LEARNING OUTCOMES

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.

AIMS AND LEARNING OUTCOMES

DESCRIBE the principles for data analysis and large-scale data analysis

UNDERSTAND the differences between data management (OLTP) systems and data analysis (OLAP) systems

UNDERSTAND the differences between design issues and methodologies for databases and for datawarehouses

UNDERSTAND the main issues in data quality, data integration, and data governance

UNDERSTAND the main issues in data warehouse design, with specific reference to conceptual design, ROLAP logical design, view selection, physical design and ETL design

UNDERSTAND the main issues in large scale data analysis 

SELECT the most adequate systems and languages for a given analysis context

USE some of the presented systems for data exploration, data reconciliation, data warehouse storage, data reporting and OLAP querying

USE some of the presented systems for construncting a datawarehouse from a given operational dataset and for performing non-trivial analyses on it

SOLVE exercizes related to the design of data warehouses and OLAP queries