ETMA 339

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Credit: 3 hours.

Covers foundational skills in applied data analysis with a primary focus on optimization. Concepts related to sensors and data will first be discussed followed by data acquisition and basic digital signal processing. Foundations of optimization will be introduced with an emphasis on application. This will include linear and non-linear, single and multiple objective, spatial, and stochastic optimization methods. Assignments will contain real world examples in the topic areas of agriculture, construction, manufacturing, and the environment.

Prerequisite: MATH 234 or equivalent; ACE 262, CPSC 241, ECON 202 or equivalent, STAT 107; and CS 105 or equivalent, or consent of the instructor.

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