Machine learning has moved from research laboratories into credit scoring, customer retention, fraud detection, supply planning, public health surveillance and programme targeting. Decision makers are expected to approve budgets, sponsor projects and accept risks for systems they may only partly understand. Without a working grasp of what data science can and cannot do, they either overspend on fashionable technology or overlook sound opportunities in their own data.

The Executive Data Science & Machine Learning in Practice Training Course gives leaders a practical grounding. Participants follow a complete project from framing a business question, assembling and cleaning data and exploring patterns, to building, testing and interpreting simple models. They see how supervised and unsupervised methods work, why models fail, how to judge accuracy against business cost, and what happens when a model moves into production and drifts. Strategy sessions then address data governance, privacy, ethics, talent models, build or buy choices, value tracking and the realistic place of generative AI.

Demonstrations and guided notebooks make concepts tangible without requiring programming experience. By the end, each participant has a prioritised use case portfolio, a business case and a governance checklist ready to take back to their executive team.

By the end of the course, participants will be able to:

• Frame business problems as data science questions with measurable success criteria.
• Assess data readiness, quality and access constraints for a proposed project.
• Interpret exploratory analysis, statistical tests and visualisations.
• Distinguish the main machine learning approaches and match them to use cases.
• Evaluate model performance using metrics that reflect the cost of errors.
• Identify bias, explainability and privacy risks and set governance controls.
• Describe deployment, monitoring and MLOps requirements for lasting value.
• Build a business case, team structure and roadmap for data science investment.

The programme is intended for decision makers who sponsor, manage or rely on analytical work, including:

• Directors and heads of department in large organisations
• Chief information, data and digital officers
• Senior managers in banking, insurance and telecommunications
• Government and parastatal executives
• Programme directors in health, development and humanitarian bodies
• Strategy, innovation and transformation leads
• Analytics and BI managers moving into leadership roles
• Risk, audit and compliance executives overseeing AI use

Participants leave the course with:

• A ranked portfolio of data science use cases for their organisation.
• A list of challenge questions for reviewing any model or vendor proposal.
• A data readiness checklist and project charter template.
• A simple scorecard for judging model performance against business value.
• A responsible AI and governance checklist.
• A structured business case and twelve-month roadmap.
• An operating model sketch showing data team roles, reporting lines and external partners.
• A one-page briefing on data and AI use, ready for their executive committee.

The course is designed for leaders, so concepts are shown rather than coded from scratch. Delegates experience:

• Instructor-led demonstrations of notebooks, models and dashboards
• Guided no-pressure labs where code is supplied and results are interpreted
• Case files from banking, health, public service and development programmes
• Executive role-plays reviewing model proposals and vendor pitches
• A final business case coached by the facilitator

A CPD-accredited Certificate of Completion issued by Vision Reach Global Consultancy is awarded to each leader who attends all ten days, takes part in the guided labs and executive role-plays, and presents a final business case.

The certificate records the course title, dates and delivery mode, while the use case portfolio, scorecard and roadmap prepared during the programme give a ready record of applied learning for board or appraisal discussions.