Gain insights into using SQL for anomaly detection. Delve into applying mean, standard deviation, and z-score to identify and investigate data anomalies, enhancing your analytical skills.

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Learn how to identify anomalies in your data using SQL. Understand how to apply mathematical concepts such as mean, standard deviation, and z-score to detect when an anomaly occurred in your data and how to investigate past data to improve and refine the model.

Simple Anomaly Detection using SQL

To get a sense of the data, let's draw a stacked bar chart by status:



The chart shows a period of 12 hours. It looks like we have a nice trend with two peaks at around 09:30 and again at 18:00.

We can notice right away that at around 11:30, there was a significant increase in 500 status code errors. The burst died down after around 10 minutes. This is the type of anomalies we want to identify early on.

It is entirely possible that there were other problems during that time; we can't spot them with a naked eye.

Understand the data set provided in the previous lesson. 

Getting a Sense of the Data

Understand the data set provided in the previous lesson.

Detecting Anomalies

Analyzing A Server Log

Backtesting

Improving Accuracy

Getting a Sense of the Data