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

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/01
9
A
SECS-S/06
9
C
SECS-P/12
English language B1 level
3
E
-

2° Year  activated in the A.Y. 2021/2022

ModulesCreditsTAFSSD
9
B
SECS-P/01
9
B
SECS-P/03
9
B
SECS-S/01

3° Year  activated in the A.Y. 2022/2023

ModulesCreditsTAFSSD
9
B
SECS-P/05
6
B
SECS-P/02
Training
6
F
-
Final exam
3
E
-
ModulesCreditsTAFSSD
9
A
IUS/01
9
A
SECS-P/01
9
A
SECS-S/06
9
C
SECS-P/12
English language B1 level
3
E
-
activated in the A.Y. 2021/2022
ModulesCreditsTAFSSD
9
B
SECS-P/01
9
B
SECS-P/03
9
B
SECS-S/01
activated in the A.Y. 2022/2023
ModulesCreditsTAFSSD
9
B
SECS-P/05
6
B
SECS-P/02
Training
6
F
-
Final exam
3
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°- 3°

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.




S Placements in companies, public or private institutions and professional associations

Teaching code

4S008960

Coordinator

Remo Tantalo

Credits

6

Language

Italian

Scientific Disciplinary Sector (SSD)

SECS-S/01 - STATISTICS

Period

Secondo semestre (lauree) dal Feb 20, 2023 al May 31, 2023.

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.