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ISLA IPGT 24860

Fundamentals of Business Intelligence and Data Analysis

Analytics and Business Data Science
  • ApresentaçãoPresentation
    The main objective of this course is to introduce students to the potential of decision-making in organisations based on large data sets and their analysis in the context of Business Intelligence and data analysis. To promote understanding of data based on visualisation and the presentation of clear and strategic reports. Understand ETL (Extract, Transform, Load) processes, Analytics and Data Science KDD (Knowledge Discovery in Databases) and Data Mining. Understand how to use Business Intelligence and data analysis to support strategy formulation and decision-making. Learn effective data visualisation (Dashboards and Reports). Develop social skills, namely teamwork and collaboration, communication, critical and agile thinking.
  • ProgramaProgramme
    Description of contents Data, Information, Knowledge and Intelligence Fundamental concepts, data, information, knowledge, and intelligence. Importance of data quality in decision-making. ¿Data Mining Concept of ETL and the stages of the process. Examples of application in an organisational context ETL (Extract, Transform, Load) Stages of the ETL process and BI. Examples of data integration tools. Analytics e Data Science Types of analytics (descriptive, predictive, and prescriptive). Role of Data Science. KDD process. Data mining.   Business Intelligence ¿BI concept and objectives. Key performance indicators (KPIs) Business Intelligence Architecture ¿Components and layers of a BI architecture (data sources, staging, data warehouse, data marts). Overview of OLAP and dimensional models. Power BI Introduction to Power BI and the data model. Construction of interactive reports and dashboards to support decision-making.    
  • ObjectivosObjectives
    Understand how to use business intelligence and data analytics to outperform traditional companies in your industry. Learn effective data visualisation (Dashboards and Reports) with Power BI. Develop social skills (soft skills), namely teamwork and collaboration, communication, critical and agile thinking. Understanding and implementing types of analytics (descriptive, predictive, and prescriptive); - The role of data science in decision support and business value creation; - The KDD (Knowledge Discovery in Databases) process; - Data mining.
  • BibliografiaBibliography
    Deckler, G. (2022). Learn Power BI Second Edition. A comprehensive, step-by-step guide for beginners to learn real-world business intelligence. Editor: Packt Publishing. ISBN: 9781801810074. Meier, M., Baldwin, D., Strachnyi, K., (2021), Mastering Tableau 2021: Implement advanced business intelligence techniques and analytics with Tableau, 3rd Edition, Packt. O'Connor, E. (2018), Microsoft Power BI Dashboards Step by Step, 1st Edition, Microsoft. Santos, M., Y., Ramos, I. (2017). Business Intelligence da Informação ao Conhecimento. Data Science. FCA. Shmueli, G., Bruce, P. C., Gedeck, P., & Patel, N. R. (2019). Data mining for business analytics: Concepts, techniques, and applications in Python. Wiley. Raschka, S., & Mirjalili, V. (2019). Python machine learning: Machine learning and deep learning with Python, scikit-learn, and TensorFlow 2 (3rd ed.). Packt Publishing.  
  • MetodologiaMethodology
    The teaching methodology consists of the presentation and discussion of topics, and whenever possible present existing technologies, through the implementation of application examples that demonstrate the concepts involved. At the end of each topic, exercises are proposed to consolidate learning. Assessment methodology: Curricular Assessment: Practical work with a weight of 100% in the final grade.
  • LínguaLanguage
    Português
  • TipoType
    Anual
  • ECTS
    2
  • 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

     

    100%

     

     

     

     

     

     

     

    Adicionalmente poderão ser incluídas informações gerais, como por exemplo, referência ao tipo de acompanhamento a prestar ao estudante na realização dos trabalhos; referências bibliográficas e websites úteis; indicações para a redação de trabalho escrito...