Objectives
The course presents the main methodological and technological approaches to the
design and implementation of decision support systems based on business intelligence (datawarehousing, data mining, data science). The first module covers themes such as conceptual and logical Data Warehouses design, data analysis using analytic SQL, algorithms for selecting materialized views, data warehouse systems technology (indexes, star query optimization, physical design, query rewrite methods to use materialized views). The second module presents technologies and systems for data access, for building and analyzing data warehouses, for reporting, and for knowledge discovery in databases. The accent of the module is on the use of tools and on the analysis of application problems by means of non-trivial samples and case studies.

Syllabus
Module I: Decision support databases
– Information systems and computer-based information systems in organizations.
– Decision Support System Based on Data Warehouses.
– Data Models for Data Warehouses and On-line Analytical Processing.
– Conceptual and logical design in Data Warehouses.
– Algorithms for Selecting Materialized Views.
– Data Warehouse Systems Technology: Indexes, Star Query Optimization,
– Physical Design, Query Rewrite Methods to Use Materialized Views.
– Case studies.
Module II: Laboratory of Data Science
– Introduction: Tools for data science and Business Intelligence.
– Data Access. Location, Format and API for Accessing Data in Text Files. Standards for Data Connectivity.
– Extract Transform and Load. Tool for ETL. Case studies.
– Data Warehousing and OLAP. Tools for Dimensional Modeling. Case Studies.
– Tools for Reporting and Multidimensional Browsing. Case Studies
– Data Mining. Tools for Knowledge Discovery. Case Studies.