Prescriptive Analytics

This course focuses on enhancing decision-making abilities through mathematical modeling and simulation. Learners will explore how to structure and solve complex business problems using logical thinking and analytical tools, transforming how decisions are approached in real-world contexts. The course introduces a range of optimization techniques, including linear, non-linear, and integer programming, as well as forecasting and basic machine learning methods, to develop effective prescriptive models. Through extensive hands-on practice using Excel, participants will work with real-world datasets across functions such as operations, supply chain, marketing, human resources, and finance. Each module integrates practical case studies, enabling learners to identify decision variables, gather relevant data, model the problem, and evaluate solutions using sensitivity analysis. Emphasis is placed on interpreting model outcomes, collaborating with stakeholders, and effectively communicating results for implementation. In addition to technical modeling skills, the course fosters soft skills like problem framing, teamwork, and engagement with decision-makers. Designed for learners with prior experience in business analytics and Excel, the course is ideal for those seeking to apply data-driven strategies in managerial or data science roles. By the end of the course, learners will be equipped to apply prescriptive analytics for impactful, actionable business decisions.