Business Intelligence and Data Engineering

Course Description

Data warehousing, decision support, OLAP and data mining, what many people often call Business Intelligence (BI), has reached a maturity height with an abundance of systems, platforms and methods. It has evolved from a domain-specific area for large and highly sophisticated corporations, to an essential component of any modern business entity or institution. Although the field of BI is relatively new, the concept of using data to support decision-making is not. Performing complex data analysis and knowledge extraction over large volumes of data exists since the inception of information systems, in the form of executive information systems, statistical packages and artificial intelligence prototypes. The crucial difference between that era and today is the “democratization” of data analysis: by natively equipping DBMS with analytical capabilities and by providing practitioners with expressive data models, powerful integration tools and intuitive query languages, BI research brought to the masses clean, integrated and aggregated information in a timely manner.

Learning Outcomes

Upon completion of the course, students will be able to:

• Understand the benefits of data analysis for an organization
• Understand the different phases of data analysis
• Design the architecture and schema of a data warehouse
• Implement ETL processes in a data warehouse environment
• Create data cubes using a data warehouse
• Create multidimensional analysis/OLAP reports
• Use data visualization tools to produce dashboards
• Know basic data mining concepts: categorization, clustering, association rules
• Know NoSQL data management systems and their use in specific applications
• Understand what data streams are and how they are used for real-time analysis
• Use commercial or open source DB systems and visualization tools for all the above.