The course tries to cover a lot of material that is somewhat math-heavy. It's not easy to digest some of the concepts without some self-study using the book. Exercises and quizzes only cover a fraction of what is taught. An improvement could be to cover less topics and use the time gained to give some more examples and clarify some of the math.

I have completed the specialization and the topic was new to me and I was interested to learn. However, the course discussion forum is not as practical and helpful as other courses that I have completed. There is no person in charge to provide any response and not enough knowledgeable learners around. So basically other than posting the final report, it has no use. One recommendation for improving the courses quality for better is to add to the JMP tool sections more and also the quiz and exams be more comprehensive and through to cover more.

Need to use R or Python and freeware rather than courtesy access to JMP. I spent a lot of time fitting R models to the data but it is a lot of work. Hence, I'm not going to complete the Experimental Design courses. The lecturer is very, very good and I enjoy listening to his wisdom.

Very exhaustive information about random models and nested and split-plot designs. Thank you to Professor Douglas C. Montgomery and Coursera Team.

Comprehensive and practical course in the Design of Experiments specialization. Helps reinforce the need for a physical experiment to align with constraints on randomization.

THIS FULL COURSE WAS EXCELLENT. IT WILL HELP IN MY PROJECT. THANK YO DOCTOR MONTGOMERY SIR.

Great