good

Nice

good

good

well

s cd

good

GOOD

Good

Nice

Gud

yes

na

NA

很棒

ok

Ok

5

The quality of this course is very good. It helped me to get a basic understanding of exploratory data analysis. Whereas the first weeks topic was more or less early for me, the seconds weeks topic about statistics was more challenging and I also had to do some own research to deepen the contents discussed in the lectures.

The course is very good and provides a detailed knowlegde of exploratory data analysis and a very basic revision of statistics and hipothesis testing. Only some of the iPython labs have minor errors in their content and need a review, which don't affect the learning experience much, however.

I like the part that how to work on feature engineering. I understand machine learning is statistic, but I felt week3 week 4 are teaching me how to use python to analyze the data. I was hoping to learn more about machine learning models and optimize their outcome.

I really liked this course, has been extremely useful for me as a starting point for next IBM courses. One suggestion to improve - some concepts are covered a bit superficially, in my view, e.g. Hypothesis Testing. Maybe going a bit more into theory would help.

1- The Lab videos are not clear enough, the font is too small, so hard for eyes to see what is written on the screen. 2- Most of the time Jupyter lab (individual work) was not opened. moving to Spyder is easy but why not to fix this problem? Thanks.

The information was good though basic. I thought the info on hypothesis testing and probability was probably not necessary for an ML course where this should be assumed. The teacher was clearly reading off a script which was at times not so engaging.

The first part of the course was very good, in the second (week 4) I had a hard time understanding it and it seemed to me that too many concepts were given for just one week. I loved that application examples were made to reinforce the concepts.