This is a "full-stack" course on pricing strategy, ranging from the nitty-gritty data science and computational aspects of "finding the right price" to the high-level qualitative strategy issues related to pricing decisions. The course has two goals: to learn how to measure and leverage pricing power, and to learn how to think about pricing strategy beyond merely posting a price. We will study (i) how pricing interacts with other aspects of strategy, (ii) the barriers to implementing particular pricing strategies; and (iii) how to price through different selling mechanisms, such as auctions. We do this through a mixture of lectures, case discussion, modeling/analysis, and guest lectures from practitioners at the top of their fields. Key topics include 1) the design of pricing schemes that segment the market, such as product bundling and nonlinear pricing; 2) data science techniques of special relevance to pricing; 3) antitrust issues; and 4) the use of auctions as a pricing strategy, with a special focus on online ad auctions of the kind run in Facebook’s Newsfeed and Google Search. There are six homework assignments and a final exam. The assignments are both individual and group-based, and involve significant use of Excel. Students will learn to use Solver and simulation tools in Excel and how to use key regression/data science techniques that arise in pricing practice.