學生對 加州大学圣克鲁兹分校 提供的 Bayesian Statistics: From Concept to Data Analysis 的評價和反饋

4.6
2,503 個評分
658 條評論

課程概述

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....

熱門審閱

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.

JB

Oct 17, 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

501 - Bayesian Statistics: From Concept to Data Analysis 的 525 個評論（共 646 個）

Sep 05, 2018

I think the course would benefit by recommending a textbook that would supplement the lecture material. It's nice to have a reference to refer to after viewing the lectures.

Feb 05, 2018

This is a good course, I've learn a lot about Bayesian statistics with very little prior knowledge about this subject and even about statistics and probability in general.

Sep 09, 2017

This is a nice, bite-sized introduction to Bayesian inference. Helpful lecture notes are provided, alongside introductions to practical computations using R and Excel.

Apr 13, 2020

Great course with clear structure and good explanations. An overview "cheat sheet" would be nice, also some hints to literature which covers the topics in more depth.

Feb 24, 2019

A little heavy on the theory for my style of learning - would have appreciated more clear, applied examples in the lectures, but overall a good and informative course

Jun 11, 2020

A lot of clearly presented content and helpful quizzes. Some lectures felt a little inconclusive but maybe I just didn't follow the Maths. Would definitely recommend

Jan 17, 2020

Assignments are the best part of the code. Videos don't provide enough conceptual knowledge. Overall considering the intricacies of the topic its a very good course.

Jul 10, 2017

Nice explanations of the theory, however there could be a bit more written materials and the pace could be slightly slower, especially regarding the last chapters.

Mar 03, 2019

I took this course both to refresh my basic understanding of statistics as well as to learn what Bayesian Statistics was about. This course was good fit for this.

Oct 24, 2020

It is a good basic course with lot of math. However, personally I found it little difficulty but yo can make sense of everything if yo watch the video 2-3 times

Aug 26, 2018

Though Bayesian statistics is not easy, and quite complex when dealing with prior and posterior. This class provides a good overview the the Bayesian statistics.

Oct 07, 2020

The course was good, but the required time was much different from the one written in the course description. It has a lot of quizzes, which take a lot of time.

May 23, 2018

Intuitive course, but somewhat fast which leads students to pause and contemplate on what the lecturer had to say. Good start to get to know Baysian Statistics.

May 27, 2018

The explanation is very in details. It would be better to have more mathematical derivation in the linear regression part besides the demonstation of using R.

May 08, 2017

Overall a good course about Bayesian inference. Only suggestion would be to spend a bit more time explaining the interpretation behind the calculated numbers.

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 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.

Sep 01, 2017

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

Sep 08, 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 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.

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.

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 19, 2020

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