ISLA IPGT 22491

Data Knowledge Extraction

• ApresentaçãoPresentation

  The course aims to provide students with technical and analytical skills in the area of knowledge extraction and data transformation into useful information to support organizational decision-making.Students should understand and apply ETL, Data Warehousing, Business Intelligence, and Data Mining methodologies, exploring integration with Power BI and Python.
• ProgramaProgramme

  Data Mining – Processes and Procedures ETL Process (Extract, Transform & Load) Tools (Data Warehouses, OLAP, BI)                - Data Warehouse – SQL Design                - Integration Services - ETL process                - Analysis Services with OLAP Cube Adaptive Business Intelligence      - Business Intelligence Architecture Knowledge Discovery in Databases (Data Mining in Python)      - Preparation and feature engineering      - Classification (K-NN + Random Forest)      - Clustering (K-Means) for segmentation      - Association rules (market basket) - Power BI — Model, DAX, and Visuals (with integrated Python) - Data integration - DAX measures - Python in the Power BI
• ObjectivosObjectives

  At the end of the course, students should be able to: Understand the complete cycle of data extraction, transformation, and loading (ETL); Design and implement data warehouse models and OLAP cubes; Apply supervised learning algorithms (Logistic Regression, Random Forest) and unsupervised learning algorithms (K-Means) in Data Mining; Develop adaptive Business Intelligence models, integrating Python with Power BI; Interpret results and indicators from analysis and forecasting systems; Create interactive dashboards with DAX and smart visualisations; Assess the quality, consistency, and usefulness of the knowledge extracted.
• BibliografiaBibliography

  Gama, J. et al. (2015). Extração de Conhecimento de Dados. Sílabo. Han, J., Kamber, M., & Pei, J. (2012). Data Mining: Concepts and Techniques. Morgan Kaufmann. Sharda, R., Delen, D., & Turban, E. (2017). Business Intelligence, Analytics, and Data Science: A Managerial Perspective (4th ed.). Pearson. Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann. Ward, M. O., Grinstein, G., & Keim, D. (2015). Interactive Data Visualization: Foundations, Techniques, and Applications. CRC Press. Data Analytics for Business Intelligence: A Multi¿Industry Approach — Sun, Zhaohao. Chapman & Hall / CRC Press. 1ª ed., Dezembro 2024. Trata de dados, analytics e inteligência aplicada a múltiplos setores. Microsoft Power BI Cookbook – Third Edition — Deckler, Greg & Powell, Brett. Packt Publishing. Julho 2024. Um guia prático de Power BI com técnicas actualizadas, ideal para modelação, DAX, visuais, integração com Python.
• MetodologiaMethodology

  The applied methodology is an expository methodology, in the theoretical contents and laboratory practice in the contents of practical application. Problem-based learning.
• LínguaLanguage

  Português
• TipoType

  Semestral
• ECTS

  6
• NaturezaNature

  Mandatory
• EstágioInternship

  Não
• AvaliaçãoEvaluation

  Descrição dos instrumentos de avaliação (individuais e de grupo) ¿ testes, trabalhos práticos, relatórios, projetos... respetivas datas de entrega/apresentação... e ponderação na nota final.

  Exemplo:

  Descrição	Data limite	Ponderação
  Trabalho prático de grupo		35%
  Teste de avaliação	24-01-2026	50%
  Trabalhos realizados em sala de aula		15%

   

  Haverá aulas de orientação tutorial remotas a acompanhar o desenvolvimento do trabalho prático.

  A falta na apresentação de um dos momentos de avaliação condiciona a época de avaliação.