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Expert roadmaps, tutorials, salary guides and interview prep across Data Science, Generative AI, Python, Cloud & Analytics — written by GloryTecks trainers in Hyderabad.
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Outlier Detection for Analysts: Methods and Tools
Outlier detection for analysts, with four standard methods run on the same 19,773 real orders: why they flag anywhere from 53 to 1,797 orders, which suits skewed business data, how a log scale reveals suspiciously small orders, what the biggest outliers turned out to be, and when to keep, fix, cap or exclude.
Model Drift and Data Drift: Detection and Response
Most drift tooling watches a model's inputs. This guide shows, on three Januaries of real New York taxi trips, a change that left every input statistic flat while the model's error grew fivefold, why significance tests mislead at scale, and how to respond to drift you detect and to change you know is coming.
Anomaly Detection: Techniques and Real-World Use Cases
How anomaly detection works in practice: rules and thresholds, z-scores and the masking problem that lets outliers hide themselves, robust median-based scores, Isolation Forest, anomalies in time series, and how to evaluate a detector when almost nothing is labelled, with fraud, sensor and data-quality use cases.