Many organisations have hired data scientists and built promising models, yet too many of those models never reach production or deliver measurable value. The gap is rarely technical skill alone. It lies in choosing the wrong problems, weak data foundations, unclear ownership, missing deployment pipelines and no plan for monitoring once a model is live. Ten days equip you to manage the full lifecycle so that analytics investment turns into decisions and savings.
Topics include use-case discovery and prioritisation, business cases and value tracking, CRISP-DM and agile delivery, data readiness and governance, team roles and hiring, and the evaluation metrics managers must understand. Hands-on labs in Python, pandas, scikit-learn and Jupyter let you build and critique a baseline model yourself. The second week covers MLOps with MLflow, Docker and CI/CD pipelines, A/B testing, drift monitoring, model risk management, responsible AI and the management of generative AI projects, ending with a capstone portfolio plan. It suits analytics managers, data science leads, heads of data, product owners and model risk officers in banking, telecoms, insurance, health and government. Afterwards you can run a model portfolio with clear governance. Classroom, online or in-house, with a CPD-accredited certificate.
Machine learning has moved from experimentation to everyday business use in credit scoring, fraud detection, demand forecasting, customer churn prediction, document processing and, increasingly, generative AI assistants. Managing this work is different from managing conventional IT projects. Outcomes are uncertain, data quality determines success, models degrade as conditions change, and the consequences of unfair or unexplainable decisions fall on the organisation rather than the algorithm.
The Management of Data Science & Machine Learning in Practice Training Course prepares managers and technical leads to direct this work with confidence. The first five days establish the foundations: identifying and prioritising use cases, framing problems, assessing data readiness, structuring projects with CRISP-DM and agile methods, building balanced teams, and understanding enough of the modelling process to ask the right questions. Participants complete guided Python labs so that terms such as overfitting, cross-validation, precision and recall become concrete. The second five days go deeper into production: MLOps pipelines and tooling, deployment patterns, experimentation, monitoring and retraining, model validation, responsible AI, cost management on cloud platforms, and the particular demands of large language model projects. An integrated capstone brings these elements together.
Every session links technique to management decisions. Labs use realistic data sets from banking, telecoms and public services, and case discussions examine projects that succeeded, stalled or failed, so participants learn to recognise warning signs early.
Graduates of the programme will be able to:
Participants leave with a practical toolkit that includes:
Management judgement is built through a blend of hands-on work and case analysis. The programme includes:
Day 1: Data Science in the Organisation
Day 2: Use-Case Discovery, Prioritisation and Business Cases
Day 3: Project Delivery with CRISP-DM and Agile
Day 4: Data Readiness, Quality and Governance
Day 5: Understanding Models in a Manager's Lab
Day 6: MLOps and Production Deployment
Day 7: Experimentation, Monitoring and the Model Lifecycle
Day 8: Model Risk, Responsible AI and Compliance
Day 9: Leading Data Science Teams and Managing Costs
Day 10: Capstone Data Science Portfolio Management Plan
It is built for people who sponsor, lead or oversee data science work:
The CPD-accredited Certificate of Completion issued by Vision Reach Global Consultancy is awarded on full attendance across the ten days, completion of the Python and MLOps labs, and presentation of the capstone portfolio plan.
The notebooks, charter and management plan created during the programme remain with each participant for continued use.
Upcoming cohorts
CPD-Accredited
Official invoice & confirmation letter provided
Team discount for 3+ seats
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Everything you need to know about this course before you register.
By the end of the Management of Data Science & Machine Learning in Practice programme, you'll be able to identify, frame and prioritise machine learning use cases using value, feasibility and risk criteria, plan and run data science projects using crisp-dm combined with agile delivery practices, assess data readiness, quality and governance requirements before modelling begins, and interpret model evaluation metrics and challenge results presented by technical teams. The full breakdown of topics is covered session by session in the Course Outline tab above.
It is built for people who sponsor, lead or oversee data science work: Data science and analytics managers, Lead data scientists and machine learning engineers moving into management, Heads of data, business intelligence and digital transformation, Product owners and product managers for data-driven products, IT and software delivery managers supporting ML deployment, Model risk, validation and internal audit staff, Business unit heads sponsoring analytics initiatives, Research and M&E managers using predictive models, and Data governance and data protection officers.
Management of 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.
Management of 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 Management of Data Science & Machine Learning in Practice starts November 9, 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 Management of 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.
Management of 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.
Fees for Management of 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 — Management of 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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