Learning Results
Given the growing amount of information from multiple sources, available to organizations it is imperative to
systematize and structure this information in order to be used effectively and efficiently to support decision
making. Thus, the objective of this course, to make known the potential of business intelligence process and
data warehouses to support and provide current information technologies and development methodologies of
business intelligence solutions.
Program
1. Introduction to Business Intelligence
2. Business Intelligence Components
3. Patterns and trends in Business Intelligence
4. Data warehouses:
4.1. Dimensional Data Modeling
4.2. Extract, Transform and Load (ETL) Process
4.3. Online Analytical Processing (OLAP) Tools
5. Introduction to Data Mining 6. Methodologies to implement Business Intelligence Projects
Internship(s)
NAO
Bibliography
M. Y. Santos e I. Ramos, Business Intelligence – da Informação ao Conhecimento – 3.a Edição Atualizada, Editora FCA, 2017.
Shmueli, G., Patel, N. R., Bruce, P. C. (2010). Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner, 2.nd ed, John Willey and Sons
E. Turban, R. Sharda, J. Aronson, D. King, Business Intelligence: a managerial approach, Prentice Hall, 2007.
• L. T. Moss, S. Atre, Business Intelligence Roadmap: The Complete Project Lifecycle for Decision-Support
Applications, Addison-Wesley Professional, 2003.
• W. H. Inmon, Building the Data Warehouse (4th Ed edition), Hungry Minds Inc, 2005.
• R. Kimball, M. Ross, W. Thornthwaite, J. Mundy, B. Becker, The Data Warehouse Lifecycle Toolkit, 2nd
Edition, Wiley, 2008.
• M. Y. Santos e I. Ramos, Business Intelligence – Tecnologias da Informação na Gestão de Conhecimento – 2.ª
Edição Actualizada e Aumentada, Editora FCA, 2009.
• Harts, Microsoft ® Office 2007 Business Intelligence: Reporting, Analysis, and Measurement from the
Desktop, McGraw-Hill, 2007.