Comentários e feedback de alunos de Análise de dados com Python da instituição IBM

There are lots of typos in the labs and assignments, which can be frustrating. I expect better quality from IBM. Content is great and easy to understand nevertheless.

This course is quite good. The bad thing is that the arguments of the last "lesson week" are treated very superficially, taking for granted some advanced knowledge.

A lot (too much maybe) is covered in this coarse

It really helps a lot when you know some statistics. Like linear regression,

Why gridsearch was covered I wonder.

While a bit fast and loose with the concepts, does contain a lot of practical code as to how exactly to bring things discussed about, which is appreciated.

Thanks for teaching me the systematic way to do data analysis! However, I found quite a few mistakes in the lectures in this course, hope it will improve!

IBM Cloud is difficult to use.

The generated link of notebook will not share the latest version, if you click the share icon before editing the notebook.

Data visualization was taught in details in course 7 and regression was taught in course 8. With no backgrounds, the codes in this course are scaring pe

The section on model development and evaluation is not so clear. It is difficult to understand if you have no prior knowledge of machine learning.

Course was great however i felt a lot of material was covered in a short period of time, this course can be 2 or 3 courses based on the content

It is missing a last project like in the two other courses... It would have been quite fun to be able to apply what we learnt in a project.

Lots of spelling and grammatical errors that made it difficult to understand some of the material. It is otherwise an interesting course.

There is no slides to download or review after class which makes it hard to go over the knowledge again without watching the video again

The video is too short and many concepts remain unclear or poorly explained. More contents need to be added to the questions and tests.

It's an excellent course; the best part is the labs. It's a pity that we (the students) have a lot of problems loading the labs.

Little bit confusing, unstructured and not easy to follow. Material inside is good though, but the course needs to be improved.

Things are too fast in this course and many things remain unexplained which makes it difficult for one to understand properly

Too many errors in the code and explanations. Makes it very difficult to understand which is the right procedure/conclusion.

This course focuses a lot on the theory and explanation. However, there isn't much hands-on practice for the coding itself.

*No response to some questions/comments on the forum

*More details/thorough clarification required for some points covered

There are many errors and this was even the toughest course I have taken yet since many things were not explained clearly

Course is flawless but when i had a technical challenge the Coursera team were clueless on how to fix it.

Otherwise good.

Few of the video requires improvement in terms of its quality. Particularly the lectures corresponding to week 4 and 5

Too complex for easy understand. Should have some documentation explaining the process and comparing the new methods.

Topics covered are important but videos end abruptly as if narrator was saying something more and video just ended

why is sharing of the notebook worth 3 points? That has absolutely nothing to do with python or data analysis!