This ebook introduces anomaly detection as a critical analytical capability for identifying unusual patterns, errors, or risks hidden within complex datasets. It explains the difference between outliers and anomalies, outlines common anomaly detection techniques, and discusses real-world use cases across industries such as manufacturing, finance, cybersecurity, and operations. The guide emphasizes practical approaches, including statistical methods, machine learning models, and visual analytics, while highlighting common challenges like false positives and data quality issues. Designed for non-experts, the ebook shows how visual data science tools enable faster detection, easier interpretation, and more confident decision-making without requiring deep data science expertise.

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