Quality, Governance & DataOps

Data you can trust. Statistics for monitoring, data quality tests, observability, lineage, governance and privacy — plus MLOps for data-driven models.

• Descriptive Statistics — Summarise and visualise data before analysing it. Learn mean, median and mode, range, variance and standard deviation, percentiles, quartiles and IQR, outliers, skewness and kurtosis, histograms, box plots, scatter plots and probability plots.
• Data Quality, Observability & Governance — Making data trustworthy. Learn data quality dimensions (completeness, uniqueness, validity, consistency, accuracy, timeliness), writing checks and tests (dbt tests, Great Expectations, Soda), anomaly detection on metrics, data observability (freshness, volume, schema, distribution, lineage), SLAs/SLOs for data, incident management, data catalogs and lineage, metadata, access control and security, privacy (PII, masking, DPDP/GDPR), data governance roles (owners, stewards), and data contracts — with Python running completeness, uniqueness, validity and freshness checks.
• MLOps, Deployment & Responsible AI — Running AI reliably in the real world. Learn deployment patterns (batch, real-time API, streaming, edge), model packaging and serving (Docker, ONNX, model servers), latency and throughput SLOs, CI/CD for ML, experiment tracking and model registries, feature stores, monitoring (data drift with PSI, concept drift, performance decay), retraining strategies, A/B and shadow testing, fairness metrics (demographic parity, equal opportunity), explainability, privacy, security and AI governance — with Python computing PSI drift, disparate impact and p95 latency.