An interview with Laura Haas and Alfred Hero by Robert Lue
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 ...
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.
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.
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.
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.
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.
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.
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.
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 ...
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.
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.
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.
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.
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.
Building Bridges for Data Science Education
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A first introduction to data science education in secondary schools: Teaching and learning about data exploration with <span style="font-variant:small-caps;">CODAP</span> using survey data
Abstract In this paper, we will describe an introduction to Data Science for secondary school students. We will report on the design and implementation of an introductory unit on “Data and data detectives with CODAP” in which secondary school students used the online tool CODAP to explore real and meaningful survey data on leisure time activities and media use (so‐called JIM‐PB data) in a statistical project setting as a starting point for data science. The JIM‐PB data set served as a valuable data set that offered meaningful and exciting opportunities for data exploration for secondary school students, and CODAP proved to be a valuable tool for the first explorations of this data.
