A leader in a data driven world requires the knowledge of both data-related (statistical) methods and of appropriate models to use that data. This Business Analytics class focuses on the latter: it introduces students to analytical frameworks used for decision making though Excel modeling. These include Linear and Integer Optimization, Decision Analysis, and Risk modeling. For each methodology students are first exposed to the basic mechanics, and then apply the methodology to real-world business problems using Excel. Emphasis will be not on the "how-to" of Excel, but rather on formulating problems, translating those formulations into useful models, optimizing and/or displaying the models, and interpreting results. The course will prepare managers who are comfortable with translating trade-offs into models, understanding the output of the software, and who are appreciative of quantitative approaches to decision making. Business analytics makes extensive use of data and modeling to drive decision making in organizations. This class focuses on introducing students to analytical frameworks used for decision making to make sense of the data, starting from the basics of Excel and working up to advanced modeling techniques.

This Business Analytics course in Excel is the second of a three part series, with the intended audience business professionals, MBA students, advanced undergraduates, and analysts who already understand basic analytics and want to upskill with techniques used in operations, finance, logistics, and strategy.. Upon completing this course, learners will be able to build and solve advanced optimization models in Excel, including linear, integer, network, and nonlinear programs; apply matrix functions to scale and streamline analysis; and use VBA macros to implement multi-goal programming and explore trade-offs through Pareto-efficient solutions.This intermediate-to-advanced course is a continuation of Business Analytics: Elementary to Advanced and is designed for learners who want to move from using Excel for analysis to using it for sophisticated decision-making. Rather than treating Excel as a passive calculation tool, the course shows how it can function as a powerful optimization and modeling environment for real business problems. Learners will benefit by gaining practical, immediately applicable skills that allow them to model constraints, manage competing objectives, and justify decisions quantitatively. What makes this course unique is its hands-on, problem-driven approach and its emphasis on automation and multi-objective thinking. By combining Solver, matrix methods, and macros, learners not only solve harder problems faster, but they also gain a flexible toolkit that is engaging, creative, and directly transferable to real-world decision environments. This course will prepare you for the third course of the Business Analytics Specialization, Simulation and Optimization.

This course equips learners with advanced skills in building and analyzing models using Excel to solve real-world business problems. Participants will learn to optimize decision-making processes using Solver, conduct sensitivity analysis to refine model outcomes, and apply advanced integer programming techniques to tackle complex scenarios. The course also covers sophisticated methods for solving assignment and transportation problems and introduces simulation techniques to analyze uncertainty and variability in decision-making.By the end of this course, learners will have the tools to enhance efficiency, improve resource allocation, and drive strategic decisions using Excel’s powerful modeling and analytical capabilities. What sets this course apart is its focus on actionable insights and practical, hands-on applications of advanced techniques, ensuring students are prepared to address challenges across a range of industries. Whether optimizing operations, solving logistical challenges, or preparing for uncertainty, this course provides essential skills for making confident, data-driven decisions. Prerequisite knowledge of basic Excel functions and foundational analytics is recommended.