Decent videos on Data Analysis techniques.

But the labs are poorly constructed: typos, inconstant question and solution, un-commented code and under-explained lab result.

It's a shame since the labs in other courses in this series are very high-quality.

Few small hiccups with the training videos and quite a few in the lab-excercise

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.

This course is definitely very helpful to people who are passionate about Data science and have basic to intermediate understanding of Python but this course can be much better if it includes coding assignments rather than quiz submission. It was a great experience.

Too fast and could have included more examples.

Typos are very unprofessional and spoil impressions of the course. Tests and labs are super-easy and do not make you think, and you only need to repeat commands from the lectures.

It was difficult to retain the knowledge imparted.

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

Very Math!

Not very interactive with fewer help to learners

not in depth.... needs more clarity on a variety of topics

The content of the course is very interesting. There are lots of typos in the lab workbooks though. Additionally, i found having to use Watson Studio for the assignment / labs as opposed to plain Jupyter a little annoying.

Week 4 is somewhat hard to follow - Here, an overview over the different concepts would really help

Good content, but lots of typos. The outsourcing is extremely evident.

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

Exams should be harder

More explanations would be great. Its very difficult to understand Data exploration / evaluation sections

Lot's of errors in this course, please update and correct it.

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.

A lot of errors make the course confusing. Also, the assigments and labs are "too easy"... it is clearly shown in the videos that there is much more to be done, but the course only demands you do about 50% of what is taught. How are we supposed to really learn without practice?? Give us real and demanding projects!

Please update and explain Rigid Regression a little more

Material to learn data analysis was very good but had quite a few bugs. It was very annoying to review the assignment of a peer because it is not possible to zoom into the screenshot. Furthermore did I need to flag a person because he had copied screenshots and his notebook was empty or only with screenshots but I was still required to review a second person to complete the course.

la lala la la laa aaa

I'm not convinced that this is a great way to learn, I just feel there needs to be a better way of learning this than the approach this course takes, I kind of learned the python commands but I'm not sure I understand how to apply them in the real world. We'll see

More examples and code examples needed