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

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/08
12
C
MAT/06
12
B
INF/01

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

ModulesCreditsTAFSSD
9
B/C
IUS/01 ,M-FIL/03
Training
6
F
-
Final exam
22
E
-
activated in the A.Y. 2022/2023
ModulesCreditsTAFSSD
9
B/C
IUS/01 ,M-FIL/03
Training
6
F
-
Final exam
22
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°
1 module among the following (1st year: Big Data epistemology and Social research; 2nd year: Cybercrime, Data protection in business organizations, Comparative and Transnational Law & Technology)
6
C
M-FIL/02
6
C
IUS/02
6
C
IUS/17
6
C
SPS/07
6
C
IUS/04
Between the years: 1°- 2°
2 courses among the following (1st year: Business analytics, Digital Marketing and market research; 2nd year: Logistics, Operations & Supply Chain, Digital transformation and IT change, Statistical methods for Business intelligence)
6
B
SECS-P/10
6
B
SECS-P/08
6
B
SECS-P/10
6
B
SECS-P/08
6
B
SECS-S/01
Between the years: 1°- 2°
2 courses among the following (1st year: Complex systems and social physics, Discrete Optimization and Decision Making, 2nd year: Statistical models for Data Science, Continuous Optimization for Data Science, Network science and econophysics, Marketing research for agrifood and natural resources)
6
C
FIS/02
6
C
MAT/09
6
C
AGR/01
6
C
MAT/06
6
C
MAT/08
6
C
FIS/02
Between the years: 1°- 2°
2 courses among the following (1st year: Data Visualisation, Data Security & Privacy, Statistical learning, Mining Massive Dataset, 2nd year: Machine Learning for Data Science)
6
B
INF/01
6
B
ING-INF/05
6
B
ING-INF/05
6
B
INF/01
6
B
INF/01
Between the years: 1°- 2°
8
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

Complex systems and social physics  (It will be activated in the A.Y. 2021/2022)

Teaching code

4S009082

Credits

6

Scientific Disciplinary Sector (SSD)

FIS/02 - FISICA TEORICA, MODELLI E METODI MATEMATICI

Learning outcomes

The aim of the course is to provide the student with the skills of theoretical physics and mathematical physics methods for the modeling and characterization of large sets of data, time series, time sequences, and hierarchical structures in aggregation. Students will also be provided with the methods of mathematical physics for the study of correlation, causation, and aggregation relationships in complex social systems. At the end of the course the student has to show to have acquired the following skills: ● ability to develop formal models for the qualitative and quantitative analysis of databases, time series, and dynamics of complex systems in interaction for the detection of causal relationships, correlation structures, and forecasting schemes.