Experimentation for Improvement
Master efficient experimental design techniques for optimizing systems and processes using practical, data-driven methods.
Master efficient experimental design techniques for optimizing systems and processes using practical, data-driven methods.
This comprehensive course teaches efficient experimental design methods for system optimization and improvement. Students learn to plan and analyze multi-variable experiments, moving beyond the inefficient one-factor-at-a-time approach. The curriculum progresses from basic manual calculations to computer-aided analysis, covering factorial designs, response surface methodology, and practical optimization techniques. With a focus on real-world applications, the course combines theoretical knowledge with hands-on practice using free software tools.
4.9
(915 ratings)
37,050 already enrolled
Instructors:
English
پښتو, বাংলা, اردو, 3 more
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What you'll learn
Learn efficient experimental design techniques
Master analysis of multi-variable experiments
Understand response surface methodology
Optimize systems using practical methods
Use computer software for experiment analysis
Apply statistical concepts to real-world problems
Skills you'll gain
This course includes:
5.3 Hours PreRecorded video
14 assignments
Access on Mobile, Desktop
FullTime access
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Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.
There are 6 modules in this course
The course provides a thorough introduction to experimental design and optimization techniques. Starting with fundamental concepts, students progress through increasingly sophisticated methods for planning and analyzing experiments. Topics include factorial designs, response surface methodology, and system optimization. The curriculum emphasizes practical applications and uses real-world examples to demonstrate how these techniques can be applied in various fields.
Introduction
Module 1 · 1 Hours to complete
Analysis of experiments by hand
Module 2 · 2 Hours to complete
Using computer software to analyze experiments
Module 3 · 1 Hours to complete
Getting more information, with fewer experiments
Module 4 · 3 Hours to complete
Response surface methods (RSM) to optimize any system
Module 5 · 3 Hours to complete
Wrap-up and future directions
Module 6 · 48 Minutes to complete
Fee Structure
Payment options
Financial Aid
Instructor
Former Assistant Professor of Chemical Engineering at McMaster University
Kevin Dunn is a former Assistant Professor at McMaster University in Hamilton, Ontario, where he specialized in teaching the foundations of chemical engineering. He was deeply invested in teaching innovation, exploring the integration of software and novel techniques in engineering education. Beyond his academic role, Kevin provided consultancy services to companies on topics such as database management, optimization, and experimental methodologies. He is also the author of the textbook Process Improvement Using Data, contributing to the field with practical insights and educational resources. His commitment to enhancing engineering education and applying theoretical knowledge to real-world challenges has made a significant impact on both his students and industry practices.
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Frequently asked questions
Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.