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:
Laurea magistrale in Medical bioinformatics - Enrollment from 2025/2026Type D learning activities are the student's choice, type F activities are additional knowledge useful for job placement (internships, transversal skills, project works, etc.). According to the Teaching 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 learning activities can be covered by the following activities.
1. Modules taught at the University of Verona
Include the modules listed below and/or in the Course Catalogue (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 in which the curriculum is open; otherwise, the student must make a request to the Secretariat, sending the form to carriere.scienze@ateneo.univr.it during the period indicated.
2. CLA certificate or language equivalency
In addition to those required by the curriculum/study plan, 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 CFUs are recognized for each proficiency level starting from A2 (if not already recognized in the previous study cycle).
These CFUs will be recognized, up to a maximum of 6 CFUs in total, of type F if the study 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 justified.
Those enrolled until A.Y. 2020/2021 should consult the information found here.
Method of inclusion in the booklet: request the certificate or equivalency from 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
3. Transversal skills
Discover the training paths promoted by the University's TALC - Teaching and learning center intended for students regularly enrolled in the academic year of course delivery https://talc.univr.it/it/competenze-trasversali
Mode of inclusion in the booklet: the teaching is not expected to be included in the curriculum. Only upon obtaining the Open Badge 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 course with modules on innovation and enterprise culture that offers the opportunity to work in teams with students from all areas to solve challenges set by companies and organisations.
Upon completion of a CLab, students will be entitled to receive 6 CFU (D- or F-type credits).
Find out more: https://www.univr.it/clabverona
PLEASE NOTE: In order to be admitted to any teaching activities, including those of your choice, you must be enrolled in the academic year in which the activities in question are offered. Students who are about to graduate in the December and April sessions are therefore advised NOT to undertake extracurricular activities in the new academic year in which they are not enrolled, as these graduation sessions are valid for students enrolled in the previous academic year. Therefore, students who undertake an activity in an academic year in which they are not enrolled will not be granted CFU credits.
5. Internship/internship period
In addition to the CFUs stipulated in the curriculum/study plan (check carefully what is indicated on the Teaching Regulations) here 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 traineeships activated after 1 October 2024, it will be possible to recognise excess hours in terms of type D credits, limited only to traineeship experiences carried out at host organisations outside the University.
| years | Modules | TAF | Teacher |
|---|---|---|---|
| 1° 2° | Attention Laboratory | D |
Pietro Sala
(Coordinator)
|
| 1° 2° | Elements of Cosmology and General Relativity | D |
Claudia Daffara
(Coordinator)
|
| 1° 2° | Introduction to quantum mechanics for quantum computing | D |
Claudia Daffara
(Coordinator)
|
| 1° 2° | Introduction to smart contract programming for ethereum | D |
Sara Migliorini
(Coordinator)
|
| 1° 2° | Python programming language [English edition] | D |
Carlo Combi
(Coordinator)
|
| 1° 2° | Mini-course on Deep Learning & Medical Imaging | D |
Vittorio Murino
(Coordinator)
|
| 1° 2° | BEYOND ARDUINO: FROM PROTOTYPE TO PRODUCT WITH STM MICROCONTROLLER | D |
Franco Fummi
(Coordinator)
|
| 1° 2° | APP REACT PLANNING | D |
Graziano Pravadelli
(Coordinator)
|
| 1° 2° | HW components design on FPGA | D |
Franco Fummi
(Coordinator)
|
| years | Modules | TAF | Teacher |
|---|---|---|---|
| 1° 2° | Attention Laboratory | D |
Pietro Sala
(Coordinator)
|
| 1° 2° | LaTeX Language | D |
Enrico Gregorio
(Coordinator)
|
| 1° 2° | Python programming language [Edizione in italiano] | D |
Carlo Combi
(Coordinator)
|
| 1° 2° | Rapid prototyping on Arduino | D |
Franco Fummi
(Coordinator)
|
| 1° 2° | Programming Challanges | D |
Romeo Rizzi
(Coordinator)
|
| 1° 2° | Protection of intangible assets (SW and invention)between industrial law and copyright | D |
Mila Dalla Preda
(Coordinator)
|
Epidemiological methods and biostatistics (2024/2025)
Teaching code
4S009837
Teacher
Coordinator
Credits
6
Language
English
Scientific Disciplinary Sector (SSD)
MED/01 - MEDICAL STATISTICS
Period
Semester 1 dal Oct 1, 2024 al Jan 31, 2025.
Courses Single
Authorized
Learning objectives
The course aims at providing the students with the theoretical and practical tools needed to evaluate the frequency of diseases in human populations and the associated risk factors, i.e. expertise in epidemiology, biostatistics and information technology applied to the analysis of biomedical data. Knowledge and understanding: Knowledge and skills concerning the main designs of epidemiological studies, the statistical methods for the analysis of biomedical data and the programming syntax of a statistical software (STATA/R). Applied knowledge and understanding: a) To know the statistical techniques used for the analysis of biomedical data; b) to perform data analysis using a statistical software (STATA/R); c) to interpret the results obtained. Making judgements: Ability to choose the appropriate statistical methods in relation to the type of data and the design of the study. Communication skills: Ability to communicate the results of an analysis of biomedical data clearly and concisely. Lifelong learning skills: Ability to apply autonomously the statistical and epidemiological methodologies learned during the course on various biomedical problems.
Prerequisites and basic notions
The course requires basic knowledge of descriptive statistics and probability theory. There are no preparatory courses.
Program
1. Introduction to epidemiology
- Definition and key features
- Traditional classification of epidemiology
2. Measures of occurrence
- Outcomes
- Prevalence
- Cumulative incidence
- Incidence rate
3. Measures of association and public health impact
- Determinants
- Epidemiological associations
- Attributable risk (AR) and AR%
- Relative risk (RR) and odds ratio (OR)
- Effect modification
4. Epidemiological studies
- Ecological studies
- Cross-sectional studies
- Cohort studies
- Case-control studies
- Experimental studies
5. Causal interpretation of an empirical association
- Statistical vs. causal associations
- Causal models in epidemiology
- Validity of a study (random error, bias, confounding)
- Types of bias
- Methods to control confounding
- Hill’s positive criteria for causality
6. Principles of statistical inference
- Point estimate and sampling distribution
- Confidence interval
- Hypothesis test
- Test of statistical significance
7. Stratified analysis
- Effect modification vs. confounding
- Stratum-specific estimates
- Testing homogeneity
- Pooled estimate
- Testing the stratified null hypothesis of no association
8. Basic statistical models in epidemiological research
- Linear regression model
- Logistic regression model
9. Statistical methods for survival analysis
- Kaplan-Meier non-parametric estimator
- Log-rank test
- Cox regression model
Bibliography
Didactic methods
The course is structured in theoretical frontal lessons (24 hours) and practical lessons in the computer lab (24 hours) on the use of a statistical software (STATA) for the quantitative analysis of biomedical data. The teaching material (slides of the theoretical lessons, STATA/R files used for the practical lessons) is made available to the students on the e-learning web page of the course (Moodle platform).
Learning assessment procedures
The final examination is a written test in the computer lab (duration of the test: 2 hours). The test is the same for attending and non-attending students and for Erasmus students. The test covers potentially all the topics listed in the program. The aim of the test is to verify the ability to solve a biomedical problem by analyzing health data using STATA statistical software. Alternatively, students can use R statistical software. The commands, results and interpretation of the analysis are reported in written form. In addition, students have to answer some questions to ascertain the understanding of theory.
Evaluation criteria
The written test consists of linked questions that guide the analysis of a health dataset and of questions to ascertain the understanding of theory. Each question is assigned a score.
Criteria for the composition of the final grade
The final grade (expressed in thirtieths) is given by the sum of the scores obtained by correctly answering the individual questions. The test is considered passed if the student gains a minimum grade of 18/30.
Exam language
Inglese - English
