Optimization Modeling for Decision Making

In a data-driven business landscape, organizations must constantly determine the absolute best course of action under tight constraints. Prescriptive analytics provides the mathematical framework to move beyond predicting the future and start actively shaping optimal outcomes. This course guides you from foundational decision theory to building practical optimization models. You will learn how to translate real-world business constraints—such as limited budgets, raw materials, or time—into structured mathematical formulations that yield clear, actionable strategic decisions. What you'll learn: - Understand the core principles of prescriptive analytics and decision theory - Formulate linear programming models for resource allocation and production planning - Apply modern Python optimization libraries to solve complex constraint problems - Analyze sensitivity to understand how changes in constraints impact your optimal decisions - Implement multi-period decision models for supply chain and inventory management You will begin by mastering essential terminology and mathematical formulation before progressing to hands-on modeling scenarios. Through step-by-step written explanations and practical code-based exercises, you will learn to tackle realistic business dilemmas systematically. This course is designed for beginners, business analysts, and aspiring data professionals. No prior background in advanced mathematics or optimization is required. Start mastering the art of optimal decision-making today.