Skip to main content

ISLA IPGT 24859

Introduction to Data Science in Organizations

Analytics and Business Data Science
  • ApresentaçãoPresentation
    The course unit Introduction to Data Science in Organisations addresses the role of data as a strategic asset and the impact of data science and artificial intelligence on business models and decision-making. Its scope covers the fundamentals of data science (data engineering, visualisation, machine learning, deep learning, predictive and prescriptive analytics, NLP, generative AI and LLMs), the roles within data teams and project management methodologies (CRISP-DM and Data Driven Scrum), complemented by the analysis of real-world big data and AI cases and by ethical reflection on their responsible use. The course unit is relevant within the study programme as it provides the conceptual and methodological foundation for the remaining course units, enabling students to understand an organisation's data ecosystem and identify opportunities for value creation from data.
  • ProgramaProgramme
    Introduction to data science: the data revolution and applications by sector Key concepts: machine learning, deep learning, predictive and prescriptive analytics, NLP, generative AI and LLMs Data teams: roles, responsibilities and interactions Data project life cycles: CRISP-DM and Data Driven Scrum Big data in practice: case studies (Netflix, John Deere, Chipotle) Ethics and critical thinking in the use of data and AI AI applications in education and entrepreneurship: development and presentation of student projects
  • ObjectivosObjectives
    By the end of the course unit, students should be able to: Understand the fundamental concepts of data science and artificial intelligence (machine learning, deep learning, predictive and prescriptive analytics, NLP, generative AI and LLMs); Identify data and AI applications across industry sectors and their impact on business models; Distinguish the roles within a data team and apply project management methodologies (CRISP-DM and Data Driven Scrum); Analyse real-world big data and AI cases, assessing data sources by business value and feasibility; Critically reflect on the ethical implications of the use of data and AI; Research and present the application of AI to a specific domain, developing research and communication skills.
  • BibliografiaBibliography
    [Preencher APELIDO], [Preencher Primeiros Nomes] - [Preencher Título]. [Preencher Edição. Local de publicação :Editor, Ano de publicação. Descrição física. Série. ISBN)] [Preencher APELIDO], [Preencher Primeiros Nomes] - [Preencher Título]. [Preencher Edição. Local de publicação :Editor, Ano de publicação. Descrição física. Série. ISBN)  
  • MetodologiaMethodology
    The course unit combines active, student-centred methodologies: case-based learning grounded in real-world cases (Netflix, John Deere, Chipotle), collaborative teamwork applying different ethical lenses to case studies, the use of collaborative digital tools and supporting multimedia resources, and project-based learning, involving autonomous research and the oral presentation of an individual project on the application of AI to a specific domain, with continuous feedback from the lecturer.
  • LínguaLanguage
    Português
  • TipoType
    Anual
  • ECTS
    2
  • NaturezaNature
    Mandatory
  • EstágioInternship
    Não
  • AvaliaçãoEvaluation

     

    Descrição

    Data limite

    Ponderação

    Teste de avaliação

    29-11-2025

    50%

    Trabalho Individual

    29-11-2025

    50%