# 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
2,153 个评分
566 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

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.

##### JH

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.

## 51 - Bayesian Statistics: From Concept to Data Analysis 的 75 个评论（共 555 个）

Sep 19, 2017

An excellent introduction to Bayesian Analysis with some practical examples and applications. The lessons serve as a solid foundation towards understanding the philosophical underpinnings of the Bayesian approach to decision analysis under uncertainty. Thanks to Prof Herbert Lee for making the easy to understand without sacrificing rigour.

Mar 07, 2018

This is a great course! Much better (and cheaper) than the course I took in grad school. Full of practical knowledge, and isn't too overwhelming on the mathematics/statistical theory. It's just right. Good for anyone interested in Bayesian statistics, though some background with probability distributions may help climb the learning curve.

Dec 27, 2016

I really enjoyed this course. The lectures were short and clearly explained, and particularly highlighted why Bayesian statistics is different and what is useful about it. I would have like a bit more walk-through on some of the derivations in weeks 3 and 4. More R exercises and further resource recommendations would have been useful.

Sep 11, 2017

Outstanding course to understand Bayesian statistics. Teacher is very pedagogical and the course delivery with equations written on the transparent board make everything easy to follow.

As an area for development, I would have like more information on Bayesian linear regression in week 4, through background lecture or dedicated video.

Jan 21, 2017

Very clear lectures masterfully delivered by prof. Lee. The quizzes are good, if somewhat on the easy side. Don't be discouraged by the choice of R as the tool for assignments. R is flawed as a programming language, but you won't need to do any programming, only one-liners to evaluate various statistical functions and plot results.

Nov 21, 2016

This is the first online course I have ever taken so I don't have anything to compare it to, but this course was excellent! The lectures and materials were very clear and I will be adopting some of Prof. Lee's approach into my own teaching practice. The bar has been set very high for any future online courses that I will take!

Aug 11, 2017

Herbert Lee's Tests are fun (Best!) to learn during the test! Lectures are succinct; Format of writing on the glass towards you and then flipped was right & original. Went on to try Kaggle problems independently. For usable feedback need tiny bit more on Poisson, Gamma, non conjugate intuitively & darker shirts as background.

Sep 11, 2016

Good course. This course is quite challenging for people who don't major in math or physics. However, it isn't so difficult to understand as the post half of this course has a lot in common. In my experience, understanding the concept of priors and posterior estimation is the core of this course. Have fun learning this course.

Mar 01, 2017

It's a great course, there is a lot of information and it might seem at times overwhelming, but it's organized nicely and prof. Lee has a very comfortable time explaining all the concepts. A few more examples would have made this course easier, but that does not mean it would have been better. It's as good as it gets

May 25, 2020

This is a great course for anyone with no prior knowledge of Bayesian statistics. The instructor did a great job explaining the concepts and provided good examples. I also liked the quizzes and activities in R/Excel. I learned a lot from this course! I plan to take a few more courses in Bayesian stats.

Oct 30, 2017

Prof Lee derived the formulas in an upbeat way, which helped me learn. I'd suggest putting the actual lectures into pdf for later reference, like is done for supplementary material. Homework assignments were challenging and educational. You might suggest a review of prob distributions as pre-requisite.

Aug 03, 2018

Fantastic first course. The only concern I have is with the software choices. I have neither R nor Excel, but was able to easily use google Sheets. It might be worth mentioning to students that this is an option. There is even a stats package that claims parity with one of the listed packages for excel.

Oct 05, 2016

Very nice course that in my opinion nicely fits between Bolstad and Gelman in difficulty (talking in popular Bayesian Data Analysis books). Herbert Lee does a very good job at building one's intuition and understanding in the general Bayesian inference. Good starting point for moving on with Bayes.

Apr 22, 2018

Amazing. Simple, fast, dense, very well taught. I loved the professor, his commentaries and way to explain the contents. Thought the exercises were OK, maybe simpler than I taught but the comments in them helped me a lot to understand the topics. 10/10, a new and better way to teach! Very useful.

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.

Jun 30, 2017

A well organized course, learned important concepts in statistics and probability that will definitely help anyone wanting to specialize in machine learning or take up data science. Clear and concise explanation of theory focusing on application that is adequately tested in the exams.

Apr 29, 2020

An excellent course on the basics of Bayesian approach to statistics. It has excellent explanations, from the concept to applications and allows gaining understanding both on the basic underlying ideas, as well as a deeper insight on Bayesian methodologies. I definitely recommend it!

Dec 13, 2019

I've reviewed probabilities and basic Bayesian methods in this course. The quizzes have good explanation and the additional reading materials are helpful. I'm learning the next course: Techniques and models, which is also great (except that we don't have free access to the quizzes).

Feb 24, 2018

As a primer to Bayesian Statistics, this course covers the basics at a brisk pace. No time is wasted in explaining the basics of Probability theory; which I have always found, at best, to be distracting in the other similar courses I have taken. Thank you, Herbert Lee and Coursera.

Apr 28, 2019

There are books and courses out there teaching you how to use machine learning tools to solve real problems. But there aren't so many like this starting from the Bayesian way. Besides, this is a good entry point for me to read the book "Pattern Recognition and Machine Learning".

Jan 09, 2017

Excellent introductory course to bayesian statistics. I'd like to thank Professor Lee, University of Santa Cruz, Coursera and all supporting staff for the opportunity. I'd enjoy if you provided intermediate and advanced courses on bayesian statistics that covers more topics.

Sep 23, 2017

I took this course due to my interest in machine learning and graphical models. I like the approach and execution. I recommend it for anyony interrested in statistical inference. Some topics require looking up external sources, like wikipedia, but it is not an issue.

Mar 18, 2017

This class is very much an intro, so if you're looking for advanced topics you it might not be challenging. But this is a really good intro. The lectures are good and the supplemented material is great. I wish there was more R, but I'm very happy with the class.

Jan 15, 2018

Interesting, challenging, informative, entertaining, Herbie Lee is an excellent presenter of a very well prepared introduction to what seems to be a more rational and coherent approach to extracting, understanding and evaluating quantative information from data

Jul 03, 2017

It's a great course to understand the fundamentals of the Bayesian Statistics. The easy quiz which meant not to deter the students could be improved a bit. For serious learning, reviewing the questions in honor sections and the supplemental materials is a must.