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Back to Bayesian Statistics: From Concept to Data Analysis

Learner Reviews & Feedback for Bayesian Statistics: From Concept to Data Analysis by University of California, Santa Cruz

4.6
stars
2,682 ratings
699 reviews

About the Course

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....

Top reviews

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

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426 - 450 of 687 Reviews for Bayesian Statistics: From Concept to Data Analysis

By Ankur S

Jan 17, 2020

Very good course

By Justin C

Aug 25, 2018

Excellent Course

By Gaurav a

Dec 26, 2017

Very encouraging

By Martin K

Feb 23, 2017

Best course yet!

By Andrei M S

Sep 23, 2020

Learned a lot.

By Jakob R

May 10, 2017

Great course!

By 조휘용

Jun 29, 2020

good course!

By Efren S

Dec 18, 2017

Great stuff!

By FNU R M

Aug 15, 2019

Nice Course

By Binghao L

Apr 11, 2019

nice course

By Joshua M

Oct 10, 2017

Good course

By Zito R

Feb 27, 2018

Excellent!

By Rigoberto J M A

Nov 6, 2017

Excellent.

By Vinicius P d A

Apr 19, 2017

Very good!

By Hortensia M

Apr 12, 2021

excelent!

By FERDINANTOS K

Nov 14, 2020

THANK YOU

By Benjamin S K

Sep 12, 2020

recommend

By How

Sep 28, 2018

Completed

By Jinxiao Z

Jun 21, 2018

excellent

By shashi r

Sep 15, 2016

Awesome.

By Xinyi J

Apr 8, 2019

Great!

By Anna B R

Dec 17, 2017

Great!

By Wai Y L

Jun 10, 2017

Good!

By Benjamin A A

May 21, 2018

j

By Artem B

Feb 7, 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