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 technicaladministrative 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 semestre  Oct 1, 2019  Jan 31, 2020 
II semestre  Mar 2, 2020  Jun 12, 2020 
Session  From  To 

Sessione invernale d'esame  Feb 3, 2020  Feb 28, 2020 
Sessione estiva d'esame  Jun 15, 2020  Jul 31, 2020 
Sessione autunnale d'esame  Sep 1, 2020  Sep 30, 2020 
Session  From  To 

Sessione estiva di laurea  Jul 17, 2020  Jul 17, 2020 
Sessione autunnale di laurea  Oct 13, 2020  Oct 13, 2020 
Sessione autunnale di laurea  Dicembre  Dec 9, 2020  Dec 9, 2020 
Sessione invernale di laurea  Mar 10, 2021  Mar 10, 2021 
Period  From  To 

Festa di Ognissanti  Nov 1, 2019  Nov 1, 2019 
Festa dell'Immacolata  Dec 8, 2019  Dec 8, 2019 
Vacanze di Natale  Dec 23, 2019  Jan 6, 2020 
Vacanze di Pasqua  Apr 10, 2020  Apr 14, 2020 
Festa della Liberazione  Apr 25, 2020  Apr 25, 2020 
Festa del lavoro  May 1, 2020  May 1, 2020 
Festa del Santo Patrono  May 21, 2020  May 21, 2020 
Festa della Repubblica  Jun 2, 2020  Jun 2, 2020 
Vacanze estive  Aug 10, 2020  Aug 23, 2020 
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.
Academic staff
Pintossi Chiara
chiara.pintossi@univr.itVallini Giovanni
giovanni.vallini@univr.it 045 802 7098; studio dottorandi: 045 802 7095Study 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 

Modules  Credits  TAF  SSD 

Modules  Credits  TAF  SSD 

1° Year
Modules  Credits  TAF  SSD 

2° Year activated in the A.Y. 2020/2021
Modules  Credits  TAF  SSD 

3° Year activated in the A.Y. 2021/2022
Modules  Credits  TAF  SSD 

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 (2019/2020)
Teaching code
4S02690
Credits
12
Language
Italian
Scientific Disciplinary Sector (SSD)
MAT/05  MATHEMATICAL ANALYSIS
The teaching is organized as follows:
Matematica Modulo 1
Credits
6
Period
I semestre
Academic staff
Chiara Pintossi
Statistica
Matematica Modulo 2
Credits
2
Period
I semestre
Academic staff
Chiara Pintossi
Learning outcomes
Mathematics: This course aims at providing the students with the mathematical tools (settheoretic 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. At the end of the course students should be able to use appropriately the mathematical language and the notions of the syllabus and furnish valid arguments in support of the solution of the proposed problems. Statistics: 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 reallife 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

MM: matematica

1. PRELIMINARY. a) Sets and operations on sets. b) Natural numbers N, integers Z and rational Q c) Real numbers. Operations and arrangement in R. Sets of limited or unlimited real numbers. Upper extremity and lower extremity of sets of real numbers. Intervals. Distance. d) Polinomial and irrational equations and inequalities, with modules and systems e) Analytical geometry in the Cartesian plane: distances between points, line, circumference, parabola, ellipse and hyperbola. Mutual positions and geometric problems 2. ELEMENTARY FUNCTIONS. a) Real functions of real variable, graph, domain, image. Composition of functions. Inverse function. Monotone functions. Limited functions and unlimited functions. Maxima and minima. Upper and lower extremes of functions. Sign and zeros of a function. Diagram operations: translations, symmetries. b) Absolute values. Powers with natural, rational and real exponent. The polynomial functions x ^ a, irrational, exponential a ^ x, logarithmic. The trigonometric functions. c) Algebric, exponential and logarithmic inequalities, systems of inequalities. 3. CONTINUOUS LIMITS AND FUNCTIONS. a) Distance and surroundings, right and left surroundings. Function limits. Continuity in one point. Elementary limits. Algebra of limits. Limits of compound functions. Comparison theorem. Some indeterminate forms. Comparison between infinites and infinitesimal. Horizontal, vertical, oblique asymptotes. b) Continuous functions and their fundamental properties. Theorem of the zeros. Weierstrass theorem. . 4. DERIVATIVES AND APPLICATIONS. a) Definition of derivative in a point. Right derivative and left derivative. Straight tangent to the graph. Derived function. Derivatives of elementary functions. Rules of derivation of sum, product, quotient, compound function, inverse function. Derivability and continuity. Relative maximum and minimum points. Fermat, Rolle and Lagrange theorems. Consequences of the Lagrange theorem: derivable functions with null derivative, derivable functions with the same derivative, sign of the first derivative and intervals of monotony of the function. Search for points of maximum or minimum relative through the sign of the derivative. Second derivative, its sign and convexity. b) Qualitative study of the graph of a function. c) Subsequent derivatives. Local approximation of functions with polynomials. Theorems of De l'Hospital. Taylor's polynomial and Taylor's theorem. Use of the theorem for determining limits. 5. INTEGRAL. a) Primitive functions (indefinite integrals). Elemental integrals. Definition of definite integral. Fundamental theorem of integral calculus. b) Calculation of areas through the use of integrals. c) Overview of improper integrals on unlimited intervals. 6. DIFFERENTIAL EQUATIONS. Definitions of differential equation (in normal and nonnormal form) and of order of a differential equation. Solution and general solution of a differential equation. Examples of differential equations. Cauchy problem. 7. LINEAR ALGEBRA. a) Geometric vectors. Vectors in R ^ n. Matrixes with real coefficients. Produced between matrices and its property. Linear systems in matrix form Ax = b. Systems resolution with the Gauss method. b) Rank (or characteristic of A). Determinant of square matrices. RouchéCapelli theorem. Cramer's theorem. Inverse of a square matrix. c) The scalar product and its properties. Standard (or form) of a vector. Orthogonal vectors. Elements of analytical geometry. Vector product in R ^ 3.

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 • goodnessoffit 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: 9788808062970 Lecture slides are available at: http://profs.scienze.univr.it/~chignola/teaching.html
Bibliography
Activity  Author  Title  Publishing house  Year  ISBN  Notes 

Matematica Modulo 1  Walter Dambrosio  Analisi matematica Fare e comprendere Con elementi di probabilità e statistica  Zanichelli  2018  9788808220745  
Matematica Modulo 1  Guerraggio, A.  Matematica per le scienze con MyMathlab (Edizione 2)  Pearson  2014  9788871929415  
Matematica Modulo 1  Dario Benedetto Mirko Degli Esposti Carlotta Maffei  Matematica per scienze della vita  Casa Editrice Ambrosiana. Distribuzione esclusiva Zanichelli  2015  9788808184849  
Matematica Modulo 1  Sergio Invernizzi Maurizio Rinaldi Federico Comoglio  Moduli di matematica e statistica Con l'uso di R  Zanichelli  2018  9788808220714  
Statistica  Michael C. Whitlock, Dolph Schluter  Analisi Statistica dei dati biologici  Zanichelli  2010  9788808062970  
Matematica Modulo 2  Walter Dambrosio  Analisi matematica Fare e comprendere Con elementi di probabilità e statistica  Zanichelli  2018  9788808220745  
Matematica Modulo 2  Guerraggio, A.  Matematica per le scienze con MyMathlab (Edizione 2)  Pearson  2014  9788871929415  
Matematica Modulo 2  Dario Benedetto Mirko Degli Esposti Carlotta Maffei  Matematica per scienze della vita  Casa Editrice Ambrosiana. Distribuzione esclusiva Zanichelli  2015  9788808184849  
Matematica Modulo 2  Sergio Invernizzi Maurizio Rinaldi Federico Comoglio  Moduli di matematica e statistica Con l'uso di R  Zanichelli  2018  9788808220714 
Examination Methods

MM: matematica

The final exam is written and must be completed in 3 hours. Neither midterm tests nor oral exams will take place. The exam paper consists of 6 exercises. The total of the marks of the exam paper is 30. Any topic dealt with during the lectures can be examined. Students are not allowed to use books, notes or electronic devices during the exam. The mark of any exercise will take into consideration not only the correctness of the results, but also the method adopted for the solution and the precise references to theoretical results (e.g. theorems) taught during the lectures. The pass mark for the exam of the Mathematics module is 18.

MM: statistica

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. Eight 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.
Type D and Type F activities
years  Modules  TAF  Teacher 

3°  Python programming language  D 
Maurizio Boscaini
(Coordinatore)

3°  Model organism in biotechnology research  D 
Andrea Vettori
(Coordinatore)

years  Modules  TAF  Teacher 

3°  LaTeX Language  D 
Enrico Gregorio
(Coordinatore)

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: only in this way will you be able to receive notification of all the notices from your teachers and your secretariat via email and soon also via the Univr app.
Erasmus+ and other experiences abroad
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, structureproperties relations, functional and advanced materials, molecular architecture, organic chemistry  Colloid chemistry 
Multifunctional organicinorganic hybrid nanomaterials for applications in Biotechnology and Green Chemistry  Synthetic Chemistry and Materials: Materials synthesis, structureproperties relations, functional and advanced materials, molecular architecture, organic chemistry  New materials: oxides, alloys, composite, organicinorganic hybrid, nanoparticles 
Stampa 3D di nanocompositi polimerici luminescenti per applicazioni in Nanomedicina  Synthetic Chemistry and Materials: Materials synthesis, structureproperties relations, functional and advanced materials, molecular architecture, organic chemistry  New materials: oxides, alloys, composite, organicinorganic 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 genomicofunzionali del processo di embriogenesi somatica in vite  Various topics 
Attendance
As stated in the Teaching Regulations for the A.Y. 2022/2023, 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.