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ENGR 531 - Engineering Risk Analysis

  • 3 credits

Successful engineering project management includes estimation and proactive risk identification and development of mitigation techniques. System uncertainty is reduced when project risks are identified, quantified, and mitigation strategies implemented. Tools, techniques, and methodologies used by successful project managers will be examined.

Course Objectives:
System uncertainty quantification, inherent in every endeavor, is reduced using risk analysis, risk attitudes, risk modeling, quantitative risk management, probabilities and impacts, and engineering tools.
Students successfully completing this course will be able to:
• Identify, analyze, quantify, and mitigate risks
• Apply tools, techniques, and methodologies to implement risk management
• Assess discrete and continuous probability events, commonly used probability distributions, and calculate functions of random variables
• Understand the use of Bayes' rule, Markov chains, fault tree analysis, decision programming


ECE 303 (Introduction to Communications Principles) or STAT 303 (Introduction to Communications Principles) or STAT 315 (Statistics for Engineers and Scientists). Credit not allowed for both ENGR 531 and ECE 531

Important Information

Military personnel admitted to a College of Engineering online degree program may be eligible for a 15% tuition discount. Tuition discounts can only be given if you provide the appropriate discount code at the time of registration. Call (877) 491-4336 or email to learn more. Discounts are not applicable to Denver sections.

Textbooks and Materials

Section 801


  • Risk Assessment: Tools, Techniques, and Their Applications (2019)
    Ostrom and Wilhelmsen
    ISBN: 9781119483465

Textbooks and materials can be purchased at the CSU Bookstore unless otherwise indicated.


James Cale
James Cale

Dr. James Cale is an Associate Professor in the Systems Engineering Department at Colorado State University, with joint appointments in the Electrical and Computer Engineering and Mechanical Engineering Departments. His research focuses on the modeling, control and design optimization of energy sources and systems. His background and interests are in the areas of energy conversion, power-electronic drive systems, microgrids, finite-inertia power systems, computational and applied electromagnetics, design optimization, hardware-in-the-loop, and machine learning algorithms.
Prior to joining CSU, he led the Integrated Devices & Systems group at the National Renewable Energy Laboratory in Golden, Colorado. Before that he worked in senior design engineering roles at Advanced Energy Industries and Orbital ATK (since acquired by Northrop Grumman). James earned his doctorate in electrical engineering (with honors) from Purdue University, where he was funded by an NSF IGERT fellowship. He earned his BSEE from Missouri University of Science & Technology (summa cum laude). He is a member of Tau Beta Pi and is a Senior Member of IEEE.

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