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:

• 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.
• Train regression, classification and clustering models with scikit-learn.
• Validate and tune models using cross-validation, suitable metrics and hyperparameter search.
• Interpret models and check them for bias, leakage and overfitting.
• Package, deploy and monitor a model as a service with versioning and drift checks.
• Communicate results and limitations clearly to non-technical decision makers.

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
• Postgraduate students and early-career data scientists

Participants complete the programme with:

• A reproducible Python project that takes a real dataset from raw files to a validated model.
• A library of notebooks for cleaning, feature engineering, modelling and evaluation.
• A model evaluation checklist covering leakage, bias and performance metrics.
• A deployed prototype model with a simple API and monitoring plan.
• A one-page template for explaining model results to executives.
• A development plan for taking machine learning from pilot to routine use in their organisation.

This is a coding-intensive programme in which participants spend most of each day at the keyboard. Learning methods include:

• Guided Jupyter notebooks that introduce each technique with explained code
• Laboratory exercises on realistic finance, health, telecom and development datasets
• Pair work and code review to build good habits in readable, reproducible work
• A ten-day capstone project completed progressively and presented on the final day
• Starter templates and notebooks participants keep for use at work

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.