Databases II


At the end of the course unit the students must have acquired the following skills:

  • How to collect data from various sources and on different formats and prepare it for analysis using languages and platforms for data transformation and loading.
  • How to create analysis, visualizations and data reports using modern platforms.
  • How to scale from personal use platforms to enterprise level platforms.
  • How to present the analysis results in an actionable way in organizations.

General characterization





Responsible teacher

Miguel de Castro Simões Ferreira Neto


Weekly - Available soon

Total - Available soon

Teaching language

Portuguese. If there are Erasmus students, classes will be taught in English


Knowledge acquired in the Databases I namely data modeling and SQL


  • Elmasri, R., & Navathe, S. B. (2017). Fundamentals of database systems. Hoboken, NJ: Pearson.
  • Petkovic, D. (2017). Microsoft SQL server 2016: a beginner's guide. New York: McGraw Hill Education.
  • Kimball, R., & Ross, M. (2013). The data warehouse toolkit: the definitive guide to dimensional modeling. Hoboken, NJ: Wiley.
  • Larson, B. (2017). Delivering business intelligence with Microsoft SQL server 2016. New York: McGraw-Hill Education.

Teaching method

Theoretical classes with presentation of models related to different functional contexts and practical classes with tutorials and exercises.

Evaluation method

Option 1

  • 2 tests (35%+35%, minimum grade in the average of both: 9 values)
  • Group project with presentation and discussion (30%, minimum score: 9 values).
  • The average between the tests and the work has, of course, to be at least 10.

Option 2

  • 2nd Season Exam (100%)

Subject matter

  • Fundamental concepts of operational databases and databases for decision support.
  • Conceptual, logical and physical design of databases for decision support.
  • Architectures of decision support systems.
  • Fundamental concepts of data modeling.
  • Dimensional and tabular data modeling.
  • Fundamental concepts of data extraction, transformation and loading.
  • Design and implementation of data extraction and loading systems.
  • Introduction to data analysis, visualization and reporting.
  • Data exploration using specific languages of this application domain.
  • Information security and privacy in the context of decision support databases.


Programs where the course is taught: