Data Wrangling with SQL

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Outlier Detection

Outlier detection is the process of identifying data points that deviate significantly from the normal behavior of a dataset. Structured Query Language (SQL) can be used to perform outlier detection on large datasets. In SQL, outlier detection typically involves analyzing the statistical properties of the dataset, such as the mean, standard deviation, and range. SQL queries can be used to identify data points that fall outside of the expected range of values, or that have a large deviation from the mean. Several methods can be used to perform outlier detection in SQL, including clustering-based methods, distance-based methods, and density-based methods. These methods can be applied to different types of datasets and can ...