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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
2,915 évaluations
758 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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601 - 625 sur 752 Avis pour Bayesian Statistics: From Concept to Data Analysis

par xu w

2 sept. 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.

par Tuhin S

1 sept. 2017

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

par Bae,Bongsung

8 sept. 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.

par Yalong L

10 oct. 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.

par Taylor J W

1 janv. 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.

par Deleted A

27 août 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.

par Philippe B

28 déc. 2020

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

par Việt P H

28 juin 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.

par Sydney W

18 août 2020

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

par Seth T

11 déc. 2020

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

par Massimo G

17 nov. 2019

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

par Xu Z

7 avr. 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).

par Alex C

17 févr. 2020

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

par Björn A

21 juin 2020

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

par David L

28 nov. 2018

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

par Ethan V

2 nov. 2017

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

par Sameer G

4 nov. 2017

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

par Jan J

28 août 2019

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

par abhisingh03

14 janv. 2017

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

par Leonardo G Z

29 juin 2021

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

par Jakob L

16 mars 2019

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

par Rohit J

4 févr. 2018

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

par tommaso c

23 nov. 2021

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

par Angela C

2 janv. 2022

T​he material is quite hard for someone who has only basic knowledge in statistics. But the excercises are helpful.

par Muksitul I

2 juil. 2018

Well explained and articulated. You can apply it straight to your work problems. I really enjoyed doing the course.