Outliers deviate from the norm—significantly enough to give marketers pause. But outliers can tell us more about our data, how we gather it, and what is in it, if we examine the entire data set ...

Data analytics deals with making observations with various data sets, and trying to make sense of the data. When dealing with very large data sets, automated tools must be used to find patterns and ...

The bigger your dataset, the greater your chance of stumbling into an outlier. It’s practically a certainty you’ll find isolated, unexpected, and possibly bizarre data you never expected to see in ...

Outliers have the potential to skew analysis when they aren’t properly accounted for. Addressing outliers, specifically in trade cost analysis (TCA) data, is crucial for traders because it ensures the ...

OAKLAND, CA, Oct. 7, 2020 – With its automated business analysis (ABA) platform, Outlier discovers and elevates unexpected changes in consumer behavior, customer demographics and buying patterns. For ...

After previously detailing how to examine data files and how to identify and deal with missing data, Dr. James McCaffrey of Microsoft Research now uses a full code sample and step-by-step directions ...

Traditionally, companies have gathered data from a variety of sources, then used spreadsheets and dashboards to try and make sense of it all. Outlier wants to change that and deliver a handful of ...

This article explains how to programmatically identify and deal with outlier data (it's a follow-up to "Data Prep for Machine Learning: Missing Data"). Suppose you have a data file of loan ...