4.6
2,931 个评分
762 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

## 601 - Bayesian Statistics: From Concept to Data Analysis 的 625 个评论（共 755 个）

Mar 30, 2018

Very good introduction to bayesian statistics, but I would have liked a bit more written material to complement the videos, who were rather short and fast.

Jan 12, 2018

It is interesting learning the mathematics behind the analysis, but it could have been more complete, with a little less theory and more data analysis.

Sep 2, 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.

Sep 1, 2017

Great course with easy to understand examples. One can explore deeper into the world of Bayesian statistics after completing this preliminary course.

Sep 8, 2020

All the weeks were great, but the week 4 seems to be in complete and lack of explanations. Some refinement on the week 4 materials would be great.

Oct 10, 2019

The first question in Week 4 Honor Quiz, the coefficient for intercept, I got 138 which you show incorrect, would like to know the correct answer.

Jan 1, 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.

Aug 27, 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.

Dec 28, 2020

Great! Clear, systematic... but: requires a good basic knowledge of mathematics and lacks practical examples to illustrate the models presented

Jun 28, 2020

A nice course. I gave me a fundamental knowledge about Bayesian Statistics. The lectures are sometime a bit confusing but overall, it's great.

Aug 18, 2020

more examples of solving problems would have help. or having direct references to sources that explain the technical aspects of the material.

Dec 11, 2020

The course could use slightly more explanation of how Bayesian statistics is applied to real world problems (vs. frequentist application).

Nov 17, 2019

Very good method and quality of teaching, I'd recommend more solved and commented exercises for each topic exposed, before each week test.

Apr 7, 2017

Very concise and easy to follow to the end. The linear regression part could be more clear (i.e., with a lecture on the background).

Feb 17, 2020

The last section, normal data, which is very important, could have been instructed in a slower, less hasty way with more details.

Jun 21, 2020

Great course to get acquainted with Bayesian statistics and inference. Just wished seeing a bit more of mathematical background.

Nov 28, 2018

Need more information about linear regression, given material is not enough to understand topic and effectively find solution.

Nov 2, 2017

A bit dry overall, but I appreciate the rigor and precision, along with the practical examples in R. I learned a great deal.

Nov 4, 2017

Hi , this course opened a door for me in Data analysis. Very intuitive & must course for any person exploring data science.

Aug 28, 2019

Good course, but it could really use some PDFs with lecture notes ( as in contents of videos, not supplementary material).

Jan 14, 2017

This course has given me some good new insights into perceiving data and has got me started nicely I am very great full.

Jun 29, 2021

Overall the course is nice for the basics. However, I expected a little bit more coding and less mathematical formulas.

Mar 16, 2019

Good introduction and interesting topics. However, some of the model analyses are not appropriate and feels artificial.

Feb 4, 2018

As a graduate student pursuing Machine Learning, this was a great course for me to get introduced to Bayesian Models.

Nov 23, 2021

D​ifficult to work on assignments because it was unclear how to implement the knowledge discussed during the lecture