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For more than two decades, the CSU system has been a leader nationally in adopting programs to strengthen students’ math preparation and smooth transitions between high school and college. Its announcement at yesterday’s trustees meeting that the system will double down on these efforts—rather than increase admissions requirements—is welcome news, particularly given concerns about students’ math learning.

Debates over math education too often focus solely on course content or—even worse—superficial questions about course titles, at the expense of the important issues of what students are learning and how they learn it. Yet, evidence abounds that, regardless of course content and titles, actual math learning is often limited and shallow.

In my new role, I continue to build a community of educators, policymakers, and student advocates dedicated to the sustainable advancement of high-quality, equitable mathematics education. Together, we can create math pathways that open, rather than close, doors for students.

The field of data science grew out of statistics. It employs statistical methods. There is significant overlap between the disciplines, but they are distinct enough that numerous universities have seen fit to have majors, and even departments, for both. Data science is multidisciplinary. In addition to statistics, data scientists use programming to manipulate real-world data to answer questions...

Is data science mathematical enough? Is it really math? Shouldn’t it be taught as an elective or in a social science class? Doesn’t introducing students to data science interfere with them learning the mathematical canon? What is important to note is that arguing for the inclusion of data science in high school shouldn’t be misconstrued as an attack on mathematics itself.