$125 Million in Combined Giving Establishes the Halıcıoğlu School of Data Science and Computing

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 ...

Easy-to-Use Cloud Computing for Teaching Data Science

One of the biggest hurdles of teaching data science and programming techniques to beginners is simply getting started with the technology. With multiple versions of the same coding language availab...

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.

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.

California Alliance for Data Science Education (CADSE) - Home

The California Alliance for Data Science Education (CADSE) seeks to advance data science education in California Community Colleges (CCCs), California State Universities, and the University of

Collaboratory at Columbia: An Aspen Grove of Data Science Education · Issue 3.4, Fall 2021

Many universities recognize the rapidly growing impact of data science in all fields of study and the professions and seek to embed this expertise widely across their educational offerings. There is often broad interest in developing new data science curricula, with some universities even allocating funds toward this purpose. Yet, it is often unclear what resources are needed for effective data science education, and how resources ought to be prioritized. Although university leadership might be aware of a growing number of successful data science acceleration programs and pedagogical models, many of which are either general purpose or specific to a particular discipline, there remains a lack of clarity about how these models might address their own specific needs. This article presents the Collaboratory Program at Columbia University (termed “the Collaboratory”), which is both a set of “data science in context” educational approaches, as well as a meta-model for an accelerator program that allows different institutions to respond flexibly to their own disciplinary heterogeneity in terms of data science educational needs. The novelty of the Collaboratory lies in its crowd-sourcing approach to creating new data science pedagogy and its ability to kindle transdisciplinary collaboration in doing so. By offering seed funding, it fosters proactive efforts to embed data science “in context” into more traditional domains through a cohort of compelling, transdisciplinary, crowd-sourced data science education proposals each year. Collaboratory educational offerings are required to be developed through a partnership between two faculty members, a data scientist and a domain expert from another field, or a larger team with complementary expertise. Over the past 5 years, the Collaboratory has supported the development of a wide spectrum of data science pedagogical models spread across more than 40 academic departments, centers, institutes, and professional schools at Columbia University. As a result, the Collaboratory has to date served the learning needs of more than 4,000 students. Furthermore, it has cultivated a thriving ecosystem that includes a funding mechanism and a community-support structure that all contribute to its agility and success. Here, we offer our experience and best practices in developing and managing the Collaboratory, which, we hope, will contribute to a blueprint for data science education leaders everywhere.

Equity, Scalability, and Sustainability of Data Science Infrastructure

We seek to understand the current state of equity, scalability, and sustainability of data science education infrastructure in both the U.S. and Canada. Our analysis of the technological, funding, and organizational structure of four types of institutions shows an increasing divergence in the ability of universities across the United States to provide students with accessible data science education infrastructure, primarily JupyterHub.

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: 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.

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

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.

$200M Gift to USC Will Advance AI Research

Mark Stevens, a venture capitalist and a board member of NVIDIA, and his wife, Mary, donated $200 million to the University of Southern California for artificial intelligence research. The university’s computing school will be renamed the USC Mark and Mary Stevens School of Computing and Artificial Intelligence. Prior to this gift, the couple donated a total of $92 million to the university, of which Mark Stevens is an alum. Several centers at USC are already named in their honor.

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.

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.

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.

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.