# 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
1,809 个评分
468 个审阅

## 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.

## 326 - Bayesian Statistics: From Concept to Data Analysis 的 350 个评论（共 457 个）

Sep 20, 2017

Very good introduction!

Nov 26, 2016

A good course but neither notes nor lectures were not in much details. But still it was worth my time. I strongly recommend it if you want a subtle introduction to Bayesian Statistics.

Jan 01, 2018

Very good intro to Bayesian statistics. I only rate 4/5 because the second week was disproportionately more difficult than the other three weeks.

Nov 22, 2016

As a grad student myself, I liked the way this course was presented in short video format and in only 4 weeks. Definitely there are much more to learn about Bayesian Statistics and one can go way deeper, but this course gives the required basic Bayesian knowledge to someone who wants to get familiar in a short time.

Jan 01, 2018

I can learn the concept, but need to understand the details well in other ways. eg. reading, or searching online

Aug 28, 2017

Very helpfull course. I will use the principles taugh for other topics like machine learning. Thans for sharing.

Sep 23, 2016

The course is a great introduction into Bayesian statistic analysis. I particularly liked the detailed explanations of where the parameter formulas came from. Also a great thing, in my opinion, was to write the explanations on the glass instead of just displaying the final results. It kind of provided a sense of interactivity and made the material more digestible for a person with not such a strong background in math. It greatly smoothed the learning curve for me and kept interested and motivated to finish the course. In the end the pace accelerated a bit but was still manageable. Four weeks seems a great duration for such a course - not becoming boring and tiring. Honors tests were quite easy, I'd prefer to have a little more challenge. Overall I'd recommend the course for everyone who wants a quick introduction into Bayesian statistics. It provides a solid background for further studies.

Apr 29, 2018

Excellent course, but the lack of the written notes is a big minus

Jul 30, 2017

The course helped me get started with Bayesian stats. This course is good if you have seem probability and stats (distributions, pdf, cdf etc.) and want to learn about the Bayesian interpretation. The course picks up pace from 3rd week and the final week seem a bit rushed. I thing more examples of explicit frequentist vs. bayesian interpretation will benefit the learners. Also, 4th week could really use some additional explanatory content. Thanks for this course, I learned something fun and useful! :)

Nov 11, 2017

Very Useful

It was little difficult to understand what the professor , but anyway it was good.

Sep 03, 2017

this is a very good introductory course on Bayesian Statistics. Thought you will not learn deep from this course, it will give you a good big picture.

Apr 17, 2017

I think that, besides lesson 11 and 12, everything was very well explained. I was a bit confused with lessons 11 and 12 since I am not new to econometrics. Perhaps I found it confusing the theory background related to the lessons themselves. Just my opinion, very good course.

Jul 09, 2017

This course provides a solid overview of simple Bayesian models and common distributions used in those models. It also provides an initial understanding of conjugate prior distributions and non-informative prior distributions. The R code used in this course is very simple; easy for a beginner, but perhaps a bit simple if you are already familiar with programming in R and doing commonplace frequentist statistical analyses (regressions, ANOVA, etc). Overall, this course is definitely worth taking if you are interested in Bayesian statistics and need a good place to start. There are quite a bit of videos and supplemental materials which allow for a broadened understanding of the materials. Thanks, Dr. Lee!!

Sep 24, 2016

A great introduction. I feel like I know a lot more about bayesian statistics now. But I do mostly feel like there is quite a bit I don't know, and while I passed, I feel like there is quite a bit more I need to do to really 'get it'. The professor recommended some books in a discussion forum and I'll be going through some of those next I am sure. I also feel, looking back, I should have had some additional math preparation before starting. The calculus was vaguely familiar but with the pace of the lectures, I felt occasionally lost. I would have found it helpful if there was a quick primer on calculus to know and review at the beginning of the course. All in all great course. Loved the presentation method.

Aug 28, 2017

I've always found stats kind of boring but, the material covered in this course is invaluable. Dr. Lee presents everything clearly and concisely.

Mar 07, 2017

It is a nice introductory course on Baysian s

Sep 19, 2017

Satisfied with the course in general. Good investment of my time!!

Feb 23, 2017

Good course, but in my opinion misses of lectures/pdf to ease understanding.

May 22, 2017

I feel like I have a much better understanding of Bayesian statistics after taking this course. I learned a lot, even though it didn't take very long to get through all of the class material. My only criticism is that the 4th week seems pretty scattered. It covers a lot of different topics in not a lot of detail. Ideally, this material should be broken up into 2 weeks and covered in greater depth.

Feb 07, 2018

This is a great course and I have learned a lot. The teacher is extremely knowledgeable and formulates things very clearly. However, this is really a math course. For me it was hard to stay motivated because the language of the course is mathematics, the teacher juggles with the concepts that my mind was still trying to process and absorb. I was able to finish all exercises, including the honors ones, but when I finished the week 3, I had to redo it completely again and buy a book on Bayesian statistics by John Kruschke which helped me immensely to rethink the basic concepts again. This course could be excellent if it included more reiterations of concepts, was explained in more general language, the pace was slower and most importantly included more practical applications. The typical statistical examples of coin flipping are fun, but too abstract. In the end, I want to know how I can apply Bayesian statistics. A lot of knowledge of mathematics was assumed and I had to look up a lot of concepts myself. The derivations sometimes also went too quick and supplementary materials were quite dense. I think this course is a perfect refresher course for someone who has mathematical background and has taken a Bayesian statistics course some time ago. But for the beginner with some mathematical background (I am familiar with the frequentist statistics, machine learning, calculus) it was too much of a challenge. If it were not a Coursera course, where I can rewind endlessly and work at my own pace, but a regular university course, there will be p=.9 that I would drop out, while my prior for dropping out would be p=.05

Jun 13, 2017

This was a great introduction to bayesian statistic. I have background in Computer Science and Engineering but I have not yet been introduced to Bayesian Statistics. The Quizzes were where the learning was happening for me. Personally I learn the best when I code things up. I wish they had also included coding examples in Python (which is what I used for the quizzes) since that is one on the most popular languages for data science.

Jul 02, 2018

Well explained and articulated. You can apply it straight to your work problems. I really enjoyed doing the course.

Feb 22, 2018

Good course , but contains some challenging material .Thank you Professor L

Nov 12, 2016

A very good course which enlights me the study of bayesian statistics, thank you.

Oct 27, 2017

Pretty good overview of Bayesian statistics.