If you are an engineer in Pharma, medical device or automobile looking for a basic course on design of experiments. this is a perfect course designed for you.

It was a great experience while doing experimental design basics. I learned a lot about the Strategy of experimentation and basic statistical concepts.

The course provides you with a good foundation in how to approach experimental design systematically. It was possible to complete the course using Python with the statsmodels library instead of JMP. The only recommendation that I have is that a how to in Python or R video is added to the lecture series. Thank you for sharing your knowledge sir, your passion for the field is contagious.

The peer review assignment is daunting as you are completely at the mercy of your classmates who might leave proper feedback on what you actually did wrong. Apart from that, great class.

A great introduction to designing experiments in manufacturing and engineering settings, useful for students who wish to pursue process or validation-related career in manufacturing sites. Focus a lot on introducing the theory and fundamentals of statistical methods such as t-tests, ANOVA, blocking principle, random effects.

maybe, this course need to be presenting more colorfull and creative. I mean, every week i only watched lots of video, and then read some material, then finally solved the quiz questions. All of that was borring sometimes. This course needed more attractive things like games, video history of some previous scientist in doing the design experiment and many more.

As companies tend to become more data-driving -making data-driven business decisions- this course provides invaluable learnings on understanding the origin of the variability in your data. For this reason, this course is not just for scientists and engineers, but for every professional who has to deal with data on a daily basis. Moreover, I see this course also as a prerequisite for any machine learning scientist. ML scientists tend to focus a lot on building models, whereas the quality (variability) of the data is sometimes neglected (see e.g. the recent chat with Andrew Ng of DeepLearningAI on From Model-centric to Data-centric AI). Btw, the majority of the Design of Experiments Specialization can be done in Python using the statsmodels library.

It is a great course to learn about the design of experiments. The course follows the standard textbook by the Douglas C. Montgomery, which simplify the learning process even more. The only thing which I think the course should have included is the demonstration of exercise with R Studio, which is more widely used in academic circles because it is open-source and freely available.

I am impressed by the quality of this course. It is really hard to find statistics courses that are easy to understand and don't ignore technical details. Lecturer is an amazing teacher. Would strongly recommend it if you are keen to create a strong foundational knowledge on the experimental design!

This was an excellent course that gave me knowledge in experimental design with which it is possible to apply to any industry or field.

Very nice refresher for someone who took Experimental Design years ago. I just wish that more examples were presented.

This course introduced me to the language and tools used by DoE practitioners. I recommend this course to any professionals who lack any formal DoE courses and work in any field were experimentation is required.

Why not 5 stars? I am not a fan of the course's reliance on JMP. My ability to apply what I've learned will be severely limited if I expect my employer to purchase the software. As an alternative, I did all the computational work using the "statsmodel" library in Python, but this required a significant amount of additional research.

peer assessment is poor, it should be canceled.

I was disappointed with the fact the Dr. Montgomery does not really explain the concepts very well.

The course and the Prof. helps me to enter the DOE field. Really recommended to freshman. The analysis is mainly done and shown in JMP which is a really expensive software. I hope the course can include the analysis done by free tools, like R. That will be much helpful for people who have no access to JMP in the reality.

Out of the four courses belonging to the DoE specialization, this and the second are the ones giving you the most, in my opinion. That's because they are the most practical and it's easy to translate into exercises and examples what you learn.

Excellent introductory course! I studied DOE as an undergraduate, but these highly effective classes highlighted several important aspects that I had missed in the past. I feel more confident designing experiments now.

I have used Dr. Montgomery's book off and on since the early 1990s! It is an enjoyment to watch his lectures. The only caveat is that it is a short course, which should have been obvious to me.

Thankyout for the class of Experimental Design Basics, I learned many things from this class. I came to know that in everyday life experimental design is very useful for the general public.

Agradezco las enseñanzas impartidas por el Profesor Montogomery.

El curso es muy bueno para afianzar los conocimientos den Experimental Designs. Muchas Gracias!!!!

This course was very practical and I thank Professor Montgomery for her excellent teaching. I also thank the Coursera team for providing this opportunity.

excepcional! excelente para quem quer não só ter um pensamento critico melhor, mas também sobre como aprender mais sobre essa área sensacional

Excellent course—very practical and concise, with a well-balanced approach between mathematical rigor and practical execution skills.

This course provides basic knowledge about experiment design, leading to the foundation of a quality manager or project management.

Content was delivered in a clear and concise manner. Referring the Text would be a great help that supplements the video lectures.