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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
2,852 évaluations
743 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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651 - 675 sur 736 Avis pour Bayesian Statistics: From Concept to Data Analysis

par Gil S

3 mars 2019

Clear and consise introduction to Bayesian statistics

par Yuan R

5 nov. 2016

Good and simple introduction for Bayesian statistics.

par sunsik k

23 août 2017

well instructed basic course of Bayesian statistics.

par Alexei M

13 mai 2018

More examples are required as well as more practice

par Venkataraghavan P K

10 févr. 2019

Loved the theory & analytical part of the course.

par Bishal L

7 mars 2017

It is a nice introductory course on Baysian s

par JhZhang

14 mars 2020

深入浅出,结合理论推导、实际应用与直观理解,挺好的一门课,让我对贝叶斯推断有了极大的兴趣

par Carson M

27 oct. 2017

Pretty good overview of Bayesian statistics.

par xuening

25 janv. 2017

from week 3, the learning curve become steep

par Wenbin M

9 févr. 2020

The normal distribution part lacks detail.

par Andres O

31 mai 2021

M​uy interesante. Simpre puede mejorar

par Ezra K

13 févr. 2020

Good overview of Bayesian statistics.

par Xindie H

27 janv. 2019

Nice and easy introduction course.

par Witold W

29 août 2017

Liked it and can recommend it.

par Chuck M

11 janv. 2017

A good course - recommended.

par Valentina D M

29 mars 2018

Need more material on R.

par Ankit P

26 mai 2020

Excellent fundamentals.

par Spyros L

20 sept. 2017

Very good introduction!

par Guim G P

18 août 2020

Very useful!

par kaushal k

28 août 2020

good

par Linda S

24 août 2020

In the course, I liked that there were questions asked during the videos. That makes you think about the content, the professor was just talking about.

Anyway from my point of view, the supplementary material should have covered more of the content of the course. That would have helped me a lot.

Also, I sometimes felt lost when the video started, some introducing words why this topic is now discussed, or an overview about the topics handled in the topic area would have helped me to understand the connections. What would have also helped are overview slides (also in the supplementary material e.g.) Also I had sometimes the feeling, that the answers to the questions of the quizzes were not always included in the videos. For this, I would have been glad to have a extensive supplementary material.

To sum up, I was able to learn a lot, but I could have learnd a lot more with better supplementary material or a clearer structure.

par Johannes M

6 juin 2017

I am working in the field of epidemiological, medical research. Overall I would recommend taking this course. It needs to be pointed out, however, that if you are outside of the field of mathematics this specific course entails a lot of research (using google etc) that needs to be undertaken to understand the course material. Maybe in the future the course directors can compile a summary of all important formulae etc so that professionals from sectors other than mathematics can follow more easily and can focus much on this particular course on Bayesian statistics and not so much on conducting additional research to understand the basic course material. Furthermore, alongside a summary formula sheet it would be good to have all explanations included, what the parameters (alpha, beta etc) stand for with regards to the specific context. Thank you very much for this course!

par Suyash C

24 déc. 2017

Plus Points of the course -

It starts with a context of where and why bayesian statistics comes into play. Good real world examples and questions are posed to drive home this point at the start of the course.

Where it could have been more helpful -

1) Somewhere in between the course gets lost in math expressions and distributions drifting away from real world implications. This would be ok for someone looking for pure math/stats. However it would become less relevant for someone coming from data science/business side. More real world use cases could have been there. (2) Better guidance on which other streams of data science/business can have application of this knowledge would be helpful (3) More comprehensive set of resources (pdf ones) would be great

par Francesco L

1 févr. 2019

The topic of the course is very interesting and the subject warrants it. Yet, especially the coverage in the last week of the course appears to be shallow and too many concepts are pushed down as valid or true without a lot of theoretical justification. Besides, some of the interesting conclusions are part of the quizzes rather than an integral part of the lectures. I also think that a course like this should allow the students to receive more written material in the form of PDF files that would cover all the matters being explored. What is made available is fragmented and does not cover all the topics in an organic fashion. I believe the course could be improved substantially.

par Yogi T C

22 juin 2019

I don't have background in math and statistics, in the first week of the lecture i can catch up with the lesson, but coming into week 3 and 4 it's really hard to me to understand what's happening, since the lecture / videos only talking about the formulas and only taught us how to use the formula. Actually for person like me who want to know Bayesian Statistics application in the real world and also fundamentals of it it's quite not recommended to took this lecture, honestly. However in the general understanding this lecture quite can help me how Bayesian thinking works what is the connection between likelihood, prior, how to choose prior, etc.