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The structure is excellent, with videos, readings, and quizzes. The labs are a great way to practice the material learned in the videos and readings. Additionally, the breadth of content is great. I feel like it covers a great amount of Python to get started on your first projects. That said, there are a lot of errors in the material and the labs. I reported most as I came across them, but it can be frustrating when something is explained incorrectly or you are given contradictory information. There are many grammatical mistakes in the lab explanations, as well. The mistakes do not ruin the course, as most of the information is phenomenal, and a lot of detail is packed in. However, it significantly hurts the experience and makes the process frustrating. This is on top of the frustration of labs including code that is not explained. There are some functions used in the sample code, and keywords used, that are not explained in comments or the lab preamble. Maybe the idea was not to overexplain, but I found this is very frustrating when it happened. All that said, this course is still worth 4 stars. It's a lot of information with a lot of resources given. I'm glad I took the course (although with the caveat that it was free since I completed it during the 7-day free Coursera Plus trial).

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.