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> Models for schools, districts, and states

There are many curriculum models through which individual schools, school districts, or entire states can integrate data science into K-12 education. Many stakeholders have already developed curricula and lesson plans for K-12 data science, or are actively working to do so.


Explore four possible models below, both for the mathematics curriculum, and across other subjects. Many have already been adopted by schools and districts, and some will be soon by entire states. Models like these can also complement and strengthen K-12 computer science education, and help bring CS into other subjects through modules or individual activities through simple, introductory coding.

1-Year Elective

A year-long elective course in late high school

Merge & Purge

Combines the best of

Geometry and Algebra 2, adding room for data science


Creates multiple pathways in high school mathematics


A problem-based curriculum centered on computational thinking 


Less Modification

More Modification


1-Year Elective Course

>   Model: a one-year elective dedicated to teaching the basics of data science in late high school.

>   Example Program: UCLA CenterX Introduction to Data Science (IDS)

>   Implementation? Los Angeles Unified School District (LAUSD) adopted the course in 2014. For the 2020 school year, 3,200 students across 51 schools in Southern California are currently enrolled. The course is also approved as a "C" course in the University of California A-G requirements, and validates the Algebra II requirement. Two other courses—one in pilot phase and another in development for the 2021-2022 school year, are available below.


Available Curricula:

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We'd be happy to help you identify the right program or model for your school / district.


Merge & Purge

>   Model: Geometry & Algebra II are cut to ½-years, and ½-years of Data Science & Statistics are integrated.

>   Partner: Center for RISC / University of Chicago

>   Implementation? The Oregon Mathematics Project is proposing a similar 2+1 Curriculum Model.


Program Description:

This model proposes a "merge and purge" of traditional K-12 mathematics, typically organized into the Algebra I—Geometry—Algebra II sequence. By removing less relevant content, typical topics in Geometry and Algebra II are each condensed to a 1/2-year. The new space would then be filled by integrating a full year of topics in data science, statistics, and mathematical modeling using contemporary computational tools.  This teaches some of the same mathematical logic necessary for critical thinking skills, with more relevant, twenty-first century content. Better yet, practical skills for modern careers and daily life are taught more directly. The path to more advanced mathematics, such as Calculus, is also preserved.



>   Model: A data science and statistics sequence becomes one available pathway during high school.

>   Partner: Dana Center Mathematics Pathways / U.T. Austin Charles A. Dana Center

>   Implementation? Pilots are being developed in partner districts in Georgia, Texas, and Washington.


Program Description:

"Mathematics pathways are part of a growing national movement designed to change the way mathematics is taught in higher education. In the “pathways model,” students are placed into course sequences designed to align with their personal interests, chosen fields of study, and career goals. Historically, college–level study has required that students, regardless of their academic or career goals, adhere to standardized mathematics course sequences that may not be relevant to their needs. In fact, traditional mathematics courses have been found to be the most significant barrier to degree completion for all fields of study."


CBM: Computer-Based Mathematics

>   Model: A problem-centered, complete redesign of math curriculum that assumes computers exist.

>   Partner: / Wolfram

>   Implementation? Ongoing pilot in Estonia (surprised? Read about Estonia's success in education).


Program Description:

"The CBM curriculum is unique in assuming computers by default, and so avoiding the need to learn most of the complex hand–calculation skills that were vital to our predecessors. Written from core guiding principles that firmly focus on the needs of learners for jobs and everyday life in the near future, the new curriculum is problem–centred versus the traditional mechanics–centred curriculum. Students are taught to solve problems using the tools available to them, rather than learning isolated, out–of–context skills, like completing a long division problem or calculating standard deviation."

Want to bring one of these models to your school, district, or state?

Superintendents, legislators, board members, or other policymakers, please:

Ready to get serious? Pilot a program:

Open Pilots

> Programs looking for schools to pilot content

CourseKata Statistics and Data Science

CourseKata is an interactive online textbook that provides a rigorous introduction to modern data science and statistics. Concepts are taught in the context of data analysis, which students learn to do using R. Students go back and forth between doing and understanding, learning the practice of data analysis while, at the same time, learning to think using statistical ideas. Every bit is taught in relation to the whole, resulting in a more coherent understanding of the domain. Can be a replacement for Algebra II, or an option preparing students for advanced data science courses and careers. We invite you to preview the materials. If you teach introductory statistics and want to join our project, find out how to use the book with your students. Preview the course here:

Adaptation of U.C. Berkeley Data8 

Although the course was designed as a college course with no-prerequisites, it is currently being adapted and piloted at several high schools. The UC Berkeley Foundations of Data Science course combines three perspectives: inferential thinking, computational thinking, and real-world relevance. Given data arising from some real-world phenomenon, how does one analyze that data so as to understand that phenomenon? The course teaches critical concepts and skills in computer programming and statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. It delves into social issues surrounding data analysis such as privacy and design.

Merge & Purge (UChicago)

This model proposes a "merge and purge" of traditional K-12 mathematics, typically organized into the Algebra I—Geometry—Algebra II sequence. By removing less relevant content, typical topics in Geometry and Algebra II are each condensed to a 1/2-year. The new space would then be filled by integrating a full year of topics in data science, statistics, and mathematical modeling using contemporary computational tools. This teaches some of the same mathematical logic necessary for critical thinking skills, but with more relevant, twenty-first century content. The Center for RISC is currently seeking ambitious schools or school districts to pilot the model, and would be supported by our Center's team for developing and implementing such a program.

Page updated March 2021. Please contact us for more information or questions.


GAISE II: Statistics & Data Science Education

The Pre-K–12 (i.e., precollege) Guidelines for Assessment and Instruction in Statistics Education II: A Framework for Statistics and Data Science Education report (GAISE II). This report presents a set of recommendations toward data literacy at the elementary, middle, and high school level. GAISE II (Bargagliotti et al., 2020) incorporates enhancements and new skills needed for making sense of data today while maintaining the spirit of the original Pre-K–12 GAISE (Franklin et al., 2007), which we shall review briefly. Now more than ever, it is essential that all students leave secondary school prepared to live and work in a data-driven world, and this report outlines how to achieve this goal. 


Endorsed by the American Statistical Association (ASA) and the National Council of Teachers of Mathematics (NCTM).

California Mathematics Framework

The California Department of Education (CDE), Instructional Quality Commission, and State Board of Education are commencing the revision process for the Mathematics Framework for California Public Schools: Kindergarten Through Grade Twelve (Mathematics Framework).


The 2021 Draft Framework now has a full chapter on data science education (see Chapter 5), and is available for receiving public comment through April 2021. We encourage everyone to leave comment by contacting

K-12 Computer Science Framework

Data education can and should have multiple homes in K-12. Imparting data literacy can be a goal of many other school subjects (mathematics, biology, social studies), and help reinforce content through hands-on data explorations in service of other content standards. Professional, career-track data science often involves some coding, and can be taught within computer science classes. 

The Association for Computing Machinery,, Computer Science Teachers Association, Cyber Innovation Center, and National Math and Science Initiative have collaborated with states, districts, and the computer science education community to develop conceptual guidelines for computer science education. These guidelines are designed to transform computer science into a subject accessible for all.

NextGeneration Science Standards

In addition to the national Common Core State Standards (CCSS) 'Statistics & Probability' thread, the Next Generation Science Standards (NGSS) Practice 4 'Analyzing and Interpreting Data' (p. 9) includes a comprehensive yet flexible section on data that can be applied for dedicated data education progression through K-12.

The Next Generation Science Standards (NGSS) are K–12 science content standards. Standards set the expectations for what students should know and be able to do. The NGSS were developed by states to improve science education for all students. A goal for developing the NGSS was to create a set of research-based, up-to-date K–12 science standards. These standards give local educators the flexibility to design classroom learning experiences that stimulate students’ interests in science and prepares them for college, careers, and citizenship.


Mathematical modeling overlaps with and complements data science in its emphasis on teaching mathematics in a way that is contextual to real world problems and situations, translating numeric information into equation-based and  computational problems.  

Guidelines for Assessment and Instruction in Mathematical Modeling Education (GAIMME) Second Edition, a freely downloadable report, enables the modeling process to be understood as part of STEM studies and research, and taught as a basic tool for problem solving and logical thinking. GAIMME helps define core competencies to include in student experiences, and provides direction to enhance mathematical modeling education at all levels. The second edition includes changes primarily to the “Early and Middle Grades (K–8)” chapter.

Page updated February 2021. Please contact us for more information or questions.

Frameworks & Assessments

> Existing places where data education already exists

Standardized Testing?

From the GAISE II report:

Several national and international standardized assessments include a focus on data analysis and statistics:


PISA, which is given to 15-year-old students around the world to assess ability to apply knowledge to real-world situations, is expected to focus on mathematics in 2021 with computer simulations and conditional decision making identified as topics for special emphasis.


SAT: According to College Board, 29% of the items on the mathematics portion of the SAT address problem solving and data analysis (which includes proportional reasoning and probability).


NAEP: Data analysis, statistics, and probability is one of five content strands assessed on the National Assessment of Educational Progress (NAEP).


And AP Statistics and AP Computer Science represent advanced, college-level courses which can be part of a data-focused education pathway in high school coursework.