ENGX 2000

Quantitative Engineering Analysis 1

Franklin W Olin College of Engineering · UGRD · Fall 2026

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Catalog description

Quantitative Engineering Analysis 1 is the first in a series of interdisciplinary math, science and engineering courses. The application of quantitative analysis of mathematical models and/or data can enable, improve, and speed up the engineering design process. Using quantitative analysis to answer engineering questions, you will be able to make the choices necessary to successfully complete an engineering design. Whether you are selecting the best part from a catalog, choosing an appropriate material, sizing a component, determining the effect of certain influences on your design, or optimizing your design within a parameter space, you often need to obtain (through experiment or calculation) and interpret quantitative information to inform your decisions. There are many different approaches to getting and interpreting the data you need: you may conduct an experiment, do a rough estimation, perform a detailed calculation based on mathematical models, or create a computer simulation. If you want to engineer effectively, you must be able to choose and use appropriate quantitative tools for a given situation. In this class, you will be introduced to various approaches to perform quantitative engineering analysis through real-world examples. You will learn how to select between different tools and different approaches within the context of an engineering challenge, how to use many different tools for quantitative analysis, and how to acquire new tools on your own in the future. This course fulfills the linear algebra requirement. Credit distribution of this course is 2 MTH, 1 SCI, 1 ENGR.

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Current meeting, instructor, credit, and enrollment details

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01

6 openSeats: 84/90 seats Last recorded: Aug 15, 2026, 1:36 PM
Class #13603Fall 2026UGRD4.00 credits
6 available84 enrolled90 capacity
Days & times
MR 1300-1540
Meeting dates
Aug 31 – Dec 18
Location
Olin Main Campus Miller Academic Center 318/326; 328/Aud
Instructor
Faculty Adams PHD, Sarah S. Michalka, Sam Preston, Victoria L. Shuman, David

Section notes

Instructional method: In person

Details checked 4 hours agoSeats checked 4 hours ago
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