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Management Science II

90-760

Units: 6

Description

This course, along with its companions (90-722 Management Science I and an optional elective 94-833 Multicriteria Decision Making & Decision Analysis), are introductory courses in analytics and management science that survey various hands-on quantitative and modeling methods useful to decision makers and analysts. We will develop a practical toolkit for solving real-world decision-making problems.

90-760 focuses on making better decisions in the face of uncertainty, which we’ll tackle in three sections:

  • Forecasting: Describe the uncertainty about future events using various forecasting methods (qualitative, time-series, categorical, and causal models).
  • DMUU: Learn approaches to decision-making under uncertainty (DMUU), specifically simulation and stochastic modeling methods.
  • Applications: We will apply DMMU methods in multiple domains with policy relevance.

Learning Outcomes

By the end of the Management Science course sequence, students will be able to…

  • Model and solve quantitative problems in Excel – working with spreadsheets should feel second nature!
  • Identify the right management science technique for your problem. You will become familiar with different problem-solving approaches and their limitations. Even if you aren’t the one implementing them day-to-day, you’ll know when to call upon the right specialists and consultants when the occasion arises.
  • Apply management science techniques as an “end user modeler.”
  • Take an end-to-end quantitative analytical approach to policy and business problems – from formalizing a problem in mathematical terms to analyzing the model results. In short, you should be able to use the language of mathematical modeling.

In 90-760, we will cover a broad set of “management science” techniques (whereas 90-722 focused on optimization). By the end of this mini, you will have achieved these ILOs in the context of modeling and solving problems involving decision-making under uncertainty, with the core tools being forecasting, simulation, and stochastic models.

Prerequisites Description

This course has no formal prerequisites but assumes knowledge of college pre-calculus (or its equivalent), including summation notation, graphing, and interpreting linear and nonlinear functions.

Syllabus