## 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.

## Academic calendar

The academic calendar shows the deadlines and scheduled events that are relevant to students, teaching and technical-administrative staff of the University. Public holidays and University closures are also indicated. The academic year normally begins on 1 October each year and ends on 30 September of the following year.

## Course calendar

The Academic Calendar sets out the degree programme lecture and exam timetables, as well as the relevant university closure dates..

Period | From | To |
---|---|---|

I sem. | Oct 3, 2016 | Jan 31, 2017 |

II sem. | Mar 1, 2017 | Jun 9, 2017 |

Session | From | To |
---|---|---|

Sessione invernale Appelli d'esame | Feb 1, 2017 | Feb 28, 2017 |

Sessione estiva Appelli d'esame | Jun 12, 2017 | Jul 31, 2017 |

Sessione autunnale Appelli d'esame | Sep 1, 2017 | Sep 29, 2017 |

Session | From | To |
---|---|---|

Sessione estiva Appelli di Laurea | Jul 12, 2017 | Jul 12, 2017 |

Sessione autunnale Appelli di laurea | Nov 21, 2017 | Nov 21, 2017 |

Sessione invernale Appelli di laurea | Mar 13, 2018 | Mar 13, 2018 |

Period | From | To |
---|---|---|

Festa di Ognissanti | Nov 1, 2016 | Nov 1, 2016 |

Festa dell'Immacolata Concezione | Dec 8, 2016 | Dec 8, 2016 |

Vacanze di Natale | Dec 23, 2016 | Jan 8, 2017 |

Vacanze di Pasqua | Apr 14, 2017 | Apr 18, 2017 |

Anniversario della Liberazione | Apr 25, 2017 | Apr 25, 2017 |

Festa del Lavoro | May 1, 2017 | May 1, 2017 |

Festa della Repubblica | Jun 2, 2017 | Jun 2, 2017 |

Vacanze estive | Aug 8, 2017 | Aug 20, 2017 |

## Exam calendar

Exam dates and rounds are managed by the relevant Science and Engineering Teaching and Student Services Unit.

To view all the exam sessions available, please use the Exam dashboard on ESSE3.

If you forgot your login details or have problems logging in, please contact the relevant IT HelpDesk, or check the login details recovery web page.

Should you have any doubts or questions, please check the Enrolment FAQs

## Academic staff

Fatone Francesco

francesco.fatone@univr.it 045 802 7965Monaco Ugo Luigi

hugo.monaco@univr.it 045 802 7903; Lab: 045 802 7907 - 045 802 7082Spena Angelo

angelo.spena@univr.it 045 683 5623Vallini Giovanni

giovanni.vallini@univr.it 045 802 7098; studio dottorandi: 045 802 7095## Study Plan

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 enrolment year.

Modules | Credits | TAF | SSD |
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Modules | Credits | TAF | SSD |
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Modules | Credits | TAF | SSD |
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1° Year

Modules | Credits | TAF | SSD |
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2° Year

Modules | Credits | TAF | SSD |
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3° Year

Modules | Credits | TAF | SSD |
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#### 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.

### Mathematics and statistics (2016/2017)

Teaching code

4S02690

Credits

12

Coordinatore

Scientific Disciplinary Sector (SSD)

MAT/05 - MATHEMATICAL ANALYSIS

Language

Italian

The teaching is organized as follows:

##### Matematica

##### Statistica

### ???OrarioLezioni???

## Learning outcomes

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MM: Matematica

------------------------

This course aims at providing the students with the mathematical tools (set-theoretic and algebraic structures, differential and integral calculus in one or several real variables, ordinary differential equations) whose knowledge is indispensable for the achievement of the degree. A particular attention is paid to the concrete application of the learned notions.

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MM: Statistica

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The aim of the course is to make the students acquainted with basic statistical ideas and methods and their applications in the correct planning of experiments, data sampling, analysis, and presentation. The course conjugates concepts of basic statistics and probability theory with real situations as they emerge in a standard biotechnology laboratory. The students acquire appropriate skills to understand how biological systems work and more generally to cope with real-life problems in different applied scientific fields. At the end of the course the students are able to: - analyse experimental observations and prepare professional reports - appropriately plan experiments - autonomously acquire new skills in specific fields of applied statistics

## Program

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MM: Matematica

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1) Some notions of set theory. 2) The complete ordered field of the real numbers. 3) Euclidean distance and induced topology on the real line. Absolute value of a real number. 4) Cartesian plane. 5) Real functions of one real variable. 6) Polynomials and polynomial functions. Power, exponential and logarithmic functions. Trigonometric functions. 7) Limit of a function of one real variable. 8) Continuity of a function of one real variable at one point. Fundamental theorems on continuos functions. 9) Derivative of a function. Derivation rules. Fundamental theorems on differentiable functions. 10) Monotonicity of a function. Local and global minima and maxima of a function. 11) Convex functions. 12) Riemann integral. Integration rules. Improper integrals. 13) Ordinary differential equations. 14) Linear algebra. Matrices and operations on them. Determinant of a square matrix. 15) Distance between two points in the plane and geometrical loci. Conics. 16) Functions of more variables. Level curves and level sets. 17) Topology in R^2. Continuity of a function of 2 variables. 18) Differentiable functions of 2 variables. Partial derivatives. 19) Local and global minima and maxima of a function of more variables.

------------------------

MM: Statistica

------------------------

Each class introduces basic concepts of probability theory and applied statistics through combination of lectures and exercises. The exercises focus on the analysis of real experimental data collected in the teacher's lab or in other biotechnology labs. Lectures - brief introduction on the scientific method: the philosophical approach of Popper, Khun, and Lakatos and the concept of validation/falsification of hypotheses - variables and measurements, frequency distribution of data sampled from discrete and continuous variables, displaying data - elements of probability theory: definition, a brief history of probability, the different approaches to probability, the rules for adding and multiplying probabilities, Bayes' theorem - discrete probability distributions: the Binomial and the Poisson distributions and the limiting dilution assay with animal cells - continuous probability distributions: the concept of probability density, the Normal distribution and the Z statistics - statistical inference: the problem of deducing the properties of an underlying distribution by data analysis; populations vs. samples. The central limit theorem - the Student distribution and the t statistics. Confidence intervals for the mean. Comparing sample means of two related or independent samples - mathematical properties of the variance and error propagation theory - planning experiments and the power of a statistical test - the χ2 distribution and confidence intervals of the variance - goodness-of-fit test and χ2 test for contingency tables - problems of data dredging and the ANOVA test - correlation and linear regression The program follows the topics listed in the textbook up to chapter 17 (included) with the following extras: key aspects in probability theory, probability distributions in the biotechnology lab (practical examples), error propagation theory Reference textbook: Michael C. Whitlock, Dolph Schluter. Analisi Statistica dei dati biologici. Zanichelli, 2010. ISBN: 978-88-08-06297-0 Lecture slides are available at: http://profs.scienze.univr.it/~chignola/teaching.html

## Examination Methods

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MM: Matematica

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Written exam. The exam consists in 6 exercises to be solved in 3 hours. The minimum pass mark for Mathematics unit is 18 out of 30.

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MM: Statistica

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At the end of the course students are expected to master the basic concepts of probability theory and of validation/falsification of hypotheses, and to apply these concepts to the analysis of experimental data collected in a generic biotechnology laboratory. To pass the final written test, students are asked to solve 4 exercises within a maximum of 2 hours. The exercises concern the analysis of problems as they are found in a biotechnology laboratory. During the test, students are allowed to use learning resources such as books, lecture slides, handouts, but the use of personal computers or any other electronic device with an internet connection is not allowed. Four points are assigned to the solution of each exercise and all points are then summed up. To pass their test students must reach a minimum score of 18 points. The final score of the whole course in Mathematics and Statistics is calculated as the weighted mean of the marks obtained by students in both tests by taking into account the number of credits assigned to each course as weights: final grade = (2/3) x1 + (1/3) x2 where x1 and x2 are the marks obtained by students in their tests of Mathematics and Statistics, respectively.

## Bibliografia

Activity | Author | Title | Publishing house | Year | ISBN | Notes |
---|---|---|---|---|---|---|

Matematica | Guerraggio, A. | Matematica per le scienze con MyMathlab (Edizione 2) | Pearson | 2014 | 9788871929415 | |

Statistica | Michael C. Whitlock, Dolph Schluter | Analisi Statistica dei dati biologici | Zanichelli | 2010 | 978-88-08-06297-0 |

## Type D and Type F activities

**Modules not yet included**

## Career prospects

## Module/Programme news

##### News for students

There you will find information, resources and services useful during your time at the University (Student’s exam record, your study plan on ESSE3, Distance Learning courses, university email account, office forms, administrative procedures, etc.). You can log into MyUnivr with your GIA login details.

## Attendance

As stated in point 25 of the Teaching Regulations for the A.Y. 2021/2022, attendance is not mandatory. However, professors may require students to attend lectures for a minimum of hours in order to be able to take the module exam, in which case the methods that will be used to check attendance will be explained at the beginning of the module.Please refer to the Crisis Unit's latest updates for the mode of teaching.

## Graduation

## List of theses and work experience proposals

theses proposals | Research area |
---|---|

Studio delle proprietà di luminescenza di lantanidi in matrici proteiche | Synthetic Chemistry and Materials: Materials synthesis, structure-properties relations, functional and advanced materials, molecular architecture, organic chemistry - Colloid chemistry |

Multifunctional organic-inorganic hybrid nanomaterials for applications in Biotechnology and Green Chemistry | Synthetic Chemistry and Materials: Materials synthesis, structure-properties relations, functional and advanced materials, molecular architecture, organic chemistry - New materials: oxides, alloys, composite, organic-inorganic hybrid, nanoparticles |

Stampa 3D di nanocompositi polimerici luminescenti per applicazioni in Nanomedicina | Synthetic Chemistry and Materials: Materials synthesis, structure-properties relations, functional and advanced materials, molecular architecture, organic chemistry - New materials: oxides, alloys, composite, organic-inorganic hybrid, nanoparticles |

Dinamiche della metilazione del DNA e loro contributo durante il processo di maturazione della bacca di vite. | Various topics |

Risposte trascrittomiche a sollecitazioni ambientali in vite | Various topics |

Studio delle basi genomico-funzionali del processo di embriogenesi somatica in vite | Various topics |

## Gestione carriere

## Further services

I servizi e le attività di orientamento sono pensati per fornire alle future matricole gli strumenti e le informazioni che consentano loro di compiere una scelta consapevole del corso di studi universitario.