This mini course expands on the core Analytics sequence through a deeper dive into the art of modeling, centered around essential tools in optimization. The goal of course is to move beyond predictive analytics and build richer prescriptive models for optimizing business operations in a wide range of contexts. We will focus on modeling decision problems so they can be stated simply, solved efficiently, and, most importantly, translated into clear managerial insights. Every class will be entirely “hands on,” with an emphasis on active problem solving and case-based learning. Each of the first three weeks will center on a core optimization technique, with the first session devoted to modeling skills and the second applying those skills to a data-driven case. We begin with linear programming, expanding from the types of problems introduced in core Analytics to richer models of strategic planning and control. From there, we turn to optimization with logical and binary constraints, emphasizing applications like investment planning, ride-hailing services, and compute scheduling. We then conclude with non-linear models and heuristic solution methods, which allow us to analyze problems in revenue management, election analytics, and system design. Throughout, we will balance classical management settings with contemporary challenges. Cases and examples will draw from diverse areas such as cloud resource allocation, prescriptive policing, data mining, personalized advertising, and product design in consumer markets. The final three sessions shift from theory to practice, bringing optimization to life through real-world cases led by industry experts and the instructor. Each class will feature a guest speaker who shares their perspective on designing and implementing optimization in high-stakes settings, highlighting both the opportunities and pitfalls of operationalizing these methods. Students will be expected to actively engage, applying the modeling tools and analytical skills developed in the first three weeks to interpret, challenge, and extend the insights raised in these practitioner-led discussions. By the end of the course, students will not only have deepened their command of optimization modeling, but will also have built the skill to communicate the insights of their models in ways that influence managerial action.