A great introduction to regression and bayesian analysis in python. I get that the content is hard, but they sum it all well. I would recommend for those who have prior knowledge of statistics.

The code examples may be more precise with detailed comments. Some codes are not understood, in other words codes can be refactored in a way that can be more suitable for reproducible studies.

Very good course, I like many practices and evaluations focused on database of real cases, perhaps it would be advisable to reproduce results from the same sources .....

JL

Very informative. But had few confusions in the last course. Also the python code explanations were not good as the instructor was rushing through it without explaining.

Good course giving a fair view on fitting statistical models. Could do to elaborate on some of the theoretical models using more illustrations for more understanding.

Good for advance topics like Marginal and Multilevel modelling. The Bayesian model could be explained in a detailed manner by providing more python assignments.

The course is great, the only improvement I would make is to be a little more didactic in the last two units because it is a more complicated subject.

The course was wonderful however, sometimes I felt that a little bit more details could be provided when python code was being explained for week 2.

It was very technical and a lot of the mathematics behind the models were not explained properly. The codes were also not explained properly

Overall it's very good for someone who has a fair background in statistics, except for some small mistakes in slides and notebooks.

Me gusto sobre todo los modelos de nivel combinados con estadistica bayesiana ,eso fue lo mejor y de verdad invaluable del curso

Week 3 starts to get unreasonably difficult and hard to understand. Apart from that, the course is still worthwhile to take.

Great course. In my view, the lectures were too long and the assignments a bit easy. But, overall, great course.

I think the notebook walkthroughs, while useful, could use some extra reinforcement in the statistical concepts

The course is great, but I would suggest that the subject of week 3 be divided into two weeks.

Great course. It really improved my understanding of statistical modeling methodologies.

Starting from basics of Statistical model to the depth its fine course.

team work

None

The content of this course is very thorough, but unfortunately it does not make very good use of the online asynchronous nature of a platform like Coursera. Most of the course consists of lengthy video-lectures paging through slides (and occasionally walking through notebooks). The hands-on parts seem like a second thought, and are mostly made of either reading long Jupyter notebooks, or running simple pre-coded ones to answer a short quizz. Statistical modeling is a topic that shoudl naturally lend itself really well to a "learn by doing" method, but unfortunately this course took the more traditional academic approach (nothing wrong with the later, it's just less engaging for me, especially when sitting in front of a computer).

The final course was definitely a step up in terms of difficulty from the previous two courses. The assignments aren't that hard, but lot of the material are discussed without getting into depth, which makes it difficult to really get a good idea about the inner workings of the statisticsa methods used. I wish the course developers planned the specialization to be a 05 or 06 course specialization, so that the materials covered will be well spread and learners will be eased into the new concepts. Giving a low rating owing to the structure of the course.

While the topics are interesting, like other courses in this specialization, this one does not really teach Python. Rather, it uses it as a tool in prepared notebooks that you can follow along with, but largely need to do your own research to understand the various syntax and variables used. This really is more of a Stats course, where the Python element doesn't add much other than some visuals of how to read results or view charts/plots using Python. It really isn't an integral part of the course material.

I do not feel like this course had given me great knowledge, there is a lot of theory and almost none practice of python, specially in the last two weeks. Topics are interesting and they are good as an opener to learn statistics but there is not enough python about them. I am disappointed on this specialization (specially on this course), I only finished the course because it was the one left to complete the specialization.

There is some good lecture content, but the assessments don't really give you a chance to "do stats" and demonstrate mastery of the material.

E.g., the week 3 Python assessment consists of just running Python code--you don't actually write any code--and answering the questions is as easy as, e.g., picking the parameter with the largest number.

It feels like Brady is reading off the slides and squeezing in a lot of information in a 10-12 min talk. I would prefer the course slows down and would introduce a case example before jumping into models full blown. The slides look wordy. Circling out the numbers when they are mentioned in the talk would help students focus as well.