Detecting & Showcasing Outliers In Power BI – Free Training Workshop

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The next free training workshop from Enterprise DNA for all Power BI users is here.

You can register for the event below:

Detecting & Showcasing Outliers In Power BI – Webinar Registration

This event is open to all (100% free) as part of the Enterprise DNA Webinar Series.

The workshop planned this time round is really taking things up a level from an analytical perspective.

I want to really showcase how incredible Power BI is, as an advanced analytics tool.

There is no other tool that comes close to the value / cost ratio of Power BI at this present time and this workshop will showcase why.

Understanding outliers that sit within your data is incredibly important because they can add the greatest value or on the flip side be the greatest detractors of value from your expected results.

So knowing how you can showcase these effectively and then dynamically breaking down the; what, when and why of these outlier results is extremely powerful from an analytical point of view.

View the solution we’ll be working on by clicking on the link below:

Detecting & Showcasing Outliers in Power BI – Showcase

During the session we’ll be covering the below;

  • Creating scatter charts and breaking-out outlier results
  • Comparing customers who are considered outliers versus those that aren’t
  • Showcasing dynamically how outlier customer groups change over time
  • Drilling down into what causes outliers through time
  • ………….and much more

 

This will be one of the best analytical sessions around Power BI yet, I’m very confident in that.

There will be many techniques used that can be replicated across many different scenarios in many different industries.

If you are using or planning to use Power BI as an analytical tool, then this workshop is certainly one you’ll want to get involved in.

This is a totally free event and anyone can register. You can so at the below link:

Detecting & Showcasing Outliers in Power BI

See you then.

Sam

 

Detecting Outliers

 

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