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. 2025/2026 A.A. 2024/2025

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
9
B/C
IUS/01 ,M-FIL/03
9
B
MAT/06
12
B
INF/01

2° Year  It will be activated in the A.Y. 2025/2026

ModulesCreditsTAFSSD
Final exam
21
E
-
It will be activated in the A.Y. 2025/2026
ModulesCreditsTAFSSD
Final exam
21
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°
1 module among the following
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 modules among the following
6
C
SECS-P/10
6
C
SECS-P/08
6
C
SECS-P/10
6
C
SECS-P/08
6
C
AGR/01
Between the years: 1°- 2°
1 module among the following 
- A.A. 2024/2025 Complex systems and social physics - Network science and econophysics - Statistical methods for business intelligence not activated
- A.A. 2025/26 Network science and econophysics not activated
6
C
FIS/02
6
C
FIS/02
6
C
SECS-S/01
Between the years: 1°- 2°
1 module among the following
6
B
MAT/09
6
B
MAT/06
6
B
MAT/08
Between the years: 1°- 2°
2 modules among the following
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°
Further activities: International students (ie students who do not have an Italian bachelor's degree) must compulsorily gain 3 credits of Italian language skills level B2.
6
F
-
Between the years: 1°- 2°
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

Big data epistemology  (2024/2025)

Teaching code

4S009088

Teacher

Massimiliano Badino

Coordinator

Massimiliano Badino

Credits

6

Also offered in courses:

Language

English en

Scientific Disciplinary Sector (SSD)

M-FIL/02 - LOGIC AND PHILOSOPHY OF SCIENCE

Period

Semester 2 dal Mar 3, 2025 al Jun 13, 2025.

Courses Single

Authorized

Lessons timetable MoodleMoodle Seminars 0

Learning objectives

The course will allow the student to acquire the skills necessary to apply the key concepts of epistemology (knowledge, methodology, justification, explanation, etc.) to the specific case of data science and to the discussion of consequences and implications of big data for society in general. At the end of the course the student has to show to have acquired the following skills:
- recognize and discuss the main epistemological issues relating to the knowledge produced by the collection and manipulation of big data, in particular for what concerns the topics: (1) epistemological specificity of big data; (2) the impact of big data on scientific work; (3) Big Data and cultural authority of science
- having acquired, through detailed analysis of real life situations, the tools for a more conscious and critical approach to the work of data analyst, as well as for the management and dissemination of big data in public domains.

Prerequisites and basic notions

Basic knowledge of philosophy and critical thinking.

Program

The course is dedicated to exploring the epistemological, social, and political issues related to the use of big data, machine learning, and artificial intelligence. The program is divided into two main modules:

(A) Producing knowledge in the digital age. This module addresses the epistemological questions raised by the use of machine learning and big data in the production of scientific knowledge. Examples of such questions include: How do big data change our scientific practices and methods? What are the limitations of the computational approach to science? Do big data make theories redundant? What are the epistemological characteristics of statistical learning? The structure of the module is as follows:

(A.1) Introduction to the epistemology of computability: complexity and undecidability.
(A.2) The concepts of data, probability, and information.
(A.3) From statistical inference to machine learning and big data.

(B) Socio-epistemology of big data. The second module deals with the socio-epistemological and political impact of machine learning and big data on scientific practice and society in general. Examples of the issues addressed in this module include: What is the impact of using big data on the social structure of scientific research? How can artificial intelligence be made more explainable and accountable? How are digital environments such as social networks influenced by machine learning? The structure of the module is as follows:

(B.1) Scientific research and big data.
(B.2) Explainable artificial intelligence.
(B.3) Truth and post-truth in digital environments.

Didactic methods

Classes will be held in presence and recorded.

Learning assessment procedures

The course will combine introductory lectures and class discussions. The final assessment consists of two elements:

(1) A written paper (max 3000 words) (40%)
(2) Oral exam (60%)

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 final assessment consists of two elements:

(1) A written paper (max 3000 words) (40%)
(2) Oral exam (60%)

Criteria for the composition of the final grade

The final assessment consists of two elements:

(1) A written paper (max 3000 words) (40%)
(2) Oral exam (60%)

Exam language

English