Decision Analytics and Modeling

Description from DukeHub

Successful management requires the ability to recognize a decision problem, understand its essential features, and make a smart choice. However, many decision problems, particularly those involving uncertainty or many variables, are difficult to grasp intuitively. In these cases we may benefit from using a computer-based mathematical model to explore and evaluate the possibilities in a systematic fashion. This course introduces several commonly used modeling frameworks and provides an introduction to the art and science of modeling decisions. The ideas and skills learned in this course are applicable in most areas of business. The course is divided into 3 parts: (a) the use of decision trees for structuring decision problems under uncertainty; (b) Monte Carlo simulation, a technique for simulating decision situations with many uncertainties; (c) optimization, an approach for finding the best possible solution in problems with many decision variables and constraints. This course is for MQM: Business Analytics students.

Skills

• data and statisticsdescription: "modeling" and 2 more

AI-resilient: 1 of 5 1/5

• No: Physical, lab or field work
• No: Live work with people
• ✓Yes: Judgment someone is accountable for · "decision"
• No: A craft you practice with your hands or body
• No: Small, in-person format

The checks lean on Microsoft Research, Working with AI (2025), which found generative AI applies least to physical work and hands-on work with people. A signal, not a promise. How the score works

More in DECISION

• Spreadsheet Modeling and Decision Analysis DECISION 517F
• Data Visualization DECISION 522Q
• Decision Models DECISION 611
• Forecasting DECISION 614
• Information Management DECISION 617

All DECISION courses · All of Fuqua