An AI management system stands or falls on evidence. ISO/IEC 42001 requires an organisation to determine what is monitored and measured and to evaluate the performance of its AI management system, the NIST AI RMF devotes an entire function to measurement, and high-risk systems under the EU AI Act must meet data governance and accuracy requirements. Yet many governance teams cannot produce a reliable fairness test, a drift report or a traceable record of where training data came from. The gap between policy and proof is usually an analytics gap.

The Data and Analytics for AI Governance, Responsible AI & ISO 42001 Training Course closes that gap. The first five days build the foundations: a metrics catalogue for trustworthy AI, data governance and provenance, data quality and representativeness, model performance measurement and fairness analytics. Days six to ten go deeper, covering explainability methods and model cards, production monitoring for data and concept drift, evaluation of generative AI and large language models, audit analytics and governance dashboards, and a capstone in which participants assemble a complete assurance pack for one model.

Almost every session is a lab. Participants work in Jupyter notebooks and build dashboards in Power BI, using anonymised or synthetic datasets from lending, recruitment, health and public services. Facilitators connect each technical result back to the control or requirement it evidences, so that analysts learn to speak the language of auditors and auditors learn to read the output of analysts.

After the ten days, participants will be able to:

• Translate responsible AI principles and ISO/IEC 42001 monitoring requirements into a catalogue of measurable indicators.
• Profile, validate and document training data for quality, representativeness and provenance.
• Evaluate classification and regression models with metrics chosen according to the harm each type of error causes.
• Quantify bias using group fairness metrics and test mitigation options with Fairlearn and AI Fairness 360.
• Generate and critically interpret global and local explanations with SHAP, LIME and counterfactual methods.
• Configure drift monitoring and alert thresholds for models in production.
• Design evaluation sets and scoring rubrics for generative AI and large language model applications.
• Build a governance dashboard and assurance pack that auditors and boards can rely on.

Participants typically come from the technical and assurance teams that generate or rely on AI evidence:

• Data scientists and machine learning engineers
• MLOps and data engineering specialists
• Data governance managers, data stewards and data quality analysts
• Model validators and quantitative risk analysts
• Internal auditors and IT auditors reviewing AI systems
• Business intelligence and reporting analysts
• Data protection and privacy analysts
• Statisticians and analysts in national statistics offices, regulators and ministries
• Responsible AI and AI governance officers who want a firmer technical grounding

The labs leave each participant with reusable assets, among them:

• A metrics catalogue linking trustworthiness characteristics to tests, thresholds and owners.
• Reusable Jupyter notebooks for data profiling, performance evaluation and fairness testing.
• A datasheet and provenance record for a training dataset.
• A bias audit report comparing fairness metrics before and after mitigation.
• A model card that brings together performance, fairness and explanation results.
• A drift monitoring configuration with alert thresholds tied to risk appetite.
• A Power BI dashboard summarising the health of an AI portfolio.
• A complete assurance pack for one model, ready for internal audit or management review.

Delivery is lab-led, with each concept demonstrated in code and then tied to the governance requirement it supports. Expect:

• Guided coding labs in Jupyter notebooks using Python, pandas, scikit-learn, Fairlearn, SHAP and Evidently
• Anonymised and synthetic datasets from lending, recruitment, health and public service delivery
• Pair work in which one participant builds a test and another reviews it as an auditor would
• Dashboard-building sessions in Power BI
• Critique of published model cards and transparency reports
• Starter notebooks so that participants with basic Python can keep pace
• A capstone assurance pack presented to a mock review board

Participants who attend the full programme and complete the coding labs and the capstone assurance pack are awarded a CPD-accredited Certificate of Completion issued by Vision Reach Global Consultancy.

The certificate lists the course, dates and delivery mode, while the notebooks, model card and dashboard built during the labs form a practical portfolio of applied work.