By: Jason Lee Morgan
When Deisy graduated high school in Compton, California, in 2014, she had no idea how central data would become to her career. Today, she writes code to analyze public health data and describes data as “a form of capital”—a tool that gives her flexibility and power in her work.
But she also reflects on a missed opportunity: she wishes she had been introduced to data science in high school.
Deisy’s sentiment is not unique. With one in five Americans wishing they had learned more about data science in school, Deisy’s experience underscores the need for broader access to meaningful data science education.
As a data science and mathematics teacher, DoD STEM Ambassador, and curriculum specialist in Compton, I’ve seen how project-based data science resonates with students of different interests, strengths, and aspirations. Not all data science courses are created equal. Some lean heavily on computational skills and analysis. The Stanford-based curriculum I use embraces authentic project-based learning. It encourages student-led inquiry, combines multiple subjects, involves multi-week investigations, and ends with students publicly sharing their work. Students begin with the human first—the “Who” and “Why” of a question—before tackling the “How” of coding, modeling, and statistical analysis.
One powerful example from my classroom was a unit where students investigated skin tone representation in urban comedies. This wasn’t the original assignment —the students adapted it themselves, taking on extra work because they cared about the issue of representation and colorism. They generated questions, collected and coded data, and analyzed it using categorical and numerical lenses. Students weren’t just meeting standards on categorical data, conditional probability, or functions—they were using math to explore the issues that mattered to them. By the end, they wrote analytical op-eds, weaving together statistical evidence with social insight and personal voice.
Data science also fosters the technical and critical thinking skills essential in today’s AI-driven world. As more sectors become automated, students gain an advantage when they know how to understand, question, and evaluate the data that powers these systems. AI reflects the human choices and biases embedded in its data and design, and the more people who can interrogate and improve the data behind AI, the more likely we are to build systems that are accurate, fair, and innovative. Data science becomes more than analysis; it becomes a tool for advocacy and systemic transformation.
Even so, some worry that if students take data science instead of calculus, it might limit their options in STEM fields. While this may be true for certain specialized pathways, the reality is that data science is a rigorous, relevant math pathway that equips students with the analytical and quantitative skills they need in today’s world and in college. Rubicelia, one of my former students, entered college confident in her ability to tackle quantitative analysis. Her early exposure to real-world datasets gave her a head start, allowing her to support her peers. Andrea, now an applied mathematics major at the University of California, Merced, explored data on the representation of Latina women in STEM during her high school data science class. That investigation didn’t just teach her technical skills; it inspired her to change the numbers she saw. Her experience in the course fostered a growth mindset and the grit to succeed in a demanding university math field. These are just two of many examples that show data science doesn’t close doors—it opens new ones to pathways students may have never imagined.
That’s why I’m calling on state and local education leaders to embrace a broader vision of math—one that reflects the diverse ways we use it in our lives and careers. This means ensuring that all high school students have access to high-quality data science courses and protecting its legitimacy as a recognized math pathway. It also means investing in professional learning that equips educators not just with content knowledge, but with the skills to teach data science in ways that are engaging, inspiring, and mathematically uplifting. A great teacher can do more with data science than traditional math courses, which are often constrained by restrictive pacing, narrow prerequisites, and an overemphasis on testing. Counselors, too, need targeted support and training to help students navigate emerging math pathways and to address persistent myths and misperceptions about what counts as “real” math.
Most importantly, we must listen to all stakeholders—especially the students—when making decisions about math pathways. Access to data science education isn’t just about preparing students with tools for success in the workforce —it’s about joy, inspiration, and human flourishing. It’s about evoking the human spirit of ingenuity, curiosity, and the hunger for impact that exists within our young people. Too often, traditional math education smothers that spirit. But it doesn’t have to. Quantitative know-how, when paired with curious, passionate souls, is a powerful mix—and students are telling us that meaningful data science education nurtures this combination in incredible ways.
Are we listening?
Jason Lee Morgan, 2025 DoD STEM Ambassador, has spent 19 years advancing culturally relevant and engaging STEM education as a math and data science educator, curriculum specialist for Compton Unified School District, and founder of the twiGs Math Equity Project, which promotes equitable and enjoyable achievement for students of color in Compton, Long Beach, and South Central LA. He also pioneered Compton Unified’s first data science course in partnership with Stanford University’s YouCubed initiative.
The post Data Science: A Student’s Tool for Understanding, Advocacy, and Systemic Change appeared first on Getting Smart.