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.
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...
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 ...
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.
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.
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 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.
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.
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.
Teaching Data Science by Visualizing Data Table Transformations: Pandas Tutor for Python, Tidy Data Tutor for R, and SQL Tutor
As part of the Digital Library's transition to Open Access, new features for researchers are available in the Premium Edition. Click here to learn more.
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.
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
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.
Coding Club: a positive peer-learning community
Free and accessible tutorials on data analysis, manipulation, visualisation and more.
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.