California's proposed math guidelines claim to promote equity by offering a data-science alternative to the traditional algebra-to-calculus track. The State Board of Education delayed approval after critics charged it would lure disadvantaged students off the track to STEM success.

In an Education Next forum on math education, University of Chicago economists Steven Levitt and Jeffrey Severts argue for teaching data literacy to all students, while Boaz Barak, a Harvard computer science professor, and Adrian Mims of The Calculus Project make the case for advanced algebra and calculus.

Levitt and Severts propose streamlining algebra and geometry, which now take three years, so all students have time to "learn how to analyze, interpret, and visualize data."

We could teach them the difference between correlation and causation. And perhaps most importantly, we could help them understand the limits of data, so they would know when to be skeptical of data-based claims.

Data skills should be taught early, so they can be applied in students' other courses, they argue.

They oppose a separate data-science track, arguing it's not a lesser, easier option for students who'd struggle in advanced algebra, trig or pre-calculus.

A high school data-science class will be a superficial introduction to "data literacy," not an "equitable alternative to calculus," Barak and Mims write. It will not close academic gaps. It will -- if students never go on to calculus -- close the door to STEM futures.

Far from being relics from the “Sputnik era,” calculus and algebra are more important than ever in K–12 education.

. . . The field of data science builds on mathematics, statistics, and computer science, and a thorough data-science education requires foundations in all three fields. For this reason, taking advanced math courses (algebra II, precalculus, and calculus) is a much better preparation than high-school data science, even for students who are interested in data-science careers.

"Closing education gaps requires improved teacher recruitment, training, and retention," they argue. Without excellent math teachers, curricular changes will do little good.

Moreover, creating “data-science pathways” as alternatives to the standard pathway can and will have a particularly harmful impact on disadvantaged students. Such pathways emphasize proficiency with computational tools such as spreadsheets over the mathematical concepts (functions, equations, symbolic manipulation, and logical reasoning) that are crucial prerequisites for more advanced math and that also build the type of thinking needed for coding. Hence, in practice, data-science pathways will become lower tracks by another name.

Educated, tech-savvy parents will steer their children toward the traditional math track, they predict. Not all parents will understand the trade-offs.

A petition signed by California professors suggests teaching data literacy in science and social studies classes -- but not as a replacement for advanced algebra.

These people have interesting ideas about math, but don't seem to have interacted with a lot of students. The types of students who could do consolidated math (I'm assuming that means faster, like going from 3 years of alg. 1, alg 2, and geometry) are also the type of student who could take both calc and stats their senior year, add a year of stats and still get to calc. Many students struggle to get through those classes at the regular pace, and some struggle to manage them at any pace.

Temple Grandin makes a good argument for having alternative options available for kids who just can't do the regular classes (she never could wrap her head around the abstract…

This is a false choice: "educated, tech-savvy parents will steer their children" towards integrated mathematics, which, at the advanced placement level learned in Singapore for Oxbridge, includes both calculus and statistics, without both of which students are ill-prepared for university.

Speaking as a former physics teacher I suspect that a reason for the data science push is that it lends itself well to "projects". The students will run around doing surveys on favorite colors, ice creams, etc., formulate "hypotheses", like "seniors like yellow more than sophomores", have excel make pretty pictures that look good on powerpoint. A good time will be had by all (but there won't be much math)

I recently judged a high school science fair where the students from the STEM magnet school said that they were taking statistics in freshmen year so that they would understand the data analysis of the hands on experiments they are doing in school, at intern programs, and for science fairs. Waiting until being a junior or senior in college to take statistics is a mistake.