Live Online Policy and Regulatory Frameworks for Data Science & Machine Learning in Practice Training Course
Live online, instructor-led · next cohort Nov 2, 2026 · from USD 1,700
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.
At a Glance
How Live Online Training Works
Live online training, led by a trainer in real time, for participants anywhere.
More about live online training →- Format
- Live online, instructor-led
- Duration
- Mon - Fri (10 Days)
- Fee
- USD 1,700 (KES 160,000)
- Next start dates
- Nov 2, 2026, Nov 30, 2026, Dec 28, 2026
- Certificate
- Certificate of Completion
- What you need
- A computer with a reliable internet connection, a webcam and a headset
About this course
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.
Training Objectives
By the end of the course, participants will 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.
- Apply data minimisation, pseudonymisation and other privacy-preserving techniques in Python.
- Test models for bias and measure fairness across protected groups.
- Produce explanations of model behaviour that suit users, auditors and regulators.
- Prepare model cards, datasheets, lineage records and validation reports.
- Monitor deployed models for drift and manage incidents and change.
What you'll learn
Key Takeaways
Participants leave with:
- A regulatory map linking legal duties to specific stages of the machine learning lifecycle.
- A risk classification worksheet for new AI use cases.
- Python notebooks for privacy-preserving preparation and bias testing.
- A model card and validation report template for review boards.
- A monitoring and incident response outline for deployed models.
- A governed capstone model with a full assessment file.
Training Methodology
The ten days alternate legal analysis with technical work so that each requirement is seen in code. Participants experience:
- Short regulatory briefings tied to concrete use cases
- Python labs in notebooks using pandas, scikit-learn and explainability libraries
- Case files of model failures and regulatory enforcement themes
- Mock review board sessions where project files are challenged
- A team capstone that is assessed against a governance checklist
Day 1: The Regulatory Landscape for Data and AI
- Why regulation of data science is accelerating
- Data protection principles and data subject rights
- Sector rules in finance, health and public services
- Overview of AI regulation and standards
- Roles: controller, processor, developer and deployer
- Exercise: mapping the rules for a sample use case
Day 2: Lawful Data Collection and Use
- Lawful bases, consent and purpose limitation for training data
- Special category and children's data
- Cross-border transfers and data localisation
- Data sharing agreements and third-party datasets
- Intellectual property and licensing of data and models
- Exercise: reviewing a data acquisition proposal
Day 3: Risk-Based AI Governance
- The EU AI Act risk tiers and obligations
- NIST AI Risk Management Framework functions
- ISO/IEC 42001 management system concepts
- Algorithmic impact and data protection impact assessments
- Human oversight and contestability of automated decisions
- Exercise: classifying five AI use cases by risk
Day 4: Model Risk Management and Accountability
- Model inventory, ownership and three lines of defence
- Independent validation and challenge
- Documentation standards for models and data
- Procurement and oversight of third-party and generative AI
- Board and executive reporting on AI risk
- Exercise: drafting a model governance policy outline
Day 5: Policy into Practice: Integrating Compliance with the Workflow
- Embedding review gates in the machine learning lifecycle
- Compliance by design checklists for data science teams
- Working with legal, security and audit functions
- Handling regulator enquiries and complaints
- Week one synthesis and gap assessment
- Workshop: a compliance plan for a project of your choice
Day 6: Privacy-Preserving Data Preparation
- Data minimisation and feature selection under legal constraints
- Pseudonymisation, anonymisation and re-identification risk
- Differential privacy and synthetic data concepts
- Secure storage, access control and encryption of datasets
- Deletion, retention and data subject request handling in pipelines
- Lab: privacy-preserving preparation with pandas
Day 7: Bias, Fairness and Testing
- Sources of bias in data, labels and models
- Fairness definitions and their trade-offs
- Measuring group disparities with scikit-learn
- Mitigation at the data, model and decision stage
- Documenting limitations and acceptable use
- Lab: bias audit of a credit scoring model
Day 8: Explainability and Documentation
- Global and local explanation methods including SHAP
- Explaining decisions to affected individuals and regulators
- Writing model cards and datasheets for datasets
- Data lineage, versioning and experiment tracking
- Reproducibility and audit trail design
- Lab: producing a model card from a trained model
Day 9: Deployment, Monitoring and Incident Response
- Pre-deployment approval and validation checklists
- Monitoring performance, drift and fairness in production
- Change control and retraining governance
- AI incident definition, escalation and notification
- Retirement and decommissioning of models
- Lab: building a drift monitoring routine
Day 10: Capstone: A Governed Machine Learning Solution
- Assembling the project file: data, model, assessment and monitoring plan
- Group presentation to a mock review board
- Peer challenge and facilitator feedback
- Common audit findings and how to prevent them
- Building a roadmap for organisational capability
- Personal action plan and course review
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
- Policy advisers shaping digital and data regulation
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.
Choose Where You Want to Learn
Policy and Regulatory Frameworks for Data Science & Machine Learning in Practice Training Course Venues
KenyaNairobiNext Nov 9· 13 dates$3,000- KenyaMombasaNext Oct 26· 13 dates$3,200
- KenyaNaivashaNext Nov 2· 13 dates$3,000
- KenyaKisumuNext Oct 26· 13 dates$3,000
- KenyaMachakosNext Nov 2· 13 dates$3,200
UgandaKampalaNext Nov 2· 13 dates$3,800
RwandaKigaliNext Oct 26· 13 dates$3,800
TanzaniaArushaNext Nov 9· 13 dates$3,800- TanzaniaDodomaNext Oct 26· 13 dates$3,600
- TanzaniaDar es SalaamNext Oct 26· 13 dates$4,000
- TanzaniaZanzibarNext Nov 16· 13 dates$5,200
NigeriaLagosNext Nov 16· 13 dates$5,600- NigeriaAbujaNext Nov 9· 13 dates$5,600
South AfricaPretoriaNext Nov 2· 13 dates$6,400- South AfricaJohannesburgNext Nov 2· 13 dates$6,800
- South AfricaCape TownNext Oct 26· 13 dates$7,600
UAEDubaiNext Oct 26· 13 dates$7,600
GhanaAccraNext Nov 16· 13 dates$9,000
Scroll sideways to see all 18 cities →
Frequently Asked Questions About Live Online Policy and Regulatory Frameworks for Data Science & Machine Learning in Practice Training Course
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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