ISLA Santarém 22491

Data Knowledge Extraction

• ApresentaçãoPresentation

  The extraction of knowledge, patterns or database trends is an essential element in the construction of decision support systems. It is an area closely linked to database techniques, statistics and machine learning. Some skills to acquire stand out: The importance in extracting data knowledge in the more general context of building decision support systems in the information and knowledge society; Identify some of the techniques, methodologies and tools of knowledge extraction from a high volume of data; Apply knowledge extraction techniques in experimental context.
• ProgramaProgramme

  Introduction to Business Intelligence, Data Mining, CRISP-DM methodology Data Warehouse and OLAP Systems Adaptive Business Intelligence Forecasting and Optimization Data Mining: classification, regression, segmentation Learning Models (e.g. Decision Trees, Neural Networks) Learning Statistics Tools (Data Warehouses, OLAP, BI, Data Mining) 1Data Warehouse - ETL Processes 2Open Source Business Intelligence - OLAP Servers/Clients 3Data Mining - Report Creation, Dashboards 4Analysis of Open Source Business Intelligence Platforms 4.1Pentaho Business Intelligence Server 4.2 SpagoBI 
• ObjectivosObjectives

  Intended learning outcomes (knowledge, skills and competences to be developed by the students). The objectives of the curricular unit are: -Identify the main techniques, methodologies and knowledge extraction tools from a high volume of data; -Present data mining techniques; -Present the learning models; -Present and use the tools (Data Wharehouse, OLAP, BI and Data mining). At the end of the course unit students should be able to: -Work with database techniques, statistics and machine learning. -Build decision support systems for today's large and medium enterprises. -Recognize the role and importance of data knowledge extraction in the broader context of building decision support systems in the information and knowledge society; -Apply knowledge extraction techniques from large data in real and experimental context.
• BibliografiaBibliography

  Gama, J. et al, (2017). Extração de Conhecimento de Dados, Sílabo. Han J., Micheline K. e Jian P. (2022). Data Mining - concepts and techniques, elsevier science technology Kirk, Andy (2024). Data Visualisation: A Handbook for Data Driven Design. Sage Publications Ltd Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2025). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann. Sharda, Ramesh (2024). Business Intelligence, Analytics, Data Science, and Ai, Global Edition. Pearson Education Limited
• MetodologiaMethodology

  The applied methodology is an expository methodology, in the theoretical contents and laboratory practice in the contents of practical application
• LínguaLanguage

  Português
• TipoType

  Semestral
• ECTS

  6
• NaturezaNature

  Mandatory
• EstágioInternship

  Não
• AvaliaçãoEvaluation

  Metodologia de avaliação - contínua:

  • Trabalho prático (Relatório e projeto); 60%;
  • Teste final teórico-prático; 40%;

   

  Todos os estudantes que não tenham concluído com sucesso a avaliação podem realizar um exame final teórico-prático na época de avaliação definida pela instituição.