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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 ...

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.

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.

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.

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.

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.

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.

Teaching Programming for First-Year Data Science

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.

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.

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.

Foundations of Data Science for Students in Grades K-12

On September 13 and 14, 2022, the Board on Science Education at the National Academies of Sciences, Engineering, and Medicine held a workshop entitled Foundations of Data Science for Students in Grades K–12. Speakers and participants explored the rapidly growing field of K-12 data science education, by surveying the current landscape, surfacing what is known, and identifying what is needed to support student learning, develop curriculum and tools, and prepare educators. To support these conversations, four papers were commissioned and discussed during the workshop. This publication summarizes the presentations and discussion of the workshop.

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.

$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.

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 ...

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...