insufficient statistic, as the name of the course is Data Analysis, i would expect more classes about the different distributions of data, pdf and pmf..

One of the best courses in the IBM Data Science Specialisation.

I would like to have more quiz questions and opportunities to practise what was covered.

There must be a slightly high level of Quiz, assignment and Project and must have to add some more advanced concepts about statistics and probability.

I would have given it 5 stars but they barely went over polynomial regressions and pipelines and it was a major portion of the end of class assignment.

pros: well organized, clearly explained each step, useful

cons: frequent errors in both videos and the lab, especially on the questions part in the lab

Final exam was too complicated. I have 2 masters degree and for me it was clear, but for other it is too complicated.

P.S. it is my personal opinion

Course is good, but in this short course it is covering lot of thing thatswhy lot of topics are just touched intead of going little bit deep into it.

You learn a lot, good intro to data science with python. Labs have typos and can be confusing at times though, the only thing that could be improved.

A very interesting and easy course. Anyone can catch up with big concepts with little effort. Thank you Coursera and IBM for this wonderful course.

Course is nicely designed and pare explained well.

I would have liked to see the steps along with the final answer to the peer assignment questions.

Very good course. I wish there were more hands on exercises. We only had a chance to practice in one lab per module; otherwise, I learned a lot.

Great course to start learning python applied to analysis, but after this, I prefer to use R. Less complicated and can obtain the same results.

The course material is good. However, I found the videos a bit fast and the automated audio speech is not so good. The lab sessions are great.

I find this course useful. But some of the contents are little advanced all of a sudden and feels some important explanations are not covered.

Through out the course i have learned alot like data visualisation mainly.I think i have completed successfully basics for machine learning.

I feel like this section needs some more hands on labs. Great topic over view and application. Not to much in the way of math unfortunately.

Very informative course, overall it is very well-done, however, there are some issues regarding a few depreciated attributes or parameters.

Exercises could be more difficutl, however I really appreciate the content and the way the course is given. Congratulations to the authors.

Good course for beginners. Can introduce little more concepts such as multi-collinearity, model accuracy etc to make it even more complete.

A great explanation of the concepts and methodology in data analysis , i wish we couldve gotten more peer reviews like the last excercise

It was a good course overall. Would prefer explanations at a slower pace and more examples for each of the techniques explained.

Thank you!

Content : 5/5

Labs : 5/5

Final Assignment : 3/5 (It was quite easy to complete as there we instructions and code already written for you).

need better practise questions preferably to write program instead of multiple choice answers plus needed more theory of the topics given

Very good course, i belive its recommended to explore and read documentation of the different python libraries for better understanding.