# 学生对 加州大学圣克鲁兹分校 提供的 Bayesian Statistics: From Concept to Data Analysis 的评价和反馈

4.6
2,537 个评分
667 条评论

## 课程概述

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....

## 热门审阅

GS
Aug 31, 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.

JB
Oct 16, 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

## 26 - Bayesian Statistics: From Concept to Data Analysis 的 50 个评论（共 654 个）

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.

Jan 4, 2020

The course only gives a lot of equations and formulas without explaining why this process should be done

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.

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.

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.

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.

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.

Jun 2, 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.

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

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.

Dec 2, 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.

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).

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.

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.

Nov 25, 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.

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.

Jan 20, 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.

May 1, 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.

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!

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!

Aug 3, 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.

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...

Nov 19, 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.

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.

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.