Anomaly detection methods help data science teams identify unusual patterns, unexpected behavior, and potential errors within large datasets. Techniques such as statistical analysis, clustering, isolation methods, and machine learning models support applications in fraud detection, cybersecurity, and quality ...
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Causal inference techniques help data scientists identify cause-and-effect relationships rather than simple correlations, enabling more accurate predictions and informed business decisions. These methods are widely used in healthcare, finance, marketing, and policy analysis to evaluate the impact of ...