Anomaly Detection
Anomaly detection is a data analysis technique that identifies rare items, events, or observations that deviate significantly from the majority of data, often indicating critical incidents like fraud, system failures, or security breaches. It involves statistical, machine learning, or rule-based methods to flag unusual patterns in datasets, enabling proactive monitoring and alerting in various domains such as finance, cybersecurity, and industrial systems.
Developers should learn anomaly detection to build robust monitoring systems for applications, detect fraudulent activities in financial transactions, identify network intrusions in cybersecurity, and prevent equipment failures in IoT or manufacturing. It is essential for creating data-driven applications that require real-time alerting, quality control, or risk management, particularly in high-stakes environments where early detection of outliers can prevent significant losses or downtime.