An introduction to Anomaly Detection

Dataiker Alumni
An introduction to Anomaly Detection

NYC UG June 2020 Bevy.jpg


On June 24th, as part of the launch of the New York user group, we’ll identify the outliers with @pmasiphelps (Lead Data Scientist, Dataiku) who will present an anomaly detection project in Dataiku DSS, based on the data in The Washington Post’s The Opioid Files.

Anomaly detection is a step in data mining that identifies data points, events, and/or observations that deviate from a dataset's normal behavior. Anomalous data can indicate critical incidents, such as a technical glitch, or potential opportunities, for instance a change in consumer behavior.

Anomaly detection is an approach that can be useful across an array of industries and for a variety of purposes, including IT and DevOps, manufacturing, healthcare, banking and finance, and in the public sector.

If you’re interested in learning more about Anomaly Detection, please join us next week!

For more resources about Anomaly Detection:

Please note: The dataset that is used in this presentation is the publicly available data presented by The Washington Post. 

Looking for more resources to help you use Dataiku effectively and upskill your knowledge? Check out these great resources: Dataiku Academy | Documentation | Knowledge Base

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