It is a very good course for beginners and learners with some level of statistical understanding. Thanks to all of the mentors especially I want to mention Professor Brady West. He is an excellent teacher, very articulate, and helps understand the topic from the very core of it. However, the python lab videos could be improved. Sometimes the modules were not clearly described. Overall, I have a very positive experience taking the course.

All instructors were very knowledgeable. Special mention goes to Prof. West. I found the last section (week 4) very insightful, detailed and rigorous. I would have loved seeing a deep discussion on the theoretical and practical choices behind the Null and the Alternative Hypothesis. I am still slightly confused on the purpose of the Alternative hypothesis. Overall a great course!

I Used to have some trouble in understanding Hypothesis testing as a concept, but after completion of this course I got a solid Idea on the whole concept. Thanks to the instructors for making it easy. But you can add a cheat sheet for the formulas of various Confidence Interval calculations and Hypothesis Testing. It will a good way for us to summarize while revising the topics.

Excellent course! I really enjoy the combination of Statistics-based Python assignments. The Jupyter Notebooks are well written, easily documented, and there is plenty of lecture material to confidently complete the assignments. I find this makes it much easier to learn both Statistics and Python simultaneously, without any frustrating"This wasn't covered in lecture!" moments.

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.

my favorite course in this specification. The subject on hypothesis testing is well designed in this course, the instructors are good, reading are insightful, python programming illustrations are easy to understand even for a new programmer like me. Shout out to Julie, a five star instructor who has a beautiful voice!

Great course...It is well organized, tutors made complex concepts very simple, I learnt how to find CI and do hypothesis testing in python. Overall very good experience. Hope the course material is accessible to me later as well, I need go through it again to reconfirm my understanding of complex concepts

It's a great course. I struggled a lot before to understand hypothesis testing, confidence intervals and how to interpret such stuff. Now, I consider myself fully able to easily handle these concepts. The teaching approach is gradual, step by step, and will convey the info very successfully.

It is a excellent course, a lot of examples, guides and lectures than are very helpfull. The only thing that I could change is the use of only normal distribution in all examples, in the real life we must use some other probability distributions and is important to talk more about these.

I learned a huge lot from this Course! I would have loved to have more references on how to account for sample weights and the non-parametric testing part. But the content was super clear and my inderstanding of how statistical inference works change from earth to heaven!

No online course comprehensive than this one, applied Python skills along with important theoretical statistics concepts emphasized throughly. It has an applied approach also not delving into deep theory rather focusing the student to what and how to apply.

All are excellent, except that for peer-reviewed exercise, I think more guidance about correct answers should be given, because I found some peers didn't fully grasp the concepts and wondered if they could grade the other people memos appropriately.

Excellent course the professors transmitted in a synthetic way the essential of the statistics, in order to have a global vision.The practical cases allowing to directly apply the new tools, this training is simply brilliant!

Great Course. Very lucidly taught. The instructors have done a commendable job in breaking down complex statistical inferential methods into simple parts and explaining the same with diagrams, examples and resources.

O​verall it was pretty heavy. A certain delay was observed with the perr-reviewed assignment, but the content is quite good. It is a step-up from Course 1 however, and the pace has increased quite significantly.

Exceptional course- Brady T West explains everything so brilliantly and I love the recaps and plentiful examples. There are a few things referred to that then don't feature in Python Labs though e.g. Chi-Square

Material was presented in an organized fashion. Very helpful discussion forum. My questions were answered the same day, usually in a few hours. It was the best beginner statistics class that I've ever taken.

G​reat course. Wish there was a little more time spent covering how to do things in python (those videos went very fast and sometimes reading the documentation doesn't help much), but overall Very good!

It was great. I could get a experience hands on and every skill were very useful.

In other stats courses, I mostly felt hard to embrace the thoughts. Here, the instructors were very very insightful.

Very good course content and mentors & teachers. The course content was very structured. I learnt a lot from the course and gained skills which will definitely gonna help me in future.

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.

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.

If you are interested in statistics and statistical analysis, this course gets you grounded in the essential aspects of statistics. Excellent instructors.

A very in-depth learning material for inferential statistics. Very good explanation of p-value which clarifies some of the prevailing misunderstandings.

Great course with practical experience with Python. There are many courses that teach statistics with R but this is the first one to do so in Python.