PRESENTAZIONE

L'insegnamento introduce le competenze alla base dell'analisi di dati strutturati e della Business Intelligence (BI). Partendo dai principi di modellazione di dati strutturati a fini analitici, gli studenti svilupperanno una comprensione approfondita della progettazione di data warehouse e acquisiranno esperienza pratica di formulazione di interrogazioni di analisi OLAP in SQL. Verranno inoltre introdotti strumenti per la creazione di report e cruscotti e framework per l'analisi dei dati su larga scala. Nelle attività pratiche, gli studenti lavoreranno con grandi set di dati in un ambiente di data warehouse per progettare e popolare un data warehouse, interrogarlo e creare dashboard, utilizzando, oltre a server OLAP, strumenti dedicati a ETL e BI.

OBIETTIVI E CONTENUTI

OBIETTIVI FORMATIVI

Learning the theoretical, methodological, and technological fundamentals of data management and analysis in decision support systems, with a specific reference to data warehousing architectural and design issues, as well as key elements of data integration and governance, data quality and cleaning, ExtractionTransformation-Loading processes, conceptual, logical, and physical design of data warehouses, storage architectures and scalable parallel processing, use of data warehouses for business reporting and online analytical processing.

OBIETTIVI FORMATIVI (DETTAGLIO) E RISULTATI DI APPRENDIMENTO

DESCRIBE the principles for data management and analysis in decision support systems
DESCRIBE the elements of data integration and governance, data quality and cleaning 
UNDERSTAND the differences between transactional and analytical data processing and the requirements they pose on the system
UNDERSTAND the different possible architectures for data warehousing, both in small-scale and large-scale data analysis context 
UNDERSTAND the issues and the main different approaches for Extraction-Transformation-Loading processes
APPLY the methodology to design a data warehouse, starting from operational data and business questions through conceptual design, logical design, view selection and physical design
UNDERSTAND the use of precomputation and materialized views in data warehousing, and SELECT the most appropriate set of views 
UNDERSTAND the main OLAP operators and the SQL extensions to express them, and APPLY them to formulate analytical queries
SELECT the system and the methodology for storing data for analysis, for ETL, and for sharing data analysis outcomes with the user, suitable in a given application context

USE some of the presented systems for data warehousing, for solving non-trivial data analysis task

DOCENTI E COMMISSIONI

Commissione d'esame

GIOVANNA GUERRINI (Presidente)

GIANNA REGGIO

BARBARA CATANIA (Presidente Supplente)

ESAMI

Calendario appelli