Back to Bayesian Statistics: From Concept to Data Analysis

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2,694 ratings

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704 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 Borja R S

•Apr 25, 2020

The teachers are clearly experts in what they do, but sometimes I think it is that same expertise that makes them jump to conclusions too easily, making it difficult for beginners to follow.

By Ran W

•Jul 25, 2020

This course gives a very brief background on conjugate prior. However, the lectures on Bayesian linear regression is too superficial. I wish the lectures could have gone into more detail.

By Carlos

•Apr 8, 2020

Too much time spent on the beginning and too little on later more complicated concepts such as the posterior predictive. It felt as if that was just a side note in the extra readings.

By Augusto S P

•Sep 24, 2017

The course is good for beginners in statistics. In my opinion it would be better to invest more time explaining different topics about bayesian regression and bayesian time series.

By Oliver B

•Jun 1, 2020

Solid mathematical grounding, but would have benefited from more time spent on the history of Bayesian inference, when to use it, why it can be used etc..

By Pranav H

•Jul 1, 2018

The course could have given more information on tiny details which can confuse people during the exercises. But overall a good learning experience

By Alessandra T

•Jun 29, 2017

We still don't understand how Bayes differs to Frequentist... A worked example comparing the two at the end would have been nice.

By Ken M

•May 1, 2019

It would have been great if more graphs had been provided, for easier visualization of the e.g. distributions, or concepts.

By roger

•Jul 24, 2019

It would be better to add more explain about those equations and connect the math stuffs with the real world samples

By Max H

•Jul 14, 2019

It would be much better if there was a more sufficient introduction to the various distributions used in the course.

By Victor D

•Jul 9, 2019

Very informative as an introduction to concepts, but nowhere near the deep dive I'm now interested in taking.

By Isra

•May 4, 2020

Good course!!... Additional examples of real life explained and done in R or excel will make it great

By Binu M D

•Sep 21, 2019

Too much theoretical than practical applications. No need to give both R and Excel videos.

By A A

•Nov 26, 2018

Would have liked more problem solving and real-world application examples.

By Jyh1003040

•Jun 15, 2020

The workload is manageable however the homework is somewhat challenging.

By Hassan S

•May 11, 2020

Not well organized.

No sufficient materials, references, etc.

Very short.

By sokunsatya s

•May 31, 2018

Overall, it's Ok. but the explanation is too short and incomplete.

By aref h

•Aug 24, 2017

better to come up with more examples and more mathematical details

By Rajesh k

•Jan 1, 2019

This course could be taught in better understanding way

By Tawan S

•Jun 3, 2019

For some derivations, the explanations are too sparse.

By Damir M

•Apr 9, 2017

A bit too short.

By Marjan H

•Jan 3, 2021

I expected better teaching quality. The instructor is undoubtedly one of the bests in his area, but I personally did not like his teaching in this course. I felt he knows a lot of interesting concepts but intentionally does not teach them. The whole course was like somebody was reading from a textbook without adding any comments for students to actually grasp the concepts. In general I liked the course but I expected to learn much more from it.

By Patrick K W

•Jul 28, 2019

It's alright because it gives you an overview of what is covered in a Bayesian Stats class, but the material is presented quite poorly and I had to do a lot of second hand reading to answer the questions. It is not particularly enlightening even and the formulas are presented without proper grounding, context, and intuition. I can recommend this only for dedicated self-studiers who already have some sort of grounding in Bayesian reasoning.

By Vinod M

•Apr 26, 2021

Week 4 explanations are just theoretical where professor is literally not giving any intuition and rushing through the concepts with equations which did not make any sense to me. Till week 3 I could kind follow. I did this course with the intend of giving a based for Machine Learning study and I am an thoroughly disappointed the way it ended up.

By Dennis R

•Dec 31, 2020

Good content. However, way of presentation is not very engaging. Presenter's voice very monotonous and free of any engagement. In my opinion, scribbling formulas to the board does not make a helpful learning experience.

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