Back to Bayesian Statistics: From Concept to Data Analysis

4.6

stars

2,533 ratings

•

665 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 Johannes M

•Jun 6, 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!

By Leandro G G

•Oct 22, 2019

This course provides a good overview to Bayesian statistics, but a larger dose of explanations of would be very useful. Mr Lee discusses, in the beginning, the differences between frequentist and bayesian paradigm. I feel that this would be beneficial in the other parts of the course, too. I feel that many of the lectures simply go too fast. After lectures full of Math, it would be useful to present lectures analyzing what had just been taught, in order to better grasp the content. And in general, this happens through the whole course - most lectures are basically math, without much time for grasping the intuition and underlying logic. For example: in the final part, under linear regression, it might be be difficult to grasp what a bayesian predictive interval means. All in all, I recommend this MOOC, but you might find hard to fully grasp it.

By Suyash C

•Dec 24, 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

By Francesco L

•Feb 1, 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.

By yogi t c

•Jun 22, 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.

By Joseph R R

•Oct 10, 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.

By Tianchi L

•Aug 15, 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

By Yann F

•Nov 7, 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.

By Francois S

•Sep 5, 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.

By Jens L R

•Jan 31, 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.

By Edoardo C

•Apr 20, 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).

By Glenn

•Jun 9, 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.

By Dmitry S

•Sep 20, 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.

By Ahmed S

•Jan 4, 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.

By Yuzhong W

•Oct 3, 2016

The lectures from week 1 to week 3 are nice and useful to me, but I think there should be more details about the content in week 4. For example, I think the lecture about the Jeffreys prior skipped many things and I did not understand this concept very well.

By Damel

•Nov 29, 2019

Most of the support material should be prior reading. Lecturing could be more useful i.e. explaining ore about why we use certain distribution and how to apply them. Most of it as just reciting formulas and felt like a waste of time...

By Olexandr L

•Jul 1, 2017

It was quite difficult to learn from just the material provided here, and I had to look for info on the web. Also, adding modern real life examples and going into detail would make this course better

By Jesús R S

•Jul 19, 2017

Good course as an introduction to bayesian statistics if you want to pursue more advanced courses in the field or to get some practise working with distributions under the bayesian framework.

By Silvia Z

•May 8, 2020

In general, the course is useful, but in half of videos the explanation focused mostly on formulas, and less on theory. I personally had difficulty in learning theory of Bayesian statistics.

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

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