In data science classes, students write computer programs to help analyze large sets of data.

In data science classes, students write computer programs to help analyze large sets of data.

High school math, and algebra, in particular, is in crisis. Although some students thrive on the pathway to calculus, most do not. Algebra I is the single most failed course in American high schools. Thirty-three percent of students in California, for example, took Algebra I at least twice during their high school careers. And students of color or those experiencing poverty are overrepresented in this group.

Some argue that algebra as part of the pathway to calculus is less and less relevant in today’s world and that students would be better served by taking fewer courses in algebra and more in fields such as statistics and data science. The University of California, for example, has ruled that statistics and data science courses can be taken in place of Algebra 2 to meet its admission requirements.

Others push back against this approach, arguing that high-level participation in careers in science, technology, engineering and math will, ultimately, require calculus, and that luring students away from Algebra 2 and into data science will cut them off from these career opportunities, including jobs in data science! Furthermore, many worry that students from disadvantaged backgrounds, who are at greater risk of failing Algebra I, will be those most likely to be tracked into these alternative math pathways, and thus more likely to be lost from the STEM pipeline.

Students should not be forced to choose between math courses that are more engaging, relevant and modern (the data science path) and those that give them opportunities to study the math they will need if they wish to pursue STEM-related careers (the calculus path). All students could benefit from learning about statistics, data science and coding. But if they plan to work in data science, or in other STEM-related fields, they also will need a deep understanding of algebra.

Arguments about what content should be included in high school mathematics fail to acknowledge the elephant in the room: *W**e haven’t yet figured out how to teach the concepts of algebra well to most students*.

Many students who pass Algebra 1 do not master the content in sufficient depth to prepare them for Algebra 2, much less higher-level STEM courses. And many students who do well enough in algebra find it boring and not relevant to their own lives. Students cannot be faulted for their lack of interest in learning about the “steps” required to solve for X or dumb acronyms like FOIL (a mnemonic to help students remember how to factor polynomials). This view of what algebra is cannot sustain most students’ motivation to pursue STEM-related careers.

Data science is not an alternative to algebra; in fact, it very well may be the key to figuring out how to teach algebra well. High school algebra, in our view, desperately needs data science, a catch-all term for the quantitative reasoning and mathematical ideas that go into working with data collected in the real world. Data science has the potential to make algebra relevant and interesting for students who want to understand and improve the world. Data science may be the best answer we have to the question most often asked by high school algebra students: How will I ever use this?

But just as algebra needs data science, data science needs algebra. The basic functions taught in high school algebra (e.g., linear, polynomial, etc.) are used to model patterns in data. We have been pursuing this possibility by developing a statistics and data science curriculum for high school and early college that emphasizes concepts core to both algebra and data science: functions and modeling. Our students don’t learn about functions as mathematical abstractions, but they use them as imperfect models that can help us understand and predict variation in the world.

Whereas in math, functions can make exact predictions, in data science the predictions are almost always wrong; models always have error. The reason this is true is that real data is always messier than the world of pure mathematics. It is this messiness, however, that draws students in. Finally, they see how functions that bored them in theory can help them to make better (even if not perfect) predictions in real contexts. They learn that imperfect models are better than no model at all.

We teach our students about linear functions. But they often ask whether other functions exist to model more complex patterns in data, such as curves. Teachers are thrilled that students want to learn about exponential, logarithmic, and polynomial functions, which many students were previously exposed to without realizing their value. Students become hungry to learn some algebra!

Imagine a world where students feel a need for algebraic functions rather than feeling forced to learn them by the so-called “math people.” Imagine a generation of students who think an exponential function might be helpful for them to learn. If algebra can embrace data science and data science can do the same for algebra, we can all look forward to a world where students feel dissatisfied with their current knowledge and *want* to learn more math.

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**Ji Y. Son** is a professor of psychology at California State University, Los Angeles.** James W. Stigler** is a distinguished professor of psychology at UCLA. They research how students learn in complex domains, and are co-founders of CourseKata.org, a statistics and data science curriculum used by high schools and colleges.

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