Most organisations hold more data than they use, and many analytics projects stall between a promising notebook and a model that people trust and rely on. The gap is rarely a missing algorithm. It is untidy data, weak validation, unclear business questions and no plan for keeping a model healthy once it is live. Ten days of laboratory-led work close that gap.
You start with Python, pandas and exploratory analysis, then move through data cleaning, feature engineering, regression, classification, clustering, time series forecasting, text analysis and model tuning using scikit-learn, with gradient boosting and an introduction to neural networks. Later days cover cross-validation, bias and fairness, explainability with SHAP, packaging models as APIs, versioning with Git and MLflow, drift monitoring, and communicating findings to decision makers. A capstone project takes a real dataset from question to deployed prototype. Data analysts, statisticians, M&E and research officers, software developers, bank, telecom and health analysts, and public sector planners with basic spreadsheet or coding skills will benefit. Classroom, online and in-house formats are available, and a CPD-accredited certificate is issued. Afterwards you can deliver a reproducible predictive model with a clear business case.
Data science has moved from experiment to expectation. Banks score credit with models, utilities forecast demand, health programmes predict stock-outs and governments target services with analytics. Yet the people asked to deliver these results often learnt statistics, programming and domain knowledge separately, and have never carried a project end to end. Models that look excellent on a laptop fail when data changes, when users do not trust them or when no one owns their maintenance.
The Practical Data Science & Machine Learning in Practice Training Course is a ten-day, laboratory-based programme that follows the real project lifecycle. The first week builds foundations: Python and pandas, data quality, exploratory analysis, visualisation, feature engineering and core supervised and unsupervised methods. The second week goes deeper into ensemble methods, imbalanced data, time series, natural language processing, model selection, interpretability, responsible use of data, deployment and monitoring, and finishes with an integrated project.
Every topic is taught through guided notebooks and exercises on realistic datasets, so participants write and run code throughout. They leave with a portfolio-ready project, reusable templates and a clear route for applying machine learning safely in their own organisations.
By the end of the course, participants will be able to:
Participants complete the programme with:
This is a coding-intensive programme in which participants spend most of each day at the keyboard. Learning methods include:
Day 1: Data Science Workflow and Python Foundations
Day 2: Data Cleaning and Exploratory Analysis
Day 3: Statistics for Machine Learning
Day 4: Feature Engineering and Preprocessing
Day 5: Classification Models
Day 6: Advanced Supervised Learning and Model Tuning
Day 7: Unsupervised Learning and Time Series
Day 8: Text Analytics and Neural Networks
Day 9: Interpretability, Ethics and Deployment
Day 10: Monitoring, Communication and Capstone
The course is aimed at professionals who have some analytical or programming background and want to apply machine learning in their work, including:
Participants who complete the ten days of laboratories and present their capstone project are awarded a CPD-accredited Certificate of Completion by Vision Reach Global Consultancy.
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 Practical Data Science & Machine Learning in Practice programme, you'll be able to frame business problems as data science questions with measurable success criteria, clean, transform and explore data using python, pandas and visualisation libraries, engineer features and handle missing values, outliers and categorical variables, and train regression, classification and clustering models with scikit-learn. The full breakdown of topics is covered session by session in the Course Outline tab above.
The course is aimed at professionals who have some analytical or programming background and want to apply machine learning in their work, including: Data analysts and business intelligence developers moving into predictive modelling, Statisticians, economists and research officers, Monitoring, evaluation and research staff in NGOs and development agencies, Software developers and IT specialists adding data science skills, Credit, risk and fraud analysts in banks, SACCOs and insurers, Telecom, retail and utility analysts working with customer data, Health informatics and public health data officers, Government planners and statistics bureau staff, and Postgraduate students and early-career data scientists.
Practical 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.
Practical 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 Practical Data Science & Machine Learning in Practice starts November 2, 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 Practical 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.
Practical 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 Practical 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 — Practical 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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