Teacher is a bit erratic. It makes the course hard to follow.

Poor concepts exposition with a bad teaching method.

This class is not very well explained.

Pedagogy needs to be improved.

Could be better ...

Too much, too soon.

For beginners.

Homework, lectures, and the quiz are completely out of sync. Bayes rule is introduced and appears in the homework but no where else. Things appear on the quiz that aren't in the home work or lecture. This was put together from scraps of another lecture, but in an incoherent fashion. When Caffo tells the viewer that they'll need to use other resources, he wasn't kidding. I dropped this the first time when I kept introducing things that completely had not been introduced, took another stats class, then came back and aced it. I don't mind accelerated learning or using other resources, but there's guide for which concepts are needed and where coverage for them can be found. This leaves little recourse but to know stats already, or go learn it before taking this course. Otherwise you don't know enough to even go find the pieces you need. Incidentally, the dude who does the lectures for Khan Academy does a fantastic job and the lectures are a joy to watch, though some people might prefer something that moves less slowly and carefully and perhaps they would prefer something that glosses over the fundamental concepts more. If that's the case, I can't say enough good things about Biostatistical Analysis by Zar but thoroughly, logically categorizing statistical methods with short, clear examples, references to the original research, and building up one concept after another in logical order. The chapters are short, but the first 16 or so should give you a good enough foundation to deal with about any intro stats class. As it is, Caffo's presentation needs some serious testing and remodeling, but there's no indication that it'll match what Khan Academy did regardless of how much work goes in. At best, it's a bitter pill you can swallow if you already know the concepts.

The instructor is horrible. He does not understand what it takes to explain mathematical ideas clearly. I do not even understand what kind of audience the instructor is trying to target. For the most part, formulas are not derived but just thrown at you. So, watching this course is definitely going to be a waste of time for someone who (like me) want to understand all mathematical details behind the statistical concepts. At the same time, he is explaining thing using very formal language (probably borrowed from some bad math textbook), so do not expect that you will be able to learn things at least at the conceptual level. have a solid background in statistics, so all the ideas covered in this class are familiar to me. Fortunately, I did not have to learn them from Brian's class.

I thave a M.Sc in Economics and after not using Statistics for a while I took the course to refresh my knowledge. My conclusion is that this module is a waste of time! The teaching skills of the Tutor are not very good (to say it mildly). All the needed materials are there (in theory), but when it comes to statistics, one cannot emphasize enough how important it is to give illustrative examples and plots. This was not done here, either at all, or very badly. When lecturing Statistic, what I want to see is someone drawing a lot of Graphs and explaingn how and why curves shift and how that changes the numbers and tests. This is how intuition is build for what is going on. Otherwise it only becomes dry Stat...

This course, which is part of Data Science Specialization Course, which is a BEGINNER specialization, doesn't explain as it should to BEGINNERS. They try to explain, complex topics in 3 minutes ... If I didn't need the certificate, I would definitely not waste my time on this course. Youtube videos from khan academy or Brandon Foltz (Statistics 101) are much more valuable, you really get the topic and they are free. The professors didn't want to spend time preparing good material, from my point of view, the preparation is very poor.

The course is more oriented to teach you to be a "data monkey". You know the code you need to write, but you don't get what are you doing ... Where do these formulas come from?

Very disappointed with how the transition from the old Coursera platform to the new platform has been handled: lots of instances of the "see lecture X" in the quizzes where the reference is now just wrong because the lectures got renumbered, an almost complete lack of community TA/mentors, and no explanations from anyone as to how the new platform works.

Perhaps the worst of all has been the almost complete lack of acknowledgement of any problems from the folks at JHU. This feels like it's just been dumped on the students without any real testing or any appropriate resources to sort out any problems.

Content covered in this course was hard to learn, both because it was pitched at a level that realistically was more akin to a wrap up of content already covered (when in fact it was all new content) and because the instructor, Brian Caffo, has not a style that was conducive to teaching.

The instructor often would launch into a topic, and then speed through a calculation with basically no explanation.

In terms of time, this was one of the most intensive courses in the specialisation, and I'd recommend taking this course alone (not concurrently with other courses) for that very reason.

This is 3rd time I a trying this course. Labeling someone just reading the slides out loud as a course is ridiculous. I have to express that this is horrible, Please don't callout a course. Call it Audio Slides.

I have a Master's degree in engineering and have won scholarship all my life. This is the first time I am trying out on-line course. The courses were okay till I came to this sections mostly done by Brian Jaffe. Knowing and teaching is two different things, Brian! I will continue, with help from other materials outside the course. But I have ti rate this as 1 star.

The worst professor in this specialization. The subject really interesting, and I have been studying for a while in my Master's and PhD in engineering, so I could understand the bulk of the course. This is a very important subject in data analysis and these poor explained classes could make lot of people give up the specialization. Statistics involves much of mathematics and calculus which make it a natural challenge for most of the people. Please, improve these classes in order not to disappoint the student who want to become data scientists.

I found the lectures to be very lacking. The lecturer seems to make too many assumptions on what the student knows. The pacing is off on what is important to know, and what isn't. There should be more examples on how the information can be utilized in R. The quizzes should be restructured to require writing some form of R script to solve the problem. The swirl exercises don't help either. Furthermore, I was hoping that there would be more depth on how this may be utilized in a real world setting.

The lectures for this class are incredibly weak. Later lectures by the same professor are reasonable and decently structured. These lectures need to be redone. The quizzes are either out-of-order or expect you to do a lot of research on your own beyond the class notes and topics. The class project is unbelievably simple, and the final metric for the class project includes duplication and one portion of the grade assigned simply if you feel the person you're grading "tried".

I am finding this course to have a flavor where the material written on the slides are just read out loud. The content doesn't seem interesting. I was determined to complete the Specialization but I am leaving it as, unfortunately, I am feeling sleepy just by listening to the course material. This was not the case at all before taking this course. I hope the teaching methodology can be enhanced to make it more engaging. Thanks.

The lessons require intermediate level in statistics and it is a complete waste of time watching the videos without doing an initial course of statistics. Thereby, It requires much more time than expected to learn the topic, which includes reviewing basic concepts and doing the (optional) assignments. Moreover, the questions in all quizzes are more than challenging very tricky.

The course is poorly laid out and the concepts are poorly explained. You'll need either previous college level statistics courses or be willing to spend a lot of time outside of the class to understand what's being taught. The quizzes have little to do with what is presented in the lecture. Unless you are going for the data science certificate, I would look some place else.

Many videos lacked associated pdf slides so confusing to watch. Some topics on slides were not covered in videos. A supplemental video for those would be great even of optional.

Brian Cato is a good presenter, however, more examples needed to be done showing how to work out various statistical problems both by traditional method and using R.

Explanations not clear and feels like he's reading rather than explaining things.

Consecutive videos feel like they are disconnected. Videos stop in the middle of him talking something. Thank god for the swirl assignments which make things much clearer!

Also the course proceed very fast not giving enough time to the concepts.

Very hard to follow, many of the maths symbols are not well explained. To little time spent on each concept and many concepts don't get a proper explanation. I had to re-learn almost every concept externally as I learnt little from the videos. Some of the lessons on khan academy are much easier to follow.

I've not found this course organized pedagogically speaking. The organization, insofar as "here is a list of stuff I'll be teaching" makes sense. But a lot of the "teaching" is ill suited for someone looking to learn with a minimal statistical background. It's incredibly frustrating and disheartening.

Terrible instruction in the videos and unclear directions. I'd avoid this course if possible, but it's required for the specialization. New videos should be shot and inspiration taken from more instructive and interesting guides, like The Cartoon Guide to Statistics by Gonick & Smith.