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Updated 27 Aug

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Simple Anomaly Detection using SQL

Name: Simple Anomaly Detection using SQL
Price: 59 USD
Author: Haki Benita

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.

4.7

19 lessons

Created by Haki Benita

What's included

19 lessons across 4 modules
4 quizzes with instant feedback
AI Tutor on every lesson

Join 3.1 million developers at

What You'll Learn

Identify anomalies in time-series data by computing mean and standard deviation in SQL
Calculate z-scores in SQL to flag data points outside defined acceptable ranges
Tune z-score thresholds in SQL to balance sensitivity and false alerts
Prepare server log data by filling time gaps with zeros using PostgreSQL functions
Backtest anomaly detection with SQL window functions and dynamic thresholds on past data
Reduce repeating alerts by detecting first-occurrence anomalies using SQL thresholds
Apply weighted mean, median, and MAD in SQL to improve anomaly detection accuracy

Learning Roadmap

19 Lessons4 Quizzes

Detecting Anomalies

Learn how to use SQL and z-scores to detect and optimize anomaly detection.

Introduction

What are Anomalies?

Understanding Z-score

Optimizing Z-score

Quiz 1

Analyzing A Server Log

Walk through server log analysis, data preparation, and anomaly detection to pinpoint issues.

Getting a Sense of the Data

Preparing the Data

Identifying Anomalies

Quiz 2

Backtesting

5 Lessons

Work your way through backtesting anomalies with thresholds, eliminating repeats, and experimenting for optimal results.

Improving Accuracy

5 Lessons

Apply your skills to improve anomaly detection accuracy using weighted mean, median, and MAD.

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

About this course

WHY I WROTE THIS COURSE

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.

ABOUT THE AUTHOR

Haki Benita

Haki is a technical lead and a tech blogger interested in databases, web development, software design and performance tuning.

Learn more about Haki

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