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.

Study Plan

A.A. 2024/2025 A.A. 2023/2024 A.A. 2022/2023 A.A. 2021/2022 A.A. 2020/2021

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

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

ModulesCreditsTAFSSD
9
A
IUS/01
9
A
SECS-P/07
9
A
SECS-P/01
9
A
SECS-S/06
9
B
SECS-P/08
9
C
SECS-P/12
3
E
-

2° Year  activated in the A.Y. 2022/2023

ModulesCreditsTAFSSD
6
A
IUS/04
6
B
SECS-P/07
9
B
SECS-P/01
9
B
SECS-P/07
9
B
SECS-P/03
9
B
SECS-S/01
9
C
SECS-P/09

3° Year  activated in the A.Y. 2023/2024

ModulesCreditsTAFSSD
9
B
IUS/07
9
B
SECS-P/05
9
B
SECS-S/06
6
B
SECS-P/02
2 MODULES TO BE CHOSEN AMONG THE FOLLOWING
6
C
SECS-P/02
6
C
SECS-S/01
6
C
SECS-P/01
6
C
SECS-S/06
6
C
SECS-P/05
6
C
SECS-P/02
6
F
-
Prova finale
3
E
-
ModulesCreditsTAFSSD
9
A
IUS/01
9
A
SECS-P/07
9
A
SECS-P/01
9
A
SECS-S/06
9
B
SECS-P/08
9
C
SECS-P/12
3
E
-
activated in the A.Y. 2022/2023
ModulesCreditsTAFSSD
6
A
IUS/04
6
B
SECS-P/07
9
B
SECS-P/01
9
B
SECS-P/07
9
B
SECS-P/03
9
B
SECS-S/01
9
C
SECS-P/09
activated in the A.Y. 2023/2024
ModulesCreditsTAFSSD
9
B
IUS/07
9
B
SECS-P/05
9
B
SECS-S/06
6
B
SECS-P/02
2 MODULES TO BE CHOSEN AMONG THE FOLLOWING
6
C
SECS-P/02
6
C
SECS-S/01
6
C
SECS-P/01
6
C
SECS-S/06
6
C
SECS-P/05
6
C
SECS-P/02
6
F
-
Prova finale
3
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°- 3°
12
D
-

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.

A Basic activities
B Characterizing activities
C Related or complementary activities
D Activities to be chosen by the student
E Final examination
F Other training activities
S Placements in companies, public or private institutions and professional associations

Data Analytics and Big Data  (2023/2024)

Teaching code

4S008960

Teacher

Alessandro Zonin

Coordinator

Alessandro Zonin

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

Lessons timetable MoodleMoodle Seminars 0

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 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 and Power BI. Data cleaning and data preparation. Data quality defects. Duplicate data. Missing values. Machine Learning and Generative AI. 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, social media analysis and social network analysis. • 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

Vai alla bibliografia
Visualizza la bibliografia con Leganto, strumento che il Sistema Bibliotecario mette a disposizione per recuperare i testi in programma d'esame in modo semplice e innovativo.

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

Written exam with multiple choice questions and any open questions. For students who wish to improve their grade it is possible to submit a homework.

Students with disabilities or specific learning disorders (SLD), who intend to request the adaptation of the exam, must follow the instructions given HERE

Evaluation criteria

The written test lasts one hour and thirty minutes and covers the entire course program. During the test it is not possible to use notes or other teaching materials.

Criteria for the composition of the final grade

For students who obtain a score of at least 28/30 in the written test, it is possible to improve the grade by submitting a homework.

Exam language

Italiano