LC
12 mars 2022
The course is extremely structured. It is a fantastic "boot camp" for students who would like to learn statistics, opening the door of future data analysis.
AC
27 juil. 2018
Please, make a reboot of this course with some improvements in the material.\n\nMore examples\n\nMore solved exercises.\n\nMore homework.
par Michael K
•1 oct. 2016
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.
par Paulina J
•16 avr. 2017
The course is a bit chaotic.
par Andre T d C
•28 juil. 2018
Please, make a reboot of this course with some improvements in the material.
More examples
More solved exercises.
More homework.
par Burak H
•20 juin 2017
The last week was a bit rushed and unclear.
par Huynh L D
•2 juil. 2016
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.
par Lu-Wei C
•12 mars 2022
The course is extremely structured. It is a fantastic "boot camp" for students who would like to learn statistics, opening the door of future data analysis.
par N N
•20 mai 2020
Fantastic introduction for statistics. Some quiz questions do not contain all the information required to answer them.
par Vivek
•8 sept. 2018
Outstanding professor -- more rigorous than other similar classes. Just the right degree of challenge in the quizzes.
par CHEN W O
•16 août 2019
Thank you Dr Brian for the in-depth teaching from fundamental to application in real-world healthcare research
par Joseph L
•31 août 2018
Excellent! A great leading course to build up one with clear background to explore the data science.
par Bekishev R
•1 nov. 2020
Great course, although some topics require additional knowledge
par Vasin S
•25 mai 2021
This is amazing course for reviewing categorical statistics.
par Santhosh K K
•8 mai 2020
it is very useful to improve my knowledge
par Nagaraju K
•8 juil. 2020
It's very much informative
par BOORA V
•8 mai 2020
5
par Luis G G E
•1 oct. 2018
Goode videos and teacher. The videos are old and it will be perfect to refresh them and make it more interactive.
par Sven S
•1 mai 2021
Quite a different course as compared to Mathematical Biostatistics Boot Camp 1. Many different tests for contigency tables are presented, however, without the proper mathematical derivation. It is just: Here is another test for this - more like a cookbook. Videos and quizzes are sometimes incoherent as if being put together from different sources without much care.
par Edit T
•25 sept. 2016
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.
par Alexander K
•19 juin 2021
The instructor does a poor job of conveying information and seems to spend most of his time demonstrating that he understands the subject well. He would do better to focus on core concepts instead of talking through tangent details and implications of them. He expects the learner to visualize everything he is saying, which is mostly impossible without any prior understanding of the subject (which ... the learner does not have or they would not be taking this course.)
par Gu F
•5 mars 2017
"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.
par Konstantinos P
•8 mai 2022
From the practicioner's point mostly, not really mathematical