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.

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.

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).

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.

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.

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.

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.

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...

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

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.

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.

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.

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.

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.

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.

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..

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

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

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

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

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

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

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

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

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