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 ...

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 ...

In an era driven by complex data, scientists are increasingly encountering information that doesn't lie neatly on flat, ...

Z-score is a standard measurement used in statistical analysis that looks at data with a normal distribution. It provides a ...

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 ...

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 ...

Examples of outlier data include a person's age of 99 (either a very old applicant or possibly a placeholder value that was never changed) and a person's country of "Cannada" (probably a transcription ...