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 A.A. 2019/2020 A.A. 2018/2019 A.A. 2017/2018 A.A. 2016/2017 A.A. 2015/2016 A.A. 2014/2015 A.A. 2013/2014 A.A. 2012/2013 A.A. 2011/2012 A.A. 2010/2011 A.A. 2009/2010

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 Informatica - 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
6
C
MAT/01
6
A
MAT/02
12
A
MAT/05
12
A
ING-INF/05
6
C
MAT/06
12
A
INF/01
English B2
6
E
-

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

ModulesCreditsTAFSSD
12
B
INF/01
12
B
INF/01
6
A
FIS/01
6
B
INF/01
6
C
MAT/01
6
B
INF/01
12
B
ING-INF/05

3° Year  It will be activated in the A.Y. 2026/2027

ModulesCreditsTAFSSD
6
B
INF/01
6
B
INF/01
6
B
INF/01
6
B
INF/01
6
C
INF/01
Un insegnamento a scelta
6
C
INF/01
6
C
INF/01
6
C
INF/01
6
C
FIS/01
6
C
INF/01
6
C
INF/01
Final exam
6
E
-
It will be activated in the A.Y. 2025/2026
ModulesCreditsTAFSSD
12
B
INF/01
12
B
INF/01
6
A
FIS/01
6
B
INF/01
6
C
MAT/01
6
B
INF/01
12
B
ING-INF/05
It will be activated in the A.Y. 2026/2027
ModulesCreditsTAFSSD
6
B
INF/01
6
B
INF/01
6
B
INF/01
6
B
INF/01
6
C
INF/01
Un insegnamento a scelta
6
C
INF/01
6
C
INF/01
6
C
INF/01
6
C
FIS/01
6
C
INF/01
6
C
INF/01
Final exam
6
E
-
Modules Credits TAF SSD
Between the years: 2°- 3°
Training
6
F
-
Between the years: 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

Probability and Statistics (2024/2025)

Teaching code

4S02843

Credits

6

Coordinator

Matteo Calgaro

Language

Italian

Scientific Disciplinary Sector (SSD)

MAT/06 - PROBABILITY AND STATISTICS

Courses Single

Authorized

MoodleMoodle Seminars 0

The teaching is organized as follows:

Teoria

Credits

4

Period

Semester 2

Academic staff

Franco Zivcovich

Lessons timetable
Laboratorio

Credits

2

Period

Semester 2

Academic staff

Matteo Calgaro

Lessons timetable

Learning objectives

The course aims at providing the fundamental concepts of descriptive statistics and probability, with the task of modeling real problems by means of probability methods and applying to real problems statistic techniques. At the end of the course the student will have to demonstrate to understand the main statistical techniques for describing problems; to be able to interpret results of statistical analyses; to be able to develop know-how necessary to continue the study autonomously in the context of statistical analysis.

Program

------------------------
UL: Teoria
------------------------
(1) Descriptive Statistics. Describing data sets (frequency tables and graphs). Summarizing data sets (sample mean, median, and mode, sample variance and standard deviation, percentiles and box plots). Normal data sets. Sample correlation coefficient.
(2) Probability theory.
Elements of probability: sample space and events, Venn diagrams and the algebra of events, axioms of probability, sample spaces having equally likely outcomes, conditional probability, Bayes’ formula, independent events.
Random variables and expectation: types of random variables, expected value and properties, variance, covariance and variance of sums of random variables. Moment generating functions. Weak law of large numbers.
Special random variables: special random variables and distributions arising from the normal (chi-square, t, F).
(3) Statistical inference. Distributions of sampling statistics. Parameter estimation (maximum likelihood estimators, interval estimates). Hypothesis testing and significance levels.
(4) Regression. Least squares estimators of the regression parameters. Distribution of the estimators. Statistical inferences about the regression parameters. The coefficient of determination and the sample correlation coefficient. Analysis of residuals: assessing the model. Transforming to linearity. Weighted least squares.
------------------------
UL: Laboratorio
------------------------
The course includes a series of laboratories with exercises in a scientific computing environment. Firstly, the scientific computing environment and the main functions and tools useful for statistics are introduced. Then, exercises in descriptive statistics, probability, calculation of the probability density function (pdf) and cumulative density function (cdf) for models of random variables, random data generation, parameter estimation, hypothesis testing for distributions, and linear regression will be proposed. Labs complement lessons by consolidating learning and developing practical problem-solving skills.

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

Lectures and laboratory sessions with R.

Sustainable Development Goals - SDGs

This initiative contributes to the achievement of the Sustainable Development Goals of the UN Agenda 2030. More information on sustainability