Digital Transformation in Data Science & Machine Learning in Practice Training Course in Accra, Ghana
Classroom training in Accra · next cohort Nov 16, 2026 · from USD 9,000
Many digital programmes stop at putting forms online, while the real gains come when data and models change how decisions are made and work is done. Loan applications can be read and scored in minutes, sensor readings can trigger maintenance before a breakdown, and customer queries can be routed and answered automatically. Across ten days you learn to find these opportunities and deliver them: mapping processes and their data with process mining, assessing digital and data maturity, modernising legacy reporting onto cloud data platforms and lakehouse architectures, and designing machine learning into end-to-end workflows.
Hands-on labs use Python, pm4py, Tesseract OCR, FastAPI and cloud ML services such as Azure Machine Learning and Amazon SageMaker to build intelligent document processing, real-time scoring APIs, event-driven pipelines and a retrieval-augmented generative AI assistant. Change management, digital operating models and adoption metrics receive equal weight. The programme is aimed at digital transformation leads, data and analytics managers, enterprise architects, process improvement specialists and product owners in banking, insurance, telecoms, utilities and government. You finish with a transformation blueprint for one process in your organisation. Classroom, online and in-house formats are available, each with a CPD-accredited certificate.
At a Glance
Training in Accra, Ghana
Accra is Ghana's capital and a stable, English-speaking business hub for West Africa.
More about training in Accra →- Format
- Classroom, in Accra
- Duration
- Mon - Fri (10 Days)
- Fee
- USD 9,000
- Next start dates
- Nov 16, 2026, Dec 14, 2026, Jan 11, 2027
- Certificate
- Certificate of Completion
- Airport
- Kotoka International (ACC)
- Time zone
- Greenwich Mean Time (UTC+0)
- Currency
- Ghanaian cedi (GHS). Fees are listed in USD.
- Business districts
- Airport City, Cantonments, Ridge and Osu
About this course
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.
Training Objectives
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.
What you'll learn
Key Takeaways
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.
Training Methodology
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
Day 1: Data-Driven Digital Transformation
- What changes when machine learning, not just digitisation, drives transformation
- Examples from mobile money, agritech, health and public services
- Digital and data maturity models and self-assessment
- Value levers: automation, prediction, personalisation and new services
- Common reasons transformation programmes stall
- Framing a transformation hypothesis for one process
- Workshop: maturity assessment of participants' organisations
Day 2: Process Discovery and Opportunity Mapping
- Process mapping with BPMN to expose analytics opportunities
- Event logs and the principles of process mining
- Discovering process variants, bottlenecks and rework with pm4py
- Deciding what to automate, what to augment and what to leave to people
- Sizing value and feasibility for candidate use cases
- Building an opportunity portfolio
- Lab: process mining a loan approval event log
Day 3: Modern Data Platforms
- From legacy data warehouses to cloud data platforms and lakehouse architecture
- Ingestion patterns: batch, change data capture and streaming
- Data modelling and transformation with SQL and dbt
- Data catalogues, lineage and access management
- Choosing among Azure, AWS and Google Cloud data services
- Data residency, connectivity and cost considerations
- Lab: building a small analytical pipeline in a cloud sandbox
Day 4: Machine Learning for Process Automation
- Classification, forecasting and anomaly detection in operational processes
- Preparing operational data for automated decisions
- Building and evaluating a model in Python with scikit-learn
- Decision thresholds and when to route cases to human review
- Combining business rules, robotic process automation and machine learning
- Measuring cycle time and quality improvement
- Lab: an automated triage model for service requests
Day 5: Data Governance and Trust in Digital Services
- Data protection, consent and lawful use in automated services
- Fairness and transparency in customer-facing decisions
- Securing data platforms and model endpoints
- Explaining automated decisions to customers and staff
- Data quality ownership in digitised processes
- Exercise: trust assessment of an automated decision service
- First-week review: assembling the opportunity case
Day 6: Intelligent Document Processing
- Document types and the hidden cost of manual data entry
- OCR with Tesseract and cloud document AI services
- Extracting fields with layout analysis and named entity recognition
- Document classification and validation rules
- Human-in-the-loop review queues driven by confidence scores
- Measuring straight-through processing rates
- Lab: from scanned application form to scored, structured record
Day 7: Real-Time Decision Services and Integration
- Packaging models with Docker and serving them through FastAPI
- Event-driven architecture and streaming with Apache Kafka
- Integrating predictions into mobile apps, USSD, web portals and core systems
- Latency, availability and scaling for customer-facing models
- Feature stores and consistency between training and serving
- Integration testing and staged release
- Lab: publishing a real-time scoring API and calling it from a client
Day 8: Generative AI in Transformation
- Large language models and where they fit in business processes
- Retrieval-augmented generation over internal documents
- Embeddings, vector search and chunking strategies
- Prompt design, evaluation and guardrails
- Use cases: knowledge assistants, drafting, summarisation and customer support
- Cost, privacy and hallucination risks
- Lab: building a document assistant with retrieval-augmented generation
Day 9: Scaling with MLOps and Cloud ML Services
- From one model to a portfolio: platform thinking
- Azure Machine Learning, Amazon SageMaker and Google Vertex AI compared
- Automated pipelines, model registry and CI/CD
- Monitoring drift, performance and cost across many models
- Reusable components and shared feature pipelines
- Build, buy and partner decisions for AI capability
- Lab: automating retraining and deployment for a model pipeline
Day 10: Operating Model, Change and the Transformation Project
- Operating models: centralised, hub-and-spoke and federated data teams
- Skills, roles and data literacy programmes
- Change management and adoption metrics for automated processes
- Benefits tracking and transformation dashboards
- Integrated project: redesigning one process end to end with data and machine learning
- Presenting the transformation roadmap to a mock executive board
- Personal action plan and course review
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
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.
Choose Where You Want to Learn
Digital Transformation in Data Science & Machine Learning in Practice Training Course Venues
GhanaAccraNext Nov 16· 13 dates$9,000Viewing
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
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Frequently Asked Questions About Digital Transformation in Data Science & Machine Learning in Practice Training Course in Accra
Everything you need to know about this course before you register.
Digital Transformation in Data Science & Machine Learning in Practice Training Course runs as classroom training in Accra, Ghana, in Monday-to-Friday cohorts that start every four weeks. The venue, daily timings and joining details are sent in your Joining Instructions email ahead of the start date.
The next Accra cohort starts on November 16, 2026, and a new intake follows every four weeks. Pick a date under "Upcoming sessions in Accra" above and click Register; your seat is confirmed once payment or a signed purchase order is received.
The classroom fee for Digital Transformation in Data Science & Machine Learning in Practice Training Course in Accra is USD 9,000 per delegate, covering tuition and course materials. Register 3 or more delegates together and a 5% team discount is applied automatically.
Accra is the only classroom city for Digital Transformation in Data Science & Machine Learning in Practice Training Course in Ghana at the moment. You can also join a live online cohort from anywhere in Ghana, or book it in-house at your organisation's own premises.
Yes — Digital Transformation in Data Science & Machine Learning in Practice Training Course can be delivered at your offices in Accra or at a venue you choose, on dates that suit your team, with case studies tailored to your sector. Message our training advisors to request a proposal.
By the end of the Digital Transformation in Data Science & Machine Learning in Practice programme, you'll 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, and automate document-heavy workflows with ocr, field extraction and classification models. The full breakdown of topics is covered session by session in the Course Outline tab above.
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, and Operations managers in logistics, utilities, manufacturing and agribusiness.
Digital Transformation in 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.
Yes — delegates who meet the attendance requirement receive a Certificate of Completion for Digital Transformation in 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.
Digital Transformation in Data Science & Machine Learning in Practice Training Course is pitched at intermediate 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.
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