There are few quizzes to test skills, and lectures are not interactive. There are better course on this site for statistics learning.

Not the best. Presumes a lot of knowledge that people probably don't have, even people in the field. Professor's communication style was pretty indirect and lectures were somewhat confusing and hard to follow.

If you like those "go find the answer elsewhere" type of courses, go for it.

With an "Advanced Statistics for Data Science Specialization", I expect to gain intuition on statistical concepts relevant for Data Science. However, this course is only about mathematical proof of certain concepts. Expect more explanation about the mathematical meaning of "the limit of N going to infinity for N/(N+1)" and how the outcome is said to become 1, than about _any_ statistical concept you might encounter in this course (well, at least for the first two weeks, because that's when I quit).

For example, it is mathematically proven that the distribution of the average of dice rolls (properly normalized), in the limit becomes that of the standard normal. But, the outcome of a dice roll is bound from 1 to 6 and so is the average. How does this relate to the unbound standard normal distribution? You are on your own to discover that in the normalizing you shift and scale the range of 1 to 6 to a larger range (proportional to something like square root of N), reaching +/- infinity in the limit. But THAT is NOT explained AT ALL! And yes, you _can_ read it in the slides, as there _is_ a formula that divides something by "s/sqrt(N)".

Course provides very little detail on the derivation of the formulas used and tested on homework and quizzes. It is better to just buy a book and learn it on your own rather than be frustrated with the lack of detail.

Very well balanced between mathematical reasoning and applications to analysis. It needs knowledge on algebra and rules of differentiating and integrating (go do some other mooc courses, or khan academy, if you lack these skills, and then come right back to it). Linear algebra I would say is not much of a requirement, neither R, just be prepared to get a little lost in some slides, but nothing you would really need for you to absorb the main content. The 1st assignment is the one actually quite challenging, but persevere through this one and you surely can complete the course, because it gets quite easier afterwards.

This is indeed an advanced course, but as long as you study it with the right method, you could get through it (and obtain not-so-introductory level of knowledge). Just as everyone says in the review, you need to write down the notes. Personally, I also tried to do the examples given in the lectures before the solution is given, so I have a chance to try to apply the concepts on my own. That really helped me with the quizzes.

Additionally, you need to be comfortable with single variable calculus (especially derivatives and integrals) before starting on this course.

Great course, insuring the basics for statistical inference. Well thought explanations and a good examples. Does get a bit hard to follow with Lecture 14 (last one). Practice exercises and homework are great for applying the covered concepts. Pity there isn't much activity on the forum. Still, this course is a must if you want to have the stats ABC in order and move forward to other data science topics in full understanding of what's happening behind the scenes.

Very thorough introduction to statistics (Definitely more generic than just "bio"). Some of the homeworks are very mathematical, but the main body of work is understandable without too much of mathematical effort. Extremely well paced and self-contained which is a signature of the best coursers materials.

Good starting point for all scientist eager to truly understand the tools we all use. I'm looking now for a follow-up course from professor Brian.

Great, concise introduction to probability and statistical testing. Be prepared to solve some integrals. Course material is pretty smooth though sometimes smaller errors are not corrected. Questions in the quizzes sometimes refer to notation or material covered in the next part. This can be a bit annoying. B. Caffo sometimes gives lengthy background motivation and then really quickly almost glosses over the math.

This course is great. It covers many mathematical statistics principles required by biostatistics, with moderate difficulty. This is not a course for people without foundation. Its curriculum is more suitable for students who have a certain foundation in statistics and mathematics and want to start biostatistics learning, deeply understand more topics or do some reviews about the basic statistics knowledges.

Brian is an incredible professor. Really try to convey topics I was struggling to understand in an approachable manner. You will get a much better intuition of sample versus population parameters, confidence intervals and much more. Different methods for statistics (frequentist, likelihoods and bayesian) . I will move on to part 2

Does this really need to be called mathematical BIOstatistics? The content itself is regular mathematical statistics, just with examples from biology. Add in a couple of examples from elsewhere and the scope of the class is broadened. Still, as someone with a STEM background but not in biology, I found this course to be excellent.

A very compact and insightful course-specialization in concrete concepts in Mathematical Probability Theory. It sets the background for the next courses of the specialization. I would say that it is an excellent online course in the related topics of Probability Theory, with an introduction to R (programming).

Honestly, the course gave me a hard time. I'm sure that I was not able to get all the concepts completely but I'm thankful I get to use this course as a way to expose myself to these advanced concepts. Besides, it takes time and practice to fully understand these concepts. Thank you so much!

One of the greatest introductory courses on statistics available for free in the internet. If you are interested in the subject you should really go for it, but realize that indeed it is very difficult and you will need to do a lot of extra research, especially at the exercises. Great couse.

This course was informative. The strongest aspect of the course were the exercises; they were challenging, requiring me to think about the material and apply the material in a way that was not necessarily demonstrated in the lectures. I would have preferred more exercises, in fact.

This course should be part of the Data Science specialization. Actually, you can supplement the Statistical Inference course with these two Boot camp courses really well!

A great revision of statistics, very rigorous and thorough cover of all distributions and hypothesis tests.

A very rare course which approaches introductory statistics rigorously. The stat courses I had at university introduced all these concepts without any justification, so I enjoyed the critical approach used here.

This course is phenomenally well developed with great curriculum and materials for building astrong base to enter into statistics with a strong base of knowledge.

Topics presented well for comprehension, good lecture quality.

These topics are covered in grad level statistics courses at certain universities.

A whirlwind tour of statistics. Questions are very tough. You have to rack your brains hard, to solve them. But well worth the effort.

Love the course, and its willingness to delve into the basics of the theory behind biostatistics. Wish there were more like it.

I liked the course. Need good maths skills. Would prefer to have a review/HW after each lecture than at the end of each week.

I think this a good course for practicing concepts, but the quizzes are not quite appropriate for a beginner's course.