Policies and principles mean little if nobody can show, with numbers, that an AI system is accurate, fair, explainable and stable once it goes live. Auditors, regulators and certification bodies increasingly ask for that evidence, and it comes from data. Across ten hands-on days you learn to produce it: profiling and validating training data against ISO/IEC 5259 quality dimensions, documenting provenance and lineage, measuring model performance and calibration, and quantifying bias with fairness metrics such as demographic parity and equalised odds.
Labs use Python, pandas, scikit-learn, Fairlearn, AI Fairness 360, SHAP, Evidently and Power BI, linked to the monitoring and measurement requirements of ISO/IEC 42001, the Measure function of the NIST AI RMF and the data governance duties in the EU AI Act. The second week tackles explainability, drift monitoring in production, evaluation of large language models, audit analytics and a governance dashboard, ending with a full assurance pack for one model. Data scientists, ML engineers, data governance leads, risk analysts, internal auditors and BI specialists in finance, health, telecoms and the public sector will benefit. Classroom, online and in-house delivery are available, with a CPD-accredited certificate.
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:
The labs leave each participant with reusable assets, among them:
Delivery is lab-led, with each concept demonstrated in code and then tied to the governance requirement it supports. Expect:
Day 1: Measuring Trustworthy AI
Day 2: Data Governance, Provenance and Lineage
Day 3: Data Quality and Representativeness
Day 4: Model Performance Evaluation
Day 5: Fairness and Bias Analytics
Day 6: Explainability and Model Documentation
Day 7: Monitoring Models in Production
Day 8: Evaluating Generative AI and Large Language Models
Day 9: Governance Dashboards and Audit Analytics
Day 10: Capstone: An Assurance Pack for One Model
Participants typically come from the technical and assurance teams that generate or rely on AI evidence:
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.
Upcoming cohorts
CPD-Accredited
Official invoice & confirmation letter provided
Team discount for 3+ seats
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Everything you need to know about this course before you register.
By the end of the Data and Analytics for AI Governance, Responsible AI & ISO 42001 programme, you'll 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, and quantify bias using group fairness metrics and test mitigation options with fairlearn and ai fairness 360. The full breakdown of topics is covered session by session in the Course Outline tab above.
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, and Responsible AI and AI governance officers who want a firmer technical grounding.
Data and Analytics for AI Governance, Responsible AI & ISO 42001 Training Course typically runs as 10 Days. It's available as in-person classroom, live virtual, and in-house corporate training — every course can also be delivered on-site for your team on dates that suit you.
Data and Analytics for AI Governance, Responsible AI & ISO 42001 Training Course is scheduled in-classroom in Nairobi, Kenya, Mombasa, Kenya, Naivasha, Kenya, and Kisumu, Kenya, and 14 other locations, plus a live interactive virtual classroom you can join from anywhere. Check the schedule panel above for exact upcoming dates and fees in each location.
The next live virtual cohort of Data and Analytics for AI Governance, Responsible AI & ISO 42001 starts October 19, 2026, with new classroom cohorts also running on a rolling basis. Pick a date and location in the schedule panel above, then click "Register for the Course" — it takes a few minutes and your seat is confirmed once payment or a signed purchase order is received.
Yes — delegates who meet the attendance requirement receive a Certificate of Completion for Data and Analytics for AI Governance, Responsible AI & ISO 42001 Training Course from Vision Reach Global Consultancy, issued in the name you register with, so double-check the spelling at checkout.
Data and Analytics for AI Governance, Responsible AI & ISO 42001 Training Course is pitched at intermediate professionals. If you're unsure whether it's the right fit for your current role or background, message our training advisors before you register and they'll help you confirm.
Fees for Data and Analytics for AI Governance, Responsible AI & ISO 42001 Training Course vary by delivery location and format and are shown in real time in the schedule panel above once you pick a date. Register 3 or more delegates on the same course together and a 5% team discount is applied automatically — larger cohorts can request a custom corporate quote.
Yes — Data and Analytics for AI Governance, Responsible AI & ISO 42001 Training Course can be delivered on-site at your offices (or virtually for distributed teams), with case studies and examples tailored to your industry and the specific challenges your team is working through. Switch to the "In-House" tab in the schedule panel above to request a proposal.
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