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 time subjects room
Monday 7 November 2022 16-18 New Paradigms for Data Architectures 278
Tuesday 8 November 2022 16-18 Emerging Issues - Data Privacy 278
Wednesday 9 Nov 2022 16-18 GDPR - Regulations 248
Monday, November 14, 2022 16-18 GDPR - Roles 278
Tuesday 15 November 2022 16-18 Data Analysis & Design: W6H 278
Wednesday 16 November 2022 16-18 Data Modeling - ER 248
Monday 21 November 2022 16-18 ER: Identification Keys + Exercise 278
Tuesday 22 November 2022 16-18 ER: Hierarchies + Exercise 278
Wednesday 23 November 2022 16-18 Exercise + Introduction to Data Warehouse 248
Monday 28 November 2022 16-18 Data Warehouse-Multidimensional Model 278
Tuesday 29 November 2022 16-18 Big Data: Definition 278
Wednesday 30 November 2022 16-18 Big Data: NO Sql models 248
Monday 5 December 2022 16-18 Students' Feedback+Basic Concepts Review 278
Tuesday 6 December 2022 16-18 Big Data Picture 278
Wednesday 7 December 2022 16-18 Italian Agency for Digital Public Administration 248
Monday 12 December 2022 16-18 Big Data: AI 278
Tuesday 13 December 2022 16-18 Big Data: ML and Deep Learning 278
Wednesday 14 December 2022 16-18 Big Data: Use Cases 248
Monday 19 December 2022 16-18 Review of Course Topics + Exercise 278
Tuesday 20 December 2022 16-18 Review of Course Topics + Exercise 278
Programme
BIG DATA AND DECISION AUTOMATIONdate time subjects room
Monday 7 November 2022 16-18 New Paradigms for Data Architectures 278
Tuesday 8 November 2022 16-18 Emerging Issues - Data Privacy 278
Wednesday 9 Nov 2022 16-18 GDPR - Regulations 248
Monday, November 14, 2022 16-18 GDPR - Roles 278
Tuesday 15 November 2022 16-18 Data Analysis & Design: W6H 278
Wednesday 16 November 2022 16-18 Data Modeling - ER 248
Monday 21 November 2022 16-18 ER: Identification Keys + Exercise 278
Tuesday 22 November 2022 16-18 ER: Hierarchies + Exercise 278
Wednesday 23 November 2022 16-18 Exercise + Introduction to Data Warehouse 248
Monday 28 November 2022 16-18 Data Warehouse-Multidimensional Model 278
Tuesday 29 November 2022 16-18 Big Data: Definition 278
Wednesday 30 November 2022 16-18 Big Data: NO Sql models 248
Monday 5 December 2022 16-18 Students' Feedback+Basic Concepts Review 278
Tuesday 6 December 2022 16-18 Big Data Picture 278
Wednesday 7 December 2022 16-18 Italian Agency for Digital Public Administration 248
Monday 12 December 2022 16-18 Big Data: AI 278
Tuesday 13 December 2022 16-18 Big Data: ML and Deep Learning 278
Wednesday 14 December 2022 16-18 Big Data: Use Cases 248
Monday 19 December 2022 16-18 Review of Course Topics + Exercise 278
Tuesday 20 December 2022 16-18 Review of Course Topics + Exercise 278
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 - The right to the protection of personal data: between European regulations and adaptation of national regulations" Roma Tre University - Department of Law Project co-financed by the "Rights, Equality and Citizenship" Program of the European UnionType 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
Strongly recommendedType 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.