Excellent course

tougher content

Nice course !!

GREAT COURSE

great course

good content

Good content

interesting

good course

Good one!!

Excellent!

great

Nice.

ok

The content is what you would expect for this subject, but it is not quite presented in a logical and ordered way. The lecturer's style is also very uncomfortable, especially in the first week or two, where it feels like the content is just read (and fast), and not explained on a level expected for a course having no prerequisites. If students don't have any previous statistics experience or knowledge, they would find some of the concepts very difficult, especially as presented in this course, as it appears that the assumption is made that students have a certain level of statistics knowledge already.

I have done the Basic Statistics course on Coursera (University of Amsterdam) and that course takes a more methodical and logical approach to the basic concepts, and if I hadn't done that course already I would have really struggled with grasping the concepts explained in this course. Even having done the Basic Statistics course I struggled anyways, and had to resort to additional information like Statistics for Dummies and various other internet / YouTube videos for more methodical and clear explanations.

This is a great course taught by a clever teacher but...

The content is presented in a very dry, not easy to grasp, manner. In several cases, I had to use external sources to understand the content and/or derive it by myself. When I finally understood the content I couldn't understand why it is presented in such a cryptic manner when the concepts are rather simple to grasp and the math not so advanced.

Professor Caffo is a good communicator in some occasion (the module on Power for example was incredibly well communicated). But most of the time he just throw us some result without properly setting the context and concepts, as if it was understood that we already know most of what he is talking about. (Not the case!)

I plan to make a document that follows the course module and fill in the missing piece of contextual information, derivations and concepts. But this takes a lot of time. If/when it will be completed, I will try to find a way to share it with future generation of students. Because, honestly, the content of this course is not so hard and shouldn't be!

As someone who's new to the world of data science and doesn't have a university degree this course was very hard to get a good grasp on.

That's partly the "cause" of how the course was taught which was assuming you had all the knowledge at hand of all the stuff Statistical Inference is about.

For people that are starting this stuff it might be nice to have a introductory course of Statistical Inference as I did not finish this course by just watching the course video's and additional information, I had to look up additional resources which explained the material better.

Still, a big thank you for explaining statistical inference and opening my eyes regarding this topic, it surely helped getting me to the next step in what Data Science is all about and makes it ever more interesting!

The course is a very quick run through of basic statistics and not very intuitive for people without much statistics/maths background. The swirl exercises is a very good practical learning tutorial that supplements the course, but overall it still lacks on the conceptual aspect. Personally, I have to occasionally refer to other basic statistics materials to be able to follow the flow and understand the lectures.

For the course project, there is a huge discrepancy in what the project expect the students to perform and the peer grading criteria. As a basic statistic course, the correctness of the estimation/ calculation/ assumptions is integral in any analysis but the grading criteria mostly neglect all this aspect. Hopefully the course admin can rectify this aspect of the course.

This is of course my personal opinion, with all due respect for the Tutors. Plus, it has to be noted that I am writing this as a Mathematics graduate, and this course was most probably not meant for people with any background. However, I have seen similar opinions from people like me. Probability calculus and statistics are both enormous areas of mathematics. Introducing them in a 4-week course seems a really bad idea to me. The probability part was in my opinion far better than the statistical, the origin of every new concept was clear. In my opinion, the optimal solution for the course would be to create a separate, longer course in PC and stats and require knowledge of the two for taking Data Science Specialization.

The course is not meant for beginners, but seems to be advertised as such. Knowledge of Elementary Statistics is a must. The course is fast-paced and most people would not be able to finish it in 4 weeks or understand all the concepts in the course without outside help. Use of Discussion Forums and Mentors such as Leonard Greski is invaluable for completing the course successfully. There are several minor flaws in the videos and textbook that need to be addressed. This course would be much better off broken into two (Elementary + Inferential Statistics) and buffered with longer videos and step-by-step instruction and help.

There is no doubt that topics covered in the course are fundamental and critical. However, instructors rushed through most topics and explain them in a not very intuitive way. To be fair, the "power" section in the course is actually pretty organized. Swirl exercises can improve your understanding to some degree, make sure to take them.

This is definitely not the 1st course if you are completely new to statistics. I'd suggest taking other similar Coursera courses or reading articles from different resources (e.g. http://www.sthda.com/english/) to better internalize all concepts.

A topic such as statistical inference is not complicated, and could be taught in a much more straight forward and comprehendible fashion. Just look at the tons of material and (good old fashion books) that relate this material in a much more concise manner. Moreover, the material in this class including the R-files are not well synchronized (gives low quality impression). A lot of time is needed to sort out the documentation between R-files, the book (Statistical Inference for Data Science) and the slides. I find many errors and sometimes inconsistent notation.

I have mixed feelings about this class. We are rushed through the concepts, I had to study a lot on my own to deeply understand the mechanisms - lucky for me I studied advanced mathematics in College, I mostly had to revive my memories. The video lectures are of very average quality, but the practical exercises in swirl helped a lot. Just the one final project is not enough imho. I can't judge yet if what I learned is enough to properly apprehend the algorithms at play in ML - we'll see. All in all, not the best course so far from the specialization.

This course covers the very important things about statistics, I totally agree with that. But I find that if Coursera can make the entire course easier to understand for the layman, it will be the best. After I took the course, I need to visit youtube to do some researches to understand the more complex stuffs like power t test. Maybe coursera should look at Khan Acedemy and see if they can get some idea from it.

I usually go to https://www.youtube.com/watch?v=uhxtUt_-GyM&list=PL1328115D3D8A2566 to look for those chapters that I need to revise.

This course goes on a very fast pace and simply does not have the charm of all the other courses in the specialization. I understand that a lot of content is covered within a month, but there should be supplementary course material available. Moreover, TAs should be more active on the forums. I have seen most of the questions just being discussed among the students. A little disappointed. Will probably have to watch all the material again to have confidence with it.