I am also a lifelong minority activist, Bernie supporter etc. My bio is here.
The SFUSD had been the centerpiece of CMF advocates' promoting the new curriculum. But after the failures in the SFUSD, the advocates distanced themselves from the district. E.g. Prof. Jo Boaler, a CMF coauthor, said that the CMF "is absolutely not based on what is happening in SF" and that the San Francisco district is "barely mentioned in the [CMF], if ever." When Prof. Boaler was challenged on that latter point in Twitter, she replied, "You are basing that on an old version [of the CMF]." Even the current, January 2021 version cites SFUSD in Chapters 1, 2, 3, 8 and 11. The CMF people have prominently mentioned SFUSD many, many times, for instance in articles by Prof. Boaler herself, such as this one and this one. In other words, the failed SFUSD curriculum has indeed played a key role; it's wrong for the CMF folks to distance themselves from it.
SFUSD remains the CMF people's only full-scale experiment of the CMF principles, and it clearly has been the "star witness" in the CMF leaders' public statements promoting the plan.
Yet the CMF people's Data Science course is in turn is a watered-down version of the existing Statistics course, with a reduced emphasis on probability math. The Statistics coverage on the binomial and geometric distributions, and Bayes' Rule, has been deleted in the Data Science course.
Open-ended data science fits right in to the CMF desire to teach kids that "There is no right answer." There is a grain of truth to that, but kids must learn how to get the right answer when there is one. As one critic rather sarcastically but in my view aptly put it, "...if you throw a bunch of data at students, they can group it, type it into a spreadsheet, plot graphs and so on. It's pretty easy to convince yourself they are doing something called 'Data Science'. They don't have to do anything particularly useful with the data because there are no right answers."
Similarly, the SFUSD combined 2 one-year courses, Algebra 2 and Precalculus, into a single "condensed" course. A teacher in the district noted that many of the original topics were dropped in the process of squeezing the material into half the allotted time; and clearly, many that were not dropped were covered in less depth. And Prof. Boaler notes that the CMF calls for Algebra (not clear, 1 or 2) to be combined with Geometry, again necessarily dumping some material. Geometry is traditionally of central value to building students' general reasoning powers, so weakening it is of particular concern.
And we all use mental math on a daily basis. We don't want to produce high school graduates who can't calculate in their heads when they see a "10% off" sale in a store, or need a calculator to find how many eggs there are in 3 one-dozen cartons.
Star teacher Jaime Escalante inspired impoverished Latino kids in East LA to achieve national-level fame in the AP Calculus Test, not via a novel curriculum but by instilling confidence in them, and leading them in hard work. Of course very few teachers can invest the tremendous energy he devoted to accomplish this, including working very closely with parents, but the point is that he did not achieve it by tinkering with the curriculum or watering down his teaching methods.
The philosophy of the CMF and similar approaches, emphasizes (a) use of applied examples and (b) collaborative work. Some comments:
I applaud the use of applied examples to motivate kids. But the kids must master the fundamentals -- yes with drill! -- in order to get even small benefit from those examples. As noted above, one needs to develop a "sixth sense," a feel for numbers, to understand and apply them.
I do not oppose collaborative work. But as one teacher put it, math is not a passive activity. Children must do extensive work on their own, with collaboration coming later.
I do not disagree with the CMF's point that students need to be able to integrate disparate concepts. I emphasize this greatly in my own teaching. However, this must occur only after students master the individual concepts separately; teaching them together confuses even well-qualified university students.
I am the author of several books on data science, including this one and this one that actually have the term in their titles. But for that very reason, I strongly oppose trivializing the topic, which requires a firm mathematical basis for understanding, and whose misunderstanding can lead to serious errors. We've seen many statistical misconceptions during the Covid-19 pandemic. The vast majority of teachers do not have the background and experience with data needed to give sound courses in this area.