Retour à Bayesian Statistics: From Concept to Data Analysis

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2,918 évaluations

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759 avis

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....

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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par Andres O

•31 mai 2021

Muy 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 Жидок Ф А

•12 avr. 2022

Good, but concise

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 Garis N K

•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.

par Joseph R R

•10 oct. 2016

Liked the course, but it was a little easy (took four days total to do the material for the whole course). Many questions were left unanswered (such as how dependent the credibility intervals are on the choice of prior distribution and the assumed distribution of the data), and it didn't touch on later topics that are interesting (MCMC sampling). Again, good beginning course, but I was looking for more in depth study.

par Tianchi L

•15 août 2019

-1 star: Some discussions and derivations do not have adequate context and background. I expected more thorough explanation on concepts and more advanced topics. There are also a few minor typos that confused me. It is only a helpful introductory level course on Bayesian without depth.

-1 star: quizzes are not challenging enough and they only require plugging in numbers into equation. Not a good way to study

par A l

•7 nov. 2020

The first two weeks are very clear, after that, new notions are thrown without any definition, the calculations are not done, only results are given. I finished the course by brute-forcing the exams because I wanted to finish fast to take another course... No help in the forums too. For me this is a course to avoid except the first two weeks that helped me a lot.

par Francois S

•5 sept. 2017

Nice introduction to Bayesian concepts. Presentation sometimes focused on the details of the calculations and could gain from more perspective. Sections relating to Normal variables - variance unknown and Linear Regression could be more explicit. Useful overall as an introduction, but require to get additional external material to get to the bottom of it.

par Jens R

•31 janv. 2017

It was pretty intuitive and easy to follow the first couple of weeks, but then the assumed knowledge of beta and gamma distributions and their frequentist usage, stood in the way of me fully grasping the Bayesian part of it. In the end I just copied the examples from the lectures and passed the tests ... without really getting it.

par Edoardo C

•20 avr. 2020

Overall I liked the course but I would have preferred a more formal treatment in many cases - sometimes numbers were plugged into the formulas without first explaining their formal structure more in detail.

I did not like also the fact that the course was implemented in R and Excel (but that's a matter of taste of course).

par Deleted A

•9 juin 2020

I didn't think the lectures were very good. The instructor wasn't careful with his notation, which was very confusing, and the initial lectures where he used a pastel green marker on a green background and wearing a pastel green shirt made his blackboard text nearly invisible.

However, the assignments were execellent.

par Dmitry S

•20 sept. 2016

The material is good, but I've found the lectures challenging to understand even having some background in math. It would be good if all the definitions and key facts were stated more prominently in the lectures, as opposed to algebraic transformations which most readers can hopefully do on their own.

par Ahmed S

•4 janv. 2017

This course requires solid grounding in mathematics. No meant of Social Science graduates without proper training in statistics/mathematics. The course was good in the sense that we could how probability distributions are used to model real world problems.

Study material was certainly not adequate.

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