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Avis et commentaires pour d'étudiants pour Bayesian Statistics: From Concept to Data Analysis par Université de Californie à Santa Cruz

4.6
étoiles
2,931 évaluations
762 avis

À propos du cours

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....

Meilleurs avis

GS

31 août 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

16 oct. 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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201 - 225 sur 755 Avis pour Bayesian Statistics: From Concept to Data Analysis

par Eben E

12 avr. 2020

This was a were educational course. I had trouble understanding R programming but with this topic, most of the programs became more clear to me.

par Cooper O

27 juin 2017

A Fantastic course. Detailed learning materials, Lots of opportunities to test your knowledge, and difficult enough to make you learn something!

par Nitin K

1 juin 2017

I loved everything about this course. It reminded me of my time in school. Papers and pencils. I look forward to attending the follow up course.

par Tiannan S

6 juil. 2020

As a computer science student, I feel Bayesian approach is much more intuitive and more computationally friendly than the frequentist paradigm.

par Fernando D L

11 mars 2019

It's a good course to know the principal concepts of Bayesian statistics. Also, the course has excellent examples to understand thew concepts.

par O K

2 mars 2018

I really appreciated the content, and the way it was taught by Prof. Lee. His explanations were intuitive, without loss of mathematical rigour.

par Giovanni G

29 juil. 2020

Consistent and mathematically dense. If you want to go through every passage this course gives you solid understanding of Bayesian statistics.

par RIcardo G M

15 déc. 2019

Very good course. Concepts are very well explained, and quizzes are really helpful to apply and further

understand the explanations provided.

par Kuntal B

13 nov. 2019

Thanks, Coursera. This is a good course. It would be helpful if we get any proper class notes on Jeffrey's prior and Multivariate regression.

par Artem B

3 juil. 2019

Great course with a lot of simple, but illustrative exercises. It may be useful to have some basic prior knowledge of econometrics/statistics

par Michael W

16 janv. 2019

Great introductory course. It was challenging but doable for someone who has not take college level mathematics or statistics in a few years.

par Robert K M

11 févr. 2018

Invaluable. Excellent quizzes. A few terms could have been better defined, and a few more examples wouldn't hurt, but overall excellent.

par Damian C

10 nov. 2016

Very well presented course. Interesting and intuitive introduction into the fascinating Bayesian world.

Many thanks and congratulations!!!

par Howard H

6 oct. 2021

Excellent introduction to Bayesian statistics -- lectures, readings, and quizzes provide excellent support to learning the main concepts.

par Ariel A

12 oct. 2017

Great course, it has the right proportion of theory and practice. It's a great start for anyone who wants to dive into Bayesian Analysis.

par Hari S

5 févr. 2020

Thought is a simple manner. Made complex concepts look very easy. Would surely recommend this course. Thanks Prof. Herbert Lee and team.

par Vignesh R

8 oct. 2018

Awesome course that helped me overcome the Bayesian statistics way of thinking hurdle. Now, I want to go on and learn MCMC, Metropolis !

par Qinyu X

2 févr. 2020

The course is generally great. Nonetheless, it is not recommended for those without a statistical background and knowledge of calculus.

par Naseera M

12 févr. 2017

Very good course. Prof. Lee explains each concept well. Bayesian Stats makes more sense to me now than before!!

Thanks so much Prof. Lee

par Gustavo C

4 oct. 2018

I loved this course, I learned a lot and I hope I will be able to use this knowledge when I go back to college for my Master's degree.

par Evgenii L

2 mai 2018

A very good course. Even better if you continue with the 2nd course that teaches about how to implement Bayesian data analysis in JAGS

par Joseph G

18 déc. 2016

I enjoyed the lecturer, the material is relevant, and the tests are well tailored to ensure you are absorbing the correct information.

par Rodrigo G

16 janv. 2020

Give you great insight. Very intuitive. Although we went through the last week rather quick (more explanation would have been better)

par Jenna K

13 mai 2019

The lectures are at the right pace; concise and challenging. Great examples. Thank you so much for providing us with great materials.

par Matthew S

5 avr. 2020

Pretty challenging course. Well organized and well delivered. I learned from the exercises and also the feedback from the exercises.