R4 Stage D · 160 h (56 T / 104 P)
Data Scientist
Converts business questions into measured, defensible, decision-grade analysis and models.
Tech stack
PythonSQLscikit-learnXGBooststatsmodelsSHAPProphetPlotlyStreamlitPower BI/Tableau
Modules
M1 · Problem framing and metric design
36 h TOPICS
- Translating business objectives into measurable targets
- Primary versus guardrail metrics
- Proxy-metric risk
- Baselines
- Cost of errors
- Decision thresholds
- Stakeholder alignment
Lab: Convert an ambiguous business objective into a measurable analytical plan
Course material for this module is in production.
M2 · Inference and experimentation
44 h TOPICS
- Sampling and estimation
- Confidence intervals
- Hypothesis testing
- Multiple comparisons
- Power and sample-size calculation
- A/B test design and analysis
- Sequential testing pitfalls
- Quasi-experiments
- Confounding and causal reasoning
Lab: Design, power and analyse a controlled experiment; write the decision memo
Course material for this module is in production.
M3 · Advanced modelling and interpretability
44 h TOPICS
- Regularisation
- Ensembles
- Time-series forecasting and seasonality
- Survival and uplift concepts
- Calibration
- Feature attribution with SHAP
- Partial dependence
- Fairness assessment
- Model limitations
Lab: Build a calibrated, explained model with a documented limitations section
Course material for this module is in production.
M4 · Communication and decision support
36 h TOPICS
- Narrative structure
- Executive summaries
- Visual encoding principles
- Dashboard design
- Uncertainty communication
- Recommendation framing
- Handling challenge from stakeholders
Lab: Present findings and defend the recommendation to a challenging panel
Course material for this module is in production.
Track project
End-to-end business analytics engagement: framing, data preparation, calibrated model with interpretability, decision dashboard, quantified recommendation with expected impact and uncertainty, delivered as a stakeholder presentation.
Job-ready exit standard
Frames a business problem, models it and communicates a decision; SQL fluency assumed.