Ratings and Reviews for Inferential Statistical Analysis with Python

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!

This course went into great detail about how to calculate confidence intervals and p-values, what these mean statistically, and some cautionary tales and misconceptions regarding them. It was really repetitive, which I appreciated, as it really drills the concepts in, but if you really catch on quickly to mathematical concepts you might find this a bit annoying. The applications with Python are quite useful - the course goes into detail not only with how to manually input values, but encourages you to write your own functions and ultimately access the statsmodels library to simplify this. It's nice to know what's going on under the hood in the libraries we so often depend on.

Great course! Clear, concise, interesting. I highly recommend it.

I enjoyed being part of this course and it was really a great experience.

Best course with in depth coverage of topics related to Hypothesis testing and inferential statistics.

Great in-depth content of further statistics, applied using Python Jupyter Notebooks. Python Code was comprehensive and enabled easy following.

I really don't see the reason for all the hate for this course and the specialization. Pros: Robust syllabus on statistics and mathematics that covers all the important concepts in inferential stats Ample example python notebook files for students to reference High quality lectures and content Manageable assignments and quizzes Lots of guided examples (week4) and excellent readings written by UoM on statistics and data analysis theory and practices. Student forum support from lecturers is excellent Cons (minus 1 star): While the material in this course is good, we should be given some notes with formulas and diagrams to accompany us at the start of week 2 and 3 (the hardest ones) A person without a background in python will struggle in this specialization because you need to have programing skill and experience and the introductory practices are not enough. You need to have some prior experience with stats or a pre-college/college year 1 text book to accompany you if this is your first time learning stats. The start-middle phase content at each chapter is explained and NOT skipped, but it could use more elaboration. I had to source elsewhere on the internet for the gaps in my knowledge (which were easily found). It is just missing a few elementary level explanations (how to calculate P values and what tests to use in different scenarios) to understand the more complex topics. I learned hypothesis testing in high school and had to refer to my textbooks for a few explanations and diagrams. Summary: Very satisfied with this course for what I got out of it, I gained multiple skills and a lot of familiarity with theory and examples.

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.

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.

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