Ratings and Reviews for Inferential Statistical Analysis with Python

Great course with practical assignments and examples. Learned alot and refreshed stuff I should know well.

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.

Great course for people who loves statistics and wants to apply these concepts in Python.

I guess I managed to learn some coding tricks with Pandas in this course, but I'm not sure what else it was supposed to teach. To the extent that I learned any statistics concepts, it was by searching on google to try to find explanations of whatever the instructors were talking about. They seem to have no interest in explaining anything, to be honest. They throw various equations at you without any indication of where these equations come from or why they work. Sometimes very important points, such as degrees of freedom in a test, are only mentioned as an aside. Really felt like a waste of my time and money. Hopefully there are some better statistics courses out there.

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.

Clear and detailed explanation of inferential statistics. The course approach is more by blackboard than what can be interpreted by the title "with python". Although there are some examples in python, there are not many exercies for the student

Mistake in the course instructions and very redundant material. A better understanding of the concepts rather than a series of walk-throughs for different scenarios, would've been better suited to me. Recommended external resources were good. Overall, an ok course, but definitely not the best in terms of design.

This "Inferential Statistical Analysis with Python" course went in depth into the topics of confidence interval and hypothesis testing in ways that were not covered in school. You will learn how to perform hypothesis tests for key areas such as : 1. (Single) population mean 2. (Single) population proportion 3. Difference in proportions 4. Difference in means for paired data 5. Difference in means for independent observations/groups The course covered lecture videos, well-prepared readings, Jupyter notebooks to introduce concepts as well as practice notebooks, lab walkthroughs, written assignment and quizzes. Brady may speak alittle too fast, especially when it comes to long sentences, so you may need to rewind certain segments of the videos numerous times to revisit some concepts as you reflect and learn. Through the Jupyter notebooks, you will better understand challenges when working with datasets and the nuances of how to perform these hypothesis tests in real life, compared to "sampling design and hypothesis testing" on paper (back in school in the past). The discussions forums was also actively monitored by a TA who got back to me usually within a day, which helped to unblock conceptual roadblocks quickly. You may encounter some issues with the Coursera platform: 1. Labs may fail to load at times, even after following instructions to restart the machine which your lab runs from 2. Your discussion forum comments may disappear right after you post. Remember to copy your comment somewhere else (e.g. on a notepad) as you may need to refresh the page and post your comment again.

Could be made more organized, like the first course in the specialization series. Seems there are some missing gaps (or assumptions of things being covered) that made it a challenge to smoothly proceed in the first 2 weeks of content.

Practical course with numerous examples to illustrate statistical concepts