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.
Broadening Access to Data Science Education in High School and Higher Education through Open Source Tools, Infrastructure, and Training
Equitable data science education requires a multifaceted approach, involving high school and higher education, community involvement, and accessible tools. A renewed investment in public digital infrastructure is needed to support these efforts. Nonprofits play a crucial role in supporting these efforts, and increased representation in leadership can enhance their impact. By addressing these disparities, we can ensure a more inclusive future in data science.
Doing Data Science on the Shoulders of Giants: The Value of Open Source Software for the Data Science Community
Open source software is ubiquitous throughout data science, and enables the work of nearly every data scientist in some way or another. Open source projects, however, are disproportionately maintained by a small number of individuals, some of whom are institutionally supported, but many of whom do this maintenance on a purely volunteer basis. The health of the data science ecosystem depends on the support of open source projects, on an individual and institutional level.
Data Science Education K-12 Research to Practice Conference. Online registration by Cvent
Data Feminism
Today, data science is a form of power. It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to d...
New fellowship program connects SDIS students with real-world opportunities in data management - School of Data and Information Sciences
SDIS alumni leaders and the UNC Research Data Management Core are creating hands-on learning opportunities for master's students through a new research data management fellowship program.
Introduction to Open Science
This course introduces the principles and practices of open science, with an emphasis on reproducible research workflows, transparent reporting, and collaborative scholarship. Students will critically examine reproducibility, explore tools that support openness (such as Git, GitHub, and Quarto), and apply these tools in hands-on assignments and a final open project. Students will also explore how open science practices vary across disciplines, including education, humanities, social sciences, industry and government, and STEM contexts. This course emphasizes applying open science principles to real-world data problems in alignment with the principles of producing reproducible research.
$125 Million in Combined Giving Establishes the Halıcıoğlu School of Data Science and Computing
The University of California San Diego has established the Halıcıoğlu School of Data Science and Computing thanks to $125 million in support from alumnus Taner Halıcıoğlu ’96. The school positions UC San Diego at the forefront of data science, AI and advanced computing education and research.
Democratizing Data
Democratizing Data builds a community-driven data ecosystem by identifying how datasets are used and reducing barriers to accessing high-quality public data. The initiative enhances the discoverability, usability, and relevance of data for researchers, policymakers, and stakeholder communities. A suite of tools and strategic partnerships supports this work by connecting users to the data, insights, and networks needed to inform decisions and generate impact.
Dynamic Data Science
<p>Use our Common Online Data Analysis Platform (CODAP) to explore dynamic data science activities and gain fluency in data moves to examine large datasets.</p>
Governing by dismantling: tech oligarchy and the stifling of public data infrastructure
Published in Science as Culture (Ahead of Print, 2026)
Data Feminism
A new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism.
A Polycentric Governance Lens on Data Infrastructures
Funding policies for data infrastructure promote open data sharing to drive positive social impact. However, concerns regarding the long-term management of data within and across distributed infrastructures can hinder data sharing. We draw upon the concept of polycentric governance to demonstrate how collaborative practices of data curation, in preparing and maintaining data for (future) sharing, provide a solid foundation for understanding data governance within data infrastructures. Based on a qualitative case study of a distributed ecological network, we investigate the conditions under which data are managed as a shared resource by local actors to ensure the long-term (re)usability of data. We contribute to CSCW by conceptualising data curation as a complex form of governance practice with multiple centres of decision-making, each of which operates with some degree of autonomy in data infrastructures. A polycentric governance lens on data infrastructures advances the CSCW conception of data curation as a collective governance practice that can cultivate a data democracy culture within and across organisations, empower individuals to be accountable for their data, and foster a mindset shift toward decentralised data governance.
A Data Utopia for Science-of-Science
Here I want to briefly sketch out a vision for how to solve a key set of problems facing science-of-science researchers, using the relatively new idea of a ‘data trust.’ In my ideal wor…
Nicholas Vincent | Data Leverage & Public AI
Prof. Nick Vincent is an Assistant Professor in Computing Science at Simon Fraser University. He studies the content ecosystems and data supply chains that fuel
Eric and Wendy Schmidt Center for Data Science & Environment | Research UC Berkeley
Launched in 2022, the Eric and Wendy Schmidt Center for Data Science & Environment (DSE) at Berkeley is a partnership between UC Berkeley's Department of Environmental Science, Policy, and Management and the Division of Computing, Data Science, and Society, with the financial support of Eric and Wendy Schmidt, working to take on these challenges.
Callysto: Bringing Jupyter and Computational Thinking to the K-12 Curriculum
Cybera and the Pacific Institute for Mathematical Sciences are participating in the new national CanCode program, to develop coding and digital skills from kindergarten to grade 12. With the launch of the Callysto project, we are creating tools and frameworks that help teachers bring computational thinking into their math, science, social sciences, and humanities courses - giving students the analytical skills needed to comprehend the digital world. Leveraging the Jupyter 'All-in-One' Science Platform to put friendly yet powerful compute tools in classroom settings, we are creating showcase modules and workshop training for K-12 teachers to incorporate data and computing into the broad curriculum. At this presentation, you will learn about how Callysto will: Bring computational thinking to classrooms through K-12 teacher training Create opportunities to participate in free workshop training sessions and content creation Help K-12 teachers gain public recognition for participating in this educational training project Note: This presentation is related to our 2017 BCNET presentation on "Bringing The Thunder: Deploying Jupyter Notebooks For Research, Education, And Innovation." View Slidedeck