Titlebook: Self-Service AI with Power BI Desktop; Machine Learning Ins Markus Ehrenmueller-Jensen Book 2020 Markus Ehrenmueller-Jensen 2020 Power BI.P

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FUSC

GROG

Entropion

Characterizing a Dataset,average, and standard deviation. You can easily visualize the value distribution and the amount of missing values and gain insights about the data even before you build your first report. Part of this metadata can be loaded into the data model, and you can build reports on it, if you want.

Monolithic

Transforming Data with R and Python,ed in Chapter 8 (“Creating Columns by Example”). In cases where you hit the limitations of Power Query, or when you already have a transformation written in R or Python, you can (re-)use them inside Power Query.

Itinerant

Execute Machine Learning Models in the Azure Cloud,hese models are easy to use because you do not have to train them. If you need more flexibility (as you want to take care of training and tuning the model yourself) or if you want to come up with your very own (or your favorite data scientist’s) model then you are more than welcome to do so with the help of Azure Machine Learning Services.

輕而薄

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Discovering Key Influencers,ibutes are making, for example, your Bike product category so different from Accessories. You bring the field value, categories, and measures; Power BI will bring the insight into how those categories and measures are key influencers on the field value.

Derogate

Adding Smart Visualizations,before you can use their full capabilities (such Power BI visuals are hinted with .). In this chapter, we will look at a selection of visualizations offered by Microsoft under the section “Advanced Analytics.” All of the following are free to use:

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