Mastering DAX Workshop

Brisbane, AU

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DAX is the native language of Power Pivot for Excel, Power BI Desktop and SSAS Tabular models in Microsoft SQL Server Analysis Services. The training is aimed at users of Power Pivot for Excel, Power BI Desktop and at Analysis Services developers that want to learn and master the DAX language.

The goal of this three-day, classroom course is for the expert trainer to teach all the features of the DAX language, providing the knowledge to write formulas for common and advanced business scenarios. All the attendees will receive a copy of the book, The Definitive Guide to DAX - 2nd edition.

The classroom course includes hands-on labs: you will apply the concepts using your own notebook.

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Topics

  • DAX Basics
  • Table Functions
  • Evaluation Contexts
  • Filter and Row Context
  • Evaluation Contexts and Relationships
  • CALCULATE
  • Context transition
  • Iterators
  • Time Intelligence Calculations
  • DAX as a Query Language
  • Advanced Table Functions
  • Advanced Evaluation Context
  • Understanding Cross filters
  • Data lineage
  • Advanced Relationship Handling
  • Physical Relationships
  • Weak Relationships
  • Examples of Complex Relationships
Mastering DAX Outline (PDF)

Prerequisites

Attendees need basic knowledge of Power BI Desktop, or Analysis Services Tabular, or Power Pivot for Excel.

We strongly suggest attendees watch the free Introducing DAX video course prior to attending the Mastering DAX classroom course. This ensures proper basic knowledge of the language to start Mastering DAX. A minimum of 3-6 months of DAX experience is usually suggested to get the most out of the Mastering DAX classroom course.

All hands-on labs are based on Power BI - reasonable user interface knowledge is suggested, though the same DAX concepts can also be applied to Power Pivot for Excel and to Analysis Services Tabular.

Location

Saxons Training Facilities

300 Adelaide St
Brisbane, AU
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Teaching this course

Marco Russo

 Consultant & Mentor
Trainer for 173 other courses at SQLBI

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