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.
Projet d'apprentissage appliqué
Projects across this program give you practical experience. You'll identify cognitive biases through post‑mortems and create debiasing checklists; generate statistics and redesign dashboards to improve clarity; build and prune CART models and compare ensemble methods; construct regression and classification models, validate assumptions and handle class imbalance; design feed‑forward neural networks and analyze learning curves; plan and execute A/B tests; build cross‑validated random forests and implement drift monitoring; apply decision‑theory and risk‑assessment frameworks; run Monte Carlo simulations; and perform hypothesis tests and calculate confidence intervals. These projects produce checklists, dashboards, modeling notebooks, experiment plans and decision frameworks demonstrating your ability to apply statistical rigor and predictive modeling techniques to real business questions.
