Knowing how an algorithm works is not the same as using it to cut loan losses, keep pharmacies stocked or send field teams to the farms that need them. Value comes from matching the right method to a specific operational question, working with messy institutional data and putting results where decisions are made. Over ten days you work through a sequence of sector case labs in Python. Credit risk scorecards with logistic regression and gradient boosting, demand forecasting with lag features and Prophet, customer segmentation and churn prediction, and fraud detection with isolation forests and imbalanced-class techniques form the core.
Later cases add text classification of complaints and reports, geospatial features with GeoPandas and satellite imagery, and transfer learning on images. You then deploy a model with FastAPI or Streamlit, track it with MLflow and design monitoring before a team capstone on a sector problem of your choice. Analysts, data scientists, risk and actuarial staff, supply chain planners, M&E specialists and developers who already know basic Python will gain most. Afterwards you can scope, build and hand over a working model for a live business question. Classroom, online and in-house delivery is available, with a CPD-accredited certificate.
Most organisations no longer ask whether machine learning works; they ask where it will pay off and how to make it stick. Lenders want sharper credit decisions, distributors want fewer stock-outs, telecoms operators and insurers want to keep customers, and public agencies want to target scarce resources. Each of these problems has its own data quirks, error costs and operational constraints, and generic tutorials rarely prepare analysts for them.
The Applied Data Science & Machine Learning in Practice Training Course is organised around use cases rather than algorithms. After a first day on scoping and a second on assembling data from databases, spreadsheets, APIs and survey platforms such as KoboToolbox and ODK, each day tackles a sector problem: credit risk classification, demand forecasting, customer segmentation and churn, fraud and anomaly detection, text analytics on feedback and documents, and geospatial and image-based modelling. The final two days turn models into working services and bring the methods together in a capstone where teams solve a full case from raw data to recommendation.
Participants should be comfortable with basic Python and pandas. Each case follows the same rhythm of problem briefing, guided build, independent extension and debrief, so participants practise judgement as well as code: which metric matters to the business, what an error costs, and when a simpler model is the better choice.
Participants who complete the programme will be able to:
Each participant leaves with a set of working assets, including:
Each day is built around a realistic case, so every method choice is tied to a business decision. Teaching combines:
Day 1: Scoping Applied Machine Learning Use Cases
Day 2: Assembling and Preparing Operational Data
Day 3: Case Lab: Credit Risk Scoring
Day 4: Case Lab: Demand and Sales Forecasting
Day 5: Case Lab: Customer Segmentation and Churn
Day 6: Case Lab: Fraud and Anomaly Detection
Day 7: Case Lab: Text Analytics for Feedback and Documents
Day 8: Case Lab: Geospatial and Image Data
Day 9: Putting Models to Work
Day 10: Sector Capstone Project
Participants should already use Python at a basic level and work with data in roles such as:
Vision Reach Global Consultancy issues a CPD-accredited Certificate of Completion to participants who attend all ten days, complete the sector case labs and contribute to their team's capstone presentation.
Alongside the certificate, participants keep their solved case notebooks and deployment code, which serve as practical evidence of applied machine learning work when discussing development with their managers.
Upcoming cohorts
CPD-Accredited
Official invoice & confirmation letter provided
Team discount for 3+ seats
Need help with this booking?
Our training team can help with group pricing, invoicing, or picking the right schedule.
Everything you need to know about this course before you register.
By the end of the Applied Data Science & Machine Learning in Practice programme, you'll be able to translate an operational problem into a machine learning task with a target, features and a cost-based success measure, assemble analysis-ready datasets from sql databases, spreadsheets, apis and survey exports, build and calibrate credit risk classifiers, including scorecards based on weight of evidence, and forecast demand with lag features, seasonal models and time-based backtesting. The full breakdown of topics is covered session by session in the Course Outline tab above.
Participants should already use Python at a basic level and work with data in roles such as: Data analysts and junior data scientists looking for sector-specific practice, Credit risk, fraud and collections analysts in banks, SACCOs and fintechs, Actuarial and pricing analysts in insurance, Supply chain and demand planners in manufacturing, retail and health commodities, Customer analytics and CRM specialists in telecoms, utilities and retail, M&E and research officers in NGOs and development agencies, Agricultural and environmental analysts working with field and satellite data, Software developers building data-driven features into products, and Analysts in revenue authorities and public agencies targeting services or compliance.
Applied 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.
Applied 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 Applied 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 Applied 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.
Applied 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 Applied 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 — Applied 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.
Related Training
Key Skills You'll Learn
Vision Reach Global
From $3,000 · KSh 230,000
Key Skills You'll Learn
Vision Reach Global
From $3,000 · KSh 230,000
Key Skills You'll Learn
Vision Reach Global
From $3,000 · KSh 230,000
Swipe to see more courses →
VRG brings strategic discipline to complex organizational challenges. Their approach is structured, data-driven, and outcome-focused.
Levis Wabwire
Managing Director
They facilitated our strategy review, we improved departmental KPI alignment by 35% and reduced operational bottlenecks significantly.
David Oketch
Strategy Lead
Employee engagement scores increased following the leadership training program. The difference in accountability culture is evident.
Wendy Albright
Program Manager