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How to Handle Outliers, Anomalies, and Skews
Data science is about finding patterns and extracting meaningful insights from their analysis. As any practitioner knows, however, data loves throwing us the occasional curveball: a weird spike, an unexpected dip, or (gasp!) an oddly shaped cluster.
This week, we turn our attention to those jarring moments when things (and our graphs) turn out to be less smooth than we’d hoped. Our selection of highlights cover different approaches for tackling irregularity and coming to terms with the unpredictable.