In this data-driven world, companies are often interested in knowing what is the "best" course of action, given the data. For example, manufacturers need to decide how many units of a product to produce given the estimated demand and raw material availability? Should they make all the products in-house or buy some from a third-party to meet the demand? Prescriptive Analytics is the branch of analytics that can provide answers to these questions. It is used for prescribing data-based decisions. The most important method in the prescriptive analytics toolbox is optimization. This course will introduce students to the basic principles of linear optimization for decision-making. Using practical examples, this course teaches how to convert a problem scenario into a mathematical model that can be solved to get the best business outcome. We will learn to identify decision variables, objective function, and constraints of a problem, and use them to formulate and solve an optimization problem using Excel solver and spreadsheet.
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关于 Analytics for Decision Making 专项课程
The field of analytics is typically built on four pillars: Descriptive Analytics, Predictive Analytics, Causal Analytics, and Prescriptive Analytics. Descriptive analytics (e.g., visualization, BI) deal with the exploration of data for patterns, predictive analytics (e.g., data mining, time-series forecasting) identifies what can happen next, causal modeling establishes causation, and prescriptive analytics help with formulating decisions. This specialization focuses on the Prescriptive Analytics (the final pillar). This specialization will review basic predictive modeling techniques that can be used to estimate values of relevant parameters, and then use optimization and simulation techniques to formulate decisions based on these parameter values and situational constraints. The specialization will teach how to model and solve decision-making problems using predictive models, linear optimization, and simulation methods.