Gruppo opzionale:
Curriculum Official Statistics Gruppo OPZIONALE B un esame 6 cfu - (visualizza)
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6
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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.
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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.
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3
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INF/01
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24
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Attività formative affini ed integrative
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ENG |
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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.
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3
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SECS-S/01
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24
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Attività formative affini ed integrative
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ENG |
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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.
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6
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SECS-S/03
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48
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Attività formative affini ed integrative
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ENG |
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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.
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6
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SECS-S/05
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48
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Attività formative affini ed integrative
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ENG |
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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.
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6
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SECS-S/01
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48
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Attività formative affini ed integrative
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ENG |
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