Ratings and Reviews for Inferential Statistical Analysis with Python

Great course! I learnt so much, this is really helpful to make more sense out of the thousands of stats that we see everyday!

My final specialization course certificate not received, even after completing all courses in this specialization.

Very good explanations by the lecturers.

I think I have gained a sense of how scientific research is conducted. There is still a lot to be digested. The exercises are very helpful.

Dissapointing. Full of errors during all the course. I came from the first course and I found the second one dissapointing. Some issues I found I thonk they should solve: *The assessments have no sense. Which is the sense of asking if 0 or 1 corresponds to napping or not napping from a datasheet? In general I also found myself wasting a lot of time trying to understand the definition of columns of NHANES or other datasheets which I think is useless. In overall I found that the assessments do not evaluate the progress of the student. *The assessments sometimes are not related to the concepts explained during the week (Name that scenario) or in the case of Pythos quiz ask for unexplained functions (week 3). *Some assignements, like the Chocoloate assignement, are about previously not explained concepts (cross-over test desing). *The Jupyter Notebook have several errors, reported in the discussion forums but not solved. Moreover, some parts of the code shown are not explained. And finally, the way to solve a problem is different in the explanation video and notebook; this is very confusing for the student. *Week 3 explains several times the same concept (CI and p_value) but them skip to explain the Power or SampleSize in detail, which are just mentioned in a Jupyter Python notebook. Moreover, I found missing a detailed explained on when to use z_test or t_test; I would had dedicate some time to this explanations, and less to repeat the same CI and p_value examples over and over. *Week 4, is just a review of week 3. I like the way Brady T. West explains the concepts, but once agains I found extremely repetitive showing so many examples of the CI and p_value.

Some concepts not covered in much details - ttest, ztest, one tailed test, two tailed - how to use them,when to use them... one needs to go outside of the course to first understand them. These concepts are not explained but are asked directly in assignments which leads to a lot of confusion.

The best part of this that it is designed in a way that it encourages people to dig deeper and explore more. The instructors have done a great job in making the curriculam this good.

They could increase the rigor mathematically on this OR spend more time on creative code. Code was great. Math was very very easy so you don't have to listen to the videos if you don't want and just grab the lecture slides. I find on many of these courses they are either way too rigorous or not rigorous at all where the lectures are a bit of waste and you can just read the books/slides (Sounds like Undergrad math/stats lectures all over again? aha) HIGHLY Recommend course if anything for the CODE in the notebooks. If you're just getting back up to speed on Pandas I found that these helped with sorting the data and reminding you of the difference of built in methods for stats with pandas and numpy. Laslty, I did not like that only 3 people grading the assignment if we are going to do this crowd sourced grading. I found one grader in particular didn't understand what a p-value was and marked me down and another from the previous course didn't have a handle on english enough to understand what I was writing on my memo. That said thank you for the course.

Peer Graded Assignments are a joke

Highly recommend this course.