Examination Methods
Students are required to attend at least 2/3 of the exercise lessons/tutorial activity in order to be admitted to the final evaluation. The final examination, which will take place online through a Zoom meeting, will consist in a written exam, followed by an oral examination, if necessary, on the use of the software R. There will be just one date for the final examination.
