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 ...

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 ...

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 ...

In my last few articles, I've looked at a number of ways machine learning can help make predictions. The basic idea is that you create a model using existing data and then ask that model to predict an ...

Figure 1 Outlier. An Outlier is a data point or two that lie outside the norm. Source: Courtesy of Shutterstock Outliers can be annoying. A single data point or two that lie outside of the norm can ...

Marshall Hargrave is a stock analyst and writer with 10+ years of experience covering stocks and markets, as well as analyzing and valuing companies. Yarilet Perez is an experienced multimedia ...

Comparing die test results with other die on a wafer helps identify outliers, but combining that data with the exact location of an outlier offers a much deeper understanding of what can go wrong and ...