This looks great! Thanks for sharing. Interestingly enough, from looking at the table of contents, it seems this book starts with a more (and welcome) pragmatic approach, where you write some python code before, look at data visualisation techniques, etc, before delving into stats.
I haven't done the course yet, I've just found it. But, from the rationale video, the course seems to be more about weaving recurrent fundamental data science concepts throughout, emphasizing one particular concept or technique in each chapter, so I guess that it would make more sense to take it as a whole.
It is intended as a "glue" course, having completed CS fundamentals and before core data science courses, like statistics, machine learning and databases, giving students a context for what lies ahead, and just enough to be dangerous and start doing data science stuff.
If this is what you are after, you may also want to consider CMU's "Practical Data Science", which seems to have a similar approach, videos, much more machine learning and big data, and is also very current, but doesn't have such a nice companion online book (but the notes look great) and has much less statistics: http://datasciencecourse.org
Both look like great DS intro courses from top universities, we are spoilt.
And then, also from Berkeley, there is "Data 8", which is intended for those who want an intro to data science, but don't have any programming or college math knowledge yet; it also has a similar online book with working links to Jupyter notebooks: http://data8.org/sp19/ (and videos: https://www.youtube.com/playlist?list=PLXbeRfilLvMoC3QZKxRrp...)
Course design: https://youtu.be/HITIm3KoU2U
Course website: http://www.ds100.org/sp19/