Statistical Inference & Predictive Modeling Foundations

This program prepares data analysts and aspiring data scientists to apply statistical inference and predictive modelling tools to solve business problems. You'll learn to identify and mitigate cognitive biases with structured post‑mortems and debiasing checklists. Courses cover designing clear dashboards and reports, building and pruning tree‑based models, comparing ensemble methods, and applying linear and gradient‑boosted regression and classification techniques. You'll then expand to neural networks by designing feed‑forward architectures in Keras or PyTorch and applying regularisation. The program also teaches you to design and execute A/B tests, estimate confidence intervals, build random forests and supervised ML workflows, apply decision‑theory frameworks (expected utility, OODA, Cynefin), run Monte Carlo simulations, and perform statistical inference and hypothesis testing in Python or R. By the end, you'll have a comprehensive foundation in statistics, predictive modeling and machine‑learning workflows ready to drive data‑driven decisions

• Identify and mitigate cognitive biases, craft effective dashboards, design A/B tests, and apply decision-science frameworks
• Build and evaluate regression, classification, tree-based ensembles, and neural networks using Python or R to meet business goals
• Apply statistical inference, run Monte Carlo simulations, and implement production-ready ML workflows with model monitoring and governance

• Basic familiarity with statistical concepts and terminology
• Readiness to practice through applied exercises or case-based work