Today it is essential to identify data behavior patterns and detect changes and anomalies in them.
The first step of the session will be to explain what represents an anomaly in a dataset.
Later, we will analyze various techniques in Power BI Desktop and compare the pros and cons.
1.- We will look at anomaly detection with Power BI Desktop using integrated visualizations,
2.- We will complement with R charts.
The third part will briefly introduce Azure Cognitive Services and a couple of samples of invoking ACS looking at the anomalies using Python script.
In the end, we will look at the anomalies from Power BI Service without any scripting.
This session will be helpful because the audience could learn about the different approaches to looking at the anomalies on the data.