Statistical Sciences

Educational objectives

Learning goals
Develop analytical and computational skills to solve statistical decision problems.

Knowledge and understanding
At the end of the course the students have the ability to understand and solve simple and more advanced exercises of Statistical decision theory.

Applying knowledge and understanding
Students are required to apply theoretical and computational skills (using the software R) to solve inferential problems formalized as decision problems.

Making judgements
One of the main goals of practical activities is to develop the ability of comparing and choosing alternative methods, i.e. to refine judgement skills.

Communication skills
Students acquire the ability of presenting written reports of their practical laboratories.

Learning skills
The students acquire a series of skills useful for future academic and professional activities.

Educational objectives

General Objectives.
The objective of this course is to present the basics necessary for the use of a general-purpose imperative programming language. In particular, the use of the Python 3 programming language will be demonstrated.

Specific objectives
(a) Knowledge and understanding skills.
Students will know the basic constructs of the Python 3 language, will be able to understand a simple program written in Python 3 and to write programs in the same language. They will also be able to use an integrated development environment (IDE).

(b) Ability to apply knowledge and understanding.
At the end of the course, students will be able to solve simple algorithmic problems using the Python 3 programming language, correct syntactic and semantic errors using an IDE, and evaluate the correctness and complexity of the identified solutions.

(c) Autonomy of judgment.
Students will develop the ability to formalize algorithms using a programming language, choosing the constructs best suited to solve the individual problem. They will be able to evaluate the correctness, readability and generality of the solutions identified.

(d) Communication skills.
Students will acquire the ability to formally express a mental procedure for solving a problem, and to understand the crucial points of an algorithm.

(e) Learning skills
Students will be able to easily learn the use of imperative programming languages, appreciating similarities and differences from the Python 3 language.

Educational objectives

The specific aim is to enable the student to assist him with the more specific knowledge for inclusion in the future world of work.

Educational objectives

The specific aim is to enable the student to assist him with the more specific knowledge for inclusion in the future world of work.

Educational objectives

Learning goals
The primary educational goal of the course is the students' learning of the main problems, methods and models of mathematical demography.
Knowledge and understanding.
After attending the course, the students know and understand the most commonly used methodologies for analysing demographic processes.
Applying knowledge and understanding.
At the end of the course students are able to apply the specific methods and models of the discipline to concrete cases, even in multidisciplinary contexts (socio-demographic, bio-demographic, actuarial, economic).
Making judgements.
Students develop critical skills through the application of methods and models to real data of different complexity.
They also develop critical sense through the comparison of alternative solutions to the same problem.
Communication skills.
Students, through the study and development of applications to concrete cases, acquire the technical-scientific language of the discipline, which must be used appropriately in the final oral examination.
Communication skills are also developed through group activities.
Learning skills.
Students who pass the exam have learned a method of analysis that allows them to study the main demographic issues autonomously.

Educational objectives

General goals
The course has the following objectives: 1) student should learn the main topics related to nutritional epidemiological studies and the analysis of data obtained from such studies, and to address both theoretical issues (sources of bias and confounding and correction methods, causal inference, nutritional survey data analysis with complex sample design) and practical issues (study design, food composition/dietary assessment), as well as acquire the ability to analyse a nutritional database already processed.
2) students should learn the main topics of molecular, quantitative and population genetics, in particular gain a clear understanding of the main theoretical probabilistic and statistical principles underlying the transmission of genetic inheritance and how these should be taken into account in the design of genetic epidemiology studies.

Knowledge and understanding
At the end of the course, the student is expected to
1) be able to describe the main study designs adopted in nutritional epidemiology and their relative advantages and disadvantages.
2) understand how bias and confounding impact nutritional studies, understand the problems associated with the breakdown of foods/nutrients into calories and the importance of using the elements of the complex sample design when present.
3) understand the mathematical and statistical bases of genetics (molecular, quantitative and population) and in particular the Hardy-Weinberg principle
Applying knowledge and understanding
1) Be able to apply the main techniques of correction for measurement error and confounding to nutritional epidemiology studies; be able to apply simple dimension reduction methods to summarize information from numerous nutrients or food consumption timeslots.
2) Be able to apply simple genetic probabilistic and statistical models to calculate the probability of familial transmission of an allele, genotype and phenotype for simple kinship relationships and understand the decomposition of the variance of a trait into its genetic and environmental components

Making judgements
1) Understand what type of genetic risk estimate may be obtained depending on the study design/type of trait/disease of reference
2) Critically evaluate the assumptions underlying the application of methods of adjustment for confounding in the nutritional and genetic fields
Communication skills
1) Be able to communicate with nutrition/sociodemography/health experts to elicit the relationships between variables and construct a causal diagram (DAG)
2) Know how to interpret the effect of measurement error in nutritional studies and the interaction/effect modification between genetic and environmental factors
Learning skills
At the end of the course the student will have developed the conceptual and quantitative bases of nutrition and genetics to be able to address nutritional epidemiology and genetics problems other than those covered in the course, which may be illustrated in the course as case studies ( for example studies on complex dietary patterns or rare genetic diseases and/or those linked to mitochondrial DNA)

Educational objectives

General goals
Principles and methods to study the health conditions of a population

Specific goals

Knowledge and understanding
After attending the course students are able to handle problems related to the evaluation of health conditions of single units and of a population

Applying knowledge and understanding
After attending the course students are able to handle the measure of health conditions of single units and of a population

Making judgements
Judgement skills are improved by discussion and use of alternative methods and practical and real examples

Communication skills
Students will develop capacity of communicating results through oral presentation of practical problems and by the final exam

Learning skills
Students who pass the exam have acquired skills and methods in Social epidemiology and in statistical methods for public health

Educational objectives

Learning goals
The student at the end of the course should be able to use with knowledge advanced modeling and exploratory techniques specifically developed for spatially dependent data. This is achieved by assigning several homeworks on real data. Practical sessions with the R software are part of each lecture, so to allow students to implement what is taught in the theoretical part. Among the expected results, ability to elaborate spatial environmental data using R software, ability to interpret the results obtained, ability to choose the most suitable statistical models according to the hypotheses they are founded on and to their compatibility with the available data.

Knowledge and understanding
The student will be able to understand the main tools for the analysis of spatial and spatio-temporal data.

Applying knowledge and understanding
Students will be involved in the discussion and analysis of case studies using the open source statistical software R. Students will be asked to prepare and discuss a presentation of the results of their homeworks. The presentation will be given in front of the class and discussed.

Making judgements
Through the homeworks and the final presentations discussions, students will develop judgements capacity in terms of theoretical choices in representation of real world phenomena.

Communication skills
Students will be asked to prepare and discuss a presentation of the results of their homeworks. The presentation will be given in front of the class and discussed.
This procedure will help the student to develop his/her ability to communicate the results of its work.

Learning skills
One of the aims of the course is to build a statistical glossary and a dictionary of specific statistical concepts that will allow the student to read and understand scientific papers using advanced statistical tools in the analysis of environmental data.

Educational objectives

General
Managers worldwide, beyond their personal experience, rely more and more on the use of
quantitative decision models which allow to take advantage of today’s data availability. Morover,
new computational tools, including algorithms, cloud computing and distributed processing, make
it possible to both develop and compute analytical models in a very short time, meeting the
requirement of practical applications and often using real time data. Data Driven Decision Making
is the new paradigm for managers to make better, evidence based, more rational, transparent and
reliable decisions.
In this context, the primary educational objective of the course is students' learning of the main
decision problems that arise in real world and the quantitative methods to model them and to
feed them with adequate data. Students must also be able to correctly use, for decision-making
and management purposes, computer tools to analyze data generated by real problems in
different contexts (e.g. service management, marketing, transportation, operations management
and production, and finance) through the analysis of several case studies.

Specific objectives

a) Knowledge and ability to understand
After attending the course the students know and classify the main decision problems arising in
real world organization and the main analytical methods (decision and optimization models and
algorithms) to be used to support a Manager during his/her decision process.

b) Ability to apply knowledge and understanding
At the end of the course the students are able to formalize real problems in terms of decision
problems and to apply the specific methods taught in the course to solve them. They are also able
to classify the type of problem to it the most appropriate quantitative method, experimenting the
effectiveness for decisional purposes also on real problems.

c) Autonomy of judgment
Students develop critical skills through the application of modeling, decision analysis and multi
objective optimization methodologies to a broad set of practical problems. They also develop the
critical sense through the comparison between alternative solutions to the same problem
obtained using methods of analysis and realistic scenarios different from each other. They learn to
critically interpret the results obtained by applying the procedures to real data sets.

d) Communication skills
Students, through the study and the carrying out of practical exercises, acquire the technical-
scientific language of the course, which must be properly used both in the intermediate and final
written tests and in the oral tests. Communication skills are also developed through group
activities.

e) Learning ability
Students who pass the exam have learned methods of decision analysis and multiobjective
optimization that allow them to face, decision-making problems and optimization on complex
organizations.

Educational objectives

Learning goals
The course aims at providing students with a basic training in practical and theoretical bioinformatics using the most common models and tools for analyzing "omics" data in biology and molecular medicine. After completing the course, the student is expected to be able to analyze and interpret large-scale data such as, for example, a patient's transcriptomic data using appropriate methodologies implemented with matlab or other high-level programming languages . Furthermore, the student will be able to link the biological theory and the analysis techniques critically analyzing the results.
Knowledge and understanding
Students will acquire knowledge and understanding in the field of bioinformatics and computational medicine at a level including some innovative topics.
Applying knowledge and understanding
Students will be able to apply their knowledge and show familiarity with methods in bioinformatics and computational medicine; they will also acquire adequate skills both to support arguments, and to solve problems typical to the subject.
Making judgements
Students will be able to collect, analyse and interpret data in the field of bioinformatics and computational medicine, to produce autonomous judgments and critical reflections of the corresponding research themes.
Communication skills
Students will be able to communicate information, ideas, problems and solutions even to non-specialist research staff (MDs/biologists).
Learning skills
Students will develop those learning skills necessary to move towards continuous in-depth studies that characterize the rapid evolution of the discipline with a high degree of autonomy.

Educational objectives

Educational objectives
The educational goal of the course is students' learning of the main statistical methods used for causal inference. That is, how to answer research questions about the impact of certain causes on a particular outcome.

Knowledge and understanding
At the end of the course, students know and understand the main methods for causal inference.

Ability to apply knowledge and understanding
Students learn how to apply the main methods for causal inference also through the use of a statistical software.

Judgment independence
The discussion of the various methods, even with team works, provides students with the skills necessary to analyze real situations critically and independently.

Communicative skills
Students acquire the basic elements for reasoning in quantitative terms about causal inference problems. These skills will be further developed through team works on real data.

Learning skills
Students who pass the exam are able to apply the methods learned in different application contexts.

Educational objectives

The primary educational objective of the laboratory is students' learning and practice of the main
tools for Data Driven Decision Making, that is the use of computer tools to analyze data and
formalize optimization or decision models and produce decisions that create value.

Knowledge and ability to understand
After attending the laboratory, students will be able to use decision support methods (like,
the Analytical Hierchical Process), optimization solvers (like CPLEX or Gurobi) and computer
algorithms for modelling multicriteria decision and optimization problems.

Ability to apply knowledge and understanding
The models are formalized in the realm of problems. The most appropriate quantitative
method, experimenting with the effectiveness of the problem.

Autonomy of judgment
Students develop critical skills through the application of modeling, analysis and
optimization to a broad set of decision problems. They also develop the critical sense
through the comparison between alternative solutions to the same problem using
methods of analysis and realistic scenarios different from each other. They learn to
critically interpret the results obtained by applying the procedures to real data sets.

Communication skills
Students, through the study and the carrying out of the practical exercises, acquire the
technical-scientific language of the course, which should be used in the tests.
Communication skills are also developed through group activities.

Learning ability
Students who pass the exam have acquired the main methods of analysis and optimization
of decision problems that allow them to face decision-making and quantitative
management in competitive nowadays enterprises.

Educational objectives

General goals
The aim of the laboratory is to stimulate learning of the appropriate use of statistical models and methods applied to data from experimental and observational studies in biomedicine. The teaching method is based on the discussion and guided analysis of real case studies, which will be proposed to students, so that they can suggest a personal analysis approach which will be then compared with the one, ex post, chosen by the teacher. The students will have the chance to discuss how to apply to real data and problems the theoretical knowledge they have learnt in the different courses of the master's degree program.

Knowledge and comprehension
During the laboratory students will deal with real case studies from biomedical fields, and this will stimulate the learning of the fundamental steps of the process of analyzing such data. At the end of the laboratory, the students will have reached a higher degree of autonomy in the formalization of the most appropriate research paths to answer questions raised by the proposed real-world problems.

Applying knowledge and comprehension
Through the practical experience developed within the laboratory, and according to the different research questions, students will acquire a more in-depth knowledge of the basic hypotheses, they will be able to clearly state the estimand of interest, identify and estimate these quantities from the observed data.

Judgement skills
At the end of the laboratory, the students will have a higher autonomy in the application of the most appropriate methodological tools for the analysis of real case studies in the biomedical field, and a greater critical ability to evaluate the basic hypotheses, the employed procedures, the obtained results.

Communication skills
Thanks to the laboratory structure which proposes the analysis of several, different, practical case studies, also analyzed by group activities, students will acquire the ability to communicate the main results, also to non-experts. This aspect is worked out as an integral part of the laboratory.

Learning skills
The applied research activity the students will be exposed to during the laboratory defines an important occasion to reconsider the theoretical knowledge already acquired in the different master’s courses, as the analysis of real case studies allows to discuss the basic hypotheses, the main features and the potentialities of the employed analysis methods.

Educational objectives

Learning goals

The main goal of the course is to learn about common general computational tools and methodologies to perform reliable statistical analyses.

Students will be able

to understand the theoretical foundations of the most important methods;

to appropriately implement and apply computational statistical procedures;

to interpret the results deriving from their applications to real data. .

(a) Knowledge and understanding

After attending the course, students will know and understand the most important computational techniques in statistical analysis. In addition, students will be able to appropriately implement the learned tools with the statistical software R and to develop original ideas often in a research context.

(b) Applying knowledge and understanding

At the end of the course, students will be able to formalize statistical problems from a computational point of view, to apply the learned methods to solve them, also in contexts not covered in the lessons, and to interpret the results deriving from their applications to real data.

c) Making judgements

Students will develop critical skills through the application of computational methodologies to a wide range of statistical problems and through the comparison of alternative solutions to the same problem by using different tools. Furthermore, they will learn to interpret critically the results obtained by applying procedures to real datasets.

(d) Communication skills.

By studying and carrying out practical exercises, students will acquire the technical-scientific language of the discipline, which must be suitably used in the final written test. Communication skills will also be developed through group activities.

(e) Learning skills

Students who pass the exam have learned computational techniques useful in statistical analysis and to work self-sufficiently to face the complexity of the statistical problems.

Educational objectives

The primary educational objective of the course is the study of applied statistics. Given the vastness of the application domain, it will focus in particular on the quantitative analysis applied to the finance sector and market automation methodologies and algorithms. Students must know how to solve the analytical problems necessary for apply the aforementioned methods and be able to interpret the results that derive from their application to real data.

Specific objectives

a) Knowledge and ability to understand
After attending the course the students must acquire a complete profile of quantitative analyst ("quant"), both regarding the knowledge of the methodologies, as well as regards the ability to implement them in modern programming languages, such as c #, c ++, vb.net, j # and in general, the integrated Visual Studio development environment.

b) Ability to apply knowledge and understanding
At the end of the course, students are able to model all phases of quantitative analysis, including simulation and modeling processes strategies, calculation of performance indices and construction of empirical probabilities of the main performance indices.

c) Autonomy of judgment
Students develop critical skills through the creation of new strategies and the corresponding simulation study, both with backtesting and forward testing techniques. Both with simulated data, through mixtures of random processes, and through the historical series observed in the past.

d) Communication skills
Students, through the study and performance of practical exercises, acquire the technical-scientific language of the discipline, which it must be used appropriately in both the intermediate and final written tests and in the oral tests. Communication skills come also developed through laboratory programming activities and also group research activities.

e) Learning ability
Students who pass the exam have learned the methods of analysis that allow them to face concrete problems of "quantitative analysis" and if necessary to intervene on any phase of the complex process that goes from the acquisition of the financial data stream in real time, to its statistical analysis for the creation of automation strategies, to the quantitative evaluation of the methodologies. They also have the ability to implement any methodology required in a modern development environment, OOP programming, and advanced graphical interfaces.

Statistica Applicata
Obiettivi generali

L'obiettivo formativo primario dell’insegnamento è lo studio di Temi di Statistica Applicata. Data la vastità del dominio applicativo si focalizzerà in particolare sull'analisi quantitativa applicata al settore della finanza e delle metodologie e algoritmi di automazione dei mercati. Gli studenti dovranno saper risolvere i problemi analitici necessari per applicare i suddetti metodi e saper interpretare i risultati che discendono dalla loro applicazione a dati reali.

Obiettivi specifici

a) Conoscenza e capacità di comprensione
Dopo aver frequentato il corso gli studenti devono acquisire un profilo completo di analista quantitativo ("quant"), sia per quanto concerne la conoscenza delle metodologie, sia per quanto riguarda la capacità di implementarle nell'ambito di linguaggi moderni di programmazione, quali ad esempio c#, c++, vb.net, j# in generale l'ambiente
integrato di sviluppo Visual Studio.

b) Capacità di applicare conoscenza e comprensione
Al termine del corso gli studenti sono in grado modellizzare tutte le fasi dell'analisi quantitativa, inclusi i processi di simulazione, modellizzazione delle strategie, calcolo di indici di performance e costruzione delle distribuzioni empiriche di probabilità dei principali indici di performance.

c) Autonomia di giudizio
Gli studenti sviluppano capacità critiche attraverso la creazione di nuove strategie e il corrispondente studio simulativo, sia con tecniche di backtesting che forward testing.
Sia con dati simulati, mediante misture di processi aleatori, sia mediante le serie storiche osservate nel passato.

d) Abilità comunicativa
Gli studenti, attraverso lo studio e lo svolgimento di esercizi pratici, acquisiscono il linguaggio tecnico-scientifico della disciplina, che deve essere opportunamente utilizzato sia nelle prove scritte intermedie e finali che nelle prove orali. Le abilità comunicative vengono sviluppate anche attraverso attività programmazione in laboratorio e anche attività di ricerca in gruppi.

e) Capacità di apprendimento
Gli studenti che superano l’esame hanno appreso i metodi di analisi che consentono loro di affrontare problemi concreti di "quantitative analysis" e se necessario di poter intervenire su qualunque fase del complesso processo che va dall'acquisizione dello stream di dati finanziari in tempo reale, alla sua analisi statistica ai fini della creazione di strategie di automazione, alla valutazione quantitativa delle metodologie. Hanno inoltre la capacità di implementare qualunque metodologia richiesta in un ambiente di sviluppo moderno, di programmazione OOP, e interfacce grafiche avanzate.

The primary educational objective of the course is the study of applied statistics. Given the vastness of the application domain, it will focus in particular on the quantitative analysis applied to the finance sector and market automation methodologies and algorithms. Students must know how to solve the analytical problems necessary for apply the aforementioned methods and be able to interpret the results that derive from their application to real data.

Specific objectives

a) Knowledge and ability to understand
After attending the course the students must acquire a complete profile of quantitative analyst ("quant"), both regarding the knowledge of the methodologies, as well as regards the ability to implement them in modern programming languages, such as c #, c ++, vb.net, j # and in general, the integrated Visual Studio development environment.

b) Ability to apply knowledge and understanding
At the end of the course, students are able to model all phases of quantitative analysis, including simulation and modeling processes strategies, calculation of performance indices and construction of empirical probabilities of the main performance indices.

c) Autonomy of judgment
Students develop critical skills through the creation of new strategies and the corresponding simulation study, both with backtesting and forward testing techniques. Both with simulated data, through mixtures of random processes, and through the historical series observed in the past.

d) Communication skills
Students, through the study and performance of practical exercises, acquire the technical-scientific language of the discipline, which it must be used appropriately in both the intermediate and final written tests and in the oral tests. Communication skills come also developed through laboratory programming activities and also group research activities.

e) Learning ability
Students who pass the exam have learned the methods of analysis that allow them to face concrete problems of "quantitative analysis" and if necessary to intervene on any phase of the complex process that goes from the acquisition of the financial data stream in real time, to its statistical analysis for the creation of automation strategies, to the quantitative evaluation of the methodologies. They also have the ability to implement any methodology required in a modern development environment, OOP programming, and advanced graphical interfaces.

Educational objectives

General Objectives.
The objective of this course is to present the basics necessary for the use of a general-purpose imperative programming language. In particular, the use of the Python 3 programming language will be demonstrated.

Specific objectives
(a) Knowledge and understanding skills.
Students will know the basic constructs of the Python 3 language, will be able to understand a simple program written in Python 3 and to write programs in the same language. They will also be able to use an integrated development environment (IDE).

(b) Ability to apply knowledge and understanding.
At the end of the course, students will be able to solve simple algorithmic problems using the Python 3 programming language, correct syntactic and semantic errors using an IDE, and evaluate the correctness and complexity of the identified solutions.

(c) Autonomy of judgment.
Students will develop the ability to formalize algorithms using a programming language, choosing the constructs best suited to solve the individual problem. They will be able to evaluate the correctness, readability and generality of the solutions identified.

(d) Communication skills.
Students will acquire the ability to formally express a mental procedure for solving a problem, and to understand the crucial points of an algorithm.

(e) Learning skills
Students will be able to easily learn the use of imperative programming languages, appreciating similarities and differences from the Python 3 language.

Educational objectives

The specific aim is to enable the student to assist him with the more specific knowledge for inclusion in the future world of work.

Educational objectives

The specific aim is to enable the student to assist him with the more specific knowledge for inclusion in the future world of work.

Educational objectives

Learning goals
Develop analytical and computational skills to solve statistical decision problems.

Knowledge and understanding
At the end of the course the students have the ability to understand and solve simple and more advanced exercises of Statistical decision theory.

Applying knowledge and understanding
Students are required to apply theoretical and computational skills (using the software R) to solve inferential problems formalized as decision problems.

Making judgements
One of the main goals of practical activities is to develop the ability of comparing and choosing alternative methods, i.e. to refine judgement skills.

Communication skills
Students acquire the ability of presenting written reports of their practical laboratories.

Learning skills
The students acquire a series of skills useful for future academic and professional activities.

Educational objectives

Learning goals.
The lab consists of the application of machine learning techniques to the analysis of images and/or textual documents.
The language used is Python 3.x with the Tensorflow package for the application of Convolutional and Recurrent Neural Networks (deep learning).

Knowledge and understanding.
Acquire the basics of machine learning techniques.
Understanding how and why to choose between alternative methods, or possibly how to combine different methods.
Ability to handle large amounts of images or text with the help of appropriate open source software.

Applying knowledge and understanding.
Students develop critical skills through the application of a wide range of statistical and machine learning models.
They also develop the critical sense through the comparison between alternative solutions to the same problem obtained using different learning logics.
They learn to critically interpret the results obtained by applying the procedures to real data sets.

Making judgements.
Students develop critical skills through the application of a wide range of machine learning and statistical models.
They also develop the critical sense through the comparison between alternative solutions to the same problem obtained using different learning logics.
They learn to critically interpret the results obtained by applying the procedures to real data sets.

Communication skills.
Students, through the study and execution of practical exercises, acquire the technical-scientific language of the discipline, which must be used appropriately in both the intermediate and final written tests and in the oral tests.
Communication skills are also developed through group activities.

Learning skills.
Students who pass the exam have learned a method of analysis that allows them to tackle the analysis of the images or text documents by machine learning techniques.

Educational objectives

The primary educational objective of the course is the study of applied statistics. Given the vastness of the application domain, it will focus in particular on the quantitative analysis applied to the finance sector and market automation methodologies and algorithms. Students must know how to solve the analytical problems necessary for apply the aforementioned methods and be able to interpret the results that derive from their application to real data.

Specific objectives

a) Knowledge and ability to understand
After attending the course the students must acquire a complete profile of quantitative analyst ("quant"), both regarding the knowledge of the methodologies, as well as regards the ability to implement them in modern programming languages, such as c #, c ++, vb.net, j # and in general, the integrated Visual Studio development environment.

b) Ability to apply knowledge and understanding
At the end of the course, students are able to model all phases of quantitative analysis, including simulation and modeling processes strategies, calculation of performance indices and construction of empirical probabilities of the main performance indices.

c) Autonomy of judgment
Students develop critical skills through the creation of new strategies and the corresponding simulation study, both with backtesting and forward testing techniques. Both with simulated data, through mixtures of random processes, and through the historical series observed in the past.

d) Communication skills
Students, through the study and performance of practical exercises, acquire the technical-scientific language of the discipline, which it must be used appropriately in both the intermediate and final written tests and in the oral tests. Communication skills come also developed through laboratory programming activities and also group research activities.

e) Learning ability
Students who pass the exam have learned the methods of analysis that allow them to face concrete problems of "quantitative analysis" and if necessary to intervene on any phase of the complex process that goes from the acquisition of the financial data stream in real time, to its statistical analysis for the creation of automation strategies, to the quantitative evaluation of the methodologies. They also have the ability to implement any methodology required in a modern development environment, OOP programming, and advanced graphical interfaces.

Statistica Applicata
Obiettivi generali

L'obiettivo formativo primario dell’insegnamento è lo studio di Temi di Statistica Applicata. Data la vastità del dominio applicativo si focalizzerà in particolare sull'analisi quantitativa applicata al settore della finanza e delle metodologie e algoritmi di automazione dei mercati. Gli studenti dovranno saper risolvere i problemi analitici necessari per applicare i suddetti metodi e saper interpretare i risultati che discendono dalla loro applicazione a dati reali.

Obiettivi specifici

a) Conoscenza e capacità di comprensione
Dopo aver frequentato il corso gli studenti devono acquisire un profilo completo di analista quantitativo ("quant"), sia per quanto concerne la conoscenza delle metodologie, sia per quanto riguarda la capacità di implementarle nell'ambito di linguaggi moderni di programmazione, quali ad esempio c#, c++, vb.net, j# in generale l'ambiente
integrato di sviluppo Visual Studio.

b) Capacità di applicare conoscenza e comprensione
Al termine del corso gli studenti sono in grado modellizzare tutte le fasi dell'analisi quantitativa, inclusi i processi di simulazione, modellizzazione delle strategie, calcolo di indici di performance e costruzione delle distribuzioni empiriche di probabilità dei principali indici di performance.

c) Autonomia di giudizio
Gli studenti sviluppano capacità critiche attraverso la creazione di nuove strategie e il corrispondente studio simulativo, sia con tecniche di backtesting che forward testing.
Sia con dati simulati, mediante misture di processi aleatori, sia mediante le serie storiche osservate nel passato.

d) Abilità comunicativa
Gli studenti, attraverso lo studio e lo svolgimento di esercizi pratici, acquisiscono il linguaggio tecnico-scientifico della disciplina, che deve essere opportunamente utilizzato sia nelle prove scritte intermedie e finali che nelle prove orali. Le abilità comunicative vengono sviluppate anche attraverso attività programmazione in laboratorio e anche attività di ricerca in gruppi.

e) Capacità di apprendimento
Gli studenti che superano l’esame hanno appreso i metodi di analisi che consentono loro di affrontare problemi concreti di "quantitative analysis" e se necessario di poter intervenire su qualunque fase del complesso processo che va dall'acquisizione dello stream di dati finanziari in tempo reale, alla sua analisi statistica ai fini della creazione di strategie di automazione, alla valutazione quantitativa delle metodologie. Hanno inoltre la capacità di implementare qualunque metodologia richiesta in un ambiente di sviluppo moderno, di programmazione OOP, e interfacce grafiche avanzate.

The primary educational objective of the course is the study of applied statistics. Given the vastness of the application domain, it will focus in particular on the quantitative analysis applied to the finance sector and market automation methodologies and algorithms. Students must know how to solve the analytical problems necessary for apply the aforementioned methods and be able to interpret the results that derive from their application to real data.

Specific objectives

a) Knowledge and ability to understand
After attending the course the students must acquire a complete profile of quantitative analyst ("quant"), both regarding the knowledge of the methodologies, as well as regards the ability to implement them in modern programming languages, such as c #, c ++, vb.net, j # and in general, the integrated Visual Studio development environment.

b) Ability to apply knowledge and understanding
At the end of the course, students are able to model all phases of quantitative analysis, including simulation and modeling processes strategies, calculation of performance indices and construction of empirical probabilities of the main performance indices.

c) Autonomy of judgment
Students develop critical skills through the creation of new strategies and the corresponding simulation study, both with backtesting and forward testing techniques. Both with simulated data, through mixtures of random processes, and through the historical series observed in the past.

d) Communication skills
Students, through the study and performance of practical exercises, acquire the technical-scientific language of the discipline, which it must be used appropriately in both the intermediate and final written tests and in the oral tests. Communication skills come also developed through laboratory programming activities and also group research activities.

e) Learning ability
Students who pass the exam have learned the methods of analysis that allow them to face concrete problems of "quantitative analysis" and if necessary to intervene on any phase of the complex process that goes from the acquisition of the financial data stream in real time, to its statistical analysis for the creation of automation strategies, to the quantitative evaluation of the methodologies. They also have the ability to implement any methodology required in a modern development environment, OOP programming, and advanced graphical interfaces.

Educational objectives

General goals
The course aims to introduce HPC (High Performance Computing) systems, their architecture, and their principles of operation. Additionally, the course aims to introduce parallel and distributed programming, with the goal of reducing resolution times for particularly complex problems through the
coordinated use of a large number of computing units.

Knowledge and comprehension
Students will understand the principles underlying HPC systems and how to organize a resolution strategy for an algorithm that can benefit from the
presence of multiple computing units.

Applying knowledge and comprehension
Upon completion of the course, students will be able to create simple parallel and distributed applications that leverage the increased computing capacity of an HPC system. Students will also be able to execute the developed algorithms using an existing computing infrastructure.

Judgement skills
Students will develop the ability to identify particular types of problems for which the use of a parallel or distributed approach is significantly helpful.

Communication skills
The students will acquire the technical-scientific language commonly used in this discipline, also thanks to the study and to the practice.

Learning skills
Students who pass the exam will have learned the paradigms to apply parallel and distributed computing techniques to solve complex problems, utilizing the computing capabilities of an HPC system.

Educational objectives

Learning goals
Thanks to technological advances, the acquisition of data has become inexpensive and big data sets are easily obtained, for example, via Internet, e-commerce or by electronic banking services.
Such data can be stored in data warehouses and data marts specifically intended to support business decisions.
Data mining provides the tools to manage and analyse these data, to extract the relevant information and build forecasting models, fundamental tools in areas such as credit evaluation, marketing, customer relationship management.
The course will examine the data preprocessing methods and their importance.
We'll cover some of non-parametric models for classification and regression: decision trees, neural networks, support vector machines.
Ensemble learning methods (Bagging, Boosting, Stacking, Blended) will be illustrated.
The course will address also the analysis of textual data and images.

Knowledge and understanding.
Acquire the basics of data mining techniques.
Understanding how and why to choose between alternative statistical methods, or possibly how to combine different methods.
Ability to handle large amounts of data with the help of appropriate, commercial and open source, software.

Applying knowledge and understanding.
Students develop critical skills through the application of a wide range of statistical and machine learning models.
They also develop the critical sense through the comparison between alternative solutions to the same problem obtained using different learning logics.
They learn to critically interpret the results obtained by applying the procedures to real data sets.

Making judgements.
Students develop critical skills through the application of a wide range of machine learning and statistical models.
They also develop the critical sense through the comparison between alternative solutions to the same problem obtained using different learning logics.
They learn to critically interpret the results obtained by applying the procedures to real data sets.

Communication skills.
Students, through the study and execution of practical exercises, acquire the technical-scientific language of the discipline, which must be used appropriately in both the intermediate and final written tests and in the oral tests.
Communication skills are also developed through group activities.

Learning skills.
Students who pass the exam have learned a method of analysis that allows them to tackle, in subsequent statistical area teachings, the study of the formal properties of data mining procedures in more complex modeling contexts.

Educational objectives

Learning goals.
The aim of this course is to increase the knowledge of multivariate statistical models for analyzing and understanding complex (often very large) data matrices.
Students have to be able to formalize the real problems in terms of the models discussed during the course and to interpret the obtained results.
Finally, students have to program and apply these methodologies by using statistical software (in particular R / Matlab).

Knowledge and understanding.
After attending the course, students know and understand the main multivariate statistical models to deal with the studies of complex phenomena.

Applying knowledge and understanding.
At the end of the course, students are able to use and select among several multivariate statistical models to face problems related to different fields.
Students are able to critically interpret the results obtained on real data sets by using statistical software (in particular R / Matlab).

Making judgements.
Students develop critical skills through the application of statistical models on real data and the comparison among solutions obtained from different models aimed at facing the same problem.

Communication skills.
Students, through practical exercises, reading and critical evaluation of scientific papers and group activities, acquire the technical-scientific ability to communicate results obtained on real problems.

Learning skills.
At the end of the course, students have a broader knowledge of the multivariate statistical models that allow them to carry out complex analysis strategies to extract the relevant information from the observed data, often of large size.
This prerogative, combined with the knowledge of programming statistical software, responds to the increasing requests of the market (companies, research institutions, etc.).

Educational objectives

Learning goals
The student at the end of the course should be able to use with knowledge advanced modeling and exploratory techniques specifically developed for spatially dependent data. This is achieved by assigning several homeworks on real data. Practical sessions with the R software are part of each lecture, so to allow students to implement what is taught in the theoretical part. Among the expected results, ability to elaborate spatial environmental data using R software, ability to interpret the results obtained, ability to choose the most suitable statistical models according to the hypotheses they are founded on and to their compatibility with the available data.

Knowledge and understanding
The student will be able to understand the main tools for the analysis of spatial and spatio-temporal data.

Applying knowledge and understanding
Students will be involved in the discussion and analysis of case studies using the open source statistical software R. Students will be asked to prepare and discuss a presentation of the results of their homeworks. The presentation will be given in front of the class and discussed.

Making judgements
Through the homeworks and the final presentations discussions, students will develop judgements capacity in terms of theoretical choices in representation of real world phenomena.

Communication skills
Students will be asked to prepare and discuss a presentation of the results of their homeworks. The presentation will be given in front of the class and discussed.
This procedure will help the student to develop his/her ability to communicate the results of its work.

Learning skills
One of the aims of the course is to build a statistical glossary and a dictionary of specific statistical concepts that will allow the student to read and understand scientific papers using advanced statistical tools in the analysis of environmental data.

Educational objectives

General goals
Learning advanced statistical methods for relational data (theory and practice)

Knowledge and understanding
Knowledge and understanding of theory and methodology related to the study of relational data (for instance, dimensionality reduction, classification).

Applying knowledge and understanding
Ability to apply appropriate methods and approaches in the analysis of relational data

Making judgements
Ability of choosing appropriate methods, models and software in different problems; ability of discussing and interpreting results of applications of the methodologies on real data

Communication skills
Ability of using appropriate scientific language and of communicating results of the analyses in written reports

Learning skills
Students acquire skills useful to approach more advanced topics in Statistics

Educational objectives

1. Knowledge and understanding: what students should know on the course topics after having passed the exam
After passing the exam, students have got a basic knowledge on both the history and the development of network analysis as an autonomous methodology to study relational data; on the main intellectual traditions, and the most relevant scholars who have contributed to its growth in the studies on the structure and the dynamics within and between groups (Moreno, Freeman, Mit, Harvard School). Moreover, students know the main properties of a relational data matrix; some basic concepts about the nodes and the relationships (lines, directions); they are able to calculate some specific metrics (density, centrality and centralization, betweenness, closeness, clustering); to use some statistical techniques (components, core and clique), and create adequate graphs representation of social networks.

2. Applying knowledge and understanding: what students should be able to do after having passed the exam
After passing the exam, students are able a) to apply theoretical schemes to complex social phenomena, traducing them in concrete research questions, smart objectives, and working hypothesis; b) students learn to gather relational data and treat them employing appropriate social network techniques, c) they show a good confidence with using Sas Viya and Ucinet software.

3. Making judgements: activities through which critical faculties should be developed.
Critical capabilities are expected to be developed through the involvement of the students in active class-work sessions. Indeed, the teaching method aims at encouraging all students, individually or in group, to observe, to analyse, to critically comment, to interpret, and share ideas, in order to get through decision making, and problem solving about specific data analysis issues posed by the lecturer.

4. Communications skills and activities through which the ability to communicate what was learned is developed.
The ability to communicate is developed through working group and the presentation/discussion of the results of the class activities (data analysis presentations).

5. Learning skills: ability to continue studying the topics.
The competences acquired should contribute to both strengthen students’ knowledge on social networks, and improve their capabilities to learn more advanced methods and techniques of network analysis about complex social phenomena at theoretical and applied level.

Educational objectives

General
Managers worldwide, beyond their personal experience, rely more and more on the use of
quantitative decision models which allow to take advantage of today’s data availability. Morover,
new computational tools, including algorithms, cloud computing and distributed processing, make
it possible to both develop and compute analytical models in a very short time, meeting the
requirement of practical applications and often using real time data. Data Driven Decision Making
is the new paradigm for managers to make better, evidence based, more rational, transparent and
reliable decisions.
In this context, the primary educational objective of the course is students' learning of the main
decision problems that arise in real world and the quantitative methods to model them and to
feed them with adequate data. Students must also be able to correctly use, for decision-making
and management purposes, computer tools to analyze data generated by real problems in
different contexts (e.g. service management, marketing, transportation, operations management
and production, and finance) through the analysis of several case studies.

Specific objectives

a) Knowledge and ability to understand
After attending the course the students know and classify the main decision problems arising in
real world organization and the main analytical methods (decision and optimization models and
algorithms) to be used to support a Manager during his/her decision process.

b) Ability to apply knowledge and understanding
At the end of the course the students are able to formalize real problems in terms of decision
problems and to apply the specific methods taught in the course to solve them. They are also able
to classify the type of problem to it the most appropriate quantitative method, experimenting the
effectiveness for decisional purposes also on real problems.

c) Autonomy of judgment
Students develop critical skills through the application of modeling, decision analysis and multi
objective optimization methodologies to a broad set of practical problems. They also develop the
critical sense through the comparison between alternative solutions to the same problem
obtained using methods of analysis and realistic scenarios different from each other. They learn to
critically interpret the results obtained by applying the procedures to real data sets.

d) Communication skills
Students, through the study and the carrying out of practical exercises, acquire the technical-
scientific language of the course, which must be properly used both in the intermediate and final
written tests and in the oral tests. Communication skills are also developed through group
activities.

e) Learning ability
Students who pass the exam have learned methods of decision analysis and multiobjective
optimization that allow them to face, decision-making problems and optimization on complex
organizations.

Educational objectives

Learning goals
The aim of the course is to introduce students to the main methods of analysis and forecasting of the economic and financial time series. In particular, it covers
i) Linear stochastic processes. Stationarity. Invertibility. Causality. ARMA processes. Identification, estimation, interpretation and forecasting.
ii) Measurement and analysis of volatility. ARCH and GARCH models. Identification, estimation, interpretation and forecasting.
Knowledge of the econometric theory for cross-section analysis, inference and probability theory is a prerequisite.

Knowledge and understanding.
After attending the course the students know and understand the main problems related to time series (for example: absence of stationarity) and the main methods to be used to solve such problems (for example: unit root tests).

Applying knowledge and understanding.
At the end of the course the students are able to formalize real problems in terms of linear regression models and to apply the methods
specific to the discipline to solve them.
They are also able to apply the methods to concrete situations and to interpret the results.

Making judgements.
Students develop a knowledge of the analytical properties of the presented methodologies and the ability to build programs for their implementation.
They also learn to critically interpret the results obtained by applying the procedures to concrete situations.

Communication skills.
Students acquire the technical-scientific language of the discipline, which it must be used appropriately in both the intermediate and final written tests and in the oral tests. Communication skills are also developed through group activities.

Learning skills.
Students who pass the exam have learned a method of analysis that allows them to tackle the study of analytical properties in more complex modeling contexts in subsequent quantitative area teachings. They are also able to produce sound empirical analyzes and forecasts.

Educational objectives

The primary educational objective of the laboratory is students' learning and practice of the main
tools for Data Driven Decision Making, that is the use of computer tools to analyze data and
formalize optimization or decision models and produce decisions that create value.

Knowledge and ability to understand
After attending the laboratory, students will be able to use decision support methods (like,
the Analytical Hierchical Process), optimization solvers (like CPLEX or Gurobi) and computer
algorithms for modelling multicriteria decision and optimization problems.

Ability to apply knowledge and understanding
The models are formalized in the realm of problems. The most appropriate quantitative
method, experimenting with the effectiveness of the problem.

Autonomy of judgment
Students develop critical skills through the application of modeling, analysis and
optimization to a broad set of decision problems. They also develop the critical sense
through the comparison between alternative solutions to the same problem using
methods of analysis and realistic scenarios different from each other. They learn to
critically interpret the results obtained by applying the procedures to real data sets.

Communication skills
Students, through the study and the carrying out of the practical exercises, acquire the
technical-scientific language of the course, which should be used in the tests.
Communication skills are also developed through group activities.

Learning ability
Students who pass the exam have acquired the main methods of analysis and optimization
of decision problems that allow them to face decision-making and quantitative
management in competitive nowadays enterprises.

Educational objectives

Learning goals
The primary aim of the module is to make students learn the main theoretical aspects of probability theory. At the end of the module, students are able to solve analytical problems through the application of such theoretical concepts. The module prepares to attend the next module of the course, which entails statistical inference, and for the course of Stochastic Processes for Applied Sciences, for it should be considered as a necessary requirement

Knowledge and understanding.
At the end of the module, students know the main aspects of probability theory and the methods that can be used to solve problems subject to and characterized by uncertainty.

Applying knowledge and understanding.
At the end of the module students know how to formalize problems subject to and characterized by uncertainty in probabilistic terms, how to choose the appropriate methods to solve them, how to describe real phenomena using probabilistic constructs.

Making judgements.
Students develop critical thinking by applying the relevant theory to a wide range of real-world problems that can be described using probabilistic models. They also develop critical judgement attitudes by comparing alternative solutions to the same problem using different methodological approaches.

Communication skills.
Students, through the study and the development of exercises, are expected to acquire an appropriate technical-scientific language, which is evaluated in the intermediate and final written tests, as well as in the oral exams.

Learning skills.
At the end of the module, the students know the basic concepts of probability theory that allow them to attend the next module of the course, which entails statistical inference. They are also able to apply probabilistic methods to theoretical and experimental problems from the physical, natural, economic and social sciences.

Educational objectives

Learning goals
The aim of this course module is to provide an in-depth critical review of the tools of parametric statistical inference, aimed to acquire the knowledge useful for attending the master's degree course.

Knowledge and understanding.
By the end of the module, students have a thorough understanding of the general theory of point, interval estimation, and parametric hypothesis testing.

Applying knowledge and understanding.
At the end of the module, students are able to use the main statistical inference techniques on samples drawn from populations described by parametric probability models

Making judgements.
Autonomy of judgment in students is stimulated by using empirical cases, and by critically comparing different techniques and approaches to statistical inference on well defined problems.

Communication skills.
The communication skills of the students are stimulated through the discussion of the features of the available approaches to statistical inference that have been introduced in the literature

Learning skills.
At the end of the module, students have the ability to deal with real empirical cases and acquire the necessary knowledge to attend specialized courses in statistics offered by the master's degree program.

Educational objectives

1 - PROBABILITY
Learning goals
The aim of this course module is to provide an in-depth critical review of the tools of parametric statistical inference, aimed to acquire the knowledge useful for attending the master's degree course.

Knowledge and understanding.
By the end of the module, students have a thorough understanding of the general theory of point, interval estimation, and parametric hypothesis testing.

Applying knowledge and understanding.
At the end of the module, students are able to use the main statistical inference techniques on samples drawn from populations described by parametric probability models

Making judgements.
Autonomy of judgment in students is stimulated by using empirical cases, and by critically comparing different techniques and approaches to statistical inference on well defined problems.

Communication skills.
The communication skills of the students are stimulated through the discussion of the features of the available approaches to statistical inference that have been introduced in the literature

Learning skills.
At the end of the module, students have the ability to deal with real empirical cases and acquire the necessary knowledge to attend specialized courses in statistics offered by the master's degree program.
2 - STATISTICS
Learning goals
The aim of this course module is to provide an in-depth critical review of the tools of parametric statistical inference, aimed to acquire the knowledge useful for attending the master's degree course.

Knowledge and understanding.
By the end of the module, students have a thorough understanding of the general theory of point, interval estimation, and parametric hypothesis testing.

Applying knowledge and understanding.
At the end of the module, students are able to use the main statistical inference techniques on samples drawn from populations described by parametric probability models

Making judgements.
Autonomy of judgment in students is stimulated by using empirical cases, and by critically comparing different techniques and approaches to statistical inference on well defined problems.

Communication skills.
The communication skills of the students are stimulated through the discussion of the features of the available approaches to statistical inference that have been introduced in the literature

Learning skills.
At the end of the module, students have the ability to deal with real empirical cases and acquire the necessary knowledge to attend specialized courses in statistics offered by the master's degree program

Educational objectives

Learning goals.
The main educational objective of the course is the knowledge of the analysis of Generalized Linear Models in their theoretical, methodological and applicative aspects.

Knowledge and understanding.
After attending the course the students know and can apply the methods of statistical analysis to all those situations that can be represented in the Generalized Linear Models family.

Applying knowledge and understanding.
At the end of the course students are able to identify which types of situations can be analyzed in the family of generalized linear models, identifying sampling model, link function and linear predictor.
They are also able to formulate substantive questions in parametric terms and answering to these questions with the tools of statistical analysis.

Making judgements.
Students develop critical skills through the selection, estimation and validation of the statistical model in different situations that can be represented in the Generalized Linear Models family.

Communication skills.
Particular attention is paid to the technical-scientific language of the discipline, which must be used appropriately in the final test.

Learning skills.
Students who pass the exam have acquired the ability to associate to the different real situation, the Generalized Linear Model that best represent them and to evaluate the quality of this representation.
These tools are useful both for insights into the possible application fields and for the study of parametric models in general.

Educational objectives

General goals
Principles and methods to study the health conditions of a population

Specific goals

Knowledge and understanding
After attending the course students are able to handle problems related to the evaluation of health conditions of single units and of a population

Applying knowledge and understanding
After attending the course students are able to handle the measure of health conditions of single units and of a population

Making judgements
Judgement skills are improved by discussion and use of alternative methods and practical and real examples

Communication skills
Students will develop capacity of communicating results through oral presentation of practical problems and by the final exam

Learning skills
Students who pass the exam have acquired skills and methods in Social epidemiology and in statistical methods for public health

Educational objectives

Expected learning outcomes
Ability to approach the literature on Population and Economy, Population and Environment, Population and Welfare System
Handling of main demo-economic models

Skills to be acquired
Orientation in the use of the main models that link demographic and economic variables
Orientation in sources that inform about the relationship between demographic variables and environmental impact indexes

Educational objectives

Learning goals.
Learning goal of the course is to acquire a basic knowledge of methods for the analysis of survival and longitudinal data.

Knowledge and understanding.
At the end of the course, the students have a basic knowledge of regression models for survival and longitudinal data

Applying knowledge and understanding.
Thanks to practical, computer-aided, classes, students learn how to apply regression models to observed survival and longitudinal data.

Making judgements.
Reviewing different estimators make students able in making judgements on observed data.

Communication skills.
At the ned of the course, students acquire basic notation and communication skills to be used in the context of the analysis of survival and longitudinal data.

Learning skills.
Students with a positive mark acquire a basic knoledge of surival and longitudinal data analysis that can be used in different empirical application fields.

Educational objectives

Learning goals
Thanks to technological advances, the acquisition of data has become inexpensive and big data sets are easily obtained, for example, via Internet, e-commerce or by electronic banking services.
Such data can be stored in data warehouses and data marts specifically intended to support business decisions.
Data mining provides the tools to manage and analyse these data, to extract the relevant information and build forecasting models, fundamental tools in areas such as credit evaluation, marketing, customer relationship management.
The course will examine the data preprocessing methods and their importance.
We'll cover some of non-parametric models for classification and regression: decision trees, neural networks, support vector machines.
Ensemble learning methods (Bagging, Boosting, Stacking, Blended) will be illustrated.
The course will address also the analysis of textual data and images.

Knowledge and understanding.
Acquire the basics of data mining techniques.
Understanding how and why to choose between alternative statistical methods, or possibly how to combine different methods.
Ability to handle large amounts of data with the help of appropriate, commercial and open source, software.

Applying knowledge and understanding.
Students develop critical skills through the application of a wide range of statistical and machine learning models.
They also develop the critical sense through the comparison between alternative solutions to the same problem obtained using different learning logics.
They learn to critically interpret the results obtained by applying the procedures to real data sets.

Making judgements.
Students develop critical skills through the application of a wide range of machine learning and statistical models.
They also develop the critical sense through the comparison between alternative solutions to the same problem obtained using different learning logics.
They learn to critically interpret the results obtained by applying the procedures to real data sets.

Communication skills.
Students, through the study and execution of practical exercises, acquire the technical-scientific language of the discipline, which must be used appropriately in both the intermediate and final written tests and in the oral tests.
Communication skills are also developed through group activities.

Learning skills.
Students who pass the exam have learned a method of analysis that allows them to tackle, in subsequent statistical area teachings, the study of the formal properties of data mining procedures in more complex modeling contexts.

Educational objectives

Learning goals
Develop analytical and computational skills to solve statistical decision problems.

Knowledge and understanding
At the end of the course the students have the ability to understand and solve simple and more advanced exercises of Statistical decision theory.

Applying knowledge and understanding
Students are required to apply theoretical and computational skills (using the software R) to solve inferential problems formalized as decision problems.

Making judgements
One of the main goals of practical activities is to develop the ability of comparing and choosing alternative methods, i.e. to refine judgement skills.

Communication skills
Students acquire the ability of presenting written reports of their practical laboratories.

Learning skills
The students acquire a series of skills useful for future academic and professional activities.

Educational objectives

General Objectives.
The objective of this course is to present the basics necessary for the use of a general-purpose imperative programming language. In particular, the use of the Python 3 programming language will be demonstrated.

Specific objectives
(a) Knowledge and understanding skills.
Students will know the basic constructs of the Python 3 language, will be able to understand a simple program written in Python 3 and to write programs in the same language. They will also be able to use an integrated development environment (IDE).

(b) Ability to apply knowledge and understanding.
At the end of the course, students will be able to solve simple algorithmic problems using the Python 3 programming language, correct syntactic and semantic errors using an IDE, and evaluate the correctness and complexity of the identified solutions.

(c) Autonomy of judgment.
Students will develop the ability to formalize algorithms using a programming language, choosing the constructs best suited to solve the individual problem. They will be able to evaluate the correctness, readability and generality of the solutions identified.

(d) Communication skills.
Students will acquire the ability to formally express a mental procedure for solving a problem, and to understand the crucial points of an algorithm.

(e) Learning skills
Students will be able to easily learn the use of imperative programming languages, appreciating similarities and differences from the Python 3 language.

Educational objectives

The specific aim is to enable the student to assist him with the more specific knowledge for inclusion in the future world of work.

Educational objectives

The specific aim is to enable the student to assist him with the more specific knowledge for inclusion in the future world of work.

Educational objectives

Learning goals.
The lab consists of the application of machine learning techniques to the analysis of images and/or textual documents.
The language used is Python 3.x with the Tensorflow package for the application of Convolutional and Recurrent Neural Networks (deep learning).

Knowledge and understanding.
Acquire the basics of machine learning techniques.
Understanding how and why to choose between alternative methods, or possibly how to combine different methods.
Ability to handle large amounts of images or text with the help of appropriate open source software.

Applying knowledge and understanding.
Students develop critical skills through the application of a wide range of statistical and machine learning models.
They also develop the critical sense through the comparison between alternative solutions to the same problem obtained using different learning logics.
They learn to critically interpret the results obtained by applying the procedures to real data sets.

Making judgements.
Students develop critical skills through the application of a wide range of machine learning and statistical models.
They also develop the critical sense through the comparison between alternative solutions to the same problem obtained using different learning logics.
They learn to critically interpret the results obtained by applying the procedures to real data sets.

Communication skills.
Students, through the study and execution of practical exercises, acquire the technical-scientific language of the discipline, which must be used appropriately in both the intermediate and final written tests and in the oral tests.
Communication skills are also developed through group activities.

Learning skills.
Students who pass the exam have learned a method of analysis that allows them to tackle the analysis of the images or text documents by machine learning techniques.

Educational objectives

1. Knowledge and understanding: what students should know on the course topics after having passed the exam
After passing the exam, students have got a basic knowledge on both the history and the development of network analysis as an autonomous methodology to study relational data; on the main intellectual traditions, and the most relevant scholars who have contributed to its growth in the studies on the structure and the dynamics within and between groups (Moreno, Freeman, Mit, Harvard School). Moreover, students know the main properties of a relational data matrix; some basic concepts about the nodes and the relationships (lines, directions); they are able to calculate some specific metrics (density, centrality and centralization, betweenness, closeness, clustering); to use some statistical techniques (components, core and clique), and create adequate graphs representation of social networks.

2. Applying knowledge and understanding: what students should be able to do after having passed the exam
After passing the exam, students are able a) to apply theoretical schemes to complex social phenomena, traducing them in concrete research questions, smart objectives, and working hypothesis; b) students learn to gather relational data and treat them employing appropriate social network techniques, c) they show a good confidence with using Sas Viya and Ucinet software.

3. Making judgements: activities through which critical faculties should be developed.
Critical capabilities are expected to be developed through the involvement of the students in active class-work sessions. Indeed, the teaching method aims at encouraging all students, individually or in group, to observe, to analyse, to critically comment, to interpret, and share ideas, in order to get through decision making, and problem solving about specific data analysis issues posed by the lecturer.

4. Communications skills and activities through which the ability to communicate what was learned is developed.
The ability to communicate is developed through working group and the presentation/discussion of the results of the class activities (data analysis presentations).

5. Learning skills: ability to continue studying the topics.
The competences acquired should contribute to both strengthen students’ knowledge on social networks, and improve their capabilities to learn more advanced methods and techniques of network analysis about complex social phenomena at theoretical and applied level.

Educational objectives

Learning goals
The course introduces to the methods for the quantitative evaluation of public policies, focusing on the various assessment concepts, on the theoretical framework, on the statistical techniques used and on the sources of data needed.
Knowledge and understanding.
Applying knowledge and understanding.
Making judgements.
Communication skills.
Learning skills.

Educational objectives

Learning goals.
The lab consists of the application of machine learning techniques to the analysis of images and/or textual documents.
The language used is Python 3.x with the Tensorflow package for the application of Convolutional and Recurrent Neural Networks (deep learning).

Knowledge and understanding.
Acquire the basics of machine learning techniques.
Understanding how and why to choose between alternative methods, or possibly how to combine different methods.
Ability to handle large amounts of images or text with the help of appropriate open source software.

Applying knowledge and understanding.
Students develop critical skills through the application of a wide range of statistical and machine learning models.
They also develop the critical sense through the comparison between alternative solutions to the same problem obtained using different learning logics.
They learn to critically interpret the results obtained by applying the procedures to real data sets.

Making judgements.
Students develop critical skills through the application of a wide range of machine learning and statistical models.
They also develop the critical sense through the comparison between alternative solutions to the same problem obtained using different learning logics.
They learn to critically interpret the results obtained by applying the procedures to real data sets.

Communication skills.
Students, through the study and execution of practical exercises, acquire the technical-scientific language of the discipline, which must be used appropriately in both the intermediate and final written tests and in the oral tests.
Communication skills are also developed through group activities.

Learning skills.
Students who pass the exam have learned a method of analysis that allows them to tackle the analysis of the images or text documents by machine learning techniques.