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Thank you for giving the opportunity to learn Python.

As for my review of this course, I suggest proofreading the labs before publishing them because they have many spelling errors. Since one of the recommended qualities of a Data Scientist is to be detailed oriented, it would be better for all the English and non-English speaking students to have instruction without errors. This will set a good role model for them to be more aware of their work.

Additionally, it would help students who have no prior knowledge of Python to be given some context before starting the labs. There are some labs that expect more than what is explained in the videos.

In regards to creating an object in Watson Studio, I highly recommend including Alex Aklson's video in the curriculum. Screenshots that are provided for the labs are helpful, however, the video is more comprehensive, and the step-by-step process eliminates confusion. Please devote more time to the subject of Numpy as it seems to be a vast subject and needs more instruction and examples.

Overall, this was an informative course that had an enormous amount of material to cover. Thank you once again and continue teaching thousands of students like me around the world.

I thoroughly enjoyed this course. It skims the surface of basic Python you need for Data Analysis, which is exactly what I was looking for. You get a general understanding of basic Python elements, syntax, useful libraries and some examples of really simple data analysis.

The main disadvantage of this course is a couple of exercises at the end of hands-on labs that do not correspond to the course material by their level of difficulty. To me, as a person with zero programming background, it felt like I've just been explained addition on examples like 2+3 and then asked to add something like exponential numbers and square roots. Judging by the discussion forums, I am not the only one who felt this way, which was the only thing to keep me from thinking that I am too dumb for this and giving up. I believe those tasks are great as extra challenges but must be marked accordingly.

The other odd thing is that really useful info specifically for Data Analysis process is contained in optional videos and labs, so I advise future learners to draw attention to them despite their being non-compulsory to finish this course.

Thanks to all the course authors and moderators.