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.
Ratings and Reviews for Inferential Statistical Analysis with Python
Back to course Page
Reviews and Ratings
Reviews
This is a very great course. Statistics by itself is a very powerful tool for solving real world problems. Combine it with the knowledge of Python, there no limit to what you can achieve.
It's just an exceptional course, 101 out of 100 would recommend, Learned a lot from it. Thank You
Detailed explanation and clear syllablus.
It's an amazing course! very clear, very informative!!
Perfect Course, It was very useful to understand the basics of inferential statistics
Great Course. There are so many example to understand the topic. I really enjoyed every lesson of this specialization. I am going forward for the next one.
good
good
Need Intermediate - Advanced skill level in Python.
