Live Online Leadership and Performance in Data Science & Machine Learning in Practice Training Course
Live online, instructor-led · next cohort Oct 26, 2026 · from USD 1,700
Many organisations invest in data scientists and machine learning platforms yet see few models reach production or change a decision. The gap is rarely technical. It comes from weak use case selection, unclear ownership, poor data foundations and no way to judge whether the team is performing. Ten days equip data leaders to close that gap.
The first week covers strategy and team leadership: building a data and AI portfolio, scoring use cases for value and feasibility, structuring data science teams, hiring, career paths and stakeholder management. You also review the machine learning lifecycle in practice, from problem framing and feature work to model evaluation, deployment with MLOps, monitoring and drift. The second week goes deeper into performance management with delivery metrics, experiment tracking, model risk, explainability, bias, privacy and responsible AI governance, followed by applied labs on forecasting, classification and a generative AI use case, and an integrated project that ends in a board-style presentation. Heads of analytics, data science managers, chief data officers, IT directors, product owners and senior business leaders sponsoring analytics work will gain most. Classroom, online and in-house formats lead to 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
- Oct 26, 2026, Nov 23, 2026, Dec 21, 2026
- Certificate
- Certificate of Completion
- What you need
- A computer with a reliable internet connection, a webcam and a headset
About this course
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.
Training Objectives
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.
What you'll learn
Key Takeaways
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.
Training Methodology
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
Day 1: Data and AI Strategy for Leaders
- The data science value chain from data to decision
- Aligning analytics strategy with corporate objectives
- Data and AI maturity assessment
- Build, buy or partner decisions
- Common reasons machine learning projects fail
- Workshop: diagnosing your organisation's maturity
Day 2: Use Case Discovery and Prioritisation
- Translating business problems into analytical questions
- Scoring use cases for value, feasibility and risk
- Assessing data availability, quality and access
- Business cases and benefit tracking
- Quick wins versus strategic bets in the portfolio
- Workshop: building a ranked use case portfolio
Day 3: Building and Leading the Team
- Roles: data scientist, engineer, analyst, ML engineer, product owner
- Centralised, embedded and hub-and-spoke operating models
- Hiring, onboarding and retaining specialist talent
- Career paths and capability building
- Managing relations with business sponsors and IT
- Workshop: designing a team structure
Day 4: Data Foundations and Preparation
- Data pipelines, warehouses, lakes and governance
- Data quality dimensions and profiling in Python and pandas
- Feature engineering and handling missing values
- Data ownership, stewardship and catalogues
- Exploratory analysis to frame the problem
- Lab: profiling and preparing a sample business dataset
Day 5: Machine Learning Methods in Practice
- Supervised learning: regression and classification with scikit-learn
- Unsupervised learning: clustering and anomaly detection
- Time series forecasting concepts
- Overfitting, validation and cross-validation
- Choosing evaluation metrics that match business cost
- Lab: training and comparing baseline models
Day 6: Deployment, MLOps and Monitoring
- From notebook to production: pipelines and versioning
- Model registries, experiment tracking and reproducibility
- Batch versus real-time serving and integration with systems
- Monitoring performance, data drift and concept drift
- Retraining triggers and incident response
- Lab: reviewing a deployment readiness checklist
Day 7: Measuring Team and Model Performance
- Delivery metrics: cycle time, throughput and time to production
- Model metrics versus business impact metrics
- OKRs and scorecards for data science teams
- A/B testing and measuring uplift
- Performance reviews and feedback for technical staff
- Workshop: drafting a team scorecard
Day 8: Model Risk, Ethics and Responsible AI
- Model risk management and independent validation
- Explainability techniques such as feature importance and SHAP
- Bias detection and fairness trade-offs
- Privacy, consent and data protection obligations
- AI governance committees and approval gates
- Case discussion: a model that caused harm and what failed
Day 9: Advanced Applications and Generative AI
- Applied cases in credit, churn, demand and fraud analytics
- Natural language processing and text analytics for business
- Large language models: opportunities, limits and controls
- Cost management and vendor selection for AI platforms
- Driving adoption and change management with end users
- Lab: scoping a generative AI use case with risk controls
Day 10: Integrated Project and Leadership Plan
- Team project: strategy, use case, delivery plan and risk review
- Preparing a board-style investment proposal
- Presentations and panel questions
- Peer and facilitator feedback
- Building a 90-day leadership action plan
- Course review and next steps
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 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.
Choose Where You Want to Learn
Leadership and Performance in 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 Leadership and Performance in Data Science & Machine Learning in Practice Training Course
Everything you need to know about this course before you register.
By the end of the Leadership and Performance in Data Science & Machine Learning in Practice programme, you'll 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, and explain the stages of the machine learning lifecycle and challenge technical choices with confidence. The full breakdown of topics is covered session by session in the Course Outline tab above.
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, and Programme managers overseeing AI and automation projects.
Leadership and Performance 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.
Leadership and Performance in 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 Leadership and Performance in Data Science & Machine Learning in Practice starts October 26, 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 Leadership and Performance 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.
Leadership and Performance in Data Science & Machine Learning in Practice Training Course is pitched at advanced 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 Leadership and Performance in 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 — Leadership and Performance in 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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VRG brings strategic discipline to complex organizational challenges. Their approach is structured, data-driven, and outcome-focused.
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