Many organisations hold more data than they can use, and many data science projects stall because the problem was badly framed or the model never reached production. This ten-day programme teaches both the technical craft and the strategic judgement behind successful projects. Early days cover Python, pandas, NumPy, Jupyter, data cleaning, exploratory analysis, visualisation with Matplotlib and Seaborn and the statistics needed to reason about evidence. You then build regression and classification models in scikit-learn, with feature engineering, cross-validation, hyperparameter tuning, imbalanced data and model interpretation using SHAP.
Later sessions move into clustering, time series forecasting, text analytics, an introduction to neural networks, model deployment with APIs, MLOps basics, monitoring for drift and responsible use of data. Strategy sessions help you choose projects with real value, estimate return and communicate results to executives. The course is designed for analysts, data scientists in training, statisticians, software developers, researchers, M&E and business intelligence specialists and managers who commission analytics in banks, telecoms, government, health and NGOs. Afterwards you can deliver an end-to-end machine learning project and judge which ones deserve investment. Delivery is by classroom, online or in-house, and a CPD-accredited certificate is issued.
Data science has become a standard capability in competitive organisations, yet projects too often fail for reasons that have little to do with algorithms. Teams chase interesting questions rather than valuable ones, models perform well in testing but not in the field, and results are presented in terms decision makers cannot act upon. Bridging this gap requires people who can code and also think strategically.
This programme follows the life of a data science project from business question to monitored model. Participants begin by learning the Python data stack and how to explore and prepare real datasets. The middle of the programme covers supervised and unsupervised learning, model evaluation, feature engineering and interpretation, with attention to overfitting, leakage and bias. Later days address forecasting, text data, deployment, version control, reproducibility and monitoring, and finally strategy: prioritising use cases, building teams, governing data and measuring impact.
Practical labs make up most of each day. Participants work with datasets drawn from finance, health, agriculture and customer analytics, and complete an end-to-end capstone project that they present to a panel.
After the ten days, participants will be able to:
Participants finish the programme with:
Roughly three quarters of each day is spent coding. The learning approach includes:
Day 1: Data Science Workflow and Python Essentials
Day 2: Data Wrangling and Exploratory Analysis
Day 3: Statistics for Modelling
Day 4: Supervised Learning with scikit-learn
Day 5: Feature Engineering and Model Tuning
Day 6: Interpretation, Fairness and Responsible Use
Day 7: Unsupervised Learning and Time Series
Day 8: Text Analytics and Neural Networks
Day 9: Deployment, MLOps and Monitoring
Day 10: Data Science Strategy and Capstone
The programme is suited to people who build, supervise or commission analytical work, including:
Participants who attend the programme, complete the labs and present their capstone 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 Strategic Data Science & Machine Learning in Practice programme, you'll be able to frame business problems as data science questions with measurable success criteria, clean, explore and visualise datasets using python, pandas and matplotlib, build regression and classification models and evaluate them with appropriate metrics, and engineer features, tune hyperparameters and prevent overfitting and data leakage. The full breakdown of topics is covered session by session in the Course Outline tab above.
The programme is suited to people who build, supervise or commission analytical work, including: Data analysts moving into machine learning, Junior and aspiring data scientists, Statisticians and research officers, Software developers and engineers adding analytics skills, Business intelligence and M&E specialists, Risk, credit and marketing analysts in banks and telecoms, Heads of analytics and IT managers, and Government and NGO officers who manage data teams.
Strategic 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.
Strategic 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 Strategic 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 Strategic 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.
Strategic 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 Strategic 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 — Strategic 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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