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

24








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

SECSS/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, semistructured and structured data and of the different techniques that can be used to automate their processing. Successively, preprocessing 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. Realworld 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 technicalscientific 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, semistructured and structured data and of the different techniques that can be used to automate their processing. Successively, preprocessing 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. Realworld 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 technicalscientific 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.

FERRARO PETRILLO UMBERTO
( programma)
The first part of the course will provide an introduction to the Python programming language. This will include the following notions:basic syntax of the language; functions; modules; data structures; I/O management. These notions will be used, in turn, to face more complex tasks related to the analytics scenario. On a side, the student will be taught about the different formats commonly used to exchange data through files or over the network like the CSV format or the JSON format. On the other side, the student will learn about the development of applications able to gather data from the web through web scraping or web mining techniques. Finally, all these techniques will be put in practice by developing a couple of casestudies involving the extraction and the processing of data from realworld social networks. This part of the course will also feature a brief introduction to the MapReduce paradigm and to its reference implementation, Hadoop, useful for the elaboration of Big Data. The second part of the course starts with an introduction to the applications of bigdata and Internet of Thing. The student will learn some peculiarity of these data and some useful preprocessing methods, but also other problematic aspects will be considered: privacy, selection bias, data security, data storage, the computing power. Moreover, the high dimensionality of data introduces unique computational and statistical challenges that must be faced. A dimensionality reduction of the original data matrix can be useful in the initial steps of analysis and the student will learn the more useful techniques, depending on the aim of the analysis. Companies are no longer satisfied to extract detailed information from their archives, but now require the application of complex predictive models (the analytics).Some predictive models very effective with bigdata will be introduced: Forests, Gradient Boosting, Neural Networks; in order to apply these models, techiques to avoid the overfitting problem will be illustrated. Finally, the outlined strategies of analysis will be applied to real data sets of different type: numeric, textual and image.
a

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, semistructured and structured data and of the different techniques that can be used to automate their processing. Successively, preprocessing 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. Realworld 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 technicalscientific 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.

DI CIACCIO AGOSTINO
( programma)
The first part of the course will provide an introduction to the Python programming language. This will include the following notions:basic syntax of the language; functions; modules; data structures; I/O management. These notions will be used, in turn, to face more complex tasks related to the analytics scenario. On a side, the student will be taught about the different formats commonly used to exchange data through files or over the network like the CSV format or the JSON format. On the other side, the student will learn about the development of applications able to gather data from the web through web scraping or web mining techniques. Finally, all these techniques will be put in practice by developing a couple of casestudies involving the extraction and the processing of data from realworld social networks. This part of the course will also feature a brief introduction to the MapReduce paradigm and to its reference implementation, Hadoop, useful for the elaboration of Big Data. The second part of the course starts with an introduction to the applications of bigdata and Internet of Thing. The student will learn some peculiarity of these data and some useful preprocessing methods, but also other problematic aspects will be considered: privacy, selection bias, data security, data storage, the computing power. Moreover, the high dimensionality of data introduces unique computational and statistical challenges that must be faced. A dimensionality reduction of the original data matrix can be useful in the initial steps of analysis and the student will learn the more useful techniques, depending on the aim of the analysis. Companies are no longer satisfied to extract detailed information from their archives, but now require the application of complex predictive models (the analytics).Some predictive models very effective with bigdata will be introduced: Forests, Gradient Boosting, Neural Networks; in order to apply these models, techniques to avoid the overfitting problem will be illustrated. Finally, the outlined strategies of analysis will be applied to real data sets of different type: numeric, textual and image.
 How to Think Like a Computer Scientist: Learning with Python 3. 3rd Edition (Peter Wentworth, Jeffrey Elkner, Allen B. Downey and Chris Meyers) Deep Learning with Python (F. Chollet) An Introduction to Statistical Learning (G. James. D. Witten, T. Hastie, R. Tibshirani) Texts and notes provided by the teachers
(Date degli appelli d'esame)

3

SECSS/01

24







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 technicalscientific 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 selfsufficiently to face with the complexity of the statistical problems.

6

SECSS/01

48







Attività formative affini ed integrative

ENG 
