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Data Science

Overview

Data is information we gather to understand the world around us—from numbers and words to images, patterns, and more. Data helps us ask and answer questions, solve problems, and make informed decisions.
 

What’s Included in This Toolkit

This toolkit provides resources and opportunities to support data science and data literacy education. In this toolkit, you will find:

  • What is Data Science & Data Literacy?
  • Why Data Science in K–12?
  • Data Science & Computer Science
  • Data Science Learning Progression and Pathways
  • Tools and Resources
  • Professional Learning Opportunities
 

What is Data Science & Data Literacy?

Data science is the practice of collecting, analyzing, and interpreting data to solve real-world problems using statistical and computational methods.

In K–12 education, data science is not just coding or statistics. This work draws on multiple disciplines, including mathematics, statistics, computer science, science, and social studies. 

Data literacy—the foundation of this work—is the ability to:

  • Ask meaningful questions
  • Collect and analyze data
  • Work with real-world data
  • Recognize patterns and trends
  • Interpret findings
  • Communicate insights
  • Make ethical and responsible decisions

Data skills are increasingly considered a foundational literacy for all students —similar to reading, writing, and mathematics, regardless of career path. In K–12 education, these skills help students think critically about data, including its sources, limitations, and potential biases

 

Why Data Science and Data Literacy in K–12?

Data science prepares students to be informed citizens and future-ready learners in a data-driven world.

Integrating data science helps students:

  • Develop critical thinking and problem-solving skills
  • Understand real-world issues (e.g., environment, health, community)
  • Strengthen math, science, and computational thinking
  • Build readiness for AI and emerging technologies
  • Engage in authentic, interdisciplinary learning

Teaching data science also introduces important topics such as privacy, fairness, and ethics. It builds confidence in math, curiosity in science, and storytelling in writing—helping students become creators, not just consumers, of information.
 

Data Science & Computer Science

Data Science and Computer Science are deeply connected:

  • Computer Science provides the tools and systems to collect, store, and process data

  • Data Science provides context and purpose. It focuses on analyzing that data to uncover patterns and insights

Computer science also provides the computational thinking and programming skills that make data science possible—especially as datasets grow larger and more complex.

Data Science in CS Standards

In the current 2017 CSTA K–12 Computer Science Standards, Data and Analysis is one of the core concepts. This concept focuses on how students collect, represent, analyze, and interpret data to solve problems and make informed decisions. These experiences build foundational data literacy and analytical thinking skills that are essential for both computer science and data science learning.

CSTA is currently revising the standards, with a new release expected in summer 2026. Data and Analysis will remain a key concept area, with updated subtopics. Explore the updated CSTA Standards (Draft 3.0)


 

K–12 Learning Progression & Pathway

Currently, there are no stand-alone K–12 data science standards. However, data science is inherently interdisciplinary and connects across:

  • Computer Science (CSTA)
  • Mathematics/ Statistics
  • Science (STEELS)

Data Literacy and Data Scinece K–12 Learning Progression

Data Science for Everyone (DS4E) has developed K–12 learning progressions to guide instruction and support students’ development of data science and data literacy skills over time.

These progressions provide a flexible, interdisciplinary framework that integrates data science across subjects such as computer science, mathematics, and science—rather than treating it as a standalone course. Designed for state and district leaders, they outline key expectations for data literacy, core data science practices, and foundational AI concepts.

Organized across grade levels, the progressions are structured around five core strands:

  • Dispositions & Responsibility
  • Creation & Curation
  • Analysis & Modeling Techniques
  • Interpreting Problems & Results
  • Visualization & Communication
  

Data Science Pathway

The Rethinking CS Pathways: High School and Beyond initiative, led by CSTA, outlines a progression from foundational computer science learning to more advanced and specialized pathways, including data science.

This pathway helps educators understand how early experiences in computer science can develop into deeper knowledge and skills over time. It also provides a clear structure for extending learning beyond foundational courses into more focused areas of study. Students can continue their learning through two advanced levels:

  • Fundamentals Level 
  • Specialty Level 

Through this progression, students can explore interests and develop competencies that prepare them for postsecondary pathways and careers in data-related fields, such as:

  • Data Scientist
  • Data Analyst
  • Data Engineer
  • Statistician
  • Data Ethicist

This pathway highlights how data science can serve as a meaningful extension of computer science education—connecting classroom learning to real-world applications and future opportunities.

In order to provide the best and most current resources and information, we continually revise our CSinPA Toolkits. You can contribute to the toolkit by submitting your questions, revisions, or resources using this form.