Bayesian Statistics: From Concept to Data Analysis に戻る

4.6

1,530件の評価

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406件のレビュー

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.

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by 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).

by 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!

by Timo K

•Mar 13, 2019

Very good overview to the area. Efficient and clear lectures - emphasis on the quizzes that required just a proper amount of focus and time from my personal point of view.

by 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.

by 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.

by 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.

by 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.

by Aditya D

•Jul 17, 2019

The course itself is well structured and covers a lot of material.

There are points in the course where the order of reading material and videos needs to be switched. Also, it would help to update some videos with a little more explanation. It appears as if the lecturer is skipping steps.

by Jose M R F

•Jul 14, 2019

Very well explained. Lectures are given in a very nice way as the professor writes. Exercises and quizzes are very well done.

by 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.

by Lee V

•Jul 12, 2019

The lectures were good but rattled-along at quite a speed, even with pausing and "rewinding" I still found it difficult to follow, esp towards the end. I think a short explanation at the start of the video explaining what was going to be covered, what its role was and where it fitted into the big picture might have helped (background is UK maths A-level 45yrs ago and a career on the fringes of science)

by Victor D

•Jul 09, 2019

Very informative as an introduction to concepts, but nowhere near the deep dive I'm now interested in taking.

by Darjo

•Jul 09, 2019

Most of the stuff is explained quite well and I managed to understand it. I am quite satisfied overall and I am glad I completed the course. The exercises, however, were somewhat boring. I wish there were some optional exercises that are more challenging and require you to solve more realistic problems. I also wish there were more additional materials with more in depth theory and examples of how they use these concepts for solving problems that are actually of some use. I feel like these improvements would make the course much more interesting and engaging.

by Yidan Z

•Jul 07, 2019

very helpful for learning Bayesian on your own.

by Arasch M

•Jul 07, 2019

The course helps in developing a quite sound grasp of the Bayesian approach to the world. The assignments are feasible and help in gaining a deeper understanding of each subject. However there is a caveat: You definitely need to review your math skills before starting this course (esp. calculus, arithmetics and combinatorics) otherwise you'll be struggling with the particularities !

by 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

by Robert G

•Jul 03, 2019

Overall great course, the last part (linear regression) seems somewhat disconnected from the rest of the course.

by Artem B

•Jul 03, 2019

Great course with a lot of simple, but illustrative exercises. It may be useful to have some basic prior knowledge of econometrics/statistics

by Deborah S

•Jun 28, 2019

Didatic and complete course! I recomend 100%

by 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.

by Felix S

•Jun 19, 2019

Great intro to Bayesian Statistics if you have some stats background in the frequentist domain.

by Piotr G

•Jun 17, 2019

Very high quality course. Could use some modifications (e.g. few more applied examples for regression using specific priors, MCMC etc.) and implementing some simple metaphors to introduce some topics before jumping into the maths.

by Derek H

•Jun 12, 2019

Good to learn or re-learn the basics of statistic and probability, and as a foundation for learning maximum likelihood methods (which are much more useful later on). The material is digestible, to the point, and the quizzes are helpful in checking your understanding and information retention.

by Stephen S

•Jun 09, 2019

I thoroughly enjoyed this course as I found it to have a good mix of background and application.

by Juan J O O

•Jun 07, 2019

Excellent course. I learned late to use the note clipboard to take notes. At times the video lectures are hard to follow because the concepts are not easy. I had to watch the video lectures several times to fully grasp the concepts.