This Specialization equips learners with practical machine learning skills to solve real-world business problems across customer analytics, financial fraud, logistics, and supply chain domains. Learners progress through end-to-end workflows including data preparation, exploratory analysis, predictive modeling, model evaluation, and business interpretation using industry-relevant datasets and tools such as R. Emphasis is placed on translating model outputs into actionable insights that support strategic decision-making, operational efficiency, and risk management, making the program highly relevant for analytics, finance, and operations roles.
Applied Learning Project
Learners complete hands-on, project-based assignments that mirror real business scenarios, including churn prediction, fraud detection, shipping time forecasting, and demand pattern analysis. Each project requires applying machine learning techniques to real datasets, evaluating model performance, and presenting insights that directly inform business and operational decisions.
