It is one of the best course present on coursera. I would recommend everyone to take this course. It will not only help you to optimize your activities in work place but also in your personal life.
Very good course,and an excellent instructor. Thank you very much Professor Kevin Dunn.\n\nI enjoyed your course, and I really appreciate the time and effort you put into those lessons
교육 기관: francisco g h r•
The absolute best starting point if you need to learn DOE at an urgency. It is up to par and even beyond professional options that charge thousands of dollars for a 40 hour, 1 week course. Professor Dunn even offers a free coursebook to complement his excelent lectures, which goes well beyond the intensive 5-6 weeks period. Uses R for programming and gives just the necessary snippets of code needed to complete every exercise and quiz. Plenty of real-life examples and a succinct style makes this course your only stop to understand DOE and then go on your own. Statistics are not needed, but having a previous background will help you appreciate even more the quality and effort put in every lecture. Two words of advice: 1 ) Take notes, as every video is full of little details. 2 ) It is the entry-level DOE course, seasoned experimenters will finish all units in 1 or 2 weeks.
교육 기관: Ahmed S S B A•
Really interesting course, simple and helpful for all fields. It required not specific background, just start and enjoy the wonderful way Kevin illustrate the course.
교육 기관: Patrick B•
This is a great course to learn how to perform efficient experiments by varying multiple factors at a time. For scientists who do R&D research, this is an invaluable tool that will set you apart from your colleagues. In this course you learn "Experimental Design" with a bit of R code to get you started. The lessons progress at an even and steady pace that helps you retain information taught in previous sections. This was a very well executed course.
교육 기관: Daniel M•
Excelente curso, flexible y con suficiente material didáctico fácilmente digerible y cómodo. No importa si se tiene pocas bases matemáticas o estadísticas, el curso proporciona casi toda explicación necesaria para un entendimiento alto.
Ideal para ingenieros en calidad y toda aquella persona que busque el mejoramiento y mejor rendimiento de negocio, procesos, investigación, etc. Apto para TODA persona además curiosa y con ganas de aprender algo nuevo.
교육 기관: SHIVAM K G•
A very useful course for research students and academic and industry persons. The course starts from the basics of DOE and it takes to two factor RSM. The page of resources is very useful, it provides flexible R codes and beautifully designed practice problems.
교육 기관: Bettina S•
A very good introduction to the concept of Design of Experiments. The course gives you a good overview and leaves you with a manual for your own work. Furthermore the course has several extras, e.g. an own online textbook.
교육 기관: Antonino C•
Fantastic .. as i am studying to gain the black belt in Six Sigma training, I was stuck on the design of experiment, but with this course I solved it !!!!
Htank you very much
교육 기관: Omaima H•
This is my second online course and my experiences have been amazing so far. With regards to this course, I could not have asked for a better teacher, teaching method, and the timing of this course for me as I am very much able to apply it in my academics and vice versa. The best part about it is its reach, that it caters to people who are technical as well as non-technical. It definitely boosts your self-esteem and confidence. the fact that you are able to study a course outside the premises of your campus and break out of the shell, with people learning from all over the world; the assessments are transparent and merit-based, and the feedback is timely. Sitting on my couch at home in Karachi, I was able to take on a course from a Canadian university, is amazing,
교육 기관: Stéphane S•
This is one of the most useful courses around for every engineer, scientist, and researcher. However, it appears that this fundamental experimentation methodology is not being taught in every standard university curriculum. It was never covered in all my background in computer science, engineering, and research, neither at the bachelor's, nor postgraduate levels. A great addition!
The course is very well taught, and provides extensive learning material, including lecture notes, and a PDF copy of a complete course manual by the author. Thank you very much, prof. Kevin Dunn.
교육 기관: Vikas S•
It is a fantastic course ane must take. This course will check your patience as well as your zeal to attempt the quizzes. The quizzes are really hard but when you keep trying you will be highly satisfied at the end. Highly recommended to all the students who want to learn how to design an experiment. I learned the important stuff and now I will start to design my first experiment
교육 기관: Maria V B•
Amazing! Very useful information, very intense workload. It´s a difficult subject, very well explained. It has many applications in almost any life or work area. Highly recommend it.
교육 기관: Steven Z•
Satisfying course content and structure. Reasonable course speed. Nice, practical reference. Good introduction to design of experiments and response surface methods.
교육 기관: Pablo C M•
Highly recommended introductory course for anyone getting into experimentation. I found the starting point appropriate for total novices (though for someone with some background, the pacing felt very slow and repetitive at times), but there was a well-defined progression from the simplest to more advanced concepts.
I would have appreciated some more theoretical content, both foundational and advanced, and the pacing/structure could use some rethinking to avoid staleness, but overall this is a very nice introduction to important aspects of design of experiments and optimization.
교육 기관: Alamin B•
Experimentation for process improvement is an excellent course and it will make a better and wiser experimenter. My background is in healthcare field and medicine and after taking this course it help to critically appraise and read article related to research in medicine. This is my first verified certificate course and i always revisit the materials whenever i need them. This is a must attend course and you would enjoy it as i have enjoyed it.
교육 기관: West P•
Kevin was superb! It was so obvious that he had deep knowledge and experience on Experimental Design because he was able to explain the concepts in layman's terms without making it overly simple. I also appreciated how he used diverse examples in terms of the kinds of experiments used, despite his background in engineering. Thank you, Kevin! I am excited to apply what I have learned here in my work.
교육 기관: Adrienne H•
Fantastic course taught in a straightforward, logical manner. Starts out simple but gets progressively more complex through each module. I am DEFINITELY going to use the concepts from this class in my job and maybe even in my day-to-day life!
I love that there isn't pressure to complete quizzes on time (I took MUCH longer than the Coursera-estimated time, but in doing so I learned much more).
교육 기관: chintan•
This course is an excellent starting platform for those, who wants to learn more about process optimization. The designing, of course, is excellent and examples are straightforward and easy to understand in each module. I hope tutor, of course, Kevin Dunn, will come with some more advanced course for same. I will like to thank Dr. Kevin Dunn and his team for such making such course.
교육 기관: Deleted A•
This is one of the best organized course on the subject. It is vast and complicated area , however thanks to Kevin for making it so simple , most beautiful part is, the way Fractional Factorial has been introduced and integrated into course.
Once completed students would be leave with a deep first level knowledge to take it forward. Once again thanks for a beautiful course.
교육 기관: Vilmantas G•
This course is worth learning.
I like this course as It is really applicable, very well prepared, the ideas are presented in a clear way. I would definitely choose this course again (and recommend to my friends), despite the fact that I had to spend really a lot more time, than on other coursera courses, i.e. ~3-6 hours studying the material.
교육 기관: Lalo P•
Covers the most important and practical aspects of experimentation, and the use of R really is a plus here. I would definitely recommend this either if you're new to experimentation or if you have some background, a great way to learn from processes and improve them, and the case studies really emphasize the practical aspect in this course.
교육 기관: Kudzayi M•
Course is well planned, the lectures are clear and no steps are skipped during explanation. A good and fair challenge. I have definitely grown in knowledge and confidence in regards to design of experiments, I do recommend purchasing the certificate as this course is more than worth it, all be it for a free! Thank you Kevin!
교육 기관: Vishal G•
I found this course very helpful to understand more about factors and how to meaningfully generate a relation between the outcome variable. I personally work in IT, and we always run experiments for our different features. This course gives me better confidence to propose better changes in the way we run our experiments.
교육 기관: Henrique M M•
The course is greatly designed. The only observation I would do is to explain other R libraries for Design of Experiments, it would be extremely useful for scientific experiments, where reproducibility might be an issue (such as chemistry experiments).
Nonetheless, this course helped me to further develop my carear!
교육 기관: Milind D U•
Very good coverage. Due to simple explanation and textbook support able to complete this course. ready to learn advance topic. Earlier tried to complete other online courses but due to statistics got demotivated. This course has developed basic understanding and provided platform to built on. Thank you Sir.
교육 기관: Edgar C L•
Very nice discussion on the method of steepest ascent/descent. Also, this course made me interested on how to come up with a code that is evolves as the experiment progresses so that the software/AI code will be the one to decide which path to take as the results are entered. :)