Databases II
Objectives
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
Code
100014
Credits
6.0
Responsible teacher
Miguel de Castro Simões Ferreira Neto
Hours
Weekly - Available soon
Total - Available soon
Teaching language
Portuguese. If there are Erasmus students, classes will be taught in English
Prerequisites
Knowledge acquired in the Databases I namely data modeling and SQL
Bibliography
- 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.