Live Online Management of Data Science & Machine Learning in Practice Training Course
Live online, instructor-led · next cohort Nov 9, 2026 · from USD 1,700
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.
At a Glance
How Live Online Training Works
Live online training, led by a trainer in real time, for participants anywhere.
More about live online training →- Format
- Live online, instructor-led
- Duration
- Mon - Fri (10 Days)
- Fee
- USD 1,700 (KES 160,000)
- Next start dates
- Nov 9, 2026, Dec 7, 2026, Jan 4, 2027
- Certificate
- Certificate of Completion
- What you need
- A computer with a reliable internet connection, a webcam and a headset
About this course
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.
Training Objectives
Graduates of the programme will 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.
- Interpret model evaluation metrics and challenge results presented by technical teams.
- Design MLOps processes for versioning, deployment, monitoring and retraining.
- Apply model risk management, validation and responsible AI controls across the model lifecycle.
- Structure, staff and develop data science teams and their collaboration with business units.
- Track the business value, cost and performance of a portfolio of models.
What you'll learn
Key Takeaways
Participants leave with a practical toolkit that includes:
- A scored use-case portfolio with value, feasibility and risk ratings.
- A project charter and CRISP-DM delivery plan for a priority use case.
- A data readiness assessment checklist.
- A baseline model built and evaluated in a Python notebook during the labs.
- An MLOps blueprint showing tools, environments, roles and approval gates.
- A model risk and responsible AI control framework with a model card template.
- A team structure and skills plan for the data science function.
- A capstone management plan with KPIs for value tracking.
Training Methodology
Management judgement is built through a blend of hands-on work and case analysis. The programme includes:
- Guided Python labs in Jupyter notebooks, designed for managers as well as practitioners
- MLflow and deployment walkthroughs showing how models move into production
- Case discussions of successful, stalled and failed ML initiatives
- Portfolio and prioritisation workshops using participants' own use cases
- Model review exercises in which participants challenge evaluation results
- A capstone management plan presented to a panel of peers and facilitators
Day 1: Data Science in the Organisation
- What data science and machine learning can and cannot do
- Supervised, unsupervised and generative approaches in business terms
- Analytics maturity and organisational readiness
- Common reasons ML projects fail to deliver
- Roles: data engineers, data scientists, ML engineers, analysts and product owners
- Aligning data strategy with business strategy
- Exercise: assessing an organisation's analytics maturity
Day 2: Use-Case Discovery, Prioritisation and Business Cases
- Problem framing: translating business questions into ML tasks
- Use-case discovery workshops with business units
- Value, feasibility and risk scoring
- Estimating benefits, costs and break-even
- Build, buy or partner decisions
- Stakeholder mapping and executive sponsorship
- Workshop: building a prioritised use-case portfolio
Day 3: Project Delivery with CRISP-DM and Agile
- CRISP-DM phases and deliverables
- Combining CRISP-DM with Scrum and Kanban
- Defining success metrics and acceptance criteria
- Managing uncertainty, spikes and proofs of concept
- Estimating and planning data science work
- Documentation and reproducibility standards
- Exercise: drafting a project charter and delivery plan
Day 4: Data Readiness, Quality and Governance
- Data sourcing, access and ownership
- Data quality dimensions and profiling with pandas
- Feature engineering and feature stores in outline
- Data governance, lineage and catalogues
- Data protection, consent and anonymisation
- Labelling strategies and annotation management
- Lab: profiling and cleaning a customer data set in Python
Day 5: Understanding Models in a Manager's Lab
- Train, validation and test splits and cross-validation
- Regression, classification and tree-based models with scikit-learn
- Accuracy, precision, recall, F1, ROC AUC and business cost metrics
- Overfitting, leakage and other warning signs
- Explainability with feature importance and SHAP
- Reviewing a data scientist's notebook: questions to ask
- Lab: building and evaluating a churn prediction baseline
Day 6: MLOps and Production Deployment
- From notebook to production: closing the deployment gap
- Version control with Git and experiment tracking with MLflow
- Containers, Docker and model serving patterns
- CI/CD pipelines for machine learning
- Batch, real-time and edge deployment options
- Cloud ML platforms compared: Azure Machine Learning, Amazon SageMaker and Google Vertex AI
- Lab: tracking experiments and registering a model in MLflow
Day 7: Experimentation, Monitoring and the Model Lifecycle
- A/B testing and champion-challenger designs
- Measuring business impact after deployment
- Data drift, concept drift and performance decay
- Monitoring dashboards and alert thresholds
- Retraining policies and model retirement
- Incident management for model failures
- Case study: a fraud model that degraded silently
Day 8: Model Risk, Responsible AI and Compliance
- Model risk management frameworks and model inventories
- Independent validation and effective challenge
- Fairness testing and bias mitigation
- Model cards and explainability for regulators and customers
- Responsible AI principles and ISO/IEC 42001 in outline
- Privacy-preserving techniques and data protection impact assessments
- Exercise: drafting a model card and validation report outline
Day 9: Leading Data Science Teams and Managing Costs
- Team structures: centralised, embedded and hub-and-spoke
- Hiring, skills frameworks and career paths
- Collaboration between data, IT and business units
- Managing cloud compute and storage costs
- Vendor and partner management for analytics services
- Managing generative AI and large language model projects
- Role play: negotiating priorities with business sponsors
Day 10: Capstone Data Science Portfolio Management Plan
- Portfolio review and value tracking KPIs
- Governance forums and stage gates
- Roadmap for capability building
- Change management and adoption by end users
- Capstone presentations and peer critique
- Personal leadership action plan
- Course review and next steps
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
- Data governance and data protection officers
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.
Choose Where You Want to Learn
Management of Data Science & Machine Learning in Practice Training Course Venues
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
GhanaAccraNext Nov 16· 13 dates$9,000
Scroll sideways to see all 18 cities →
Frequently Asked Questions About Live Online Management of Data Science & Machine Learning in Practice Training Course
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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