Bayesian Statistics

This is definitely a challenging course. However, I took in that spirit and am really enjoying it so far. As well as Bayesian statistics, you can learn R/markdown through the very well constructed labs and the advanced, but really helpful extra pdfs put out by Merlise Clyde. I haven't done the rest of the specialisation, but did do the earlier stand-alone course fronted by Mine Cetinkaya (an absolutely brilliant lecturer).

The material and the pace is such that most of the lectures alone are not enough in one go to deliver understanding. How many lectures are? But you can watch them again, read the transcript and download the slides, as well as the supplementary pdfs. There is also a helpful list of useful Wikipedia/Stack Exchange articles on the many of the main topics. And, you can try out the ideas in R, as the lecturers encourage you to do.

You can do the quizzes as often as you want. When you get a question wrong, there are helpful hints and you are directed at learning outcomes that the question addresses.

The topic is intrinsically interesting. The course, with its mixture of R markdown files and Git not only delivers that but does it in a modern way that is conducive to learning how reproducible research can be done.

I am about to start the project, which looks like a really interesting opportunity to apply all that we have learned, while also getting the hang of putting together a markdown document along the way.

I'm currently taking the Specialization "Introduction to Statistics in R". I have completed and reasonably understood the first three courses which were very interesting, well presented with basic concepts and implications continuously re-enforced. I loved the first three courses immensely and they have re-enforced in me the desire to know and learn more about the topics and statistics in general.

The difference in content, communication style and presentation between the 4th course (Bayesian Statistics) and the earlier three is significant. Unfortunately the theoretical section of the 4th course is best explained in the accompanying book. The videos related to the theory are more similar to a recital of equations as opposed to explanations of implications.

This course is incredibly, terribly frustrating to try and learn from by following the videos only. For example: one of the lectures introduced a new word "hyperparameter" which from my memory had never been uttered in any of the previous videos nor explained.

The labs are quite good, and the final videos on the application in R are also decent. It would be better if there was more explanation on the differences between priors and why to use some over others. This is definitely a quick overview, but decent enough to be able to use the tools.

On the plus side, Bayesian statistics looks to be quite interesting.