# 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
2,362 个评分
619 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.

## 426 - Bayesian Statistics: From Concept to Data Analysis 的 450 个评论（共 608 个）

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.

Oct 08, 2019

The course is in general well structured. The professor used a lot of mathematical equations to explain the contents. I have no problem understanding them. Everything goes smoothly, until the last section: Bayesian Linear Regression (BLE). In the last section, the professor skipped all the mathematics aspects and rushed the content with R / Excel examples. This is not what I expected. Overall, I will rate the course 4 stars.

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)

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.

Jun 29, 2017

Very good course, I may have been at a bit of a disadvantage because I came from a behavioural sciences background rather than a full statistics or math background. It was interesting though, and I think I acquired the requisite skills to conduct a Bayesian analysis in future. However, at some points in the class it does become very formula heavy, which I did find tough to grasp at some points.

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 !

Sep 04, 2017

Excellent introduction to Bayesian statistics. More proofs would have been nice (perhaps an optional advanced material section?). The later half of the course increases quite a bit in difficulty and could use 1-2 more examples + applications. Professor did a great job and the quizzes thoroughly tested my knowledge. Overall, I would definitely recommend this course.

Jul 19, 2017

Great lectures. The explanation of each topic is extremely clear and avoids excessive mathematical burden. Lectures are short and concise. Quizzes or at least Module Honors could be a bit more challenging, though. It's a great course, anyway. I'll be looking forward to enroll in the next course of the sequence, entitled "Bayesian Statistics: Techniques and Models".

Nov 30, 2016

Very good course, with less emphasis in the videos and more on exercises and critical thinking, the way I like and learn the best. I particularly liked that the lecturer writes on a transparent vertical surface standing between him and the camera, very convenient. For those not so familiar with mathematics, this might hurt a bit but the payoff is super positive.

Jul 30, 2019

Really enjoyed the course! Thank you. I would have given a higher rating if: 1) the instructor had spend more time on the intuition underpinning different derivations, 2) provided more context, 3) discussed more examples from practice. However, I am definitely continuing on to "Bayesian Statistics: Techniques and Models"! Thank you once more, team UCSC!

Apr 13, 2020

I enjoyed the course. I was looking for a way to improve my knowledge of statistics and bayesian maths. I mainly used excel for the calculations. I would appreciate an additional tutorial on plotting mass and PDF function etc against Theta and real world variables to explore impact of parameters on distribution shape on prior and posterior results.

May 27, 2020

Exceptionally interesting class. Professor was knowledgeable and engaging. The key insight was to approach the "quizzes" as homework, a learning process. Some of the lectures were of variable audiovisual quality and the pacing of some sections was not uniform, but overall, a triumph. More from this professor please! Fun.

Mar 02, 2018

Herbert Lee is great at explaining the mathematics behind Bayesian statistics. However, I think the course can improve greatly by also focusing more on context and the intuition behind the mathematics. I often found that I was able to pass all quizzes, while I did not 100% understand why I was doing what I was doing.

Jun 12, 2020

Great discussion into the ideas. The quizzes are relevant to the lectures as well and pretty straightforward, you don't need to go outside of the lecture itself to be able to do the quizzes. the only thing would be it would be good if the lectures notes were in print as Prof. Lee's writing is sometimes hard to read.

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.

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.

Jan 26, 2019

I liked it as introduction to Baysian statistics. With the material provided it was quite easily possible to answer the questions. I would have preferred that the videos of the course contained all the material and that it would not have been required to have read through material.

Oct 01, 2019

The course was well explained and there were several exercises pushing the learner to understand the logic behind the mathematical concept. I think it is a suitable class for people with already a certain level of statistics knowledge, even though all concepts are well explained.

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.

Jun 01, 2020

The course was very well designed, I got to learn about a lot of new things in statistics that I had to understand. But for a Data Analyst working on large data sets and primarily working on ML this course is far too basic. Also, some of the concepts can be explained better.

Feb 05, 2019

I really liked the course.

What I think could be nice improvement would be more nsightful notes. Which means, that after every video, there should be a separate sheet with all the formulas being described in more detail, so that you can refer to them any time during quizes.

May 22, 2018

Overall the course is pretty good. They breakdown the concepts into clear and concise lectures. My only grip, is that the quizzes occur a little too frequently. They really interrupt the flow of the class. I would definitely prefer them spaced in 30-60 minute interval.

Oct 31, 2018

I found the need to search for most of the material needed to understand the lessons in other sources. Other than than it was a relatively easy class, which covers nearly the basics. This is not a tutorial on Data Analysis on R, although a short introduction is provided.

Nov 19, 2019

Course covers the concept in a very simple way. Examples and assignments are very good.

However some of the statements made throughout the lectures needs more explanation , the course did not dedicate any videos to get familiar with terminology related to probability.

Apr 17, 2020

It is a really interesting course. However, I think it should include more examples and meaningful ways to estimates some parameters. For example, how can I estimate alpha and beta for an Inverse-Gamma distribution in order to obtain a prior for the sigma-squared?