Foundations of Data Science for Students in Grades K-12

Understanding the Data in K-12 Data Science · Issue 6.2, Spring 2024

Our increasingly data-driven world is amplifying the need for everyone to develop foundational data literacy skills. In response, a growing number of K-12 data science curricula are being designed to introduce all students to data. These curricula define what data science is at the high school level and directly shape how students are introduced to and understand the discipline. Ensuring these curricula are effective, engaging, and, most critically, equitable is of paramount importance. This article presents a qualitative analysis of four curricula, focusing on the data used to introduce learners to the field of data science. The analysis uses a series of analytical lenses to evaluate the 296 distinct data sets used across the curricula and identifies trends and best practices in data set selection. The analysis includes using data collected from high school students about their interests and experiences with data to understand if and how contemporary data science curricula are tapping into students’ lived experiences to situate data science learning experiences. The findings show that the curricula use relatively recent and small data sets covering a range of topics and that learner involvement in data set selection is limited. Further, the analysis reveals gaps between the data sets used and students' self-reported interests. This work highlights the importance of data set selection, especially as it relates to supporting learners from historically excluded populations in technology fields. Finally, this article provides practical implications to assess existing curricula and advances our understanding of how to situate the field of data science in the interests, ideas, and values of today’s students.

Accelerating the Advancement of Data Science Education

We outline a synthesis of strategies created in collaboration with 35+ colleges and universities on how to advance undergraduate data science education on a national scale. The four core pillars of this strategy include the integration of data science education across all domains, establishing adoptable and scalable cyberinfrastructure, applying data science to non-traditional domains, and incorporating ethical content into data science curricula.

Integrated Data Science for Secondary Schools: Design and Assessment of a Curriculum

This summary was generated using automated tools and was not authored or reviewed by the article's author(s). It is provided to support discovery, help readers assess relevance, and assist readers from adjacent research areas in understanding the work. It is intended to complement the author-supplied abstract, which remains the primary summary of the paper. The full article remains the authoritative version of record. Click here to learn more.

Broadening Access to Data Science Education: Open Source Tools and Training in High School and Higher Education · Issue 8.3, Summer 2026

As data play a more integral part to our daily lives, there is a growing need for data science education. However, access to curriculum, tooling, and infrastructure is not readily available to many students in the United States. We review the current state of data science education tools and implementations as well as highlight a set of emerging tools and technologies. We conclude that broadening data science education requires a multifaceted approach, involving technological accessibility, instructional equity, curricular relevance, and long-term sustainability.

ADOPTING DATA SCIENCE CURRICULA: A STUDENT CENTRIC EVALUATION

With the advent of data science as a new discipline with high demand for a skilled workforce, educators are increasingly recognizing the value of translating courses and programs that have been shown to be successful and sharing lessons learned in increasing diversity in data science education. In this paper, we describe and analyze our experiences translating a lower-division data science curriculum from one university, University of California, Berkeley’s Data8 course, to other settings with very different student populations and institutional contexts at, University of Maryland, Baltimore County and Mills College during Spring 2021. It is essential to motivate students to meaningfully take part in their journey into a data science career. We wanted to consider their perceptions and motivations to take foundation courses and next steps emerging from the foundation course. We evaluated how students were receiving the course and curriculum with the adaptations across the three institutions. Two identical surveys were administered at all three institutions, once at the beginning of the semester and then at the end to study the impact of the course and its variations on the students. The survey consisted of over 60 questions including demographics, understanding the motivations on why students were in the data science class, how they perceived data science, etc. Overall, UMBC and Mills both showed gains in students’ motivations over the semester, as seen by comparing the mean scores for questions asked at the beginning of the semester versus the end. We also saw gains in student perceptions towards Data Science at the end of the semester at UMBC and Mills. With the very large size of classes at Berkeley, we performed a weighted analysis from the UC Berkeley data with respect to race, gender, first-generation status, transfer status, and international student status drawing samples close to UMBC and MILLS. This weighted sample of UC Berkeley’s students shows a clear improvement across both sets of questions after taking the Foundations class. Our findings emphasized the importance of adapting courses and programs to existing curricula, student populations, cyberinfrastructure, and faculty and staff resources in the context of the institutions. Such adaptation can help students develop their understanding of data science career pathways and help hone their motivations which can lead to a more engaged workforce supporting the data science careers.

Integrating Data Literacy Into K–12 Education · Issue 7.2, Spring 2025

In a world increasingly driven by data, the ability to understand, interpret, and make use of data has assumed a fundamental role. Yet despite its importance, data literacy remains conspicuously absent from the U.S. K–12 educational landscape. This article explores the obstacles in integrating data literacy into the educational system and proposes a multipronged approach to address these challenges.We contend that successful implementation of K–12 data literacy education necessitates comprehensive teacher preparation and professional development coupled with the integration of data literacy within curricula. Furthermore, the creation of suitable tools and data sets specifically tailored for K–12 learning is paramount. Finally, school and district leaders must champion the cause of data literacy, allocating resources and nurturing a culture that recognizes its value.Drawing inspiration from the success of large-scale initiatives such as the Moore-Sloan Data Science Environments and the National Writing Project, we present the components and conditions essential for successful advancement of data literacy education. The article concludes with a call to action inviting constituents from across various roles to unite in the endeavor of building a future where data literacy is recognized as a fundamental skill. We underscore that the widespread implementation of data literacy within K–12 education will demand sustained investment, customized local implementation, and the concerted efforts of multiple disciplines and stakeholder groups. By confronting these challenges head-on and harnessing the strategies outlined in this article, we can strive to ensure that all students are armed with the data literacy skills necessary to navigate an increasingly data-driven world.

Developing Competencies for the Future of Data and Computing: The Role of K-12

Skills in computing and data are critical for students to thrive in a data-driven world. While efforts to expand learning in these areas have grown, opportunities to participate are unevenly distributed and lack coherence across grades and subjects. Our new report identifies seven shared competencies for data and computing and how they align with existing STEM standards and classroom practice. Through addressing curriculum, teacher preparation, professional learning, technology access, assessment, and system-level coordination, the report calls for sustained, coordinated action to ensure that all students have meaningful opportunities to develop literacy in data and computing.

Innovation in Data Science Education

Data science is a new field of research focuses on extracting knowledge and value from data. The radical growth in recent years in the availability of data and of the computational resources required to process them, has led to a corresponding increase ...

Broadening Access to Data Science Education in High School and Higher Education through Open Source Tools and Training

As data plays a more integral part to our daily lives, there is a growing need for data science education. However, access to curriculum, tooling, and infrastructure is not readily available to many students in the U.S. We review the current state of data science education tools and implementations as well as highlight a set of emerging tools and technologies. We conclude that broadening data science education requires a multifaceted approach, involving technological accessibility, instructional equity, curricular relevance, and long-term sustainability.

Data Science in 2020: Computing, Curricula, and Challenges for the Next 10 Years

In the past 10 years, new data science courses and programs have proliferated at the collegiate level. As faculty and administrators enter the race to provide data science training and attract new ...

Transforming Curriculum and Building Capacity in K–12 Data Science Education · Issue 4.4, Fall 2022

The recently released and updated Pre-K–12 Guidelines for Assessment and Instruction in Statistics Education (GAISE II; Bargagliotti et al., 2020) provides guidance as to how teachers can support the development of data literacy for all students in the pre-K–12 curriculum. However, to truly meet the vision of the GAISE II report and to support all students in developing data literacy for today’s societies, significant transformations need to be made to the educational system as a whole to build capacity for such development. In this article we discuss the current state of the K–12 curriculum focusing on the mathematics curriculum where statistics and data concepts are most frequently situated, presenting some challenges and exciting examples. We then discuss areas of need for capacity building that must come at all levels, including: K–12 school curriculum, K–12 teacher professional development, K–12 teacher preparation, statistics and data science education research, and policies. We also provide a set of recommendations for building capacity to develop the data literacy of all students through the teaching of data science and statistics concepts and practices in the K–12 mathematics curriculum to support democratic equity through engaged citizenship.

3 Powerful Takeaways from the National Workshop on Data Science Education

As the field flourishes, there is much we can do to benefit its countless students.

Adopting Foundational Data Science Curriculum with Diverse Institutional Contexts

This summary was generated using automated tools and was not authored or reviewed by the article's author(s). It is provided to support discovery, help readers assess relevance, and assist readers from adjacent research areas in understanding the work. It is intended to complement the author-supplied abstract, which remains the primary summary of the paper. The full article remains the authoritative version of record. Click here to learn more.

Interleaving Computational and Inferential Thinking: Data Science for Undergraduates at Berkeley · Issue 3.2, Spring 2021

The undergraduate data science curriculum at the University of California, Berkeley is anchored in five new courses that emphasize computational thinking, inferential thinking, and the perspective gained by working on real-world problems. We believe that interleaving these elements within our core courses is essential to preparing students to engage in data-driven inquiry at the scale that contemporary scientific and industrial applications demand. This new curriculum is already reshaping the undergraduate experience at Berkeley, where these courses have become some of the most popular on campus and have led to a surging interest in a new undergraduate major and minor program in data science.

Introductory data science across disciplines, using Python, case studies, and industry consulting projects

Abstract Data and its applications are increasingly ubiquitous in the rapidly digitizing world and consequently, students across different disciplines face increasing demand to develop skills to answer both academia's and businesses' increasing need to collect, manage, evaluate, apply and extract knowledge from data and critically reflect upon the derived insights. On the basis of recent experiences at the University of Ttingen, Germany, we present a new approach to teach the relevant data science skills as an introductory service course at the university or advanced college level. We describe the outline of a complete course that relies on case studies and project work built around contemporary data sets, including openly available online teaching resources.

Highlights of the National Academies Report on "Undergraduate Data Science: Opportunities and Options” · Issue 1.1, Summer 2019

An interview with Laura Haas and Alfred Hero by Robert Lue