Landscape Analysis

State Policy Landscape

Twenty-nine states now show some policy activity related to data science education, but the depth of engagement varies enormously. DS4E tracks states across four tiers: Exploring, Developing, Implementing, and Scaling. The headline finding is velocity — eight states moved up at least one tier in a single year. New Hampshire and Oregon stand out as the only states that require data-related credits for high school graduation. Six states have adopted data science learning standards or frameworks.

Top States by Enrollment

Source: DS4E / Code.org enrollment data, 2024-25. California accounts for ~24% of all enrolled students.

Demographics

The student population skews in ways that challenge simple narratives. 49% qualify for free or reduced lunch — closely matching national averages and suggesting these courses are not exclusively reaching affluent students. Gender breakdown is 57% male / 42% female (with 1% non-binary or not reported). Racial composition: 50% White, 29% Hispanic, 13% Black, with remaining students identifying as Asian, multiracial, or other categories. Bootstrap stands out as the curriculum with the strongest equity numbers.

Key Milestone

The first-ever K-12 Data Science Learning Progressions were published in Summer 2025 by DS4E and Concord Consortium. This document, developed with input from 100+ educators across 33 states, represents the field's first attempt at a shared definition of what students should know and be able to do with data at each grade band. Subject-specific progressions (math, science, social studies) are expected in 2026.

Three Funding Orbits

The funder landscape organizes into three distinct clusters, each with its own logic and theory of change:

• Valhalla Orbit — The most structured and transparent. Valhalla Foundation operates with a 4-pillar theory of change (research, curriculum, policy, field-building) and publishes grant details. Gates co-funds several Valhalla grantees. This cluster favors rigorous, evidence-based approaches.
• Youcubed / Google Orbit — Corporate-backed, largest reach by student count. Google.org's support for Youcubed (which reaches 180K+ students) represents the single biggest bet by student exposure. This cluster favors scale and accessibility.
• Bootstrap Orbit — Diverse funder base including NSF, smaller foundations, and school district partnerships. Bootstrap's integration approach (embedding data science into existing courses rather than creating new ones) attracts funders who are skeptical of standalone course proliferation.

Major Funders

Co-Funding Patterns

The Valhalla–Gates co-funding axis is the most important structural feature of this landscape. Both funders support CourseKata, DS4E, and Skew The Script. This creates a dominant cluster where a small number of funders reinforce each other's bets, concentrating influence and creating potential fragility if either funder shifts priorities.

Warning Signal: ExcelinEd

ExcelinEd, one of the field's key policy organizations, shows concerning financial signals: 74% revenue decline and burning reserves at approximately -$5.9M/year. As the primary organization connecting data science to state policy infrastructure, ExcelinEd's potential instability represents a structural risk to the field's policy pipeline.

DS4E: The Central Hub

Data Science 4 Everyone, housed at the Center for RISC at the University of Chicago, functions as the field's coordinating body. With 3,000+ coalition members, DS4E published the Learning Progressions, conducts the national enrollment count, hosts annual convenings, and advocates for state policy adoption. Led by Zarek Drozda, DS4E evolved from Steve Levitt's early advocacy (he famously argued "data science should replace calculus") into a broad-tent coalition.

Curriculum Providers

Reach figures are self-reported and definitions vary — Skew The Script counts lesson downloads, while IDS counts full-course enrollments. Direct comparisons should be made cautiously.

Field-Builders

• ExcelinEd — State policy infrastructure. Works with governors and legislatures on standards adoption, course approval, and graduation requirements. Founded by Jeb Bush. Financial distress noted above.
• Digital Promise — District-level implementation support. Helps districts design and launch data science programs. Bridges the gap between state policy and classroom reality.
• Concord Consortium — Research and tool development. Co-developed the Learning Progressions. Builds open-source data tools (CODAP) used across multiple curricula.
• Code.org — Emerging partner. Now collecting enrollment data for DS4E. Potential to bring its massive K-12 CS infrastructure to bear on data science.

Key People

• Nancy Poon Lue — Valhalla Foundation program officer; quiet but influential architect of the field's evidence-based wing
• Zarek Drozda — DS4E executive director; coalition builder; manages the big tent
• Jo Boaler — Stanford mathematician behind Youcubed; polarizing figure in the California math wars
• Steve Levitt — Freakonomics co-author; early DS4E champion; provided initial intellectual legitimacy

Learning Progressions

The K-12 Data Science Learning Progressions, published Summer 2025, organize data science knowledge across 5 strands spanning kindergarten through 12th grade. Developed with input from 100+ educators across 33 states, they represent the field's first consensus document — its equivalent of the Next Generation Science Standards, though without regulatory force. Subject-specific progressions (integrating data science into math, science, and social studies standards) are planned for 2026.

Curriculum Models

A fundamental design choice divides the field: standalone courses vs. integrated/modular approaches.

• Standalone courses (Youcubed, IDS, CourseKata) create dedicated data science classes, typically offered as math electives or alternatives. This model is easier to count and evaluate but faces the course-scheduling problem: what does it replace?
• Modular / integrated approaches (Skew The Script, Bootstrap) embed data science into existing courses — a statistics unit in social studies, a data analysis project in biology. This model sidesteps scheduling politics but is harder to track and assess.

The May 2024 joint statement by NCTM, NSTA, ASA, NCSS, and CSTA endorsed data science "across the curriculum" — a strong signal favoring integration, though standalone courses continue to grow.

The Algebra 2 Debate

No issue generates more heat in this field than the relationship between data science and Algebra 2. Three positions have emerged:

• Replacement — Data science should be a valid alternative to Algebra 2 for graduation. This position is losing. UC's Board of Admissions and Relations with Schools (BOARS) found that Youcubed and IDS courses "do not validate" Algebra 2, dealing a significant blow.
• Integration — Data science concepts should be woven into the existing math sequence, not positioned as a substitute. This position is winning in policy circles, as reflected in the NCTM/NSTA joint statement.
• Addition — Data science as an additional elective alongside existing math requirements. This is what most commonly happens in practice, regardless of the policy debate.

The California context has made this debate uniquely toxic. Jo Boaler's advocacy for Youcubed as an Algebra 2 alternative triggered opposition from Berkeley mathematics professors, generating media coverage that framed data science education as an attack on mathematical rigor. This political toxicity has spilled over into other states.

Implementation on the Ground

DS4E's case studies reveal the diversity of implementation models:

• Genesee ISD (Michigan) — Regional service agency model; trains teachers across multiple districts
• KIPP NYC — Charter network integration; data science embedded in existing math courses
• Evanston (Illinois) — Suburban district with dedicated data science course pathway
• Loudoun County (Virginia) — Large district leveraging state-level standards
• Alabama (statewide) — State-led adoption with centralized curriculum support

"When we tell students they can take data science instead of Algebra 2, the counselors don't know what to say to families about college admissions." — High school counselor, Oxnard, CA

Three Levels of Field Leadership

Power in this field operates at three distinct levels, with different visibility:

• Funders build the architecture — Valhalla, Gates, and Griffin shape the field through grant-making, convening, and theory of change. They are invisible in public discourse. Nancy Poon Lue at Valhalla is arguably the most influential person in K-12 data science education, yet she appears in virtually no public coverage.
• Academics shape the debate — Jo Boaler, Steve Levitt, and curriculum developers at UCLA and Stanford are visible in media and policy discussions. They generate the ideas, controversies, and research that define the field's public narrative.
• Practitioners create reality — Teachers, district coordinators, and state education officials are occasionally visible but do the actual work of implementation. The 3,091 teachers currently teaching data science courses are dramatically insufficient for the field's ambitions.

The Invisible Funder Landscape

No journalist or think tank has mapped who funds K-12 data science education. This is not a gap in our research — it is a feature of the landscape itself. DS4E's own 79-page State of the Field report discusses field growth, enrollment, and state policy without once naming who pays for it. Funder strategies, co-funding patterns, and investment theses are entirely absent from public discourse.

This invisibility matters because it means the field's direction is set by a small number of actors whose strategies are not subject to public scrutiny. It also means that potential new funders have no map to navigate by — they cannot see where money is already flowing, where gaps exist, or which bets have already been placed.

DS4E as Umbrella vs. Implementation Reality

DS4E functions as the field's convening umbrella — the big tent where all stakeholders gather. But the actual implementation landscape is far more diverse than DS4E's coalition structure suggests. Curriculum providers compete for the same school placements. Funders have distinct (and sometimes conflicting) theories of change. State policy approaches vary from top-down standards adoption to bottom-up teacher networks. DS4E holds the tent together, but the tensions underneath are real.

Five White Spaces

Our research identifies five confirmed gaps where investment is needed and no funder has staked a clear position:

• Teacher preparation — Only 3,091 teachers nationally are teaching data science courses. This is dramatically insufficient. No major funder has made teacher preparation and certification their primary focus.
• Assessment infrastructure — The field has curricula and now learning progressions, but no standardized assessments. Without assessment, it's impossible to compare programs, demonstrate student learning, or satisfy state accountability requirements.
• Data and measurement — The enrollment count is a start, but the field lacks longitudinal data on student outcomes, teacher effectiveness, and program quality. Measurement infrastructure is essential for evidence-based field-building.
• Sustainability beyond grants — Most programs depend on philanthropic funding. The ESSER cliff (federal pandemic education funding expiring) threatens programs that used those dollars for data science. No clear path to sustainable, public funding at scale.
• University course recognition — The BOARS reversal in California highlights a critical gap: data science courses need university recognition to be viable for college-bound students. Without it, counselors cannot recommend these courses.

The Equity Paradox

Both sides of the Algebra 2 debate claim equity as their justification. Proponents of data science as an alternative argue that Algebra 2 is a gatekeeper course that disproportionately harms students of color and low-income students. Opponents argue that weakening math requirements under the banner of "data science" will reduce access to STEM careers for the same populations. The demographic data (49% free/reduced lunch, 29% Hispanic, 13% Black) suggests data science courses are reaching underserved students — but whether this access leads to better outcomes is unmeasured.

Fragility Signals

• CZI cut 30% of its education team — future commitment to data science education uncertain
• Schmidt Futures wound down its data science education investments
• ExcelinEd burning reserves at -$5.9M/year with 74% revenue decline
• The ESSER cliff threatens programs funded with pandemic-era federal dollars
• The California math wars have made data science politically toxic in the largest state

Growth Signals

• 8 states moved up at least one tier in DS4E's tracking in a single year
• NH and OR now require data-related credits for graduation
• The Learning Progressions provide a shared reference point for the first time
• NCTM/NSTA/ASA/NCSS/CSTA joint statement endorses data science across the curriculum
• Code.org entering the data science space brings massive K-12 infrastructure

Positioning Advice

For a new funder considering this space: the integration approach is the safest entry point. It avoids the Algebra 2 controversy, aligns with the NCTM/NSTA joint statement, and builds on existing course infrastructure rather than competing for scarce schedule slots. The five white spaces above offer clear, defensible investment theses that don't require taking sides in the field's most contentious debates.