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.
Type D and Type F activities
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:
Bachelor's degree in Bioinformatics - Enrollment from 2025/2026Type D and Type F activities
Type D educational activities are at the student's choice, Type F activities are additional knowledge useful for job placement (internships, transversal skills, project works, etc.). According to the Didactic Regulations of the Course, some activities can be chosen and entered independently in the booklet, others must be approved by a special committee to verify their consistency with the study plan. Type D or F training activities can be covered by the following activities.
1. Teachings delivered at the University of Verona.
Include the teachings listed below and/or in the Catalog of Teachingshttps://www.univr.it/it/catalogo-insegnamenti - Opens in a new window (which can also be filtered by language of delivery via Advanced Search).
Booklet entry mode: if the teaching is included among those listed below, the student can enter it independently during the period- Opens in a new window in which the syllabus is open; otherwise, the student must make a request to the Secretariat, sending to carriere.scienze@ateneo.univr.it- Opens in a new window the form- Opens in a new window in the period indicated- Opens in a new window.
2. CLA language certificate or equivalency.
In addition to those required by the curriculum, the following are recognized for those matriculated from A.Y. 2021/2022:
- English language: 3 CFUs are recognized for each level of proficiency above that required by the course of study (if not already recognized in the previous course of study).
- Other languages and Italian for foreigners: 3 cfu are recognized for each proficiency level starting from A2 (if not already recognized in the previous study cycle).
These cfu will be recognized, up to a maximum of 6 cfu in total, of type F if the teaching plan allows it, or of type D. Additional elective credits for language knowledge may be recognized only if consistent with the student's educational project and if adequately motivated.
Those matriculated up to A.Y. 2020/2021 should consult the information found here- services - cla - language exercises - science and engineering https://www.scienzeingegneria.univr.it/?ent=iniziativa&id=4688 - Opens in a new window.
Booklet entry mode: apply for the certificate- Opens in a new window orequivalency- services - recognition of external language certifications - cla Opens in a new window to the CLA and send it to the Student Secretariat - Careers for the inclusion of the exam in the career, by email: carriere.scienze@ateneo.univr.it- Opens in a new window
3. Soft skills
Discover the training paths promoted by the University's TALC - Teaching and learning centerhttps://talc.univr.it/ - Opens in a new window, intended for students regularly enrolled in the academic year of course delivery https://talc.univr.it/it/competenze-trasversali- Opens in a new window
Booklet entry mode: The teaching is not intended to be included in the syllabus. Only upon obtaining theOpen Badgehttps://talc.univr.it/it/servizi/open-badge - Opens in a new window will the booklet CFUs be automatically validated. The registration of CFUs in career is not instantaneous, but there will be some technical time to wait.
4. Contamination lab
The Contamination Lab Verona (CLab Verona) is an experiential pathway with modules dedicated to innovation and business culture that offers the opportunity to work in teams with students from all courses of study to solve challenges launched by companies and institutions. The pathway allows students to receive 6 CFUs in the D or F area. Discover the challenges: https://www.univr.it/clabverona- Opens in a new window
PLEASE NOTE: To be eligible to take any teaching activity, including electives, you must be enrolled in the year of the course in which it is offered. Therefore, it is recommended that undergraduates of the December and April sessions DO NOT take extracurricular activities of the new academic year, in which they are not enrolled, since these degree sessions are valid with reference to the previous academic year. Therefore, for activities carried out in an academic year in which they are not enrolled, no recognition of CFUs can be given.
5. Internship/internship period
In addition to the CFUs stipulated in the curriculum (check carefully what is indicated on the Educational Regulations) here- services - internships and apprenticeships - science and engineering It opens in a new window you can find information on how to activate the internship.
Check in the regulations which activities can be Type D and which can be Type F.
Please also note that for internships activated from October 1, 2024, it will be possible to recognize excess hours in terms of Type D credits, limited only to internship experiences carried out at host institutions outside the University.
| years | Modules | TAF | Teacher |
|---|---|---|---|
| 2° 3° | Attention Laboratory | D |
Pietro Sala
(Coordinator)
|
| 2° 3° | Elements of Cosmology and General Relativity | D |
Claudia Daffara
(Coordinator)
|
| 2° 3° | Introduction to quantum mechanics for quantum computing | D |
Claudia Daffara
(Coordinator)
|
| 2° 3° | Introduction to smart contract programming for ethereum | D |
Sara Migliorini
(Coordinator)
|
| 2° 3° | Python programming language [English edition] | D |
Carlo Combi
(Coordinator)
|
| 2° 3° | BEYOND ARDUINO: FROM PROTOTYPE TO PRODUCT WITH STM MICROCONTROLLER | D |
Franco Fummi
(Coordinator)
|
| 2° 3° | APP REACT PLANNING | D |
Graziano Pravadelli
(Coordinator)
|
| 2° 3° | HW components design on FPGA | D |
Franco Fummi
(Coordinator)
|
| years | Modules | TAF | Teacher |
|---|---|---|---|
| 2° 3° | Attention Laboratory | D |
Pietro Sala
(Coordinator)
|
| 2° 3° | LaTeX Language | D |
Enrico Gregorio
(Coordinator)
|
| 2° 3° | Python programming language [Edizione in italiano] | D |
Carlo Combi
(Coordinator)
|
| 2° 3° | Rapid prototyping on Arduino | D |
Franco Fummi
(Coordinator)
|
| 2° 3° | Programming Challanges | D |
Romeo Rizzi
(Coordinator)
|
| 2° 3° | Tools for development of applications of virtual reality and mixed | D |
Andrea Giachetti
(Coordinator)
|
| 2° 3° | Development and life cycle of software of artificial intelligence software | D |
Marco Cristani
(Coordinator)
|
| 2° 3° | Protection of intangible assets (SW and invention)between industrial law and copyright | D |
Mila Dalla Preda
(Coordinator)
|
| years | Modules | TAF | Teacher |
|---|---|---|---|
| 1° | Subject requirements: mathematics | D |
Franco Zivcovich
(Coordinator)
|
Probability and Statistics (2024/2025)
Teaching code
4S00021
Credits
6
Language
Italian
Scientific Disciplinary Sector (SSD)
MAT/06 - PROBABILITY AND STATISTICS
Courses Single
Authorized
The teaching is organized as follows:
Teoria
Laboratorio
Learning objectives
The course aims to provide the basic concepts of descriptive Statistics and Probability, by modeling concrete problems through the use of probabilistic methods and, at the same time, to underline the natural application of these concepts to mathematical Statistics. The course also aims to provide ac-tual tools to apply the main statistical techniques to real cases. By the end of the course, students will have to show their knowledge and understanding of the main statistical techniques for the description and analysis of the phenomena under study; to express the ability to apply the acquired knowledge and understanding skills for the interpretation of the results of the applied statistical analyses in a critical and proactive way, by using the available tools; to be able to employ the knowledge acquired to continue the studies independently in the field of statistical analysis.
Prerequisites and basic notions
-
Program
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MM: Teoria
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(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.
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MM: Laboratorio
------------------------
The course includes a series of laboratories in the computer lab with exercises in MATLAB environment. After an introduction to MATLAB and to the main functions and tools useful for statistics, some exercises will be proposed on descriptive statistics and probability; for computing the probability density function (pdf) and cumulative distribution function (cdf) of special random variables, for generating random data and estimating parameters; on hypothesis testing for distributions and linear regression. The laboratories complement lectures by consolidating learning and developing problem-solving and hands-on practical skills.
Bibliography
Didactic methods
Regular lectures with power point presentation and blackboard and laboratory exercises. Educational material will be available to students enrolled in the course on the Moodle platform. This material includes lecture presentations in PDF format and material related to laboratory activities. For further details and supplementary materials, please refer to the reference books.
Learning assessment procedures
The exam consists of a computer test via Moodle. The exam consists of theoretical questions (test with multiple choice), problems, and laboratory questions (open questions).
Evaluation criteria
To pass the exam, the students must show that: - they have understood the basic concepts of probability theory and statistics; - they are able to use the knowledge acquired during the course to solve the assigned problem; - they are able to program in MATLAB environment in the statistical and probabilistic context.
Criteria for the composition of the final grade
The final grade will be the average of the three grades (theory, exercises, laboratory). To pass the exam, a minimum score of 18 out of 33 is required. Distinction is awarded for scores above 30.
Exam language
Italiano
