I feel like the course overall was important. I'm glad I have the basic idea of how I can crunch research data using R. However, the course could be difficult and perhaps a bit deep for many people looking toward UX design. The instructor suffers from the academia cliche of having so much knowledge and skill that he can't always help students on a very introductory level. Case in point reading the class notes (a task usually unimportant and ignored by most users) is required, and yet even those notes are written from a perspective of knowledge of the subject. It's a bit like coming into a Spanish 1 class, looking to learn a bit about Spanish and the professor hands you a syllabus of which parts are written in Spanish. While I recovered from this shock, I think for many this snowballs into growing frustration and failure.

Add to this the complexity of coding across different computer operating systems and versions. There are times when you get an error and you don't know why. In a classroom the professor might tell you, "oh you forgot to _______". Here you can ask and you might get a response days or weeks later. It could also get so complex that even the professor is not sure what is going on. I had an error and there was no resolution, I had to google and solve it myself.

Overall, I'm glad I took the course. I had some really painful moments but I also solved problems and completed complex tasks, which felt rewarding. I definitely appreciate the approach of the professor, splitting time between lecture and hands on and having hands on assessment at the end.

I liked the professor. He explained things well. My concern with this course it the understanding of it. I can do command line commands but figuring out which commands to use for each quiz question is complicated. The first few weeks I figured out which sections of the coursera.R file to use. Later weeks were more complicated. Perhaps files with commands and comments on a per week basis would be best. Not giving the answers to the student, but at least giving more context per file would be very helpful.

I was enthusiastic about this class but it ended up being useless to me. While the structure allowed me to jump right in R, I felt like without any prior knowledge of statistics, I was just copy pasting without really understanding the tests, and why to use one rather than another. I guess going deeper into what things mean, or adding more context to the tests would help for designers with little background of stats. For example, even though it was explained, it would have been nice that every time we had a new dataset, to take the time to explain what type of survey it was, vs the previous ones, and to map it to the grid. This was assumed as a given, but I feel I would still have trouble figuring out which test to use on my own. So overall this was a good class but missing some content for the beginners.

The instructor and Teaching Aids haven't participated in the learning Forums for over a year- this is the most difficult course in the Specialization however there is little to no support for the students. I have a background in engineering so I faired well in the course, but for many- not so much! Course would also benefit from a more robust intro to R Programming. Thank you so much for the course , I really appreciate it! I'm only sending these criticisms in order to help - I personally did very well in the course.

I've been going through "Interaction Design" courses from University of California, San Diego since course one in order to get the specialisation. And each course was interesting, insightful, challenging. I really want to get the specialisation and I worked hard to get to this point. In total it took me around two years with pauses and breaks.

However, the last course called "Designing, Running, and Analyzing Experiments" is something different and makes it impossible for me and many others to finish. Because it requires programming and statistical skills. So for it to be finished I need to take a separate course on R language. I'm not willing to give up, but this particular course requires special skills which not everyone has. I'm not sure if this R language will be in the capstone project as well but it's just impossible to finish. Moreover, if you go to the discussion forums not only you will see that people can't finish even second week but also that many students can't even install the software that they don't know how to use. I suggest all the R language materials, assignments, quizzes, videos to be removed from this specialisation. Because it requires a special preparation and skills in programming and statistical analysis which this course wasn't meant to require from students.

It's like if I would be doing a course on Microsoft Paint and the last course would be to create a 3D model of a dinosaur in Maya assuming after learning Microsoft Paint we're able to take on the Maya 3D in no time.

I was forced to study something separately just to finish this course. And I'm not planning to use the knowledge from this course. This should be a completely discreet course not related to the specialisation.

It's true that I've learned a lot and will never see experiments the same way again. I have new-found respect for conducting even the most simple surveys. BUT this course goes way too deep into the math and code under the hood. It's absolutely ridiculous. I almost dropped out so many times. I had to invest hours of my scarce time to complete tasks that aren't suited for an Interaction Designer, but rather for a mathematician. I'd rather spend more time analysing results and optimising the design of experiments than figuring what on earth I'm being "explained" about a bunch of intricate formulas. The analysis got completely LOST in that jungle of numbers, weird names and math jargon. Wouldn't recommend.

The worst course of this specialisation. Instead of emphasising on the principles of statistical methods, this course forces you to use R and RStudio.

A tough course, especially for those who do not code. Thus, lots of work required. Sometimes there were way too many tasks (32). I gave this course 5 stars as it was a very challenging, but lots of different approaches and tests you can learn more and in depth.

This had been the hardest class ever. I don't even know how I passed but also I don't see how I can remember to use Rcode for future work within HCI

At first I should thank Dr,Wobbrock for his efforts. He teaches the course materials well but I think the this course is not well-balanced. Statistics is very wide concept and R Studio is big too. Although the course is longer than other courses of this specialization , i dont think it has the same output.

I become familiar with R.

I become familiar with distributions.

I know some of tests but if I want do a real world experiment I dont't know how can i start it now.

I think this course should get redesigned.

I reviewed all previous courses with 5 stars but I had to give 1 star to this course Not because of the instructor and not because of the importance of the material but because of the level it os not well thought through. As a beginner designer information given here without clear explanation of how those studios and softwares work you would be a big problem for anyone who just started getting into this field. Giving us basic skills of using those tools would be more helful rather than asking us to solve problems. I finished all previous courses in less than a month and obtained so many skills and information. I am very frastrated because I was looking forward to finish this specialization.

This course assumes too much knowledge about both programming and statistics. The tone of the instructor makes it seem like its an introductory course, but he does not provide an introduction to the concepts and methods that would give the student a proper grounding to successfully advance through the course.

Yes, this is not an intro stats course, and yes, it is not an intro R course. But what is it actually, particularly in terms of UX?

This course is not amazing at all. It takes so much time to complete plus you do not learn much. You learn to pretty much replace some parts of a code it's already done in R. So you work with code but you don't learn how to code either. I give this course 1 star.

Too technical, with lots of hard concepts to assimilate in a very small time. Exercises are too hard.

Amazing class. Covers a great deal of statistical methods and explains them in a plain/clear/accessible way with good examples.

It cuts through the chase and gives you what you need to know (the real essentials) from a practical standpoint. You will not get a lot of theory (for that, there are plenty additional courses) but you will get enough theory to select the right method for each scenario.

It will not teach you to program in R from zero (for that there are many other courses) but it will jump start you with snippets of code that you can read, understand, modify, and use.

Real useful stuff... done the proper way...

I could immediatly implement the concepts and instruments provided in this course to my professional activity. I must say that I have a poor training in statistics and a superficial knowledge of R, but I needed to implement more professional usertests (I'm an instructional designer with a background on education).

I really enjoyed the clear explanations; above all, how the concepts where linked to R practice, which was thrilling to me.

Thanks to the team! Great course!

Juliana

This course was extremely helpful in understanding which statistical test to use when, with applications specifically for interaction design, which is what I need :) I appreciated the clear relationship between the lectures and the quizzes & assignments. The lectures also were clear. The course was broken up into doable chunks that made it easy to take while still having a full-time job.

That course was going to drive me mad but it's one of the most useful parts of the specialization. I already know where to use it in my job as most people listen to numbers more. I think that the problems that appeared in the process of calculating the results are some bugs in R and have nothing to do with the design of the course. Good job, Jacob and Scott! You're great, guys!

Great course on experimental design providing a detailed overview of running and interpreting factorial ANOVAs beyond the 'standard' t-test and chi-squared test. Given that the bulk of the assessment is based on analysis of experiment data in the R programming environment , I can see why this course may be challenging for those without programming or statistical knowledge.

Excellent course. Very well organised. Challenging yet satisfying. Jacob Wobbrock is a very good tutor and despite the complex nature of the course material, he is engaging and thorough in his approach but leaving just enough for the student to explore and practice on their own. Very enjoyable. Thanks

This course was an deep dive into data analytics and designing experiments for analytics. Although there was a great requirement to perform the tests by yourself - which i guess some designers don't appreciate - it still was an exciting and challenging experience!

Great course! Very applied and less theoretical which is great in understanding how to analyze experiments without getting too deep into the theory. Would love a follow up course that dives deeper into the mechanics of the functions used in this course!

It's a great course. It is very well structured and provides a foundation of research and I am glad that it is a part of Interaction Design specialization. However, might be extremely challenging for people without Statistics experience.

A challenging but very fruitful course.

If you do not have sufficient statistics background, you will find it much more challenging and need to pay much effort on it.

Having said that, you can learn a lot of practical and useful concepts.

This is a thorough course, but was way harder than I expected. I needed to understand the experiment design more than to learn R programming language. I am glad I did, but it was hard for me and took a lot more effort than anticipated.