Learn to identify and address irregularities in data using SQL. This module covers detecting anomalies, backtesting techniques, and server log analysis to improve your database's efficiency.

Become a Database Professional with SQL

In this module, we’ll focus on detecting anomalies in data using SQL. We’ll explore how to apply mathematical concepts like mean, standard deviation, and z-scores to identify anomalies. Additionally, we’ll delve into backtesting techniques to investigate past data and refine our detection models. We’ll also cover server log analysis to improve and optimize the overall efficiency and scalability of databases.

Anomaly Detection

In statistics, a mean is not considered robust because extreme values influence it. Given our use case, the measure we use to identify extreme values is affected by those values we are trying to identify.

For example, at the beginning of the article, we used this series of values:



Learn how to use the median to improve the accuracy of anomaly detection.

Median