返回到 Bayesian Statistics: From Concept to Data Analysis

4.6

星

2,127 個評分

•

559 條評論

This course introduces the Bayesian approach to statistics, starting with the concept of probability and moving to the analysis of data. We will learn about the philosophy of the Bayesian approach as well as how to implement it for common types of data. We will compare the Bayesian approach to the more commonly-taught Frequentist approach, and see some of the benefits of the Bayesian approach. In particular, the Bayesian approach allows for better accounting of uncertainty, results that have more intuitive and interpretable meaning, and more explicit statements of assumptions. This course combines lecture videos, computer demonstrations, readings, exercises, and discussion boards to create an active learning experience. For computing, you have the choice of using Microsoft Excel or the open-source, freely available statistical package R, with equivalent content for both options. The lectures provide some of the basic mathematical development as well as explanations of philosophy and interpretation. Completion of this course will give you an understanding of the concepts of the Bayesian approach, understanding the key differences between Bayesian and Frequentist approaches, and the ability to do basic data analyses....

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.

篩選依據：

創建者 Jonathan

•Jul 02, 2018

So, I really wanted to LOVE this class, but instead I found that I merely liked it, and want to use this review as a way to explain why. WHAT I LIKE ABOUT THE CLASS: The material is sufficient for the topic at hand, and is structured in an appropriate way. If you work through everything you'll have a decent grasp of exactly what the class is meant to be about. It's also pretty well paced. WHAT I DIDN'T LIKE ABOUT THE CLASS: Dr. Lee usually rushes through or skips discussions what concepts mean before formalizing them mathematically. As a result it's very easy to make progress through the class without a good feeling that you actually "get" what Bayesian statistics is really about. Too many of these videos are him chopping wood through the mathematical jingo, when the material DESPERATELY needed a 3-5 minute introductory video about what concepts actually mean or how to think about them. I remember telling my girlfriend during the middle of the class that I found it frustrating because I was progressing through it quickly, and getting the quizzes right, but lacked a good intuition for how to think about Bayesian statistics. So Dr. Lee......work on those presentation skills! Think deeply about how to communicate the essentials of the concepts in each lesson, and THEN start pounding away on the whiteboard!

創建者 DM C

•Jun 11, 2018

I don't find that the lectures do a good job of relating the material to real world usage. To much focus on equations and too little on the why.

創建者 Megan G

•Jul 26, 2017

I felt like I just did a lot of calculations. The course was better in the beginning, as I felt the professor actually explained what and why were were doing what we were doing. By the middle of the course, however, I felt that the professor just jotted down equations and went really quickly. I don't actually understand why I was doing the calculations that I was doing.

創建者 DOGA T

•Sep 12, 2019

The instructor doesn't do a good job at teaching. He throws so many formulas at you without explaining any of them. The course is purely based on memorization not understanding the concepts. I have been using other online classes to be able to understand this class.

創建者 Emine Ç Ö

•May 23, 2017

Almost no intuition is given. I really got bored while watching the formulas to be written on the board without giving real meaning behind them. I would not have taken this course I was aware of these.

創建者 Scott S

•Oct 28, 2018

This course gives an introduction to the theoretical basics of Bayesian statistics. Before taking this class, I had a very confused view of the whole Frequentist vs Bayesian "debate". I understand now that Bayesian statistics is really about attaching uncertainties to beliefs and producing a clear definition of this uncertainty (especially through the notion of credible intervals).

The course really focusses on theory. I recommend knowing a bit of basic stats concepts before taking the class, such as Bayes' Theorem, basic discrete and continuous distributions, and confidence intervals. If you are not experienced with these, be aware that you will likely need to read-up on them throughout the course. R is used, but the usage is so simple that you should not shy away due to a lack of R experience.

I really have no complaints about the course. After completing it, you should understand the differences between Bayesian and Frequentist approaches. You will also understand a lot of terminology that gets thrown around in data science these days (priors, posteriors, credible intervals).

創建者 Iryna

•Feb 16, 2017

If you already know everything about the topic and just forgot some little things or you are very strong in calculus, this may be a nice refresher. Otherwise, not very useful. Really dense and little explanation. I liked the Youtube MIT course on Probability (it includes Bayesian Statistics) much more, since it has good explanation of the concepts.

創建者 Sathish R

•May 19, 2018

Herbert Lee is teaching by seeing books and write lots of equations doesn't explain how theory and equations related to real world applications. Its more like class room lessons , not like something that can be applied to real world scenarios.

創建者 Benjamin H

•Jan 05, 2019

I was baffled after the first lesson. There is no explanation or answers given.

創建者 Georgi S

•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.

創建者 Justin W

•Oct 03, 2018

This was a fantastic introduction to Bayesian statistics. Professor Lee is an excellent lecturer, with a comfortable, almost conversational style that I found easy to follow and stay focused on. The course itself is very well organized, introducing key concepts and then immediately providing examples that helped me internalize the concepts they pertained to. Quizzes were low pressure, straightforward applications of the lectures that served the purpose of allowing me to immediately apply what I had just learned.

創建者 Yifei H

•Dec 22, 2018

Very concise and helpful for an intro to Bayesian statistics. Good level of difficulty to encourage learning. This well prepares further study of more advanced topics such as MCMC and more.

創建者 Asael B I

•Feb 28, 2019

a really good course!

though sometimes the questions in quizes aren't clear enough,or not explaind else where,and sometime you could miss the big picture.

could also be good if you could add some python scripts,and maybe more reading material about the topics.

創建者 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 ;-)

創建者 Carmen R

•Apr 09, 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.

創建者 German E G

•May 07, 2020

When doing the quiz for Lesson 2, Week 1, I first failed, then I used the hints provided to do the appropriate calculations, however the numbers I obtain are considered incorrect, and I cankot pass the quiz although I checked the calculations million times and I know I am correct. There should be a demonstration of how the correct answer is obtained, simpke hints are not enough. Although I was super excited about learning Bayesian statistics, now I am forced to quit as it looks I will never be able to complete the course. The course ended up being useless and frustrating. This is truly unfortunate

創建者 Jayant G

•Jan 11, 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.

創建者 Ezequiel L C

•Mar 21, 2020

A good MATHEMATICAL introduction to Bayesian Statistics. I read some of the negative reviews and they claim to have many formulas, well, that was exactly what I was looking because after watching some PyCon Videos about Bayesian Statistics I understood the code to solve the problem but not really why that code works or how.

This course may be frustrating for those with no prior introduction to Bayesian statistics, I recommend to take this course after seeing some videos from the Scipy, PyData and PyCon conferences regarding this topic.

創建者 Gary S

•Dec 19, 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.

創建者 Anupam K

•Mar 16, 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.

創建者 kpb

•Feb 15, 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.

創建者 Francesco B

•Feb 18, 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.

創建者 Melvyn B

•Jun 02, 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.

創建者 Jeff N

•Mar 30, 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

創建者 Rob H

•Apr 17, 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.