This is not a Python introduction, but the authors approach it as if it were. Even the most basic data scientist will not calculate averages in the way described here. We'd use pandas or similar to get not just means, but other summary stats as well. For a Python course, I could understand doing it the way shown here. But not for data science.

Excellent to start your career in machine learning!!!

Over all a great course for beginner

The positives: I liked the design of the final project, and how users were encouraged to 'get out there' and find some interesting open source data sets. The lectures were well structured with good narratives and good examples.

The negatives: I would have liked a bit more focus on actual visualization libraries like matplotlib and maybe seaborn. When covering the data types (date, string, boolean etc.), it might be worth adding an extra week or so were these things are done with the help of the standard library pandas. I feel like this is what people will end up doing anyway bc there are so little alternatives in python to do processing, so a course on data processing should ideally cover that library.

The test scripts make no sense.

Great first class in this series.

The course is easy to follow, well organized, and assumes very little background. It effectively demonstrates the power of Python in large data applications and provides insights and guidance on which tools are best used.