Brett Kennedy

Learn how to identify the unusual, interesting, extreme, or inaccurate parts of your data.  Data scientists have two main tasks: finding patterns in data and finding the exceptions. These outliers are often the most informative parts of data, revealing hidden insights, novel patterns, and potential problems.  Outlier Detection in Python  is a practical guide to spotting the parts of a dataset that deviate from the norm, even when they're hidden or intertwined among the expected data points. In  Outlier Detection in Python  you'll learn how to: