The professor for the course was able to explain well on most of the concepts on the r programming behind the designing, running and analyzing experiments. He went a little fast on the videos but I was able to catch up by reviewing the transcripts and pausing /playing the videos. The part I was not happy about was that there was no mentor in this course who helped out the students on the discussion board in facing issues of setting up r programming and r studio and also understanding how to do r programming. I was a bit surprised that R is used in interactive design because I heard the main programming languages when it comes for UX researchers and designers are focused on java, c++, and sql.

Great intro to R-Studio entering pre-made functions with data tables. I feel a little more familiar with basic concepts for quantitative experiments but in terms of understanding the actual statistical functions I still don't feel confident running know when and where to apply the statistical analysis to the data (Seems out of scope for this one course and I would take this course again for more practice). This was the most difficult course in the specialization. Working in R-studio is tricky because you need to know how run the correct version of R, and how to install missing packages in order to run the functions used in the quizzes.

Interesting to try R hands on. Could have made more definitive statements on the usefulness of each test (there was lots of 'you could have a look at this analysis, or this one, or this one' without covering why each one would be particularly good for this case). Some of the later exercises also made quite of a leap in terms of what you were expected to know about R syntax, although the follow up answers were good if you didn't understand what was expected.

Too technical for my taste. I understood the concepts but as we progressed, the code itself was extremely complicated with A LOT to grasp within a weeks time. I relied on the hints and code keys provided in examinations. I would highlighy recommend either rethinking if code should be part of this. Or improve how we can test/ increase the friendliness. No offence intended, just want to help :)

This module took me on an interesting journey with lots of twists and turns. While I love challenges and learning otherwise I wouldn't have taken this course. I'd like to suggest the following: mentor to assist students, review of codes I found lots of glitches, one quiz rather than two, cut back on timeframe. I'd like to give more stars but am being generous with 3 stars.

This is a very interesting and an important area in the Interaction Design Specialisation. This should have a dedicated specialisation on its own. While the exercises and the lectures are pretty decent,I don't think it's adequate to really grasp and master this subject. However, it's still a pretty good introduction enough to give someone a basic working knowledge.

Very, very, difficult. I'm finding myself having a harder time to pass this than structural design in architecture university, and that says a lot. At least in school we had the option to ask other students, but online, this resource is way less present. I did start getting some principles, but this needs rebalancing in my view.

This one felt really out of place with the rest of the courses. I feel like 2-3 weeks tops would have got me the basic knowledge, I've also heard from multiple people that Python would be preferable to learn for statistic analysis over R. Overall, just SO happy to have this one done so I don't have to keep stumbling through R.

I was able to follow the course and keep the pace but I got lost on factorial ANOVA. The professor is great but the course feels rushed through. It would be great if more time was spent on actual explanation of main concepts, since many here are the beginners in stats. I will try again, hopefully will get it this time.

Great overview of different analysis, however I would not be able to do the tests without trail and error, and the available codes! 9 weeks, but it went too fast through the course, i would suggest to either make the course shorter to go deep in a few concepts, OR make it much longer so learners can understand.

This came slightly unexpected, and was quite challenging which is good. It was a lot condensed into a short period. It would have been nice if it was updated as their were a few issues because of it but not a huge deal - the forums were helpful.

There is alot o f bugs on the exams and some questions are really confusing about what the point is about. I liked the way the professor explain every thing and theme was so simple even for someone that has not a background in coding like me.

I think the course requires some basic statistic knowledge which I do not know anything about, it is tough to keep out. If providing some basic reading or keynote before hand would be really helpful!

I would give it a 3.5 if there were half scores. This course requires way too much time and is quite tedious, however, the instructor explains the somewhat "dry" material very well.

The truth is that I expected more. It is one of the most complex parts that I will have to reinforce because there are many things that have not been clear to me

it's a little too much materials. I wish the course materials are less dense, though I really appreciate the efforts of the instructors creating this course

It's really interesting to learn it but it's not easy at all. Maybe you will need an statistic professor to understand it perfectly.

Way to much stuff for students who haven't learned statistic before.

9 weeks are just too short for these abundant material.

While it's a fun course (and challenging), retaining all the information could be a challenge if not put into practice.

Not finding it very useful, but it's good to know about the concepts and theories for each type of experiments.

This should really be broken up into several separate classes. It is a whole lot at 1 time.

I would have rather gone deeper into the tests, than quickly run through them.

I got to know from a professor that some information is incorrect.

Quite advanced in contrast to the rest of this specialization!

It requires a lot of knowledge of R and statistic