Educational objectives General Targets:
Prior educational teaching concern is the students understanding of the main (Economic Statistics Modeling) problems and methods for studying Panel Data Models for Finance. Furthermore, students should know both how to solve analytical problems, in order to apply the appropriate methodology, and to interpret results obtained from empirical applications to actual data.
Specific Targets:
a) Knowledge and capability in understanding.
After attending the course, students know and understand main problems of Panel Data modeling concerning the Banking System in the international and more specific European context. In particular, the course will account for the logic for building empirical models, related to the underlying economic theory (and the consequent subdivisions in endogenous and exogenous variables), with one or more equations in order to evaluate the management of the Banking System (both from the employment of the inputs and the financial products). An important part will deal with the analysis of the default risk due to Non-Performing Loans and the consequent Systemic Risk. We will study the main estimation methods for solving problems pertaining the reference sectors (quadratic methods with different types of metrics and the comparison with maximum likelihood) and the formulas implementable using the estimated functions, necessary for solving financial problems.
b) Capability of applying knowledge and comprehension
At the end of the course students are able to formalize and solve problems by means of specific methods as well as treating fundamental models of Economic Statistics with Panel Data and extend them to other specific contexts (such as that of Insurance Companies). Finally, students will be able to apply the methods studied to real data and interpret results correctly also from a theoretical point of view.
c) Autonomy in assessment.
Students develop analytical skills and capacity of facing different alternative approaches for solving actual empirical problems.
d) Communication ability.
Students learn technical language which is appropriate for the subject studied and that will be used at the oral and written exam, by means of practical exercises.
e) Learning capacity.
Students passing the exam are capable to extend the methodology studied also to other fields and derive conclusions.
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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.
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Educational objectives Learning goals.
The different techniques existing for Big Data management will be illustrated, with a particular emphasis on NoSQL databases.
The course will also deal with the problem of collecting Big Data from various sources such as from the web or from the online social networks.
This will require also the introduction of the different formats that are commonly used to encode unstructured, semi-structured and structured data and of the different techniques that can be used to automate their processing.
Successively, pre-processing techniques, including denoising and imputation of missing data, will be considered.
Then, the course will treat dimensionality reduction techniques, based on feature extraction and feature selection.
Finally, some statistical learning models, supervised and unsupervised, for the analysis of Big Data, will be presented.
Real-world problems will be addressed during the course using suitable software.
Knowledge and understanding.
The student will learn as to apply some statistical learning techniques for dimensionality reduction, based on feature extraction and feature selection.
Moreover, he will know and understand some powerful statistical learning models, supervised and unsupervised, to analyse Big Data.
Applying knowledge and understanding.
The student will be able to manage Big Data collected from various sources.
He will learn as to apply dimensionality reduction techniques, based on feature extraction and feature selection.
Moreover, he will be able to choose and apply some powerful statistical learning models to analyse Big Data.
Making judgements.
Students will develop critical skills through the application of a wide range of machine learning and statistical models.
They also will develop the critical sense through the comparison between alternative solutions to the same problem obtained using different learning logics.
They will 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 will have learned an analytical approach that allows them to tackle Big Data analysis with statistical models and machine learning methods.
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Educational objectives Learning goals.
The different techniques existing for Big Data management will be illustrated, with a particular emphasis on NoSQL databases.
The course will also deal with the problem of collecting Big Data from various sources such as from the web or from the online social networks.
This will require also the introduction of the different formats that are commonly used to encode unstructured, semi-structured and structured data and of the different techniques that can be used to automate their processing.
Successively, pre-processing techniques, including denoising and imputation of missing data, will be considered.
Then, the course will treat dimensionality reduction techniques, based on feature extraction and feature selection.
Finally, some statistical learning models, supervised and unsupervised, for the analysis of Big Data, will be presented.
Real-world problems will be addressed during the course using suitable software.
Knowledge and understanding.
The student will learn as to apply some statistical learning techniques for dimensionality reduction, based on feature extraction and feature selection.
Moreover, he will know and understand some powerful statistical learning models, supervised and unsupervised, to analyse Big Data.
Applying knowledge and understanding.
The student will be able to manage Big Data collected from various sources.
He will learn as to apply dimensionality reduction techniques, based on feature extraction and feature selection.
Moreover, he will be able to choose and apply some powerful statistical learning models to analyse Big Data.
Making judgements.
Students will develop critical skills through the application of a wide range of machine learning and statistical models.
They also will develop the critical sense through the comparison between alternative solutions to the same problem obtained using different learning logics.
They will 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 will have learned an analytical approach that allows them to tackle Big Data analysis with statistical models and machine learning methods.
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Educational objectives Learning goals.
The different techniques existing for Big Data management will be illustrated, with a particular emphasis on NoSQL databases.
The course will also deal with the problem of collecting Big Data from various sources such as from the web or from the online social networks.
This will require also the introduction of the different formats that are commonly used to encode unstructured, semi-structured and structured data and of the different techniques that can be used to automate their processing.
Successively, pre-processing techniques, including denoising and imputation of missing data, will be considered.
Then, the course will treat dimensionality reduction techniques, based on feature extraction and feature selection.
Finally, some statistical learning models, supervised and unsupervised, for the analysis of Big Data, will be presented.
Real-world problems will be addressed during the course using suitable software.
Knowledge and understanding.
The student will learn as to apply some statistical learning techniques for dimensionality reduction, based on feature extraction and feature selection.
Moreover, he will know and understand some powerful statistical learning models, supervised and unsupervised, to analyse Big Data.
Applying knowledge and understanding.
The student will be able to manage Big Data collected from various sources.
He will learn as to apply dimensionality reduction techniques, based on feature extraction and feature selection.
Moreover, he will be able to choose and apply some powerful statistical learning models to analyse Big Data.
Making judgements.
Students will develop critical skills through the application of a wide range of machine learning and statistical models.
They also will develop the critical sense through the comparison between alternative solutions to the same problem obtained using different learning logics.
They will 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 will have learned an analytical approach that allows them to tackle Big Data analysis with statistical models and machine learning methods.
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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
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Educational objectives Learning goals.
The aim of the lectures is to provide an exhaustive discussion of the main topics concerning the linear model (OLS, MLE, IV, asymptotic theory and inference) for cross-section analysis and a brief introduction to the analysis of discrete data.
Students must understand the analytical problems of these methods and be able to apply them to concrete situations.
Knowledge and understanding.
After attending the course the students know and understand the main problems related to the linear regression model (for example: absence of exogeneity) and the main methods to be used to solve such problems (for example: IV estimator).
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.
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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.
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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.
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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.
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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.
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