Data science and machine learning now influence pricing, credit decisions, supply planning, service delivery and public policy. The leaders who sponsor and manage this work face an unusual challenge: results are uncertain, specialist staff are hard to retain, and a model that performs well in a notebook can still fail in the real world. Without disciplined leadership, budgets are spent on experiments that never generate returns.

This ten-day programme prepares experienced managers and senior practitioners to direct data science as a business capability. The early days establish how to align analytics with strategy, assess data readiness, choose and prioritise use cases and design an operating model with the right roles and governance. The middle days work through the end-to-end machine learning lifecycle in enough depth for leaders to ask the right questions: problem definition, data preparation, supervised and unsupervised methods, evaluation metrics, validation, deployment pipelines and monitoring. The later days concentrate on performance: setting team objectives and KPIs, measuring business value, managing model risk and responsible AI, and leading change so that models are adopted.

Hands-on labs in Python and notebook environments let participants see what their teams do, while leadership workshops develop the decisions only a leader can take. The course ends with an integrated project and presentation.

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

• Build a data and machine learning strategy aligned with organisational priorities.
• Score and prioritise use cases by value, feasibility, data readiness and risk.
• Design the structure, roles and operating model of a data science team.
• Explain the stages of the machine learning lifecycle and challenge technical choices with confidence.
• Interpret evaluation metrics such as precision, recall, AUC and RMSE in business terms.
• Oversee deployment, monitoring and retraining through MLOps practices.
• Set team objectives and KPIs that link model delivery to measurable business outcomes.
• Govern model risk, explainability, bias and privacy under a responsible AI framework.

The programme is for leaders and senior practitioners who direct, fund or depend on analytics and machine learning work, such as:

• Heads of data science, analytics and business intelligence
• Chief data officers and digital transformation directors
• IT directors and chief information officers
• Senior data scientists moving into management
• Product owners and portfolio managers for data products
• Heads of risk, credit and actuarial functions using models
• Senior managers in banks, telecoms, retail and utilities
• Government and development agency directors commissioning analytics
• Programme managers overseeing AI and automation projects

Participants complete the programme with:

• A scored portfolio of data and machine learning use cases for their organisation.
• An operating model and role map for a data science function.
• A one-page scorecard of team KPIs linked to business value.
• A model risk and responsible AI checklist for approvals.
• A review framework for questioning model results and deployment readiness.
• A completed project presentation and a 90-day leadership plan.

The programme mixes executive discussion with technical demonstration so leaders understand both the decisions and the work behind them:

• Leadership workshops on strategy, structure and performance management
• Guided Python notebook labs that show what each lifecycle stage involves
• Case files on analytics programmes in banking, telecoms, health and public services
• Peer review of use case portfolios and team scorecards
• A team project over the final days with panel feedback

Participants who attend the full programme and complete the labs and final project are issued a CPD-accredited Certificate of Completion by Vision Reach Global Consultancy.