Just like the other two courses of this specialization I believe the content offered here is great and the main methods used for statistical inference are well explained and even possibly more important, the interpretation of results is really hammered home here which is great. A few things that weren't covered thoroughly enough (if at all) in my opinion are QQplots (maybe this is more related to course 1...) and Chi-square tests (what are they and when do we use them?). Also it would have been nice to take a little bit more time to explain the differences in using t-tests and z-tests and why we would choose one over the other. I do believe the structure of the notebooks could be improved, maybe listing all of the possible functions that can be used for statistical inference for each type of scenario (e.g. functions applicable for mean of population proportion). As always, I would have loved for answers to be provided for the "extra practice" notebooks.

This course gives a lot of important concepts such as confidences intervals, p-values and hypothesis testing, but I think it is short in terms of using it in real life because the explanations rely on examples that always fulfil the same conditions and in real life it is not possible to have always the same conditions for a problem you want to study. It would be nice if the course could be complemented (in a deep way) with applications of complex samples and non-probability samples, not only single random sample. Also, python codes are not explained in a deep way.

The teaching team is great. But the assignments are not very helpful. And yes, this is more a statistics course than a python course. The application with python, which I am more interested in, seems just the supplementary portions to the lectures of concepts of statistics. There is not much introduction to how we use python to perform statistics, how we debug, and how we interpret the outcomes of programs.

It would be better to give precise definitions of each test, at least in optional reading material. Also, sometimes different lecturers used different terminologies and sometimes concepts not covered before just popped up in the video (ex. chi-square test). In general, it seems more organization in the material needed.

The course gives details on several stats concepts. Its one of the finest course here on Coursera. You gain a significant amount of knowledge on Statistics.

As the course progressed, I felt the content was squeezed and students were bombarded with the content without giving a real life example on them.

Clear and detailed explanation of inferential statistics. The course approach is more by blackboard than what can be interpreted by the title "with python". Although there are some examples in python, there are not many exercies for the student

Could be made more organized, like the first course in the specialization series. Seems there are some missing gaps (or assumptions of things being covered) that made it a challenge to smoothly proceed in the first 2 weeks of content.

Thank you a lot. For me was an incredible course I learned many things and was very important to my career. Thanks to all the team, They are really masters.

Good theoretical foundation, but lacks explanation on python libraries extensively used in the course.

the fundamentals and intuition are greatly explained. The python part feels a little rushed though.

Great experience, though very helpful and happy working with the real world dataset and problems

A very good one, but it would be great if more challenging exercises and examples were added.

Overall solid course. Could do without peer review assignment, more of a hassle than anything.

i wish there are more (differently structured and defined) datasets to practice in the lab.

Perfect Course, It was very useful to understand the basics of inferential statistics

Good and accessible introduction to hypothesis testing and confidence intervals ...

If you want a course that's 90% statistics and 10% Python, this would be it.

Need Intermediate - Advanced skill level in Python.

good examples expected to have more examples

Need more improvements in engaging students

More mathematic explanations would be nice

Wish there were more practice problems.

Peer Graded Assignments are a joke

very very high level statistic

I really enjoyed the course.