Course topics is good and heavily dive into statistical training.

I may say that there is a lot of theoretical stuff and these need to be supported by real world simple examples.

I have spent twice the time to watch the youtube videos about the classes to settle my mind and see some examples.

Course content need to revised and realistic easy to understand content including R coding should be included.

Thanks for the effort spent so far.

I believe the theory part can be greatly improved to provide an understanding. Practical and all is good enough as someone who likes maths, I would like to see more of it in the theory classes. I did not quite understand some topics intutively for which I had to search for other materials, but swirl excercises are a big help actually. Also a big thank you to the professors/mentors who put their time and effort in this .

It was more difficult than I expected. Besides to imagine inside your head some of the theoretical concepts. Instead of "accept or reject", we have "reject" and "fail to reject".... just as an example :) And now there is this discussion about p-values omg....

https://www.vox.com/science-and-health/2017/7/31/16021654/p-values-statistical-significance-redefine-0005?imm_mid=0f55ac&cmp=em-data-na-na-newsltr_20170809

I think it could be taught a tad better. Maybe more explanations in lessons and a bit better (read: less vaguely) worded course project description would be useful. I do understand the importance of this part of the DS specialization, but I would have loved a bit more careful approach to the subject. It is probably hard for an expert to lower himself to this admittedly low level of knowledge, but please do try.

Great R material, powerpoints, and lesson materials, l however the material is extremely fast-paced. Recommend a page dedicated entirely to R formulas. This was a good refresher, but anyone who has not had at lease Stats 3 or 4 in college/graduate school should think twice before doing this program or at least have many outside hours dedicated to completing this program.

The same comment I made in some to the previous courses of this specialization also apply: it is not a course meant to teach, only to verify one's knowledge. I recommend learning about this topic somewhere else and only coming back if you really want this certificate. Also, this module is really monotonous. It was really hard to be motivated.

Caffo clearly knows his stuff. But some of the lectures start off going slow but then take a leap forward into a conceptual realm that is beyond most people if they are not at least somewhat familiar with statistical concepts. Take your time with this one and make sure to do the reading. The videos kind of cut off prematurely sometimes.

Since it is a very theoretical subject, trying to explain it without proofs and plenty of background is hard. But i feel like most of the course is just to memorize formulas without much explanation where they come from. A few examples are computing the expectation and mean of the average distribution and computing confidence intervals.

Not my favorite course in this specialization. Very poor at developing "intuition" regarding statistical inference concepts. At many times I felt that the instructor was simply reading formulas, assuming that we already had the background. I had to go to YouTube to get real-world explanations of what different concepts meant.

The course covers very important topics pretty well. The instructors knows the subject, materials are well chosen. However, the lectures could be done much better. There are many typos, the instructor is reading from the slights. Isn't it worth putting a little more effort since this course is taken by the thouthands of students?

The course is good, but not very challenging. Anybody having done any course in statistic would have little to no information from the first two weeks. Only week 4 was interesting to me, dealing with boostrapping.

The teacher is very clear and chooses the subject in a clever way. One always understands what he or she is doing.

Brian Caffo is an interesting lecturer - he dives into the key concepts and ideas that are essential to understanding the statistical concepts necessary to gain a better appreciation of the course. However, presentation and materials need a LOT of work. They can be too overwhelming and most of the times feel irrelevant.

This course should be targeted for Data Scientists, in my opinion it is more for statisticians.

Too much about the insight of statistics and some but not enough about how to use the statistic tools.

Some time the professor seems like he is just reading the slides which I think it doesn't intensive the student.

So in my opinion information that ws given in this course was not exlained well, lucky for me I was just refreshing these things, so I knew where I could lok for additional explanation. But if you don't have any background in this scope, it would be rather hard to understand why given formulas are working.

The course contains a lot I want to learn, but as someone with a limited background in statistics - I found many of the lectures not to provide clear explanations for concepts. I had to use a lot of outside material to try to learn and understand the concepts. The course lectures seem incomplete to me.

The class contents are good I guess. But I don't think the professor knew how to teach and enjoyed the teaching process. Based on my experience, all the concepts are not that hard for everyone if they would be explained in a good way. I finished this course only because I want to do the course capstone.

A good class, but I think there are some missing pieces. For example, there was a lecture on the basics of knitr, but nothing related to creating a pdf from R. In the Regression Models class there is a lecture on basic notation. I think it would have been more helpful to have that lecture in this class.

I really learned a lot in this course, but I find that I got most out of the lectures in week 3/4 when Brian actually stopped reading the slides out loud and explained the concepts i his own words. I believe the course could be improved by taking that approach in the other weeks

The lectures are really hard to understand, while the material itself is really not that hard. The lecturer talks as if he is just reminding us everything we've already learned. Had to go to other MOOC (specifically Khan Academy) to obtain proper understanding of the topic.

The course was very dry compared to the other courses I have taken. Though there was a lot to cover in the four weeks but this was not best way to do it. The course covers a lot of concepts in far too little a time span. It should have been spread into at-least two modules.

To tackle such key concepts and tools of statistics, you need the appropriate time. Too much material covered in this course. I tend to think that revising the approach to this course as two 4 weeks modules would allow to both go more in depth at a more appropriate pace.

ok course. They provide examples that make sense, although assignments don't really touch all of the material covered. The examples as well as assignments tend to be quite helpful, although I dislike how they force the specific format, which for some could be advantage.

Course is reasonably well taught. I have gotten used to stale online courses. You would think that, given the apparent work to correct errors or respond to feedback should be minimal compared to the vast number of students taking this course. Not so much, I guess.

This is a decent overview of statistical inference techniques. Make sure you understand each lecture before moving on to the next since they build on each other. The lecture notes are decent but not great. I found it cleaner and easier to take my own notes.

This course was better than the others in the program because there was much less of a gap between the lectures and the graded sections in terms of expectations. Still, I knew this material going in and would not recommend this as a way to learn it.