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.

Please, make a reboot of this course with some improvements in the material.

More examples

More solved exercises.

More homework.

Outstanding professor -- more rigorous than other similar classes. Just the right degree of challenge in the quizzes.

Thank you Dr Brian for the in-depth teaching from fundamental to application in real-world healthcare research

Excellent! A great leading course to build up one with clear background to explore the data science.

Thankful that a course like this exists, as most MOOCs are quite basic. And thanks to Coursera for running the courses even though attendance seems to be low (darn, that pesky calculus pre-requisite). Lecture quality is varied--some quite good (such as the lectures in Boot Camp I) and others seem like he hadn't looked at his notes for a long time. It's great to hear a stats professor talk about the strengths and weaknesses of many approaches. It complements a mathematical statistics book quite well. It would have been nice to have had some problems that were more challenging. Overall, while the Johns Hopkins Data Science MOOCs are pretty good, they are a bit more basic than what's available through MIT and Stanford.

Goode videos and teacher. The videos are old and it will be perfect to refresh them and make it more interactive.

It was very challenging. It would be better if a couple of practice exercises were put after the relevant lecture videos and not just after the weekly lecture videos.

The last week was a bit rushed and unclear.

The course is a bit chaotic.

"much better than the data science with R concentration provided also by John Hopkins. This course has concrete examples, and the lecturer doesn't treat his audience like the first-grade kids." I take this back. The lecturer doesn't know what he's taking about and what he is gonna talk about for at least half of the time.