Вернуться к Bayesian Statistics: From Concept to Data Analysis

4.6

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Оценки: 2,153

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Рецензии: 566

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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автор: Johan D R P

•Dec 02, 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.

автор: Georgy M

•Jan 10, 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).

автор: Vasilios D

•Aug 28, 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.

автор: sara

•Sep 22, 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.

автор: Zhu L

•Nov 26, 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.

автор: Giuseppe F

•Aug 22, 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.

автор: Davide V

•Jan 21, 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.

автор: Sinkovics K

•May 01, 2020

This is a wonderful course in Statistics that I would highly recommend to everyone who wants to take a learning path into the world of Bayesian inference and refresh their knowledge of numerous statistics concepts involved. The lectures provide excellent in-detail explanations, and additional reading material fill in the gaps if some of the concepts or derivations weren't shown in the lecture in full.

автор: Michal K

•Oct 24, 2017

Excellent course. For such broad discipline I'm sure it was difficult to choose most important material to fit 4-week course, yet professor did it perfectly. I'd love to see this course in Python, but I guess I can't have everything ;) I'd also love see some examples of using probabilistic programming packages, like Stan or PyMC3 in more real-life problems - I would give 6/5 stars for it!

автор: Paulina S

•Mar 10, 2017

This is my first course on Coursera and I am delighted by the construction, how it was led by the instructor and what I learned. Quizzez are great, I spent on some quite a bit of time, but I feel they really checked if I understand the concepts and calculations. The questions during the video are also an excellent idea to check if you follow. All in all I am very happy I took this course!

автор: Kostyantyn T

•Aug 04, 2019

I really enjoyed this course, the videos are fairly short with focus on exercises and there is a nice narrative throughout the course. Sometimes I needed to watch videos again because explanations were too fast for me to follow in real time, but I definitely enjoyed presentation style of Prof. Herbert Lee. Will be following the course up with "Techniques and Models" to learn about MCMC.

автор: Albert A M H S

•Jun 29, 2017

Followed the course in order to fill a gap I had in statistics knowledge, as I'm very interested in machine learning - deep learning, and always came upon things as MLE without really knowing well what they were talking all about. Really a very good course to get an understanding! Well explained, though maybe you'll need to brush up your Algebra and Calculus a bit to be able to follow...

автор: MaoJie T

•Nov 20, 2019

It's a fantastic course, which guides me to know what is Bayesian statistics. Before joining this course, I try my best to learn Bayesian Statistics but it's failed. However, I really grasped some key points and knowledge of Bayesian Statistics and I will join the following course about Bayesian Statistics to get more. Thanks for the professor. I am appreciated for it.

автор: Matteo V

•Jun 26, 2017

Great course that introduces the fundamentals of Bayesian Statistics. Useful for becoming familiar enough with the ideas to use in basic analysis provided you have some experience with frequentist statistical methods. For my studies, this course allowed me access to the Bayesian statistical material that is often encountered in phylogenetic analysis in bioinformatics.

автор: Ian M

•Aug 16, 2019

I think this was a very helpful course, for me personally I learn better with "real" examples, so i think if there were more of those earlier on, that would have been more helpful. I also use Python, and would prefer to use Python, so it would be nice if there were instructions on that in addition to R/Excel. Spent a lot of time translating between R and Python.

автор: Shubham A

•Nov 11, 2017

I strongly recommend this course to those who are interested in learning theoretical concepts that build Machine Learning statistics especially Bayesian. The course content was well organized and the professor presented the concepts in a very engaging way. Relevant and appropriate examples and in-video quizzes were very helpful in understanding the theory.

автор: keyvan R

•Feb 09, 2020

This is a math course. It has good quality, it is rigorous and educational. It presents the mathematical framework of the Bayesian statistics. I like it when the math of the subject is explained well, as done in this course, rather than "I don't want to get in to the math", or "it is beyond the scope of this course", which you often see in online courses.

автор: Aditya M

•Feb 03, 2020

A great course to learn not only Bayesian Statistics, but covers statistics in general to a great degree. The best part is the exercise, which are almost perfect to learn the course material. After doing tens of MOOCs everywhere, I find this course unique in terms of pushing students to apply the concepts. I loved this course and enjoyed learning with it.

автор: Suleyman K

•Feb 12, 2020

This is one of the best online courses I took. This is coming from an ex-Professor who taught 13 years. The material basic and is brief, but to the point and very well organized and presented. Having some background in statistics helps as some important details are skimped. In a such a short time, I learned well the concept of about Bayesian statistics.

автор: Ron A

•Apr 12, 2017

Excellent course. Professor Lee did a first-rate job of giving the intuition for Bayesian methods and building the foundations for further study of the topic. The course is short and to the point, but that is a feature and not a bug. It will prepare you to take further courses in Bayesian statistics or to study the topic on your own. Highly recommended.

автор: Dennis L W

•Sep 17, 2016

Out of 15 online courses I have taken over the last 3 years, this is the best. Professor Lee presents rather difficult material in a clear, detailed, style. The lesson quizzes are remarkably useful; it seems real care has been taken in aligning the questions with the key points in the lectures, and in furthering one's understanding of the same.

автор: Mark S

•Feb 08, 2017

I found this course to be really useful. It did progress through the math a bit quickly for my liking, but it was paced very appropriately and the discussion forums were helpful. Excellent examples are contained and I loved how both R and Excel modules were leveraged. Looking forward to seeing more Bayesian courses on Coursera in the future.

автор: Haozhe ( X

•Apr 25, 2020

Great course for intro to Bayesian. Before deciding to learn bayesian, I expect to choose a course which could explain concept in a simple way but, at the same time, having enough practice. This course matches my need. After taking this course, I would recommend it to anyone who want to learn some bayesian for further machine learning studies.

автор: David D

•Feb 27, 2019

Really loved this course. I am relatively new to Statistics but very familiar with the rest of the mathematical tools used in this class (Integration, sets, etc). After finishing the class, I was immediately able to apply Bayesian Inference to my job. Things were explained well, and made sense after re-watching once or twice. Excellent course!

автор: Polano M

•Mar 11, 2018

Great introduction to Bayesian Statistics.

Prof. Lee uses the right approach with a theoretical introduction that helps to graps the concepts with a right balance of math and intuition. This was my first exposure to the bayesian approach to statistics and after this course I want to learn more, both on the pratical and the theoretical side.

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