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ISLA IPGT 22491

Data Knowledge Extraction

Technology and Web Systems Engineering
  • 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
    Content description 1. Introduction to Business Intelligence, Data Mining, CRISP-DM methodology 2. Data Warehouse and OLAP Systems 3. Adaptive Business Intelligence 4. Data preparation 5. Forecasting and Optimisation 6. Data Mining: classification, regression, segmentation 7. Learning Models (e.g. Decision trees, Neuronal Networks) 7.1. Predictive Models 7.2. Descriptive Models 7.3. Advanced Topics 8. Tools (Data Warehouses, OLAP, BI, Data Mining) 8.1. Data Warehouse - ETL Processes 8.2. Open Source Business Intelligence - OLAP Servers /Clients 8.3. Data Mining - Reporting, Dashboards 8.4. Analysis of Proprietary Data Mining and Business Intelligence Platforms and Open Source 8.4.1. Power BI
  • 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, (2015). Extração de Conhecimento de Dados, Sílabo. Han J., Micheline K. e Jian P. (2012). Data Mining - concepts and techniques, Edições The Morgan Kaufmann Series in Data Management Systems, ISBN: 0123814790. Matthew O. Ward, Georges Grinstein, Daniel Keim (2015). Interactive Data Visualization: Foundations, Techniques, and Applications, 2nd Edition. CRC Press. Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann. Sharda, R., Delen, D., Turban- (2017). Business Intelligence, Analytics, and Data Science: A Managerial Perspective (4th Edition)  
  • MetodologiaMethodology
    Evaluatuin methodology - continuous: - Practical work (Report and project): 100% - All students who have not successfully completed the assessment can take a final theoretical-practical exam at the time of assessment defined by the institution.
  • LínguaLanguage
    Português
  • TipoType
    Semestral
  • ECTS
    6
  • NaturezaNature
    Mandatory
  • EstágioInternship
    Não