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 magistrale in Economics and data analysis - 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
B
SECS-P/05
One module between the following
9
C
SECS-P/01
9
C
SECS-P/03
6
B
IUS/13
12
B
SECS-P/01
12
B
SECS-P/05 ,SECS-S/06
3
F
-

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

ModulesCreditsTAFSSD
Two modules among the following
6
C
SECS-P/03
6
C
SECS-S/01
6
C
SECS-P/02
6
C
SECS-P/02
Two modules among the following
6
B
SECS-P/07
6
B
SECS-P/11
6
B
SECS-P/08
One module between the following
9
C
SECS-P/02
9
C
SECS-P/05
9
B
SECS-P/01
15
E
-
ModulesCreditsTAFSSD
9
B
SECS-P/05
One module between the following
9
C
SECS-P/01
9
C
SECS-P/03
6
B
IUS/13
12
B
SECS-P/01
12
B
SECS-P/05 ,SECS-S/06
3
F
-
activated in the A.Y. 2022/2023
ModulesCreditsTAFSSD
Two modules among the following
6
C
SECS-P/03
6
C
SECS-S/01
6
C
SECS-P/02
6
C
SECS-P/02
Two modules among the following
6
B
SECS-P/07
6
B
SECS-P/11
6
B
SECS-P/08
One module between the following
9
C
SECS-P/02
9
C
SECS-P/05
9
B
SECS-P/01
15
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°
Further language skills
3
F
-
Between the years: 1°- 2°
9
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

Machine Learning for Economics  (It will be activated in the A.Y. 2022/2023)

Teaching code

4S008979

Credits

6

Scientific Disciplinary Sector (SSD)

SECS-S/01 - STATISTICA

Learning outcomes

The goal of the course is to provide students with mathematical, statistical and computational tools for a rigorous understanding of machine learning. A central aspect is the critical discussion of how and to which extent machine learning methods are essential in large scale data analysis in order to develop a professional profile combining solid quantitative skills with an in-depth knowledge of economic and corporate dynamics to support strategic decisions based on data analysis. At the end of the course students will be able to master classical methods of machine learning, implement data analysis algorithms, choose the most suitable techniques, identify relevant structures underlying the data for prediction purposes, critically discuss the output generated by a machine learning technique.

Educational offer 2024/2025

ATTENTION: The details of the course (teacher, program, exam methods, etc.) will be published in the academic year in which it will be activated.
You can see the information sheet of this course delivered in a past academic year by clicking on one of the links below: