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.

Good course but lack of visuals and examples for illustrating complex concepts.

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.

I had never given much thought to multilevel models and their implications (for example how clustering or the interviewer effected the results). So the course was definitely interesting. However the Python notebooks that are part of the course don't give enough detail to be able to apply the theoretic material to other models.

It needs more mathematics and theories in its content presentation to better understanding what happened in the python codes. Thank you for giving me a chance to pass this course to have an overview of statistical modeling.

It is good as an introduction / overview course, but barely touches any mathematics background in fitting models to data. The assignment is not challenging enough

The first half of the course is fantastic, but the second seems to go way too fast, not elaborating enough on the concepts, and not enough practice

I was a bit disappointed by the notebooks of week3: missing some details and explanations for me.

please better explanation of python videos