Introduction to Data Modeling for Power BI Video Course

Introduction to Data Modeling for Power BI is an introductory video course about data modeling, which is a required skill to get the best out of Power BI, Power Pivot for Excel, and Analysis Services. The training is aimed at users that do not have a background knowledge in data modeling for analytical systems and reporting.

The goal of the course is introduce the primary concepts of dimensional modeling, using practical examples and demos to illustrate how to obtain the desired result without having to write complex DAX expressions. Creating a proper data model simplifies the code to write and improves the performance. The course is made of 100 minutes of lectures. You can watch the videos at any time and the system will keep track of your advances. Within the course you can download the slides and the Power BI files used in the demos.

Collapse allCurriculum

  • Presentation of Introduction to Data Modeling for Power BI

    • Presentation of Introduction to Data Modeling for Power BI
  • Slides and demos

    • How to download and use demo files
    • Demos download
    • Slides of the video course
  • Introduction to data modeling

    • Introduction to data modeling
    • Introduction
    • Scattered information
    • Business entities
  • Normalization and denormalization

    • Normalization and denormalization
    • Introducing normalization and denormalization
  • Star schemas

    • Star schemas
    • Introducing star schemas
    • Placing tables in a diagram
    • If you don't have a star schema
  • Why data modeling is useful

    • Why data modeling is useful
    • What is the role of a data model
  • Data modeling scenarios

    • Data modeling scenarios
    • Common scenarios
    • Header / detail tables
    • Back to a star schema
    • Multiple fact tables
    • Building a star schema
    • Handling multiple dates
    • Multiple relationships with date
    • Events with different durations
    • Precompute the values
  • Conclusion

    • Conclusion
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