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:

• 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.
• Engineer features, tune hyperparameters and prevent overfitting and data leakage.
• Interpret models and explain predictions using feature importance and SHAP.
• Apply clustering, time series forecasting and basic text analytics.
• Package and deploy a model as an API and monitor it for drift.
• Prioritise analytics use cases and communicate results to executive audiences.

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
• Government and NGO officers who manage data teams

Participants finish the programme with:

• A working Python environment and library of reusable code notebooks.
• A complete capstone project from problem framing to deployed model.
• A model evaluation checklist covering metrics, validation and bias.
• A deployment template using a simple API and monitoring plan.
• A prioritised list of data science opportunities in their organisation.
• A one-page storyline for presenting model results to executives.

Roughly three quarters of each day is spent coding. The learning approach includes:

• Guided live coding in Jupyter notebooks
• Lab exercises on finance, health, agriculture and customer datasets
• Pair programming and code review among participants
• Short strategy workshops on prioritising and presenting analytics projects
• A capstone project presented to a panel, with written feedback

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.