31 août 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.
16 oct. 2020
An excellent course with some good hands on exercises in both R and excel. Not for the faint of heart mathematically speaking, assumes a competent understanding of statistics and probability going in
par Leandro G G•
22 oct. 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.
par Philip M•
29 mai 2020
Found the pace of the course to be a little uneven - sometimes jumps from basic introductions (good) to somewhat advanced concepts rather quickly. The sound quality was also a bit uneven, but improved with the later videos. Please wear dark clothing so that writing on the see-through board is readable - again, this improved with later videos.
Biggest suggestion for improvement is to provide downloadable lecture notes - having to take notes while the lecture is in process is distracting, and takes us back to the bad old days of "talk and chalk".
All of that said, the class was a very useful introduction, even though the application I have in mind requires discrete Bayes rather than continuous. I will be taking a look at the second course in this series.
par Carmen R•
9 avr. 2020
This is was a really difficult course. I took a basic statistics course in college but was not prepared for the calculus and the theoretical way this course was explained. If you are looking for a stats course that explains through real-world examples rather than theory - this ain't it. The only reason I gave it 2 and not a 1 star is because I can assume that those with a deeper statistical background would probably not face the challenges I did.
par Siddhant R•
20 juin 2020
The course is more of remembering rather than understanding. Many of the formulas and distributions are used without proper derivations. I was determined to complete the second course of this series, but now I don't think I would.
30 oct. 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.
par Lukman A S•
4 janv. 2020
The course only gives a lot of equations and formulas without explaining why this process should be done
par Jayant G•
11 janv. 2018
I had a great experience. It was lot more in-depth than I originally anticipated. In the tech world, Machine Learning is a buzz word and Bayesian based algorithms / models are the key and this introduces one to the fundamentals of Bayesian statistics. I was totally hooked on to this and the quizzes with real world examples really helped understand and apply the concepts. This course definitely requires maths background to be able to complete. Course provides lot of helpful materials and a pace that can be adopted based on your time and ability. Really looking forward for another deep dive in the near future.
par Paolo P•
4 févr. 2022
The course is well organized and quality. The topics are, for a number of distributions (bernoulli, binomial, normal), how to compute posterior from prior. All lectures are organized similarly to each other: to introduce a measure, the lecturer calculates it assuming starting distributions. Prerequisites are a minimal knowledge of how to calculate derivatives and integrals, so not advanced knowledge. The tests are simple but at the same time useful to consolidate the concepts introduced in the lectures. I recommend this course.
par Gary S•
19 déc. 2016
Great intro to Bayesian Statistics. The math gets complex but the professor illustrates with examples to help with understanding. The exercises are generally similar to the examples in the lectures and honestly not as hard as they could've been. The course is only 4 weeks and moves pretty fast. Although I scored well, I may take the course again to help make sure all the details and concepts fully sank in.
I'm hungry for a deeper dive into the topic. I hope there is a follow up course in the future.
par Anupam K•
16 mars 2018
Extremely useful course. The way concepts are taught is amazing. However, if you are like me, you will have problems following the lectures at the speed at which the professor proceeds. It's a minor 'subjective' issue. The second issue is that sometimes, the equations in the quizzes may appear in the form of "cryptic codes", for the lack of better words, and you'll know it if you face it. A change of browser solves the problem, for me a shift from Chrome to Safari did the trick! Hope this helps.
15 févr. 2018
A good introduction to the concepts conveyed by revealing the equations and expressions on a whiteboard. Minimal work with data and programming - much less of this than other Coursera classes on the same topics. Also unlike other Coursera classes on the same topic, the quiz answers/hints are useful and contain the relevant equations or R commands - not merely "correct" or "you should not have chosen this answer." I found this very helpful for self learning and confirming solution approach.
par Francesco B•
18 févr. 2020
Good introduction to the Bayesian approach to inference.
As an introduction, it doesn't go very deep on some interesting arguments and it leaves out Hierarchical Modeling and estimations through Monte Carlo Markov Chain, but it would have been unfeasible in such a short time.
Finally, I would like to point out that mathematical strictness doesn't mean that the course is too technical: you have just to go through some calculations and review some concepts in order to fully understand them.
par Melvyn B•
2 juin 2017
Professor Herbert Lee is world-class. The masterful and thoroughly outstanding presentation, organization and content of this activity are among the best of the best in any subject at any institution, whether on campus or otherwise -- more remarkably so for any senior undergraduate to graduate level mathematics activity, and most especially so in the broad field of Bayesian analysis. In summary: Extremely well-done and hats off to Professor Lee. I am thoroughly impressed.
par Jeff N•
30 mars 2017
As a long time frequentist, I occasionally run into problems that are very awkward to fit into the frequentist paradigm. I was aware at a high level that the Bayesian approach could be applied more naturally. Unfortunately, I was unable to "get it" simply be reading a book on the subject. This course made it very approachable. Professor Lee showed us the difficult math (tough integrals) behind it and how we can apply the results of that math in Excel or R
par Rob H•
17 avr. 2020
Really enjoyed the course. Coming from an engineering background but little statistics study for 15 years, this course provided a great explanation of the concepts and terminology with really good quizzes and and an introduction to R. There are still some terms I have seen elsewhere that weren't covered, but it may well be that they aren't specifically related to Bayesian Statistics, or were more advanced. I look forward to taking the follow-on course.
par Johan D R P•
2 déc. 2019
This course has been highly useful to understand how hypothesis testing works, starting from experimental design using prior distributions and assumptions to posterior statistics based on data. In my college courses it was always assumed that the parameters for the distribution were fixed, so, having a way to correct them through the information hidden in the data allows to overcome those assumptions and have a clearer perspective of the data behavior.
par Фирсанова В И•
25 mars 2021
The course is great! I am a computational linguist without strong math background, however, there were no problems in completing this course. The course is provided with supplementary materials that really helped me to fill my gaps in math. The course, however, is quite challenging (well, for me it was), and I had a great fun trying to complete some quizes several times. I hope that soon I will be able to implement my knowledge on real tasks.
par Georgy M•
10 janv. 2019
I found the course very well made and beautifully presented. The material is systematic, the more advanced topics based on the previously learned information without gaps and any need to study additional sources. The examples and the tests provide additional insights. Thank you, prof. Herbert Lee, for this great course!
Was able to do the course with Python instead of R, though it got a bit complicated on the last topic (regression).
par David H•
16 déc. 2021
Great course! I'm an average person on maths and I can say this course is challenging but not overwhelming; you'll be just fine. It may require some basic previous knowledge of R if you want to work the problems using it, but having all excercises done in Excel as wells makes it really easy. So even if you have no idea of R, you still can use Excel instead. I'll take the 2nd course of this series just becuase I liked this one a lot
par Vasilios D•
28 août 2018
This course strikes a perfect balance between not being too simple or too slow on one hand, and offering an easily accessible introduction to many central topic of Bayesian statistics on the other.
I think that good knowledge of basic probability theory and one-variable calculus is necessary for getting the maximum out of this course. This, however, is strictly due to the probabilistic underpinnings of the Bayesian theory.
22 sept. 2019
I really enjoyed working through this course. It is a great introduction to Bayesian statistics. People with a little probability and statistics background can easily follow this course. I personally prefer to have more assignments for this course to better learn the concepts. Professor Lee is a great instructor, and he speaks slowly. The length of each video is short, and I like it a lot because you can finish it quickly.
par Li Z•
25 nov. 2017
A very well-organized course. Not a hard one, but one with sufficient quizzes to make sure you understand every concept by solving problems.
Another thing I like about this course, is that I had to actively write a lot of codes in Python and Matlab when doing the exercises(due to my familiarity with these two), although the course teaches a little bit R and Excel programming. This is a very effective way of teaching.
par Miguel A M•
4 janv. 2022
excellent course. I would suggest to add some more references/suggested readings and add one ' blackboard' lecture on the regression part (currently the course jumps into R/Excell with no theory given and the supplement material does not give enough infirmation to code your own functions). Nevertheless, the feedbacks provided at the exams are amazing and i managaed to get all the information I wanted from there.
par Giuseppe F•
22 août 2019
great course for those who have an understanding of the frequentist approach and would like to dip their toes in the bayesian approach. pace is right and the content is interesting throughout. Given the basic math requirements, many derivations are omitted (especially towards the end of the course, which might feel a bit rushed) but I feel the course gives the tools to explore should one want to fill the gaps in.
par Davide V•
20 janv. 2017
Short but sweet. This course is a good introduction to the subject. I particularly liked the instructor and the design of the tests, which are really complementary to the learning material and are really helpful to put in practice the somewhat abstract theory. The supplementary material is also well done. It would be nice to have a course book to follow though as referring to videos is not always easy.