Sep 01, 2017
Good intro to Bayesian Statistics. Covers the basic concepts. Workload is reasonable and quizzes/exercises are helpful. Could include more exercises and additional backgroung/future reading materials.
Jun 27, 2018
Great course. The content moves at a nice pace and the videos are really good to follow. The Quizzes are also set at a good level. You can't pass this course unless you have understood the material.
创建者 Joseph R R•
Oct 11, 2016
Liked the course, but it was a little easy (took four days total to do the material for the whole course). Many questions were left unanswered (such as how dependent the credibility intervals are on the choice of prior distribution and the assumed distribution of the data), and it didn't touch on later topics that are interesting (MCMC sampling). Again, good beginning course, but I was looking for more in depth study.
创建者 Suyash C•
Dec 24, 2017
Plus Points of the course -
It starts with a context of where and why bayesian statistics comes into play. Good real world examples and questions are posed to drive home this point at the start of the course.
Where it could have been more helpful -
1) Somewhere in between the course gets lost in math expressions and distributions drifting away from real world implications. This would be ok for someone looking for pure math/stats. However it would become less relevant for someone coming from data science/business side. More real world use cases could have been there. (2) Better guidance on which other streams of data science/business can have application of this knowledge would be helpful (3) More comprehensive set of resources (pdf ones) would be great
创建者 Jens L R•
Jan 31, 2017
It was pretty intuitive and easy to follow the first couple of weeks, but then the assumed knowledge of beta and gamma distributions and their frequentist usage, stood in the way of me fully grasping the Bayesian part of it. In the end I just copied the examples from the lectures and passed the tests ... without really getting it.
创建者 Pranav H•
Jul 02, 2018
The course could have given more information on tiny details which can confuse people during the exercises. But overall a good learning experience
创建者 Jesús R S•
Jul 19, 2017
Good course as an introduction to bayesian statistics if you want to pursue more advanced courses in the field or to get some practise working with distributions under the bayesian framework.
创建者 Yuzhong W•
Oct 03, 2016
The lectures from week 1 to week 3 are nice and useful to me, but I think there should be more details about the content in week 4. For example, I think the lecture about the Jeffreys prior skipped many things and I did not understand this concept very well.
创建者 Ken M•
May 01, 2019
It would have been great if more graphs had been provided, for easier visualization of the e.g. distributions, or concepts.
创建者 Tawan S•
Jun 03, 2019
For some derivations, the explanations are too sparse.
创建者 yogi t c•
Jun 22, 2019
I don't have background in math and statistics, in the first week of the lecture i can catch up with the lesson, but coming into week 3 and 4 it's really hard to me to understand what's happening, since the lecture / videos only talking about the formulas and only taught us how to use the formula. Actually for person like me who want to know Bayesian Statistics application in the real world and also fundamentals of it it's quite not recommended to took this lecture, honestly. However in the general understanding this lecture quite can help me how Bayesian thinking works what is the connection between likelihood, prior, how to choose prior, etc.
创建者 Victor D•
Jul 09, 2019
Very informative as an introduction to concepts, but nowhere near the deep dive I'm now interested in taking.
创建者 Max H•
Jul 14, 2019
It would be much better if there was a more sufficient introduction to the various distributions used in the course.
Jul 24, 2019
It would be better to add more explain about those equations and connect the math stuffs with the real world samples
创建者 Tianchi L•
Aug 15, 2019
-1 star: Some discussions and derivations do not have adequate context and background. I expected more thorough explanation on concepts and more advanced topics. There are also a few minor typos that confused me. It is only a helpful introductory level course on Bayesian without depth.
-1 star: quizzes are not challenging enough and they only require plugging in numbers into equation. Not a good way to study
创建者 Binu M D•
Sep 21, 2019
Too much theoretical than practical applications. No need to give both R and Excel videos.
创建者 Leandro G G•
Oct 22, 2019
This course provides a good overview to Bayesian statistics, but a larger dose of explanations of would be very useful. Mr Lee discusses, in the beginning, the differences between frequentist and bayesian paradigm. I feel that this would be beneficial in the other parts of the course, too. I feel that many of the lectures simply go too fast. After lectures full of Math, it would be useful to present lectures analyzing what had just been taught, in order to better grasp the content. And in general, this happens through the whole course - most lectures are basically math, without much time for grasping the intuition and underlying logic. For example: in the final part, under linear regression, it might be be difficult to grasp what a bayesian predictive interval means. All in all, I recommend this MOOC, but you might find hard to fully grasp it.
创建者 Jane B•
Jul 31, 2018
There should be more focus on understanding the equations. The R and excel videos were incredibly blurry.
创建者 Martin E•
Apr 13, 2017
I get lost a bit too often.
The teacher sometimes explains easy concepts and omits the difficult ones (e.g. exponential distribution is explained as "for example if you are waiting for a bus that comes every ten minutes" and then he tells you how to compute expected value and moves on, but he does not say WHAT IT MEANS - is it the probability that I will meet an oncoming bus? is it probability of waiting ten minutes for the bus? is it the average waiting time? is it average number of buses that come every hour? - but there is detailed explanation of what A squared means in lesson two (!))
The teacher often makes me confused as to where he got the numbers he is plugging in the formula or what answer the formula gives.
But I take it as a challenge and I intend to finish the course despite all of that. Sometimes it is fun to decipher the mystic equations. And maybe it is me, maybe I was not born to be a statistician. Maybe there are people that find this stuff easy and understand it right away.
I really like the quizes. They are HARD.
One last thing: Wearing white shirt and using white marker makes it impossible to read what he writes. But I take it is part of the challenge ;-)
创建者 Fabian K•
May 01, 2017
Not very much in depth and does not offer complete lecture notes, which are necessary for answering the quizzes...
创建者 Jorge P•
Feb 02, 2017
Some matters were just given formulas and there was a lack of practice. The course should cover less materials or be longer to be effective in teaching.
创建者 Mehrdad P•
Jul 04, 2019
it was an okay course, I liked that they used R occasionally in the course, but I did not like how the concepts were discussed
创建者 Patrick K W•
Jul 28, 2019
It's alright because it gives you an overview of what is covered in a Bayesian Stats class, but the material is presented quite poorly and I had to do a lot of second hand reading to answer the questions. It is not particularly enlightening even and the formulas are presented without proper grounding, context, and intuition. I can recommend this only for dedicated self-studiers who already have some sort of grounding in Bayesian reasoning.
创建者 Benjamin H•
Jan 05, 2019
I was baffled after the first lesson. There is no explanation or answers given.
创建者 Voo T V•
Jan 29, 2017
I have studied some Bayesian Statistics before. I feel like the materials itself is not sufficient for entry level, and will actually confuse some of the learners. Anyway, this is just my two cents. :)
Oct 30, 2019
I was hoping to get more intuition on bayesian statistics, but I couldn't. Hence, I think I am gonna forget what I have learned in a very very short time.