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Class Business Intelligence Applications

  • Presentation

    Presentation

    The discipline of Business Intelligence Applications has as its fundamental objective providing the student with knowledge and competence in the areas of decision support through techniques and methodologies that allow the effective exploration of data and its transformation into knowledge.  It is also intended to provide students with the possibility of developing skills in the use of tools to provision, explore, analysis and communicate information. The practical component is a key attribute of the discipline. The ability to identify and build indicators that can support analytical decisions is highly valued.

  • Code

    Code

    ULHT457-13321
  • Syllabus

    Syllabus

    • Part 0: Data, Analytics, Data Science and Business Intelligence: Trends  

      Part 1: Business Intelligence Application Areas, Context, Objectives and Relationship with Business Processes. Methodologies.

      Part 2: Modeling in Business Intelligence  

      Part 3: Data provisioning and Preparation  

      Part 4: Exploration, Description, and Visualization of Data  

  • Objectives

    Objectives

    When concluding this curricular unit the student should:  

    • Understand the Business Intelligence (BI) market, its relationship with related areas (analytics, data mining, data science) and describe how organizations survive and stand out in a strongly competitive environment, solving problems and taking advantage of opportunities;
    • Understand the need for computerized and operationalized support in the decision-making process;
    • Describe the methodology and concepts of BI and relate them to Decision Support Systems (DSS); Understand the main issues in the implementation of BI systems, nodded to the methodological approaches that facilitate their success.
  • Teaching methodologies and assessment

    Teaching methodologies and assessment

     

    The subject has a strong practical component where students have to apply knowledge and obtain visible results.

  • References

    References

    • Grossmann, W., & Rinderle-ma, S. (2015). Fundamentals of Business Intelligence. Berlin Heidelberg: Springer-Verlag

    • Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., & Wirth, R. (2000). Crisp_DM, Step-by-step data mining guide. SPSS Inc., 9(13), 1–73. Retrieved from https://www.kde.cs.uni-kassel.de/lehre/ws2012-13/kdd/files/CRISPWP-0800.pdf

     

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