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Class Business Analytics

  • Presentation

    Presentation

    To provide students with an integrated view of the practices, tools and methodologies of Business Intelligence and Business Analytics, enabling them to transform data into useful information for decision making, based on descriptive, predictive and prescriptive analysis.
  • Code

    Code

    ULHT6634-23090
  • Syllabus

    Syllabus

    Fundamentals of Business Intelligence and Business Analytics Business Intelligence Architecture Visualization and Reporting Data Quality, Governance and Ethics Descriptive and Diagnostic Business Analytics Predictive Business Analytics Prescriptive Business Analytics and Decision Support Integration of BI and BA in Organizations Practical Project: Planning and Development Practical Project: Presentation and Discussion
  • Objectives

    Objectives

    Understanding of the fundamental concepts of Business Intelligence (BI) and Business Analytics (BA) - differences and complementarities. Mastery of the architecture and operation of Business Intelligence systems - modeling of a Datawarehouse, ETL process and data analysis Descriptive analysis and data visualization capabilities Application of predictive and prescriptive techniques Quality assurance and ethics in data management Practical integration of BI and BA in an organizational context Development of problem-oriented analytical solutions
  • References

    References

    Bolton, J. (Ed.). (2019). Data Warehousing Essentials. Larsen and Keller Education. Deckler, G. (2022). Learn Power BI: A comprehensive, step-by-step guide for beginners to learn real-world business intelligence, 2nd Edition (2nd ed. edition). Packt Publishing Kelly, N. (2021). Delivering Data Analytics: A Step-By-Step Guide to Driving Adoption of Business Intelligence from Planning to Launch (1st edition). Kogan Page. Kimball, R. (2013). The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3rd Edition (3 edition). Wiley. Sharda, R., Delen, D., & Turban, E. (2023). Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support (12th edition) Pearson. Provost, F., & Fawcett, T. (2013). Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking. O’Reilly Media.
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