Data, Models, and Decisions for Strategic Management

Every modern business challenge generates data, but turning that raw information into actionable strategy requires structural thinking. This text-based course teaches you how to frame complex management problems using quantitative models, helping you move past guesswork and intuition. By the end of this course, you will transition from relying on gut instinct to making rigorous, data-backed decisions. You will understand how to structure uncertainty, analyze risk, and use optimization techniques to guide your organization through complex operational and strategic choices. What you'll learn: - Understand foundational concepts of probability, risk, and decision theory in a business context - Structure complex management problems into clear, solvable quantitative models - Apply regression analysis to identify key business drivers and forecast future trends - Evaluate uncertainty using simulation techniques and decision trees - Interpret modern data analytics outputs to drive strategic organizational change - Practice formulating optimization models for resource allocation and operational efficiency This course begins with key terminology, basic concepts, and the core principles of decision theory. You will then progress through step-by-step written explanations of probability, regression, optimization, and simulation, applying these frameworks directly to real-world business scenarios. This course is designed for aspiring managers, business analysts, and professionals looking to build a strong quantitative foundation. No prior advanced mathematics or statistics background is required. Start reading today to master the analytical models that drive successful business outcomes.