A good introduction to those who want a brief taste of statistics

Very interesting course as it included very powerfull tools.

Excellent Course...Would be great if add few more examples

It easy to understand. Not too difficult. Not too easy.

the course is more useful and cover basic concepts

Well Explained with guided project.

Extraordinary. Very interesting.

A​MAZING EXPERIENCES WITH IBM

Outstanding course!

Thank You IBM

Good

ok

Overall this course provided content to familiarize oneself with statistical analysis in python. I'm particuliarly thankful for the step by step labs and excercises available on IBM. In some cases, the course materials don't seem to cover content that is included in the evaluations. In those cases, I suggest to reference outside sources. Also the experiences with IBM Cloud have been frustrating. Partially becuase the environment is at times unavailable when needed. In addtion the environment has been undergoing upgrades and changes, and the course materials are not up to date with the changes in the cloud environment. Ultimately though, dealing with unstable computing environments and reasearching outside sources to successfully complete projects are skills possibly more valuable than knowing how to compute statistics with Python.

Good course, many subjects are covered. But be careful if you're totally new to statistics and hypothesis testing, this course is rather fit as a refresher.

Unfortunately the lecture slides are not available for download and some of the transcripts need serious amendments. In all Jupyter labs the kernel did not connect for a long time and attempts to export notebooks as pdf threw internal server errors. Such things are disturbing and could be prevented with proper monitoring and proper technical setup. The peer review in week 6 must be performed without having the approved solutions; this is not very professional.

Overall, the course gave me a brief but informative look at the basics of statistics with Python. Once again, the many practical exercises were very nice. However, the speed of the p-value and regression was a bit too ambitious for me. Would have appreciated some more details there or a good link to somewhat short and informative. But as said, overall, another very informative course.

This was an absolutely useful course to introduce the student in the topics of normal distribution, calculation of probabilities and hypothesis testing applying Python.

Visualization and statistic charts are covered as well.

Examples were given in a meaningful way, nevertheless I would give 5 stars if teachers could focus more on the theory of inferential statistics.

Quick basic statistics with python. Some topics were explained better then others. For example t-test was explained well from statistics point and how to do it in python, meanwhile linear regression was just shown how to do it in python and very quick overview of output data. No background explanation or how to do it by hand.

It was a decent course.

It could be more "learning by doing" oriented, there are some concepts like hypothesis testing that could be presented in other way, It'd be helpful if it had some real world examples of that.

I loved learning here; it was explained so well and all the modules here are too fun to learn <3

I highly recommend this course for anyone that is having problems with basic statisitcs.

very interesting course, however, IBM Watson Studio was difficult to use

Good introductory course

The course felt disjointed at times and there was a lack of clear explanations. The expectations for the final project (formatting, etc.) could have been stated more clearly to reflect the marking rubric. The final project was otherwise nice and quite summative.

Not the greatest course on this platform. The structure of the course is somehow confusing and it's got a bit old, should be updated and offer better knowledge.

Many of the concepts mentioned in the lectures or the quizzes are never clearly defined. Quizzes test concepts never mentioned in class, and one question contradicts what was taught in class.