I​ think the lecture about grouping could be improved with more practical examples, I had to search for external sources to understand the concept.

Not at all for beginnners. Fast-paced with more focus on self-learning and grinding,rather than focussing more upon the concepts. Dry presentation.

Quite bad knowledge delivery from lectures. The course is rather self learning than course. A lot of vague points and uncertainties in assignments.

The course instructor could have been more elaborate with the examples. I felt there was a wide gap between the exercises and the course material.

Assignments are challenging, but some questions are very vague and require lots of trial and error guesswork to get the autograder to accept them.

Assignments are great to reinforce your learning. But the instructor does not cover many topics and leave you with a lot of questions unanswered.

Please update the auto-grader such that is it compatible with current version of Jupyter notebook, very frustrating dealing compatibility issues

This course is a nicely organized. However assignments are not completely clear. Especially assignment 4 needs more explanation and details.

Subject matter was very good. Some of the assignments were not clear on instruction, and some of the Coursera functions were buggy or broken

Material delivered a bit too rapidly to effectively assimilate. Often, further external research is needed to find solutions to assignments.

I don't think this is a good enough course to "teach" you "data-science". All this does is give you an overview of things you need to know.

The assignment descriptions needs to be precise (with psuedo code).And the statistics part needed a lot visualization to aid understanding.

The material is valuable and taught well. The lectures are impossibly fast paced (lots of pausing) and the assignments are often ambiguous.

Assignments were more difficult than they needed to be because many of the directions were unclear. Otherwise, the class was interesting.

Great Assignments, Great learning, but requires good "prior" knowledge of Python and Pandas. This is more of a refresher course in Pandas.

Much more time needs to be invested into theory of the data frames. The course is too lightweight for the heavyweight topic it's covering.

Course materials should be scrutinized. It's like the mentor is going through a scripted page. I understood very little from this course.

Good course for the basics, but the assignments are very difficult as lectures do not cover everything which is asked in the assignments.

Great Content. Course Auto-Grader was immensely frustrating. Videos aren't very helpful except to identify where to do your self study.

The course assignments are at a very good level. But, I feel the course doesn't prepare you for that. Most of the work is self-learning.

The examples used are so small it's hard to understand how to use these skills in real-world situations if you aren't as used to Python.

There should be more Pandas API hints in lectures, it ware really hard to find optimal ways to perform operations on DataFrames I wanted

The course lessons could have been more specific and provide more explanations on many topics that are later required in the assignments

Lectures are interesting but coursework is challenging. It requires significant external reading and understanding to stand a chance.

The demanded exercises were way harder than the content taught. Also, the main teacher isn't didatic, he speaks in a monotonous way.