Models that predict credit risk, diagnose disease or rank job candidates now sit inside a growing web of privacy law, sector rules and emerging AI regulation. Teams that ignore this find their projects blocked at deployment, their data challenged by regulators or their decisions contested by the people affected. Teams that build compliance into the pipeline ship faster and defend their work better.
This ten-day programme joins the policy view and the technical one. The first week covers data protection principles, lawful bases, cross-border transfer, the EU AI Act risk tiers, the NIST AI Risk Management Framework, ISO/IEC 42001 and sector expectations on model risk. The second week is applied: you work in Python with pandas and scikit-learn on privacy-preserving techniques, bias and fairness testing, explainability with SHAP, model cards, data lineage, monitoring for drift and audit-ready documentation. A capstone assembles a governed model from data collection to deployment. Data scientists, machine learning engineers, analytics leads, compliance and legal officers, and risk managers will benefit. Delivery is classroom, online or in-house. Afterwards you can run an impact assessment, document a model for review and explain decisions to a regulator. The certificate is CPD-accredited.
Machine learning has moved from research teams into lending, insurance, hiring, health and public services, and regulators have followed. Data protection laws govern how training data is collected and used, sector supervisors expect model risk management, and new AI-specific rules classify systems by the harm they can cause. For practitioners, the difficulty is translating legal language into engineering decisions made every day.
The Policy and Regulatory Frameworks for Data Science & Machine Learning in Practice Training Course bridges that gap over ten days. The opening block builds the regulatory map: privacy principles, data subject rights, automated decision-making, intellectual property and consent, followed by AI risk frameworks and management system standards. Participants learn to classify a use case by risk, decide what documentation and human oversight it requires, and involve legal, compliance and security colleagues at the right moment.
The second block turns requirements into code and process. Participants practise data minimisation and pseudonymisation, test models for bias, generate explanations, write model cards and datasheets, version data and experiments, and set up drift and performance monitoring with incident procedures. The closing project requires a complete governed machine learning solution, with an assessment file that a reviewer could actually audit.
By the end of the course, participants will be able to:
Participants leave with:
The ten days alternate legal analysis with technical work so that each requirement is seen in code. Participants experience:
Day 1: The Regulatory Landscape for Data and AI
Day 2: Lawful Data Collection and Use
Day 3: Risk-Based AI Governance
Day 4: Model Risk Management and Accountability
Day 5: Policy into Practice: Integrating Compliance with the Workflow
Day 6: Privacy-Preserving Data Preparation
Day 7: Bias, Fairness and Testing
Day 8: Explainability and Documentation
Day 9: Deployment, Monitoring and Incident Response
Day 10: Capstone: A Governed Machine Learning Solution
The course is intended for those who build, approve or oversee data-driven systems, such as:
Participants who attend the full ten days and complete the labs and capstone receive a CPD-accredited Certificate of Completion from Vision Reach Global Consultancy.
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 Policy and Regulatory Frameworks for Data Science & Machine Learning in Practice programme, you'll be able to map the privacy, ai and sector rules that apply to a data science or machine learning use case, classify ai systems by risk tier and determine the controls each requires, establish lawful bases, consent and purpose limits for training and inference data, and apply data minimisation, pseudonymisation and other privacy-preserving techniques in python. The full breakdown of topics is covered session by session in the Course Outline tab above.
The course is intended for those who build, approve or oversee data-driven systems, such as: Data scientists and machine learning engineers, Analytics and artificial intelligence team leads, Data protection officers and privacy counsel, Compliance and regulatory affairs managers, Model risk and internal audit professionals in banks and insurers, Health informatics and public sector data specialists, Product owners for AI-enabled services, and Policy advisers shaping digital and data regulation.
Policy and Regulatory Frameworks for Data Science & Machine Learning in Practice 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.
Policy and Regulatory Frameworks for Data Science & Machine Learning in Practice 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 Policy and Regulatory Frameworks for Data Science & Machine Learning in Practice starts November 2, 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 Policy and Regulatory Frameworks for Data Science & Machine Learning in Practice Training Course from Vision Reach Global Consultancy, issued in the name you register with, so double-check the spelling at checkout.
Policy and Regulatory Frameworks for Data Science & Machine Learning in Practice Training Course is pitched at advanced 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 Policy and Regulatory Frameworks for Data Science & Machine Learning in Practice 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 — Policy and Regulatory Frameworks for Data Science & Machine Learning in Practice 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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