Back to Bayesian Statistics: From Concept to Data Analysis

4.6

stars

2,532 ratings

•

664 reviews

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

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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By Thomas J M

•May 21, 2018

Overall the course is pretty good. They breakdown the concepts into clear and concise lectures. My only grip, is that the quizzes occur a little too frequently. They really interrupt the flow of the class. I would definitely prefer them spaced in 30-60 minute interval.

By Ekaterini T

•Oct 31, 2018

I found the need to search for most of the material needed to understand the lessons in other sources. Other than than it was a relatively easy class, which covers nearly the basics. This is not a tutorial on Data Analysis on R, although a short introduction is provided.

By Mohd S K

•Nov 18, 2019

Course covers the concept in a very simple way. Examples and assignments are very good.

However some of the statements made throughout the lectures needs more explanation , the course did not dedicate any videos to get familiar with terminology related to probability.

By Luiz G S S

•Apr 17, 2020

It is a really interesting course. However, I think it should include more examples and meaningful ways to estimates some parameters. For example, how can I estimate alpha and beta for an Inverse-Gamma distribution in order to obtain a prior for the sigma-squared?

By h

•Jan 14, 2017

Pen hard to see against shirt. Was mildly irritating to wait for prof to write out stuff, maybe prewrite it?

Went too fast forward for me, would've liked complementary optional material, eg extra quizzes, to help understand and get used to the tougher parts.

By Paul B

•Oct 8, 2020

Honestly wish there were more practice problems that I could do outside of the quizzes. Just make them optional. It's just tough to iterate on the same problems and work to figure them out. Otherwise I really enjoyed the course and found it really helpful.

By Katsu

•Jul 9, 2017

Great introductions to Bayesian statistics and inference. Quiz is actually not easy just by passively viewing videos, so taking notes during lectures is strongly recommended. Do not be afraid the Honor quiz...they are not so different from the normal ones.

By Elguellab A

•Jan 29, 2019

Likely course and practical: it help us to understand some basic notion for bayesian inference. But Some concepts are less clear and I think need more development and explication (like effective sample size, Jeffreys prior). Great job over all.

By Jerry S

•Mar 13, 2017

The lectures were good, but I hope more background materials can be released. Understanding the topics needs a relative solid mathematical background. Although having completed the course, I am still confused about some concepts in this course.

By Brian M

•May 21, 2020

Really enjoyable.

My first free course, so this may be way off the mark in terms of norms, but I would have appreciated if supplementary material was either provided or suggested for doing more practice exercises, with worked through examples.

By DR A N

•Sep 4, 2017

The course was excellent !...Giving a good overview of the basics needed to navigate through this topic. However, it would have been really great if some specific examples with respect to medicine and public health practice were incorporated

By Jakob W

•Mar 15, 2018

I found it to be a solid course. It has given me better grasp of the basics. I also found it a bit dry, and significant time spent on equations rather than high-level understanding. This is fine, as long as you know what you are in for!

By Qin Z

•Jan 7, 2020

Overall the class is great, especially the first two weeks' content is simple and well-explained. But from the week 3 to the week 4, the professor only writes many formula and doesn't provide enough examples to explain those formula.

By Piotr G

•Jun 17, 2019

Very high quality course. Could use some modifications (e.g. few more applied examples for regression using specific priors, MCMC etc.) and implementing some simple metaphors to introduce some topics before jumping into the maths.

By Masoud A M

•Aug 16, 2020

The Course was concise and helpful to build a foundation for Bayesian statistics. However, it is not recommended for those who has weak or no background in statistics, as the explanation are not thoroughly explained by details.

By Curt J B

•Nov 20, 2020

The course is quite difficult to comprehend with a loose background on stats, but the lessons prove to be interesting especially when applied to sample experiments. Eager to try the next course on Bayesian Statistics.

By Yahia E G

•May 4, 2019

Very good course for beginning bayesian inference. The syllabus is easy to follow, but I also think one could benefit even more by complementing the lectures with other sources (books or other youtube explanation)

By Paul B

•Aug 19, 2020

The course provides a good explanation of a complex topic. I had trouble following some of the statistical mathematics but was able to understand the concepts and the different range of possible applications.

By Bojan B

•Apr 9, 2017

Short course that's actually mostly theoretical with a bit of R/Excel analysis. This fitted my needs perfectly. My only suggestion is that they should have released more comprehensive notes for the lectures.

By Raja G

•Dec 11, 2019

The course content is great and provides a good introduction to bayesian statistics. The assignments could be a little more challenging as a lot of the questions require just plugging numbers into formulae.

By Leszek B

•Jan 15, 2018

I could grab the concept of Bayesian statistics but did not find the course fully self-contained. I had to look elsewhere to fully understand details. More complete supplementary material could help a lot.

By Marc S

•Oct 10, 2018

Good use of R but maybe use the actual coefficient from the equations themselves rather than picking numbers pre-selected which may confuse.

Unable to look at discussion forum without posting myself.

By Michael D

•Feb 19, 2020

the notes for the lectures are missing.

In my opinion the notes, which includes the video materials could be very useful.

the course was good. I learnt some new concepts in bayesian thinking.

By Enrique D T

•Jun 23, 2020

Good course. As a recommendation to improve it, it would have been very helpful if the lectures (PDF) given with each lesson included all the formulas and explanations given in the videos.

By Michael M

•Sep 25, 2019

Very clear and informative. Would like a more extensive and combined reference material (PDF, so less need to lookup e.g. definitions of effective sample size for various distributions).

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