Not as good as other courses in this specialization. Most of the times the focus was to teach the method of performing a Bayesian Statistical process rather than teaching the actual concept.

I felt the course ramps up from the basics way too quickly. I didn't like the pacing in the course compared to other courses in the same specialization, although I did learn a lot.

Explanations not simplified as compared to the other courses in the specialisation. Very difficult to comprehend. Instructor should take more time to explain the fundamentals.

Se trata de explicar términos matemáticamente complejos de una manera muy general y vaga dificultando el entendimiento y el aprendizaje del tema.

This course gave me a hard time. The lectures from week 3 and 4 seemed difficult, some more resources would be helpful.

Like in all courses of this specialization, the peer assignment was a real bottle-neck in the completion of the course.

This is certainly a good introduction. But it did not help me to be independent on bayesian statistics

The explaining for some bayesian methods are unclear, which make it harder for new learner to follow.

Slides poor compared to 3 earlier modules and instructor not as engaging. However, the labs are good.

Find it hard to follow the lectures. The labs and supplement material is good though.

A useful course, but very demanding. Many of the lectures are fast-paced.

I would recommend to include more weeks; slow down and go deeper

Some contents are just too difficult to understand fully.

Very heavy information very quickly otherwise - great

Good course, but need more details.

not very interested

tough course.

huge jump

This course covers a good amount of bayesian statistics. However, the presentation/videos starting from week 2 really sucks. They change instructors for difference topics and obviously some instructors are not very good at explaining other than reading the material.

The videos skipped many medium steps that are actually very crucial for understanding the concepts. And no suggested reading materials at all either. Also the quiz are not very well designed either. For example, some quiz are much more simpler than the course material, which makes it not helpful at all to understand the course material itself. While some times it is the opposite.

The first three courses in this specialization are very good, but somehow this course are way below the quality of the previous ones.

Tons of interesting material. However, presented in a way which is hard to take, and harder to remember, especially if you are used to the exceptionally high standards of Coursera. The slides, which I am used to work with, are a big let down. They are hard to follow, erratic, lack thoroughness and are incomplete. It does not make it better that they refer you all the time to additional material. Also the lectures are disappointing. The lecturers do not interact with the slides, they don't explain. I wished I could have taken more from the course since I think that the topic is relevant and interesting. Really disappointed. I do hope that there will more MOOC's teaching Bayesian statistics soon.

The previous courses of the specialization were much better. This one is too fast paced and confusing. The math for this course is significantly harder than for the previous, but in my case it was not the math what was making it hard. The videos are hard to follow. I answered some of the quiz questions based on intuition and what looked reasonable rather than actually knowing how to solve them. Usually in the previous courses the project felt like the hardest part, but on this one the project felt like the easiest. What I did like about the course is that it has good breadth of topics in Bayesian statistics.

This course, compared to the others in the specialization, was a bit of a mess. The lectures were hard to follow with fewer exercises to check your learning than in previous courses. The "text" seemed to just be a bad transcript of the lectures with all sorts of errors. The labs were confusing and sometimes included incorrect or outdated instructions that caused me to waste a lot of extra time trying to figure out what was wrong. I enjoyed doing the final project, though, and learned a lot doing that.

The course presents interesting material but it is not easy to follow. It is a huge jump from the previous courses and requires far more hours to understand all the (math-heavy) material than the stated. The slides feel a bit chaotic and the language/sentences during the explanations could be much simpler. At times it feels that the instructors limit themselves to reading formulas one after another, making it hard to find a connection between them and how they are applied.

This course makes a valiant effort to provide as much coverage of Bayesian statistical methods as the prior three courses in the "Statistics in R" specialization do for Frequentist statistical methods, but the lack of supporting material (e.g. reading/text exercises directly paired with each lesson) really hampers this. The videos are quite informative, but if you don't catch on to the material based strictly on the videos, the weekly quizzes can be a bit frustrating.

This course is far different from others in the series. Mathematical formulas and other concepts are introduced without any prior background. Even if the concept is understood the application part of it still remains a mystery on where to apply it, the course could have been more elaborate explaining these concepts in-depth rather than introducing without any prior background. Words such as prior families are used without introducing them properly.