Corso di laurea: Data science for management Programmazione per l'A.A. 2022/2023

Knowledge and understanding (Conoscenza e capacità di comprensione). Students will acquire a precise knowledge and understanding of fundamental concepts in the field of cloud computing, chiefly through a guided exploration of the main technological solutions available from the public Cloud, focusing on resources and services oriented to data storage, analysis, visualization and machine learning.
Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). Based on the operating knowledge acquired, students will develop an effective "toolset" of practical, application-oriented skills in leveraging the Cloud to cater for the typical needs of a data scientist: i.e. processing large datasets with a view to revealing meaningful patterns and relationships. Cloud implementations of state-of-the-art tools and frameworks like, e.g., MapReduce/Hadoop or TensorFlow, will be employed
Making judgements (Autonomia di giudizio). The student will develop the ability to choose the suitable Cloud-based resource for the Data Science scenario of interest, properly estimating the ensuing costs and performance gains, as well as consciously assessing the tradeoffs involved.
Communication skills (Abilità comunicative). The student will acquire the communication skills required to express and discuss, at a rigorous technical level, the benefits and (mostly cost-related) downsides of the Cloud for Data Science applications. In addition, the student will gain the ability, for presentation purposes, to effectively highlight the features of very large datasets by means of cloud-based visualization services.
Learning skills (Capacità di apprendimento). Students will become capable of profitably consulting technical documentation concerning Data Science-oriented Cloud services, in order to concretely put them to effective use

Data is growing and has grown very fast in the last years.”Big Data” analytics is challenging today because of the unprecedented large data volumes. In this course, we will describe the structure of data generated in big data sensing applications, by distinguishing the type and structure of data. Then we will discuss SoA methodologies which can be used to compress this data based on its intrinsic features; finally, communication protocols for remotely delivering this data will be described and detailed. In this way students will be provided with communication engineering competences allowing them to actively communicate with experts in various fields by providing focused and competent data analysis for every application, such as in scientific, technological or business fields. Students will also be able to exploit the competences gained for design processes of collection, compression and communication of heterogeneous big data.
This course will be of interest for students attending all paths for the following reasons:
For “Business and economics data scientists”, this course will allow to complement their business competences being able also to communicate with technical experts in the field.
For students in “Data driven applications for the IoT” and “Data for sciences”, this is a fundamental course to well understand the intrinsic nature of IoT big data.

Knowledge and understanding. Students will learn the fundamental concepts of stationary processes and time series, how to estimate the features of a process, the main structures of prediction models, how to identify models starting from time series and how to validate models.
Applying knowledge and understanding. Students will be able to identify linear and non-linear models starting from time series by using popular software tools, such as MATLAB toolboxes, and validate their performances. Case studies will be proposed by using various kinds of dataset.
Making judgements. Students will be able to judge on the potential and limits of the model identification theory proposed in the course.
Communication skills. Students will be able to illustrate the basic aspects of model identification theory, interact and collaborate in teams with other experts.
Learning skills. Students will be able to autonomously extend their knowledge, drawing on the vast literature available in the field of time series model identification.

This course aims to present the theory and practice of
empirical research in public economics with particular
emphasis on the assessment of public programs in the
market economies. The course will develop analytical
knowledge of the main tools of quantitative
evaluations which underpin public interventions
efficiency and outcomes. Public policy applications
include the main programs in welfare (i.e. health,
education, and social sector).

Knowledge and understanding (Conoscenza e capacità di comprensione). Basic knowledge related to the recognition and management of financial risks from the core of the management processes of financial intermediaries.
Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). On completion, the student will be able to apply the measurement models of the main risks analyzed (credit risk, operational risk, interest rate risk on the banking book, liquidity risk)
Making judgements (Autonomia di giudizio). The ability to research, analyze and process public data and information also collected through scientific researches, with specific reference to the strategies up to the study of the problems concerning the assessment processes of the specific risks that banks incur in the activity of lending.
Communication skills (Abilità comunicative). On completion, the student will be able to communicate the results obtained, the problems encountered and the lessons learned, also based on independent judgment.
Learning skills (Capacità di apprendimento). On completion, the student will be able to individuate the components of credit risk, the organizational aspects of credit risk management and to choose the assessment tools for the diagnosis of credit risk.

Il corso fornisce conoscenze specifiche sui metodi e tecniche per affrontare i principali temi di Sanità Pubblica. In particolare, al termine del corso, gli studenti dovranno possedere: i) buona conoscenza degli strumenti e dei metodi necessari alla formulazione di quesiti e obiettivi della propria ricerca; ii) conoscenza approfondita dei metodi statistici e degli strumenti informatici per l'elaborazione del piano di analisi statistica e per l'interpretazione dei risultati; iii) capacità di comunicazione utilizzando il linguaggio proprio della Sanità Pubblica e conoscenza del contesto biomedico. Queste abilità vengono acquisite attraverso lezioni attive e interattive, seminari, laboratori ed esercitazioni e attraverso lo studio del materiale didattico.

Knowledge and understanding
The course of Private Law for Information Technology aims to analyze the impact of the Internet on legal rules and to identify the regulation of Internet behavior, with particular reference to the relationships between private subjects and to the complexity of the diffusion of artificial intelligence. The course aims also to give the keys to an adequate knowledge and understanding, as well as the normative sources and the relative interpretative principles, of the fundamental institutions of private law strictly connected to the Web in terms of subject regulation and protection of personal data, of the goods and of the circulation of internet rights, of the contract in general and of contracts on line.

2. Applying knowledge and understanding

At the end of the course, the student will be able to understand the legal issues raised by the technological context and to identify the solutions, both by reconstructing and interpreting the different situations and the legally relevant interests in the network on the private level, both by applying in practice the knowledge and tools acquired during the course of lessons and developed with a careful study of the subject.

3. Making judgements

The rapid development of information technology has exacerbated the need for robust personal data protection, the right to which is safeguarded by both European Union (EU) and Council of Europe (CoE) instruments. Safeguarding this important right entails new and significant challenges as technological advances expand the frontiers of areas such as surveillance, communication interception and data storage. This course is designed to familiarise students not specialised in data protection with this emerging area of the law. In particular, the student will be placed in a position to distinguish between positive and negative aspects, advantages and disadvantages associated with data protection regulation and the possible options between alternative forms of protection ready by the legislature. The teaching tends to let the student's ability to understand and explain key case law, summarising major rulings of both the Court of Justice of the European Union and the European Court of Human Rights.

4. Communication skills

The course must put the student in a position to transfer and apply the knowledge gained outside, using an exposure mode logical argument conforms to the main principles of matter and also suits a technically appropriate legal language. In particular, he must be able to provide opinions, advice and assistance on issues object of study.

5. Learning skills

Exceeding the matter must be based on a rigorous assessment in the examination of the achievement by the student of a level of preparation and competence that allows him to approach the study of the other teachings, also not legal, but which may present significant implications with them.

The goal of this course is to familiarize the students
with research in political science with quantitative
data. They will learn how to formulate (scientifically)
questions about the political world finding answers
using the logic of the scientific method.
On completion, students will learn how to measure political
concepts, how to define a research design and methods of data
collection; how to use statistical and graphical techniques for
describing data; and the principles of statistical inference

The course will give the main concepts and techniques
for the design of questionnaires and data analysis of
collected data. On completion, students will acquire
knowledge about: i) design of a statistical survey; ii)
techniques for questionnaire design; iii) methods for
statistical analysis of collected data and for providing
statistical reports.

Knowledge and understanding (Conoscenza e capacità di comprensione). Students will acquire a precise knowledge and understanding of fundamental concepts in the field of cloud computing, chiefly through a guided exploration of the main technological solutions available from the public Cloud, focusing on resources and services oriented to data storage, analysis, visualization and machine learning.
Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). Based on the operating knowledge acquired, students will develop an effective "toolset" of practical, application-oriented skills in leveraging the Cloud to cater for the typical needs of a data scientist: i.e. processing large datasets with a view to revealing meaningful patterns and relationships. Cloud implementations of state-of-the-art tools and frameworks like, e.g., MapReduce/Hadoop or TensorFlow, will be employed
Making judgements (Autonomia di giudizio). The student will develop the ability to choose the suitable Cloud-based resource for the Data Science scenario of interest, properly estimating the ensuing costs and performance gains, as well as consciously assessing the tradeoffs involved.
Communication skills (Abilità comunicative). The student will acquire the communication skills required to express and discuss, at a rigorous technical level, the benefits and (mostly cost-related) downsides of the Cloud for Data Science applications. In addition, the student will gain the ability, for presentation purposes, to effectively highlight the features of very large datasets by means of cloud-based visualization services.
Learning skills (Capacità di apprendimento). Students will become capable of profitably consulting technical documentation concerning Data Science-oriented Cloud services, in order to concretely put them to effective use

Data is growing and has grown very fast in the last years.”Big Data” analytics is challenging today because of the unprecedented large data volumes. In this course, we will describe the structure of data generated in big data sensing applications, by distinguishing the type and structure of data. Then we will discuss SoA methodologies which can be used to compress this data based on its intrinsic features; finally, communication protocols for remotely delivering this data will be described and detailed. In this way students will be provided with communication engineering competences allowing them to actively communicate with experts in various fields by providing focused and competent data analysis for every application, such as in scientific, technological or business fields. Students will also be able to exploit the competences gained for design processes of collection, compression and communication of heterogeneous big data.
This course will be of interest for students attending all paths for the following reasons:
For “Business and economics data scientists”, this course will allow to complement their business competences being able also to communicate with technical experts in the field.
For students in “Data driven applications for the IoT” and “Data for sciences”, this is a fundamental course to well understand the intrinsic nature of IoT big data.

Knowledge and understanding. Students will learn the fundamental concepts of stationary processes and time series, how to estimate the features of a process, the main structures of prediction models, how to identify models starting from time series and how to validate models.
Applying knowledge and understanding. Students will be able to identify linear and non-linear models starting from time series by using popular software tools, such as MATLAB toolboxes, and validate their performances. Case studies will be proposed by using various kinds of dataset.
Making judgements. Students will be able to judge on the potential and limits of the model identification theory proposed in the course.
Communication skills. Students will be able to illustrate the basic aspects of model identification theory, interact and collaborate in teams with other experts.
Learning skills. Students will be able to autonomously extend their knowledge, drawing on the vast literature available in the field of time series model identification.

This course aims to present the theory and practice of
empirical research in public economics with particular
emphasis on the assessment of public programs in the
market economies. The course will develop analytical
knowledge of the main tools of quantitative
evaluations which underpin public interventions
efficiency and outcomes. Public policy applications
include the main programs in welfare (i.e. health,
education, and social sector).

Knowledge and understanding (Conoscenza e capacità di comprensione). Basic knowledge related to the recognition and management of financial risks from the core of the management processes of financial intermediaries.
Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). On completion, the student will be able to apply the measurement models of the main risks analyzed (credit risk, operational risk, interest rate risk on the banking book, liquidity risk)
Making judgements (Autonomia di giudizio). The ability to research, analyze and process public data and information also collected through scientific researches, with specific reference to the strategies up to the study of the problems concerning the assessment processes of the specific risks that banks incur in the activity of lending.
Communication skills (Abilità comunicative). On completion, the student will be able to communicate the results obtained, the problems encountered and the lessons learned, also based on independent judgment.
Learning skills (Capacità di apprendimento). On completion, the student will be able to individuate the components of credit risk, the organizational aspects of credit risk management and to choose the assessment tools for the diagnosis of credit risk.

Il corso fornisce conoscenze specifiche sui metodi e tecniche per affrontare i principali temi di Sanità Pubblica. In particolare, al termine del corso, gli studenti dovranno possedere: i) buona conoscenza degli strumenti e dei metodi necessari alla formulazione di quesiti e obiettivi della propria ricerca; ii) conoscenza approfondita dei metodi statistici e degli strumenti informatici per l'elaborazione del piano di analisi statistica e per l'interpretazione dei risultati; iii) capacità di comunicazione utilizzando il linguaggio proprio della Sanità Pubblica e conoscenza del contesto biomedico. Queste abilità vengono acquisite attraverso lezioni attive e interattive, seminari, laboratori ed esercitazioni e attraverso lo studio del materiale didattico.

Knowledge and understanding
The course of Private Law for Information Technology aims to analyze the impact of the Internet on legal rules and to identify the regulation of Internet behavior, with particular reference to the relationships between private subjects and to the complexity of the diffusion of artificial intelligence. The course aims also to give the keys to an adequate knowledge and understanding, as well as the normative sources and the relative interpretative principles, of the fundamental institutions of private law strictly connected to the Web in terms of subject regulation and protection of personal data, of the goods and of the circulation of internet rights, of the contract in general and of contracts on line.

2. Applying knowledge and understanding

At the end of the course, the student will be able to understand the legal issues raised by the technological context and to identify the solutions, both by reconstructing and interpreting the different situations and the legally relevant interests in the network on the private level, both by applying in practice the knowledge and tools acquired during the course of lessons and developed with a careful study of the subject.

3. Making judgements

The rapid development of information technology has exacerbated the need for robust personal data protection, the right to which is safeguarded by both European Union (EU) and Council of Europe (CoE) instruments. Safeguarding this important right entails new and significant challenges as technological advances expand the frontiers of areas such as surveillance, communication interception and data storage. This course is designed to familiarise students not specialised in data protection with this emerging area of the law. In particular, the student will be placed in a position to distinguish between positive and negative aspects, advantages and disadvantages associated with data protection regulation and the possible options between alternative forms of protection ready by the legislature. The teaching tends to let the student's ability to understand and explain key case law, summarising major rulings of both the Court of Justice of the European Union and the European Court of Human Rights.

4. Communication skills

The course must put the student in a position to transfer and apply the knowledge gained outside, using an exposure mode logical argument conforms to the main principles of matter and also suits a technically appropriate legal language. In particular, he must be able to provide opinions, advice and assistance on issues object of study.

5. Learning skills

Exceeding the matter must be based on a rigorous assessment in the examination of the achievement by the student of a level of preparation and competence that allows him to approach the study of the other teachings, also not legal, but which may present significant implications with them.

The goal of this course is to familiarize the students
with research in political science with quantitative
data. They will learn how to formulate (scientifically)
questions about the political world finding answers
using the logic of the scientific method.
On completion, students will learn how to measure political
concepts, how to define a research design and methods of data
collection; how to use statistical and graphical techniques for
describing data; and the principles of statistical inference

The course will give the main concepts and techniques
for the design of questionnaires and data analysis of
collected data. On completion, students will acquire
knowledge about: i) design of a statistical survey; ii)
techniques for questionnaire design; iii) methods for
statistical analysis of collected data and for providing
statistical reports.