Technology is pervasive in our daily experience, but we must never forget that it is a tool at our service. We are surrounded by data of all kinds and nature and we ourselves, more or less consciously, are data generators. These data, however, are useful if, and only if, they become information that we are able to use to understand the reality in which we live, to guide our choices and to reduce the level of uncertainty of the complex system of which we are part. Anything that is not measurable cannot be managed or improved. Progress depends on the quality of the data and information we are able to extract and manage.
Technology makes it possible to record, store, analyze increasing quantities of data, therefore new problems arise in terms of governance, quality, reliability, certification, data protection. The boundaries between social and private are more blurred, data can make us more free and aware, or more vulnerable and orientable. In step with technology, it is necessary to develop
the ability to seek, integrate, elaborate, imagine, understand. And together with all this, ethical awareness and social responsibility are needed. In this context, great attention is paid to Artificial Intelligence and how it can be applied in different fields and how its applications are changing the world. Machine Learning in recent years has found wide fields of application, for example in the field of health.
In this course we will address these issues with an approach where the questions we ask ourselves will be more important than the answers we find, together or individually.
Technology makes it possible to record, store, analyze increasing quantities of data, therefore new problems arise in terms of governance, quality, reliability, certification, data protection. The boundaries between social and private are more blurred, data can make us more free and aware, or more vulnerable and orientable. In step with technology, it is necessary to develop
the ability to seek, integrate, elaborate, imagine, understand. And together with all this, ethical awareness and social responsibility are needed. In this context, great attention is paid to Artificial Intelligence and how it can be applied in different fields and how its applications are changing the world. Machine Learning in recent years has found wide fields of application, for example in the field of health.
In this course we will address these issues with an approach where the questions we ask ourselves will be more important than the answers we find, together or individually.
teacher profile teaching materials
date schedule topics
Monday 9 October 2023 18-20 New Paradigms for Data Architectures
Tuesday 10 October 2023 18-20 Emerging Issues - Data Privacy
Wednesday 11 October 2023 18-20 GDPR - Regulations
Monday 16 October 2023 18-20 GDPR - Roles
Tuesday 17 October 2023 18-20 Data Analysis & Design: W6H
Wednesday 18 October 2023 18-20 Data Modeling - ER
Monday 23 October 2023 18-20 ER: Identification Keys + Exercise
Tuesday 24 October 2023 18-20 ER: Hierarchies + Exercise
Wednesday 25 October 2023 18-20 Exercise + Introduction to Data Warehouse
Monday 30 October 2023 18-20 Data Warehouse-Multidimensional Model
Tuesday 31 October 2023 18-20 Big Data: Definition
Monday 6 November 2023 18-20 Big Data: NO Sql models
Tuesday 7 November 2023 18-20 Analysis Feedback from Students+Basic Concepts Review
Wednesday 8 November 2023 18-20 Big DataPicture: Data-Information-Knowledge-A.I.
Monday 13 November 2023 18-20 Public Administration Digitalization: Italian Regulatory Agency
Tuesday 14 November 2023 18-20 Big Data: AI
Wednesday 15 November 2023 18-20 Big Data: ML and Deep Learning
Monday 20 November 2023 18--20 Big Data: Use Cases
Tuesday 21 November 2023 18-20 Review Course Topics + Exercise
Wednesday 22 November 2023 18-20 Review Course Topics + Exercise
Programme
BIG DATA AND DECISION AUTOMATIONdate schedule topics
Monday 9 October 2023 18-20 New Paradigms for Data Architectures
Tuesday 10 October 2023 18-20 Emerging Issues - Data Privacy
Wednesday 11 October 2023 18-20 GDPR - Regulations
Monday 16 October 2023 18-20 GDPR - Roles
Tuesday 17 October 2023 18-20 Data Analysis & Design: W6H
Wednesday 18 October 2023 18-20 Data Modeling - ER
Monday 23 October 2023 18-20 ER: Identification Keys + Exercise
Tuesday 24 October 2023 18-20 ER: Hierarchies + Exercise
Wednesday 25 October 2023 18-20 Exercise + Introduction to Data Warehouse
Monday 30 October 2023 18-20 Data Warehouse-Multidimensional Model
Tuesday 31 October 2023 18-20 Big Data: Definition
Monday 6 November 2023 18-20 Big Data: NO Sql models
Tuesday 7 November 2023 18-20 Analysis Feedback from Students+Basic Concepts Review
Wednesday 8 November 2023 18-20 Big DataPicture: Data-Information-Knowledge-A.I.
Monday 13 November 2023 18-20 Public Administration Digitalization: Italian Regulatory Agency
Tuesday 14 November 2023 18-20 Big Data: AI
Wednesday 15 November 2023 18-20 Big Data: ML and Deep Learning
Monday 20 November 2023 18--20 Big Data: Use Cases
Tuesday 21 November 2023 18-20 Review Course Topics + Exercise
Wednesday 22 November 2023 18-20 Review Course Topics + Exercise
Core Documentation
The lessons will be available on the e-learning platform of the UniversityReference Bibliography
"Building the Data Lakehouse" Bill Inmon, Mary Levins, Ranjeet Srivastava - First Printing 2021 Copyright © 2021 by Bill Inmon, Mary Levins, and Ranjeet Srivastava ISBN, print ed. 9781634629669 ISBN, Kindle ed. 9781634629676 ISBN, ePub ed. 9781634629683 ISBN, PDF ed. 9781634629690 "Domain-Driven Design: Tackling Complexity in the Heart of Software" Eric Evans Addison-Wesley Professional First Printing 2004 ISBN: 0321125215 "The Entity Relationship Model — Toward a Unified View of Data" Peter_Pin_Shan-Chen Massachusetts Institute of Technology, USA © 2002 Springer-Verlag Berlin Heidelberg Print ISBN: 978-3-642-63970-8 "SMEData - Il diritto alla protezione dei dati personali: tra disciplina europea e adeguamento della disciplina nazionale" Università degli Studi Roma Tre - Dipartimento di Giurisprudenza Progetto cofinanziato dal Programma "Rights, Equality and Citizenship" della Unione EuropeaType of delivery of the course
The lessons will be supported by material that will be distributed at the end of each topic. The didactic approach will be oriented towards discussion and comparison on the topics that will be treated; therefore, the active participation of students in lessons will be particularly useful, to make the acquisition and consolidation of the notions learned more effective. The focus of the course will be centered on the functional understanding of the topics covered, highlighting the relationship between the use cases and the various classes of methods and tools presented, reserving an exposure to technologies oriented towards understanding their use rather than knowledge of the aspects of software engineering.Attendance
frequency is advisableType of evaluation
Students will be assessed on the basis of the acquired ability to describe the fundamental rationales of the design of BIG DATA solutions oriented to the analysis of complex phenomena for decision support, relating the methods and models covered by the course with the main classes of use cases.