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:

Bachelor's degree in Economics and Business - 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
B
SECS-P/08
9
A
SECS-P/01
9
A
SECS-S/06
9
C
SECS-P/12
English B1
3
E
-

2° Year  activated in the A.Y. 2024/2025

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

3° Year  activated in the A.Y. 2025/2026

ModulesCreditsTAFSSD
9
B
IUS/07
9
B
SECS-P/05
9
B
SECS-S/06
6
B
SECS-P/02
2 modules 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
Stage
6
F
-
Final exam
3
E
-
ModulesCreditsTAFSSD
9
A
IUS/01
9
A
SECS-P/07
9
B
SECS-P/08
9
A
SECS-P/01
9
A
SECS-S/06
9
C
SECS-P/12
English B1
3
E
-
activated in the A.Y. 2024/2025
ModulesCreditsTAFSSD
6
B
SECS-P/07
9
C
SECS-P/09
6
A
IUS/04
9
B
SECS-P/01
9
B
SECS-P/07
9
B
SECS-P/03
9
B
SECS-S/01
activated in the A.Y. 2025/2026
ModulesCreditsTAFSSD
9
B
IUS/07
9
B
SECS-P/05
9
B
SECS-S/06
6
B
SECS-P/02
2 modules 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
Stage
6
F
-
Final exam
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  (2025/2026)

Teaching code

4S008960

Teacher

Alessandro Zonin

Coordinator

Alessandro Zonin

Credits

6

Language

Italian

Scientific Disciplinary Sector (SSD)

SECS-S/01 - STATISTICS

Period

Secondo semestre L dal Feb 16, 2026 al May 29, 2026.

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

There are no specific prerequisites.

Program

The course is divided into four parts: • Introduction to data analysis and big data. Concepts, definitions, challenges, and opportunities. Data sources, types, and characteristics. Data lifecycle. Data management and data governance. • Tools and methods. Software for data extraction, manipulation, analysis, and visualization, including Threadminer, Nodiux and KNIME. Data cleaning and data preparation. Data quality defects. Duplicate data. Missing values. Machine learning and generative AI. In-depth analysis of data visualization, dashboarding, and storytelling techniques. • Data analysis across various sectors 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 best practices for communicating data and analyses. Writing reports, articles, and 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 course is structured into 48 hours of teaching (4 credits of lectures and 2 credits of exercises). The teaching, which consists of theoretical and practical lessons, is delivered in person with video recordings. To maximize teaching effectiveness and ensure the correct balance between theory and laboratory work, a typical teaching week is characterized as follows: • Theoretical lectures with possible external intervention by industry experts, in person or via videoconference • Hands-on workshops for the practical application of concepts • Discussion and analysis of case studies.

Learning assessment procedures

The exam consists of a written test; there are no oral supplements. There are no midterm tests.

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 exam lasts one hour and thirty minutes and covers the entire course syllabus. Notes or other teaching materials may not be used during the exam.

Criteria for the composition of the final grade

Written exam with multiple-choice questions and possible open-ended questions. There will be no midterm exams.

Exam language

Italiano