Corso di laurea: Statistical Methods and Applications - Metodi statistici e applicazioni A.A. 2020/2021

Primo semestre

Insegnamento

CFU

SSD

Ore Lezione

Ore Eserc.

Ore Lab

Ore Studio

Attività

Lingua

1056015 - STOCHASTIC PROCESSES (obiettivi)

Learning goals
The course provides a broad introduction to stochastic processes. In particular the aim is
- to give a rigorous introduction to the theory of stochastic processes,
- to discuss the most important stochastic processes in some depth with examples and applications,
- to give the flavour of more advanced work and applications,
- to apply these ideas to answer basic questions in several applied situations including biology, finance and search engine algorithms.

Knowledge and understanding
At the end of the course the students will be familiar with the basic concepts of the theory of stochastic processes in discrete and continuous time and will be able to apply various techniques to study stochastic models that appear in applications.

Applying knowledge and understanding
At the end of the course the students will have the tools to grasp and formalize, in the language of stochastic processes, phenomena that evolve in time and space. The students will have the tools to solve simple applied problems in new environments and broader contexts.

Making judgements
At the end of the course the students will have the tools to evaluate critically and choose between different stochastic models to model phenomena that evolve in time and space. The student will also acquire the necessary language skills to start reading academic books on the topic and research papers.

Communication skills
The students will acquire the intuition and the communication skills necessary to describe phenomena in the mathematical language of stochastic processes. In particular the student will also acquire the rationale behind the stochastic model studied (e.g. the ideas of Markovianity, transience, recurrence, equilibrium, stationarity, long and short-time behaviour...) that is necessary to communicate to specialist and non-specialist audiences.

Learning skills
The students will acquire the methodology and the language to study in a manner that may be largely autonomous and to apply the methodology to the subsequent studies in the area of statistics and finance.

9

MAT/06

72

-

Attività formative caratterizzanti

ENG

Gruppo opzionale: Curriculum Data Analyst OPZIONALE A a scelta 9 cfu - (visualizza)

9

1055949 - BAYESIAN MODELLING (obiettivi) General goals Knowledge at an intermediate and advanced level of the main issues in Bayesian statistics. Ability to apply Bayesian statistical techniques to applicative context. Knowledge and understanding Knowledge and understanding of the Bayesian approach to statistical inference, of its models and of its methodologies Applying knowledge and understanding Ability to apply Bayesian statistical methods for inferential problems in real-data problems Making judgements Ability of choosing appropriate Bayesian methods and models in different inferential problems Communication skills Ability of communicating results of the analyses in written and oral form Learning skills Students acquire skills useful to approach more advanced topics in Bayesian inference, Advanced data analysis, Statistical computing and Mathematical statistics	9	SECS-S/01	72	-	-	-	Attività formative caratterizzanti	ENG
10592813 - PROBABILITY AND STATISTICS (obiettivi) Obiettivi formativi Obiettivo formativo dell’insegnamento è l'apprendimento da parte degli studenti dei fondamenti del calcolo delle probabilità e dell’inferenza statistica. Conoscenza e capacità di comprensione Alla fine del corso gli studenti conoscono e comprendono come formalizzare l’incertezza e come fare inferenza su parametri non noti. Capacità di applicare conoscenza e comprensione Gli studenti apprendono come impostare un problema di probabilità o inferenza. Autonomia di giudizio La discussione dei vari metodi, anche con lavori di gruppo, fornisce agli studenti le capacità necessarie per analizzare criticamente, ed in autonomia, situazioni reali. Abilità comunicativa Gli studenti acquisiscono gli elementi di base per ragionare, e far ragionare, in termini quantitativi su problemi di incertezza ed inferenza. Capacità di apprendimento Gli studenti che superano l’esame sono in grado di applicare i metodi appresi in diversi contesti applicativi.	9	SECS-S/01	72	-	-	-	Attività formative caratterizzanti	ENG

Gruppo opzionale: Curriculum Data Analyst Gruppo OPZIONALE C Altre attività per 12 - (visualizza)

12

AAF1149 - altre conoscenze utili per l'inserimento nel mondo del lavoro (obiettivi) The specific goal of these activities is to enable the students to merge their academic knowledge with professional skills. By facing practical and real problems, students develop judgement and communication skills.	3	27	-	-	-	Ulteriori attività formative (art.10, comma 5, lettera d)	ENG
AAF1152 - altre conoscenze utili per l'inserimento nel mondo del lavoro (obiettivi) The specific goal of these activities is to enable the students to merge their academic knowledge with professional skills. By facing practical and real problems, students develop judgement and communication skills.	6	54	-	-	-	Ulteriori attività formative (art.10, comma 5, lettera d)	ENG
AAF1544 - LABORATORY OF STOCHASTIC PROCESSES (obiettivi) Learning goals. General learning targets: The course is organised as a series of classes where the students will have the possibility to solve and discuss the solutions of a series of advanced exercises on the theory of stochastic processes. Knowledge and understanding. At the end of the course the students will acquire the ability to solve autonomously simple and more advanced exercises on the theory of stochastic processes. Applying knowledge and understanding. During the course the students will exercise the ability to grasp and formalize, in the language of stochastic processes, phenomena that evolve in time and space and to solve simple applied problems in new environments and broader contexts. Making judgements At the end of the course the students will have the tools to evaluate critically and choose between different stochastic models to model phenomena that evolve in time and space. Communication skills. The students will exercise the intuition and the communication skills necessary to describe phenomena in the mathematical language of stochastic processes. In particular the student will acquire familiarity with the main ideas that are behind the stochastic model, e.g., the ideas of Markovianity, transience, recurrence, equilibrium, stationarity, long and short-time behaviour. Learning skills. The students will acquire autonomy in studying more advanced theoretical aspects of stochastic processes and in applying the main ideas of stochastic processes to the subsequent studies in the area of statistics and finance.	3	27	-	-	-	Ulteriori attività formative (art.10, comma 5, lettera d)	ENG
AAF1883 - LABORATORY OF MACHINE LEARNING	Erogato in altro semestre o anno
AAF1877 - LABORATORY OF FINANCIAL AND MONETARY STATISTICS	Erogato in altro semestre o anno
AAF1884 - LABORATORY OF DATA DRIVEN DECISION MAKING	Erogato in altro semestre o anno
AAF1885 - CASE STUDIES AND STATISTICAL CONSULTING	Erogato in altro semestre o anno

- - A SCELTA DELLO STUDENTE

12

96

-

Attività formative a scelta dello studente (art.10, comma 5, lettera a)

ENG

Secondo semestre

Insegnamento

CFU

SSD

Ore Lezione

Ore Eserc.

Ore Lab

Ore Studio

Attività

Lingua

10589920 - SAMPLE SURVEYS (obiettivi)

9

SECS-S/01

72

-

Attività formative caratterizzanti

ENG

Gruppo opzionale: Curriculum Data Analyst Gruppo OPZIONALE B a scelta 24 cfu - (visualizza)

24

1047208 - STATISTICAL LEARNING (obiettivi) Learning goals Devising new machine learning methods and statistical models is a fun and extremely fruitful “art”. But these powerful tools are not useful unless we understand when they work, and when they fail. The main goal of statistical learning theory is thus to study, in a statistical framework, the properties of learning algorithms mainly in the form of so-called error bounds. This course introduces the techniques that are used to obtain such results, combining methodology with theoretical foundations and computational aspects. It treats both the basic principles to design successful learning algorithms and the “science” of analyzing an algorithm’s statistical properties and performance guarantees. Theorems are presented together with practical aspects of methodology and intuition to help students develop tools for selecting appropriate methods and approaches to problems in their own data analyses. Methods for a wide variety of applied problems will be explored and implemented on open-source software like R (www.r-project.org), Keras (https://keras.io/) and TensorFlow (https://www.tensorflow.org/). Knowledge and understanding On successful completion of this course, students will: know the main learning methodologies and paradigms with their strengths and weakness; be able to identify a proper learning model for a given problem; assess the empirical and theoretical performance of different learning models; know the main platforms, programming languages and solutions to develop effective implementations. Applying knowledge and understanding Besides the understanding of theoretical aspects, thanks to applied homeworks and a final project possibly linked to hackathons or other data analysis competitions, the students will constantly be challenged to use and evaluate modern learning techniques and algorithms. Making judgements On successful completion of this course, students will develop a positive critical attitude towards the empirical and theoretical evaluation of statistical learning paradigms and techniques. Communication skills In preparing the report and oral presentation for the final project, students will learn how to effectively communicate original ideas, experimental results and the principles behind advanced data analytic techniques in written and oral form. They will also understand how to offer constructive critiques on the presentations of their peers. Learning skills In this course the students will develop the skills necessary for a successful understanding as well as development of new learning methodologies together with their effective implementation. The goal is of course to grow a active attitude towards continued learning throughout a professional career.	6	SECS-S/01	48	-	-	-	Attività formative affini ed integrative	ENG
10589423 - ALGORITHMS AND DATA STRUCTURES (obiettivi) General objectives The primary objective is to study algorithms and data structures that efficiently solve problems whose solution, using trivial approaches, would require very high resources. The algorithms studied will be implemented in Java language. Specific objectives Knowledge and ability to understand Basic data structures and their use in solving sorting, searching, and graph problems will be shown. It will be shown how the data structures described are made available in Java language. The student will be able to determine the computational complexity of algorithms, and associate them with the appropriate complexity class. Ability to apply knowledge and understanding At the end of the course the students will be able to determine algorithms to efficiently solve complex problems, in particular problems on graphs, and choose the most suitable data structures to obtain an efficient implementation of the algorithm. They will also be able to use the Java classes that implement the data structures studied. Autonomy of judgment Students will be able to distinguish the computational complexity of problems and algorithms, and to identify the computationally more costly steps in solving a problem. Communication skills Students will be able to describe, in appropriate terms, the characteristics of the main data structures, and identify the primitives needed to efficiently implement an algorithm. Learning ability Students who pass the exam will be able to take advanced courses, of an applicative nature, that require the use of sophisticated algorithms. They will also be able to appreciate software engineering and computational complexity theory teachings.	6	INF/01	48	-	-	-	Attività formative affini ed integrative	ENG
1047222 - EFFICIENCY AND PRODUCTIVITY ANALYSIS	Erogato in altro semestre o anno
1047773 - BIG DATA ANALYTICS (obiettivi) 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.
- BIG DATA ANALYTICS	Erogato in altro semestre o anno
- BIG DATA ANALYTICS	Erogato in altro semestre o anno
10589563 - DATA DRIVEN DECISION MAKING	Erogato in altro semestre o anno
10589835 - COMPUTATIONAL STATISTICS	Erogato in altro semestre o anno

Gruppo opzionale: Curriculum Data Analyst Gruppo OPZIONALE C Altre attività per 12 - (visualizza)

12

AAF1149 - altre conoscenze utili per l'inserimento nel mondo del lavoro	Erogato in altro semestre o anno
AAF1152 - altre conoscenze utili per l'inserimento nel mondo del lavoro	Erogato in altro semestre o anno
AAF1544 - LABORATORY OF STOCHASTIC PROCESSES	Erogato in altro semestre o anno
AAF1883 - LABORATORY OF MACHINE LEARNING (obiettivi) 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.	3	27	-	-	-	Ulteriori attività formative (art.10, comma 5, lettera d)	ENG
AAF1877 - LABORATORY OF FINANCIAL AND MONETARY STATISTICS (obiettivi) Learning goals Students will be introduced to the following topics 1. The Monetary, banking and financial statistics. Why Bank of Italy collects statistics and what collects. Application: recent developments of the banks. 2. The financial accountsThe financial accounts structure.Household wealth after Piketty: an international comparison.The financial structure of the companies. 3. The balance of payments and international investment positionThe Italian balance of payments: the structure and recent developments.The procedure on excessive macroeconomic imbalances in Europe.Funds held abroad by the families. 4. The sample surveys of the Bank of ItalyThe survey on Household Income: recent results and a long-term look.The survey on inflation and growth expectations.	3	27	-	-	-	Ulteriori attività formative (art.10, comma 5, lettera d)	ENG
AAF1884 - LABORATORY OF DATA DRIVEN DECISION MAKING	Erogato in altro semestre o anno
AAF1885 - CASE STUDIES AND STATISTICAL CONSULTING (obiettivi) Learning goals Prepare students to proposing solutions to real statistical problems in many research areas. Knowledge and understanding At the end of the course the students have the ability to understand and solve real practical statistical problems and to propose adequate solutions. Applying knowledge and understanding Students are required to apply theoretical and computational skills to real problems and case-studies in a wide range of domains. Making judgements One of the main goals of practical activities is to develop the ability to understand problems and to propose and compare alternative statistical approaches to solve them. Communication skills Students acquire the ability of discussing problems and of presenting oral and written reports of their practical analyses. Learning skills The students acquire a series of skills useful for future academic and professional activities.	3	27	-	-	-	Ulteriori attività formative (art.10, comma 5, lettera d)	ENG

Primo semestre

Insegnamento

CFU

SSD

Ore Lezione

Ore Eserc.

Ore Lab

Ore Studio

Attività

Lingua

10589458 - ADVANCED ECONOMIC STATISTICS (obiettivi)

6

SECS-S/03

48

-

Attività formative caratterizzanti

ENG

10589834 - Advances in data analysis and statistical modelling (obiettivi)

9

SECS-S/01

72

-

Attività formative caratterizzanti

ENG

1047802 - SPATIAL STATISTICS AND STATISTICAL TOOLS FOR ENVIRONMENTAL DATA (obiettivi)

9

SECS-S/02

72

-

Attività formative caratterizzanti

ENG

Gruppo opzionale: Curriculum Data Analyst Gruppo OPZIONALE B a scelta 24 cfu - (visualizza)

24

1047208 - STATISTICAL LEARNING	Erogato in altro semestre o anno
10589423 - ALGORITHMS AND DATA STRUCTURES	Erogato in altro semestre o anno
1047222 - EFFICIENCY AND PRODUCTIVITY ANALYSIS	Erogato in altro semestre o anno
1047773 - BIG DATA ANALYTICS (obiettivi) 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.
- BIG DATA ANALYTICS	Erogato in altro semestre o anno
- BIG DATA ANALYTICS	Erogato in altro semestre o anno
10589563 - DATA DRIVEN DECISION MAKING (obiettivi) 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.	6	MAT/09	48	-	-	-	Attività formative affini ed integrative	ENG
10589835 - COMPUTATIONAL STATISTICS (obiettivi) 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. 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. 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. 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. 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 be also developed through group activities. Learning skills Students who pass the exam have learned computational techniques useful in the statistical analysis and to work self-sufficiently to face with the complexity of the statistical problems.	6	SECS-S/01	48	-	-	-	Attività formative affini ed integrative	ENG

Gruppo opzionale: Curriculum Data Analyst Gruppo OPZIONALE C Altre attività per 12 - (visualizza)

12

AAF1149 - altre conoscenze utili per l'inserimento nel mondo del lavoro	Erogato in altro semestre o anno
AAF1152 - altre conoscenze utili per l'inserimento nel mondo del lavoro	Erogato in altro semestre o anno
AAF1544 - LABORATORY OF STOCHASTIC PROCESSES	Erogato in altro semestre o anno
AAF1883 - LABORATORY OF MACHINE LEARNING	Erogato in altro semestre o anno
AAF1877 - LABORATORY OF FINANCIAL AND MONETARY STATISTICS	Erogato in altro semestre o anno
AAF1884 - LABORATORY OF DATA DRIVEN DECISION MAKING (obiettivi) 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.	3	27	-	-	-	Ulteriori attività formative (art.10, comma 5, lettera d)	ENG
AAF1885 - CASE STUDIES AND STATISTICAL CONSULTING	Erogato in altro semestre o anno

Secondo semestre

Insegnamento

CFU

SSD

Ore Lezione

Ore Eserc.

Ore Lab

Ore Studio

Attività

Lingua

Gruppo opzionale: Curriculum Data Analyst Gruppo OPZIONALE B a scelta 24 cfu - (visualizza)

24

1047208 - STATISTICAL LEARNING	Erogato in altro semestre o anno
10589423 - ALGORITHMS AND DATA STRUCTURES	Erogato in altro semestre o anno
1047222 - EFFICIENCY AND PRODUCTIVITY ANALYSIS (obiettivi) This course has the target of providing students with the modern techniques of measuring quantitatively advanced topics in economics. In particular, our focus will be on two main interrelated directions: the analysis of production and efficiency in the private and public sectors, impact analysis and economic dynamics of sectorial and micro founded systems in the modern economy. Knowledge and understanding Students are requested to know the techniques of estimation for output and input efficiency as well as the analysis methods for sectors (both static and dynamic) Applying knowledge and understanding At the end of the course, students should be capable of interpreting and addressing a study of efficiency pertaining decisional making units, both private and public. The statistical methods related are those of inference (for firms) and impact analysis and forecasting (for sectors). Making judgements At the end of the course, students will develop skills for interpreting and assessing productivity and efficiency problems. Communication skills Students will develop capacity of communicating results through the acquisition of both theoretical and applied skills. Learning skills After the exam, students will develop capacity of making autonomously further progress as far as the new advancements of this and related subjects are concerned.	6	SECS-S/03	48	-	-	-	Attività formative affini ed integrative	ENG
1047773 - BIG DATA ANALYTICS (obiettivi) 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.
- BIG DATA ANALYTICS (obiettivi) 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.	3	INF/01	24	-	-	-	Attività formative affini ed integrative	ENG
- BIG DATA ANALYTICS (obiettivi) 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.	3	SECS-S/01	24	-	-	-	Attività formative affini ed integrative	ENG
10589563 - DATA DRIVEN DECISION MAKING	Erogato in altro semestre o anno
10589835 - COMPUTATIONAL STATISTICS	Erogato in altro semestre o anno

AAF1019 - PROVA FINALE (obiettivi)

21

525

-

Per la prova finale e la lingua straniera (art.10, comma 5, lettera c)

ENG

TOTALE

120

Primo semestre

Insegnamento

CFU

SSD

Ore Lezione

Ore Eserc.

Ore Lab

Ore Studio

Attività

Lingua

Gruppo opzionale: Curriculum Quantitative Economics gruppo A 1 esame a scelta 9 cfu - (visualizza)

9

10589834 - Advances in data analysis and statistical modelling (obiettivi) Learning goals Knowing how to reorganize multidimensional data with a complex structure for their statistical analysis. Starting from the multivariate statistical methodologies, knowing how to realize complex strategies of analysis that have an easy interpretation. To be able to make decisions based on empirical evidence giving appropriate answers to corporate information requests. Knowing how to extract relevant information from large data (big data). Knowledge and understanding Knowledge of advanced multivariate statistical methodologies and advanced formal data analysis strategies with a model-based statistical approach. Applying knowledge and understanding Understanding the most appropriate complex techniques to be able to make decisions based on empirical evidence, respond to corporate information requests and be able to extract relevant information from the observed data. Making judgements Students develop critical skills through the application of complex multivariate statistical methodologies. They learn to critically interpret the results obtained by applying the procedures to real data sets. Communication skills Students, through the study and performance of practical exercises, acquire the technical-scientific language of the discipline, which must be used in the world of work. 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 face, the world of work having acquired advanced statistical analysis tools for complex and large data.	9	SECS-S/01	72	-	-	-	Attività formative caratterizzanti	ENG
10592813 - PROBABILITY AND STATISTICS (obiettivi) Obiettivi formativi Obiettivo formativo dell’insegnamento è l'apprendimento da parte degli studenti dei fondamenti del calcolo delle probabilità e dell’inferenza statistica. Conoscenza e capacità di comprensione Alla fine del corso gli studenti conoscono e comprendono come formalizzare l’incertezza e come fare inferenza su parametri non noti. Capacità di applicare conoscenza e comprensione Gli studenti apprendono come impostare un problema di probabilità o inferenza. Autonomia di giudizio La discussione dei vari metodi, anche con lavori di gruppo, fornisce agli studenti le capacità necessarie per analizzare criticamente, ed in autonomia, situazioni reali. Abilità comunicativa Gli studenti acquisiscono gli elementi di base per ragionare, e far ragionare, in termini quantitativi su problemi di incertezza ed inferenza. Capacità di apprendimento Gli studenti che superano l’esame sono in grado di applicare i metodi appresi in diversi contesti applicativi.	9	SECS-S/01	72	-	-	-	Attività formative caratterizzanti	ENG

1056015 - STOCHASTIC PROCESSES (obiettivi)

Learning goals
The course provides a broad introduction to stochastic processes. In particular the aim is
- to give a rigorous introduction to the theory of stochastic processes,
- to discuss the most important stochastic processes in some depth with examples and applications,
- to give the flavour of more advanced work and applications,
- to apply these ideas to answer basic questions in several applied situations including biology, finance and search engine algorithms.

Knowledge and understanding
At the end of the course the students will be familiar with the basic concepts of the theory of stochastic processes in discrete and continuous time and will be able to apply various techniques to study stochastic models that appear in applications.

Applying knowledge and understanding
At the end of the course the students will have the tools to grasp and formalize, in the language of stochastic processes, phenomena that evolve in time and space. The students will have the tools to solve simple applied problems in new environments and broader contexts.

Making judgements
At the end of the course the students will have the tools to evaluate critically and choose between different stochastic models to model phenomena that evolve in time and space. The student will also acquire the necessary language skills to start reading academic books on the topic and research papers.

Communication skills
The students will acquire the intuition and the communication skills necessary to describe phenomena in the mathematical language of stochastic processes. In particular the student will also acquire the rationale behind the stochastic model studied (e.g. the ideas of Markovianity, transience, recurrence, equilibrium, stationarity, long and short-time behaviour...) that is necessary to communicate to specialist and non-specialist audiences.

Learning skills
The students will acquire the methodology and the language to study in a manner that may be largely autonomous and to apply the methodology to the subsequent studies in the area of statistics and finance.

9

MAT/06

72

-

Attività formative caratterizzanti

ENG

Gruppo opzionale: Curriculum Quantitative economics Gruppo C attività a scelta per 6 cfu - (visualizza)

6

AAF1877 - LABORATORY OF FINANCIAL AND MONETARY STATISTICS	Erogato in altro semestre o anno
AAF1883 - LABORATORY OF MACHINE LEARNING	Erogato in altro semestre o anno
AAF1152 - altre conoscenze utili per l'inserimento nel mondo del lavoro	Erogato in altro semestre o anno
AAF1885 - CASE STUDIES AND STATISTICAL CONSULTING	Erogato in altro semestre o anno
AAF1888 - READING SEMINARS	Erogato in altro semestre o anno
AAF1544 - LABORATORY OF STOCHASTIC PROCESSES (obiettivi) Learning goals. General learning targets: The course is organised as a series of classes where the students will have the possibility to solve and discuss the solutions of a series of advanced exercises on the theory of stochastic processes. Knowledge and understanding. At the end of the course the students will acquire the ability to solve autonomously simple and more advanced exercises on the theory of stochastic processes. Applying knowledge and understanding. During the course the students will exercise the ability to grasp and formalize, in the language of stochastic processes, phenomena that evolve in time and space and to solve simple applied problems in new environments and broader contexts. Making judgements At the end of the course the students will have the tools to evaluate critically and choose between different stochastic models to model phenomena that evolve in time and space. Communication skills. The students will exercise the intuition and the communication skills necessary to describe phenomena in the mathematical language of stochastic processes. In particular the student will acquire familiarity with the main ideas that are behind the stochastic model, e.g., the ideas of Markovianity, transience, recurrence, equilibrium, stationarity, long and short-time behaviour. Learning skills. The students will acquire autonomy in studying more advanced theoretical aspects of stochastic processes and in applying the main ideas of stochastic processes to the subsequent studies in the area of statistics and finance.	3	27	-	-	-	Ulteriori attività formative (art.10, comma 5, lettera d)	ENG
AAF1149 - altre conoscenze utili per l'inserimento nel mondo del lavoro (obiettivi) The specific goal of these activities is to enable the students to merge their academic knowledge with professional skills. By facing practical and real problems, students develop judgement and communication skills.	3	27	-	-	-	Ulteriori attività formative (art.10, comma 5, lettera d)	ENG

- - A SCELTA DELLO STUDENTE

9

72

-

Attività formative a scelta dello studente (art.10, comma 5, lettera a)

ENG

Secondo semestre

Insegnamento

CFU

SSD

Ore Lezione

Ore Eserc.

Ore Lab

Ore Studio

Attività

Lingua

10589921 - SAMPLE SURVEYS (obiettivi)

6

SECS-S/01

48

-

Attività formative caratterizzanti

ENG

10589568 - EFFICIENCY AND PRODUCTIVITY ANALYSIS (obiettivi)

General Targets:
Prior educational teaching concern is the students understanding of the main (Economic Statistics Modeling) problems and methods for studying Efficiency and Productivity Analysis. 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 Efficiency and Productivity Analysis. 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 degree of efficiency of a typical Decisional Making Unit. We will study the main estimation methods for solving efficiency problems pertaining a firm traditionally operating in the private sector, but we also will extend the analysis to Decisional Making Units of public and non-profit sectors (quadratic methods with different types of metrics, maximum likelihood methods and non-parametric analysis) 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 Efficiency and Productivity Analysis. 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.

9

SECS-S/03

72

-

Attività formative caratterizzanti

ENG

10589488 - FINANCIAL ECONOMETRICS (obiettivi)

9

SECS-P/05

72

-

Attività formative caratterizzanti

ENG

Gruppo opzionale: Curriculum Quantitative Economics Gruppo opzionale B2 esami per 18 CFU - (visualizza)

18

10589423 - ALGORITHMS AND DATA STRUCTURES (obiettivi) General objectives The primary objective is to study algorithms and data structures that efficiently solve problems whose solution, using trivial approaches, would require very high resources. The algorithms studied will be implemented in Java language. Specific objectives Knowledge and ability to understand Basic data structures and their use in solving sorting, searching, and graph problems will be shown. It will be shown how the data structures described are made available in Java language. The student will be able to determine the computational complexity of algorithms, and associate them with the appropriate complexity class. Ability to apply knowledge and understanding At the end of the course the students will be able to determine algorithms to efficiently solve complex problems, in particular problems on graphs, and choose the most suitable data structures to obtain an efficient implementation of the algorithm. They will also be able to use the Java classes that implement the data structures studied. Autonomy of judgment Students will be able to distinguish the computational complexity of problems and algorithms, and to identify the computationally more costly steps in solving a problem. Communication skills Students will be able to describe, in appropriate terms, the characteristics of the main data structures, and identify the primitives needed to efficiently implement an algorithm. Learning ability Students who pass the exam will be able to take advanced courses, of an applicative nature, that require the use of sophisticated algorithms. They will also be able to appreciate software engineering and computational complexity theory teachings.	6	INF/01	48	-	-	-	Attività formative affini ed integrative	ENG
10589579 - GENDER ECONOMICS	Erogato in altro semestre o anno
1047773 - BIG DATA ANALYTICS (obiettivi) 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.
- BIG DATA ANALYTICS	Erogato in altro semestre o anno
- BIG DATA ANALYTICS	Erogato in altro semestre o anno
10589452 - DEVELOPMENT FINANCE	Erogato in altro semestre o anno
1047208 - STATISTICAL LEARNING (obiettivi) Learning goals Devising new machine learning methods and statistical models is a fun and extremely fruitful “art”. But these powerful tools are not useful unless we understand when they work, and when they fail. The main goal of statistical learning theory is thus to study, in a statistical framework, the properties of learning algorithms mainly in the form of so-called error bounds. This course introduces the techniques that are used to obtain such results, combining methodology with theoretical foundations and computational aspects. It treats both the basic principles to design successful learning algorithms and the “science” of analyzing an algorithm’s statistical properties and performance guarantees. Theorems are presented together with practical aspects of methodology and intuition to help students develop tools for selecting appropriate methods and approaches to problems in their own data analyses. Methods for a wide variety of applied problems will be explored and implemented on open-source software like R (www.r-project.org), Keras (https://keras.io/) and TensorFlow (https://www.tensorflow.org/). Knowledge and understanding On successful completion of this course, students will: know the main learning methodologies and paradigms with their strengths and weakness; be able to identify a proper learning model for a given problem; assess the empirical and theoretical performance of different learning models; know the main platforms, programming languages and solutions to develop effective implementations. Applying knowledge and understanding Besides the understanding of theoretical aspects, thanks to applied homeworks and a final project possibly linked to hackathons or other data analysis competitions, the students will constantly be challenged to use and evaluate modern learning techniques and algorithms. Making judgements On successful completion of this course, students will develop a positive critical attitude towards the empirical and theoretical evaluation of statistical learning paradigms and techniques. Communication skills In preparing the report and oral presentation for the final project, students will learn how to effectively communicate original ideas, experimental results and the principles behind advanced data analytic techniques in written and oral form. They will also understand how to offer constructive critiques on the presentations of their peers. Learning skills In this course the students will develop the skills necessary for a successful understanding as well as development of new learning methodologies together with their effective implementation. The goal is of course to grow a active attitude towards continued learning throughout a professional career.	6	SECS-S/01	48	-	-	-	Attività formative affini ed integrative	ENG

Gruppo opzionale: Curriculum Quantitative economics Gruppo C attività a scelta per 6 cfu - (visualizza)

6

AAF1877 - LABORATORY OF FINANCIAL AND MONETARY STATISTICS (obiettivi) Learning goals Students will be introduced to the following topics 1. The Monetary, banking and financial statistics. Why Bank of Italy collects statistics and what collects. Application: recent developments of the banks. 2. The financial accountsThe financial accounts structure.Household wealth after Piketty: an international comparison.The financial structure of the companies. 3. The balance of payments and international investment positionThe Italian balance of payments: the structure and recent developments.The procedure on excessive macroeconomic imbalances in Europe.Funds held abroad by the families. 4. The sample surveys of the Bank of ItalyThe survey on Household Income: recent results and a long-term look.The survey on inflation and growth expectations.	3	27	-	-	-	Ulteriori attività formative (art.10, comma 5, lettera d)	ENG
AAF1883 - LABORATORY OF MACHINE LEARNING (obiettivi) 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.	3	27	-	-	-	Ulteriori attività formative (art.10, comma 5, lettera d)	ENG
AAF1152 - altre conoscenze utili per l'inserimento nel mondo del lavoro	Erogato in altro semestre o anno
AAF1885 - CASE STUDIES AND STATISTICAL CONSULTING (obiettivi) Learning goals Prepare students to proposing solutions to real statistical problems in many research areas. Knowledge and understanding At the end of the course the students have the ability to understand and solve real practical statistical problems and to propose adequate solutions. Applying knowledge and understanding Students are required to apply theoretical and computational skills to real problems and case-studies in a wide range of domains. Making judgements One of the main goals of practical activities is to develop the ability to understand problems and to propose and compare alternative statistical approaches to solve them. Communication skills Students acquire the ability of discussing problems and of presenting oral and written reports of their practical analyses. Learning skills The students acquire a series of skills useful for future academic and professional activities.	3	27	-	-	-	Ulteriori attività formative (art.10, comma 5, lettera d)	ENG
AAF1888 - READING SEMINARS (obiettivi) Learning goals. Aim of the course is to allow students to broaden their knowledge of economics, sociology, and other social sciences in an interdisciplinary way. Knowledge and understanding. Historical perspective and awareness of the existence of different interpretative positions in the context of social sciences. Applying knowledge and understanding. At the end of the course students will be able to deal with different models in a critical way. Making judgements. Students will develop critical skills through different theoretical approaches. Communication skills. Students, through the study, acquire the language of different disciplines, which must be appropriately used both in written and oral exams. Learning skills. Students who pass the exam have learned a method of analysis that allows them to tackle the study of more complex models.	3	27	-	-	-	Ulteriori attività formative (art.10, comma 5, lettera d)	ENG
AAF1544 - LABORATORY OF STOCHASTIC PROCESSES	Erogato in altro semestre o anno
AAF1149 - altre conoscenze utili per l'inserimento nel mondo del lavoro	Erogato in altro semestre o anno

Gruppo opzionale: Curriculum Quantitative economics Gruppo OPZIONALE B1 due esami a scelta 18 cfu - (visualizza)

18

10589582 - ECONOMIC HISTORY	Erogato in altro semestre o anno
10589482 - INTERNATIONAL MONETARY ECONOMICS (obiettivi) Learning goals Working knowledge of the main models of international economics and international finance. Knowledge and understanding. Upon successful completion of the course, students will be able to analyse actual economic problems in terms of competing theories and models. Applying knowledge and understanding. Upon successful completion of the course, students will be able to understand the explicit and implicit hypotheses informing the main economic policy proposals in the current debate. Making judgements. The course is explicitly based on the principle of methodological and theoretical pluralism. Students will be introduced to at least two competing models for each economic problem considered, and will understand the criteria with which to personally choose their favorite interpretation. Communication skills. Through study and hands-on sessions, students will become proficient in the jargon and technical language of the discipline, which they must use in both written and oral examinations. Learning skills. Students that successfully complete the course will have learnt a method of analysis that will allow them to tackle and understand the main economic issues of today, both in subsequent economic courses and in the fruition and participation to the public debate.	9	SECS-P/01	72	-	-	-	Attività formative affini ed integrative	ENG
10589565 - APPLIED ECONOMICS	Erogato in altro semestre o anno
1038218 - Computational Statistics	Erogato in altro semestre o anno
1055949 - BAYESIAN MODELLING	Erogato in altro semestre o anno

Primo semestre

Insegnamento

CFU

SSD

Ore Lezione

Ore Eserc.

Ore Lab

Ore Studio

Attività

Lingua

10589458 - ADVANCED ECONOMIC STATISTICS (obiettivi)

6

SECS-S/03

48

-

Attività formative caratterizzanti

ENG

Gruppo opzionale: Curriculum Quantitative economics Gruppo OPZIONALE B1 due esami a scelta 18 cfu - (visualizza)

18

10589582 - ECONOMIC HISTORY	Erogato in altro semestre o anno
10589482 - INTERNATIONAL MONETARY ECONOMICS	Erogato in altro semestre o anno
10589565 - APPLIED ECONOMICS	Erogato in altro semestre o anno
1038218 - Computational Statistics (obiettivi) 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. 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. 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. 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. 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 be also developed through group activities. Learning skills Students who pass the exam have learned computational techniques useful in the statistical analysis and to work self-sufficiently to face with the complexity of the statistical problems.	9	SECS-S/01	72	-	-	-	Attività formative affini ed integrative	ENG
1055949 - BAYESIAN MODELLING (obiettivi) General goals Knowledge at an intermediate and advanced level of the main issues in Bayesian statistics. Ability to apply Bayesian statistical techniques to applicative context. Knowledge and understanding Knowledge and understanding of the Bayesian approach to statistical inference, of its models and of its methodologies Applying knowledge and understanding Ability to apply Bayesian statistical methods for inferential problems in real-data problems Making judgements Ability of choosing appropriate Bayesian methods and models in different inferential problems Communication skills Ability of communicating results of the analyses in written and oral form Learning skills Students acquire skills useful to approach more advanced topics in Bayesian inference, Advanced data analysis, Statistical computing and Mathematical statistics	9	SECS-S/01	72	-	-	-	Attività formative affini ed integrative	ENG

Gruppo opzionale: Curriculum Quantitative economics Gruppo C attività a scelta per 6 cfu - (visualizza)

6

AAF1877 - LABORATORY OF FINANCIAL AND MONETARY STATISTICS	Erogato in altro semestre o anno
AAF1883 - LABORATORY OF MACHINE LEARNING	Erogato in altro semestre o anno
AAF1152 - altre conoscenze utili per l'inserimento nel mondo del lavoro (obiettivi) The specific goal of these activities is to enable the students to merge their academic knowledge with professional skills. By facing practical and real problems, students develop judgement and communication skills.	6	54	-	-	-	Ulteriori attività formative (art.10, comma 5, lettera d)	ENG
AAF1885 - CASE STUDIES AND STATISTICAL CONSULTING	Erogato in altro semestre o anno
AAF1888 - READING SEMINARS	Erogato in altro semestre o anno
AAF1544 - LABORATORY OF STOCHASTIC PROCESSES	Erogato in altro semestre o anno
AAF1149 - altre conoscenze utili per l'inserimento nel mondo del lavoro	Erogato in altro semestre o anno

Gruppo opzionale: Curriculum Quantitative Economics Gruppo opzionale B2 esami per 18 CFU - (visualizza)

18

10589423 - ALGORITHMS AND DATA STRUCTURES	Erogato in altro semestre o anno
10589579 - GENDER ECONOMICS (obiettivi) The main objective of the course is to provide a gender analysis of economic theory answering to one main question: why and how a gender approach to inequality can explain men and women occupational patterns, wages and poverty? Knowledge and understanding After attending the course, the students will increase their ability in dealing, both theoretically and empirically, with gender diversity in the economic context. Applying knowledge and understanding At the end of the course, students will be able to formalize real economic problems and to apply the specific methods of the discipline to solve them. Students will acquire a theoretical preparation and, thanks to the analysis of numerous practical cases, the ability to critically study policies and economic models. Making judgements Students will increase not only their theoretical skills but also their critical curiosity in reading real recent economic phenomena and economic models in a gender perspective. Communication skills Students, through discussions in the classroom and exercises, will acquire tools for critical analysis of empirical evidence and communication skills. They will also learn how to structure and present a research report. Learning skills Students who pass the exam learned methods of analysis that will allow them to tackle other economic courses.	6	SECS-P/01	48	-	-	-	Attività formative affini ed integrative	ENG
1047773 - BIG DATA ANALYTICS (obiettivi) 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.
- BIG DATA ANALYTICS	Erogato in altro semestre o anno
- BIG DATA ANALYTICS	Erogato in altro semestre o anno
10589452 - DEVELOPMENT FINANCE	Erogato in altro semestre o anno
1047208 - STATISTICAL LEARNING	Erogato in altro semestre o anno

Secondo semestre

Insegnamento

CFU

SSD

Ore Lezione

Ore Eserc.

Ore Lab

Ore Studio

Attività

Lingua

Gruppo opzionale: Curriculum Quantitative economics Gruppo OPZIONALE B1 due esami a scelta 18 cfu - (visualizza)

18

10589582 - ECONOMIC HISTORY (obiettivi) Obiettivi formativi L'obiettivo formativo primario dell’insegnamento di storia economica è consentire agli studenti di collegare prospettiva storica, nozioni essenziali di teoria economica e strumenti di statistica Conoscenza e capacità di comprensione Dopo aver frequentato il corso gli studenti conoscono le linee principali dello sviluppo della storia economica europea e mondiale dal XV al XX secolo. Capacità di applicare conoscenza e comprensione Al termine del corso gli studenti sono in grado di comprendere le linee principali dello sviluppo della storia economica europea e mondiale dal XV al XX secolo e di collegarle alle dimensioni culturali, sociali, politiche ed economiche dello svolgimento degli avvenimenti e di estendere tali capacità anche al tempo presente. Autonomia di giudizio Gli studenti sviluppano capacità critiche attraverso l’applicazione di metodologie storiche che collegano le dimensioni culturali, sociali, politiche ed economiche dello svolgimento degli avvenimenti. L'applicazione di tali capacità critiche potrà essere estesa anche al tempo presente. Abilità comunicativa Gli studenti, attraverso la frequenza delle lezioni, lo studio e la preparazione di elaborati scritti, acquisiscono le capacità di dialogo scientifico proprie della storia economica e di combinarle con quelle proprie dell'economia e della statistica. Capacità di apprendimento Gli studenti che superano l’esame hanno appreso un metodo di analisi che consente loro di affrontare i temi degli insegnamenti successivi e il confronto con i fatti economici quotidiani, collegando le dimensioni culturali, sociali, politiche ed economiche.	9	SECS-P/01	72	-	-	-	Attività formative affini ed integrative	ENG
10589482 - INTERNATIONAL MONETARY ECONOMICS	Erogato in altro semestre o anno
10589565 - APPLIED ECONOMICS (obiettivi) Learning goals The primary learning goal of this course is that of exposing students to the body of econometric techniques that are customised to economics applications. The aim of the course is to review this body of techniques, to demonstrate their use in hands-on style, drawing on as wide a range of example as possible, and to interpret each set of results in ways that are most useful to read and represent economic phenomena. Knowledge and understanding. The course is supposed to broaden students' knowledge of the various econometric techniques that appear in the economics literature, their properties and the way these are applied to data in order to verify economic theory. Applying knowledge and understanding. Upon successful completion of the course, students will be able to carry out a wide range of tasks in empirical economics, such as recognising the most suitable approaches to analyse the data at hand in order to capture and model its regularities, and intelligibly convey its messages to both economists and broader audiences. Making judgements. The course develops in a way to spurs students on researching empirical evidence of competing economic theories by respecting the nature of convenient data. Communication skills. Through study and hands-on sessions, students will acquire the terminology characterising the discipline, which they are required to use in both written and oral dissemination. Learning skills. Students who complete the course successfully will be acquainted with a method of analysis enabling them to endeavour the main economic issues from an empirical point of view.	9	SECS-P/01	72	-	-	-	Attività formative affini ed integrative	ENG
1038218 - Computational Statistics	Erogato in altro semestre o anno
1055949 - BAYESIAN MODELLING	Erogato in altro semestre o anno

Gruppo opzionale: Curriculum Quantitative Economics Gruppo opzionale B2 esami per 18 CFU - (visualizza)

18

10589423 - ALGORITHMS AND DATA STRUCTURES	Erogato in altro semestre o anno
10589579 - GENDER ECONOMICS	Erogato in altro semestre o anno
1047773 - BIG DATA ANALYTICS (obiettivi) 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.
- BIG DATA ANALYTICS (obiettivi) 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.	3	INF/01	24	-	-	-	Attività formative affini ed integrative	ENG
- BIG DATA ANALYTICS (obiettivi) 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.	3	SECS-S/01	24	-	-	-	Attività formative affini ed integrative	ENG
10589452 - DEVELOPMENT FINANCE (obiettivi) Learning goals Aim of the course is to explore the role of financial systems in the economic development process. Lectures will deal with topics related to the deepening, outreach, efficiency and stability of financial systems. The focus will be on applied and policy-oriented research, which can serve as basis for public policy discussions on the financial system issues, especially in developing and emerging markets. Knowledge and understanding Knowledge of the basic concepts and of the main theories elaborated in the field. Historical perspective and awareness of the existence of different interpretative positions. Applying knowledge and understanding At the end of the course students are able to formalize problems and to apply the specific methods of the discipline to solve them. They are also able to link methods to short-term data. Making judgements Students develop critical skills through the application of the same methodology to a wide range of economic models, which are affected by different theoretical approaches. Communication skills Students, through the study, acquire the technical-scientific language of the discipline, which must be appropriately used both in written and oral exams. Learning skills Students who pass the exam have learned a method of analysis that allows them to tackle the study of more complex models in economics.	6	SECS-P/01	48	-	-	-	Attività formative affini ed integrative	ENG
1047208 - STATISTICAL LEARNING	Erogato in altro semestre o anno

AAF1019 - PROVA FINALE (obiettivi)

21

525

-

Per la prova finale e la lingua straniera (art.10, comma 5, lettera c)

ENG

TOTALE

120

Primo semestre

Insegnamento

CFU

SSD

Ore Lezione

Ore Eserc.

Ore Lab

Ore Studio

Attività

Lingua

Gruppo opzionale: Curriculum Official statistics Gruppo OPZIONALE D un esame per 9 cfu - (visualizza)

9

10592813 - PROBABILITY AND STATISTICS (obiettivi) Obiettivi formativi Obiettivo formativo dell’insegnamento è l'apprendimento da parte degli studenti dei fondamenti del calcolo delle probabilità e dell’inferenza statistica. Conoscenza e capacità di comprensione Alla fine del corso gli studenti conoscono e comprendono come formalizzare l’incertezza e come fare inferenza su parametri non noti. Capacità di applicare conoscenza e comprensione Gli studenti apprendono come impostare un problema di probabilità o inferenza. Autonomia di giudizio La discussione dei vari metodi, anche con lavori di gruppo, fornisce agli studenti le capacità necessarie per analizzare criticamente, ed in autonomia, situazioni reali. Abilità comunicativa Gli studenti acquisiscono gli elementi di base per ragionare, e far ragionare, in termini quantitativi su problemi di incertezza ed inferenza. Capacità di apprendimento Gli studenti che superano l’esame sono in grado di applicare i metodi appresi in diversi contesti applicativi.	9	SECS-S/01	72	-	-	-	Attività formative affini ed integrative	ENG
10589834 - Advances in data analysis and statistical modelling (obiettivi) Learning goals Knowing how to reorganize multidimensional data with a complex structure for their statistical analysis. Starting from the multivariate statistical methodologies, knowing how to realize complex strategies of analysis that have an easy interpretation. To be able to make decisions based on empirical evidence giving appropriate answers to corporate information requests. Knowing how to extract relevant information from large data (big data). Knowledge and understanding Knowledge of advanced multivariate statistical methodologies and advanced formal data analysis strategies with a model-based statistical approach. Applying knowledge and understanding Understanding the most appropriate complex techniques to be able to make decisions based on empirical evidence, respond to corporate information requests and be able to extract relevant information from the observed data. Making judgements Students develop critical skills through the application of complex multivariate statistical methodologies. They learn to critically interpret the results obtained by applying the procedures to real data sets. Communication skills Students, through the study and performance of practical exercises, acquire the technical-scientific language of the discipline, which must be used in the world of work. 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 face, the world of work having acquired advanced statistical analysis tools for complex and large data.	9	SECS-S/01	72	-	-	-	Attività formative affini ed integrative	ENG
10589488 - FINANCIAL ECONOMETRICS	Erogato in altro semestre o anno

1056015 - STOCHASTIC PROCESSES (obiettivi)

Learning goals
The course provides a broad introduction to stochastic processes. In particular the aim is
- to give a rigorous introduction to the theory of stochastic processes,
- to discuss the most important stochastic processes in some depth with examples and applications,
- to give the flavour of more advanced work and applications,
- to apply these ideas to answer basic questions in several applied situations including biology, finance and search engine algorithms.

Knowledge and understanding
At the end of the course the students will be familiar with the basic concepts of the theory of stochastic processes in discrete and continuous time and will be able to apply various techniques to study stochastic models that appear in applications.

Applying knowledge and understanding
At the end of the course the students will have the tools to grasp and formalize, in the language of stochastic processes, phenomena that evolve in time and space. The students will have the tools to solve simple applied problems in new environments and broader contexts.

Making judgements
At the end of the course the students will have the tools to evaluate critically and choose between different stochastic models to model phenomena that evolve in time and space. The student will also acquire the necessary language skills to start reading academic books on the topic and research papers.

Communication skills
The students will acquire the intuition and the communication skills necessary to describe phenomena in the mathematical language of stochastic processes. In particular the student will also acquire the rationale behind the stochastic model studied (e.g. the ideas of Markovianity, transience, recurrence, equilibrium, stationarity, long and short-time behaviour...) that is necessary to communicate to specialist and non-specialist audiences.

Learning skills
The students will acquire the methodology and the language to study in a manner that may be largely autonomous and to apply the methodology to the subsequent studies in the area of statistics and finance.

9

MAT/06

72

-

Attività formative caratterizzanti

ENG

1052019 - BAYESIAN MODELLING (obiettivi)

- BAYESIAN MODELLING I (obiettivi)

3

SECS-S/01

24

-

Attività formative caratterizzanti

ENG

- BAYESIAN MODELLING II (obiettivi)

3

SECS-S/01

24

-

Attività formative caratterizzanti

ENG

Gruppo opzionale: Curriculum Official Statistics Gruppo OPZIONALE B un esame 6 cfu - (visualizza)

6

1056085 - BIG DATA FOR OFFICIAL STATISTICS (obiettivi) General learning goals - Define what subset of Big Data can be used in Official Statistics and what domains of Official Statistics can be enriched through the availability of new data sources - Establish how new data sources can be used in Official Statistics, by taking into account challenges, needs and risks in this exercise - Definition of the role of Big Data in the context of Official Statistics - Establish how to frame the measurement of social, demographic and economic phenomena through Big Data by considering challenges, needs and risks Knowledge and understanding Knowledge and understanding of statistical methods to handle Big Data in official statistics Applying knowledge and understanding Ability to apply statistical methods for official statistics problems with emphasis on Big Data Making judgements Ability of choosing appropriate methods in different problems in official statistics with emphasis on Big Data Communication skills Ability of communicating results of the analyses in official statistics with emphasis on Big Data Learning skills Students acquire skills useful to approach more advanced topics in official statistics and Big Data management	6	SECS-S/05	48	-	-	-	Attività formative affini ed integrative	ENG
10589580 - INTERNATIONAL DEMOGRAPHY	Erogato in altro semestre o anno
10589423 - ALGORITHMS AND DATA STRUCTURES	Erogato in altro semestre o anno
1047773 - BIG DATA ANALYTICS (obiettivi) 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.
- BIG DATA ANALYTICS	Erogato in altro semestre o anno
- BIG DATA ANALYTICS	Erogato in altro semestre o anno
1047222 - EFFICIENCY AND PRODUCTIVITY ANALYSIS	Erogato in altro semestre o anno
10589562 - SURVEY METHODOLOGY	Erogato in altro semestre o anno
10589835 - COMPUTATIONAL STATISTICS	Erogato in altro semestre o anno

Gruppo opzionale: Curriculum Official statistics Gruppo OPZIONALE C altre attività per 3 cfu - (visualizza)

3

AAF1149 - altre conoscenze utili per l'inserimento nel mondo del lavoro (obiettivi) The specific goal of these activities is to enable the students to merge their academic knowledge with professional skills. By facing practical and real problems, students develop judgement and communication skills.	3	27	-	-	-	Ulteriori attività formative (art.10, comma 5, lettera d)	ENG
AAF1544 - LABORATORY OF STOCHASTIC PROCESSES (obiettivi) Learning goals. General learning targets: The course is organised as a series of classes where the students will have the possibility to solve and discuss the solutions of a series of advanced exercises on the theory of stochastic processes. Knowledge and understanding. At the end of the course the students will acquire the ability to solve autonomously simple and more advanced exercises on the theory of stochastic processes. Applying knowledge and understanding. During the course the students will exercise the ability to grasp and formalize, in the language of stochastic processes, phenomena that evolve in time and space and to solve simple applied problems in new environments and broader contexts. Making judgements At the end of the course the students will have the tools to evaluate critically and choose between different stochastic models to model phenomena that evolve in time and space. Communication skills. The students will exercise the intuition and the communication skills necessary to describe phenomena in the mathematical language of stochastic processes. In particular the student will acquire familiarity with the main ideas that are behind the stochastic model, e.g., the ideas of Markovianity, transience, recurrence, equilibrium, stationarity, long and short-time behaviour. Learning skills. The students will acquire autonomy in studying more advanced theoretical aspects of stochastic processes and in applying the main ideas of stochastic processes to the subsequent studies in the area of statistics and finance.	3	27	-	-	-	Ulteriori attività formative (art.10, comma 5, lettera d)	ENG
AAF1883 - LABORATORY OF MACHINE LEARNING	Erogato in altro semestre o anno
AAF1885 - CASE STUDIES AND STATISTICAL CONSULTING	Erogato in altro semestre o anno

Secondo semestre

Insegnamento

CFU

SSD

Ore Lezione

Ore Eserc.

Ore Lab

Ore Studio

Attività

Lingua

10589920 - SAMPLE SURVEYS (obiettivi)

9

SECS-S/01

72

-

Attività formative caratterizzanti

ENG

1055990 - DATA MANAGEMENT IN OFFICIAL STATISTICS (obiettivi)

6

SECS-S/01

48

-

Attività formative caratterizzanti

ENG

1055952 - DATA QUALITY AND OTHER TOPICS OF OFFICIAL STATISTICS (obiettivi)

6

SECS-S/01

48

-

Attività formative caratterizzanti

ENG

AAF1179 - PER STAGES E TIROCINI PRESSO IMPRESE, ENTI PUBBLICI O PRIVATI, ORDINI PROFESSIONALE (obiettivi)

9

-

Per stages e tirocini presso imprese, enti pubblici o privati, ordini professionali (art.10, comma 5, lettera e)

ENG

AAF1877 - LABORATORY OF FINANCIAL AND MONETARY STATISTICS (obiettivi)

3

27

-

Ulteriori attività formative (art.10, comma 5, lettera d)

ENG

Gruppo opzionale: Curriculum Official statistics Gruppo OPZIONALE C altre attività per 3 cfu - (visualizza)

3

AAF1149 - altre conoscenze utili per l'inserimento nel mondo del lavoro	Erogato in altro semestre o anno
AAF1544 - LABORATORY OF STOCHASTIC PROCESSES	Erogato in altro semestre o anno
AAF1883 - LABORATORY OF MACHINE LEARNING (obiettivi) 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.	3	27	-	-	-	Ulteriori attività formative (art.10, comma 5, lettera d)	ENG
AAF1885 - CASE STUDIES AND STATISTICAL CONSULTING (obiettivi) Learning goals Prepare students to proposing solutions to real statistical problems in many research areas. Knowledge and understanding At the end of the course the students have the ability to understand and solve real practical statistical problems and to propose adequate solutions. Applying knowledge and understanding Students are required to apply theoretical and computational skills to real problems and case-studies in a wide range of domains. Making judgements One of the main goals of practical activities is to develop the ability to understand problems and to propose and compare alternative statistical approaches to solve them. Communication skills Students acquire the ability of discussing problems and of presenting oral and written reports of their practical analyses. Learning skills The students acquire a series of skills useful for future academic and professional activities.	3	27	-	-	-	Ulteriori attività formative (art.10, comma 5, lettera d)	ENG

Gruppo opzionale: Curriculum Official Statistics Gruppo OPZIONALE B un esame 6 cfu - (visualizza)

6

1056085 - BIG DATA FOR OFFICIAL STATISTICS	Erogato in altro semestre o anno
10589580 - INTERNATIONAL DEMOGRAPHY	Erogato in altro semestre o anno
10589423 - ALGORITHMS AND DATA STRUCTURES (obiettivi) General objectives The primary objective is to study algorithms and data structures that efficiently solve problems whose solution, using trivial approaches, would require very high resources. The algorithms studied will be implemented in Java language. Specific objectives Knowledge and ability to understand Basic data structures and their use in solving sorting, searching, and graph problems will be shown. It will be shown how the data structures described are made available in Java language. The student will be able to determine the computational complexity of algorithms, and associate them with the appropriate complexity class. Ability to apply knowledge and understanding At the end of the course the students will be able to determine algorithms to efficiently solve complex problems, in particular problems on graphs, and choose the most suitable data structures to obtain an efficient implementation of the algorithm. They will also be able to use the Java classes that implement the data structures studied. Autonomy of judgment Students will be able to distinguish the computational complexity of problems and algorithms, and to identify the computationally more costly steps in solving a problem. Communication skills Students will be able to describe, in appropriate terms, the characteristics of the main data structures, and identify the primitives needed to efficiently implement an algorithm. Learning ability Students who pass the exam will be able to take advanced courses, of an applicative nature, that require the use of sophisticated algorithms. They will also be able to appreciate software engineering and computational complexity theory teachings.	6	INF/01	48	-	-	-	Attività formative affini ed integrative	ENG
1047773 - BIG DATA ANALYTICS (obiettivi) 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.
- BIG DATA ANALYTICS	Erogato in altro semestre o anno
- BIG DATA ANALYTICS	Erogato in altro semestre o anno
1047222 - EFFICIENCY AND PRODUCTIVITY ANALYSIS	Erogato in altro semestre o anno
10589562 - SURVEY METHODOLOGY	Erogato in altro semestre o anno
10589835 - COMPUTATIONAL STATISTICS	Erogato in altro semestre o anno

Primo semestre

Insegnamento

CFU

SSD

Ore Lezione

Ore Eserc.

Ore Lab

Ore Studio

Attività

Lingua

10589458 - ADVANCED ECONOMIC STATISTICS (obiettivi)

6

SECS-S/03

48

-

Attività formative caratterizzanti

ENG

1047802 - SPATIAL STATISTICS AND STATISTICAL TOOLS FOR ENVIRONMENTAL DATA (obiettivi)

9

SECS-S/02

72

-

Attività formative caratterizzanti

ENG

Gruppo opzionale: Curriculum Official Statistics Gruppo OPZIONALE B un esame 6 cfu - (visualizza)

6

1056085 - BIG DATA FOR OFFICIAL STATISTICS	Erogato in altro semestre o anno
10589580 - INTERNATIONAL DEMOGRAPHY (obiettivi) General Aim The primary educational goal of the course is students' learning of the main concepts and basic methods of Demography. Knowledge and understanding After attending the course, the students know and understand the main international demographic sources and the measures to describe the population processes. Applying knowledge and understanding At the end of the course, the students are able to apply the learned methods to the real data, and to understand the results of these applications. Making judgements Students develop critical skills through the application of different indicators and measures to a wide range of case studies from different countries, and learn to critically interpret the results. Communication skills Students, through the study and the carrying out of practical exercises, acquire the technical-scientific language of the discipline, which must be opportunely used in the final oral examination. Communication skills are also developed through group activities. Learning skills Students who pass the exam have learned the skills necessary to address the study of more complex methods and models in subsequent teachings of demographic area.	6	SECS-S/04	48	-	-	-	Attività formative affini ed integrative	ENG
10589423 - ALGORITHMS AND DATA STRUCTURES	Erogato in altro semestre o anno
1047773 - BIG DATA ANALYTICS (obiettivi) 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.
- BIG DATA ANALYTICS	Erogato in altro semestre o anno
- BIG DATA ANALYTICS	Erogato in altro semestre o anno
1047222 - EFFICIENCY AND PRODUCTIVITY ANALYSIS	Erogato in altro semestre o anno
10589562 - SURVEY METHODOLOGY	Erogato in altro semestre o anno
10589835 - COMPUTATIONAL STATISTICS (obiettivi) 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. 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. 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. 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. 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 be also developed through group activities. Learning skills Students who pass the exam have learned computational techniques useful in the statistical analysis and to work self-sufficiently to face with the complexity of the statistical problems.	6	SECS-S/01	48	-	-	-	Attività formative affini ed integrative	ENG

- - A SCELTA DELLO STUDENTE

9

72

-

Attività formative a scelta dello studente (art.10, comma 5, lettera a)

ITA

Secondo semestre

Insegnamento

CFU

SSD

Ore Lezione

Ore Eserc.

Ore Lab

Ore Studio

Attività

Lingua

Gruppo opzionale: Curriculum Official Statistics Gruppo OPZIONALE B un esame 6 cfu - (visualizza)

6

1056085 - BIG DATA FOR OFFICIAL STATISTICS	Erogato in altro semestre o anno
10589580 - INTERNATIONAL DEMOGRAPHY	Erogato in altro semestre o anno
10589423 - ALGORITHMS AND DATA STRUCTURES	Erogato in altro semestre o anno
1047773 - BIG DATA ANALYTICS (obiettivi) 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.
- BIG DATA ANALYTICS (obiettivi) 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.	3	INF/01	24	-	-	-	Attività formative affini ed integrative	ENG
- BIG DATA ANALYTICS (obiettivi) 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.	3	SECS-S/01	24	-	-	-	Attività formative affini ed integrative	ENG
1047222 - EFFICIENCY AND PRODUCTIVITY ANALYSIS (obiettivi) This course has the target of providing students with the modern techniques of measuring quantitatively advanced topics in economics. In particular, our focus will be on two main interrelated directions: the analysis of production and efficiency in the private and public sectors, impact analysis and economic dynamics of sectorial and micro founded systems in the modern economy. Knowledge and understanding Students are requested to know the techniques of estimation for output and input efficiency as well as the analysis methods for sectors (both static and dynamic) Applying knowledge and understanding At the end of the course, students should be capable of interpreting and addressing a study of efficiency pertaining decisional making units, both private and public. The statistical methods related are those of inference (for firms) and impact analysis and forecasting (for sectors). Making judgements At the end of the course, students will develop skills for interpreting and assessing productivity and efficiency problems. Communication skills Students will develop capacity of communicating results through the acquisition of both theoretical and applied skills. Learning skills After the exam, students will develop capacity of making autonomously further progress as far as the new advancements of this and related subjects are concerned.	6	SECS-S/03	48	-	-	-	Attività formative affini ed integrative	ENG
10589562 - SURVEY METHODOLOGY (obiettivi) Obiettivi generali L'obiettivo formativo primario dell’insegnamento è l'apprendimento da parte degli studenti dei principali problemi e metodi legati alle indagini statistiche. Gli studenti devono apprendere i principali elementi teorici che conducono alla organizzazione delle indagini e ai problemi legati all’errore di non osservazione (campionamento) e all’errore di osservazione (strumento di rilevazione). Una particolare attenzione verrà dedicata ai temi legati al dato soggettivo e alla consolidata teoria che conduce ad una corretta rilevazione e interpretazione degli stessi. Obiettivi specifici a) Conoscenze Al termine del corso, gli studenti conoscono e comprendono i principi delle indagini statistiche (soprattutto nell’ambito della Statistica Ufficiale) e delle fasi operative che lo compongono. b) Competenze Al termine del corso gli studenti sono in grado di definire un progetto di rilevazione definendo le diverse fasi che lo compongono. c) Autonomia di giudizio Le conoscenze e le competenze sviluppate consentono agli studenti di osservare in maniera critica progetti di indagini esistenti e di valutare la possibilità di modifiche costruttive e finalizzate al loro miglioramento. d) Abilità comunicativa Gli studenti, attraverso lo studio e lo svolgimento di esercizi pratici, acquisiscono il linguaggio tecnico-scientifico della disciplina, con particolare riferimento alla rilevazione di dati individuali e soggettivi. Le abilità comunicative vengono sviluppate anche attraverso attività di gruppo. e) Capacità di apprendimento Gli studenti che superano l’esame hanno appreso la metodologia delle rilevazioni statistiche. Ciò consente loro di di affrontare lo studio dei successivi insegnamenti con una consapevolezza legata soprattutto alle rilevazioni nell’ambito della Statistica Ufficiale.	6	SECS-S/05	48	-	-	-	Attività formative affini ed integrative	ENG
10589835 - COMPUTATIONAL STATISTICS	Erogato in altro semestre o anno

Gruppo opzionale: Curriculum Official statistics Gruppo OPZIONALE D un esame per 9 cfu - (visualizza)

9

10592813 - PROBABILITY AND STATISTICS	Erogato in altro semestre o anno
10589834 - Advances in data analysis and statistical modelling	Erogato in altro semestre o anno
10589488 - FINANCIAL ECONOMETRICS (obiettivi) 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.	9	SECS-P/05	72	-	-	-	Attività formative affini ed integrative	ENG

AAF1028 - PROVA FINALE (obiettivi)

30

750

-

Per la prova finale e la lingua straniera (art.10, comma 5, lettera c)

ENG

TOTALE

120

Corso di laurea: Statistical Methods and Applications - Metodi statistici e applicazioni A.A. 2020/2021

Obiettivi formativi e sbocchi professionali Regolamento didattico del corso Programmazione

Conoscenza e capacità di comprensione

Capacità di applicare conoscenza e comprensione

Autonomia di giudizio

Abilità comunicative

Capacità di apprendimento

Requisiti di ammissione

Prova finale

Orientamento in ingresso

Il Corso di Studio in breve