Ratings and Reviews for Inferential Statistical Analysis with Python

Wish there were more practice problems.

Good Python tutorials that gives a good paratactical introduction to the theoretical core of the course.

Very well done. A lot of practice.

Some parts can be explained better

I found Brady T West's videos in Week 4 to be unnecessarily confusing causing me to have to go back to Week 3 lectures to clarify the steps of hypothesis testing.

It was very good course, everything was very well explained and the activities were challenging enough to practice the knowledges obtain.

In this course, they cover making confidence intervals and calculating p-values given a specific test scenario (compare sample proportion to population proportion, sample mean to population mean, two sample means to each other, etc). While they go though each statistical procedure clearly, I feel like a lot of underlying context is missing. What is the different between a z- and t-distribution? Why do we use those distributions? How do the different tests relate to each other? Etc. It feels like this course needed an extra 50-60 minutes of lecture time to tie all these concepts together. A textbook to follow along would have been great too.

I really appreciate the course and let me accumulate a lot of knowledge about statistics. And I have developed a good impression of the University of Michigan teaching level.

The content explanation is excellent and one of the best I have seen.

A complete course focused on teaching the details and intuition of experiment design, inferential analysis for decision making through confidence interval ans hypothesis testing and how to state effective questions. I would recommend this course to everyone who are seeeking for more explainability and improvements in its ability to solve complex problems through data analysis.