Digital transformation has entered a second phase. Having moved services online and installed core systems, organisations now hold large volumes of transactional, mobile, sensor and document data, and the next step is to use that data to automate decisions, personalise services and redesign processes. Machine learning is the engine of this phase, but models built in isolation from processes, platforms and people rarely change how an organisation actually works.

The Digital Transformation in Data Science & Machine Learning in Practice Training Course shows how to embed analytics and machine learning in the fabric of an organisation. Days one to five cover the foundations: gauging how ready the organisation is, discovering process bottlenecks from event logs, choosing where machine learning adds most value, moving data onto modern cloud platforms and preparing it for automation, with trust and data protection built in. Days six to ten move into delivery at scale with intelligent document processing, real-time decision services, streaming and event-driven integration, generative AI with retrieval-augmented generation, MLOps for many models, and the operating model, skills and change management needed for lasting adoption.

Every topic is practised. Participants work in Python notebooks and cloud sandboxes, automate a paper-heavy process from scanned document to decision, publish a model behind an API and build a small assistant over internal documents. They also redesign one of their own organisation's processes and leave with a costed roadmap for it.

After the ten days, participants will be able to:

• Assess an organisation's digital and data maturity and identify where machine learning can change processes, not just reports.
• Map business processes from event logs with process mining and quantify delays, rework and automation potential.
• Design target architectures for data and machine learning on cloud and hybrid platforms, including lakehouse patterns.
• Automate document-heavy workflows with OCR, field extraction and classification models.
• Deploy models as real-time decision services and connect them to digital channels through APIs.
• Build a retrieval-augmented generative AI assistant over internal documents with suitable guardrails.
• Scale machine learning across many use cases with MLOps practices, shared feature pipelines and monitoring.
• Lead the people side of change, including operating model design, skills development and adoption measurement.

The course is built for professionals driving digital change with data, including:

• Digital transformation leads and chief digital or innovation officers
• Heads of data, analytics and business intelligence
• Enterprise and solutions architects
• Data scientists and machine learning engineers moving into transformation roles
• Process improvement, Lean Six Sigma and business process management specialists
• Product owners and digital channel managers in banking, insurance and telecoms
• ICT and e-government officers in ministries, county governments and public agencies
• Operations managers in logistics, utilities, manufacturing and agribusiness

Participants leave with:

• A digital and data maturity assessment of their own organisation, with priority gaps identified.
• A process mining analysis of a sample event log with automation opportunities ranked by value.
• A target data and machine learning architecture sketch for a cloud or hybrid platform.
• A working document processing pipeline that turns scanned forms into structured, scored records.
• A model published as a real-time API that a digital channel can call.
• A prototype generative AI assistant built on retrieval-augmented generation over a document collection.
• A change and adoption plan, with metrics, for one process enabled by machine learning.
• A costed transformation roadmap ready to present to sponsors.

The programme moves between process redesign and building working solutions. Participants learn through:

• Cloud sandbox and Python labs that automate a real process from input to decision
• Process mining exercises on anonymised event logs from lending, procurement and service desks
• Architecture workshops where teams sketch target data platforms for their own organisations
• Case studies of digital programmes in African banking, telecoms, agriculture and government
• A build-along generative AI lab using retrieval over a shared document set
• A ten-day transformation project presented to a mock executive board

On attending all ten days and completing the hands-on labs and the integrated transformation project, participants are awarded a CPD-accredited Certificate of Completion issued by Vision Reach Global Consultancy.

The roadmap, maturity assessment and prototypes built during the course give each participant tangible evidence of applied digital transformation skills to share with line managers and sponsors.