Outlier Detection
Outlier detection is a data analysis technique used to identify rare items, events, or observations that deviate significantly from the majority of data points in a dataset. It involves statistical, machine learning, or rule-based methods to flag anomalies that may indicate errors, fraud, system faults, or novel patterns. This concept is widely applied in fields like finance, cybersecurity, manufacturing, and healthcare for monitoring and quality control.
Developers should learn outlier detection when building systems that require data quality assurance, anomaly monitoring, or fraud prevention, such as in financial transaction processing, network security tools, or predictive maintenance applications. It's essential for handling real-world data where anomalies can skew analysis, impact model performance, or signal critical issues, enabling proactive responses and improved decision-making.