Impact on Education and Workforce Development

[jarrodmillman]

See the parent issue #9 for background.

For the preliminary proposal, we wrote:

Education and Workforce Development. The ecosystem plays a crucial role in education,
with Python increasingly adopted as the primary language taught at universities [31, 43,
27, 3]. At UC Berkeley, for instance, the Scientific Python OSE is used across fields as diverse as astron-
omy, digital humanities, engineering, neuroimaging, and statistics, preparing students for
careers in research, government, and industry.

In the full proposal, we have space to significantly expand and improve this. Please let me know if you have better references for the above paragraph. It would also be helpful to have more concrete examples (preferably with references to github repos or published papers).

[ericvd-ucb]

At UC Berkeley, the adoption of Scientific Python has been foundational to the success of its Data Science education initiative, which includes the first new college in 50 years and the largest undergraduate major on campus. Core courses such as Data 8: Foundations of Data Science — the university’s largest single course — and Data 100: Principles and Techniques of Data Science — the largest upper-division course — both rely heavily on the Scientific Python ecosystem to deliver high-impact, scalable instruction. These courses integrate tools like pandas, NumPy, Matplotlib, and Jupyter Notebooks, combined with open-source infrastructure for cloud-based computing and autograding, enabling thousands of students each semester to engage in hands-on data analysis and computational thinking. The open-source stack not only ensures accessibility and reproducibility but also empowers educators to teach at scale without compromising on the depth and rigor of the material.

https://cdss.berkeley.edu/dsus/news/data-science-major-now-largest-campus

[ericvd-ucb]

Beyond its central role in Data Science, the Scientific Python ecosystem has enabled UC Berkeley to make a broad and deep impact across disciplines in higher education. Open-source, reproducible workflows built on tools like NumPy, SciPy, Matplotlib, and Jupyter have become foundational in a wide range of courses, from biology and physics to earth science and engineering. In some majors, the Scientific Python stack serves as the standard computational toolkit, equipping students with transferable skills early in their academic journeys. In others, it supports advanced applications such as biosciences data analysis and earthquake monitoring, giving students authentic experiences with real-world scientific workflows. Berkeley’s investment in building scalable infrastructure, developing and sharing open educational resources, and supporting faculty in adapting and modernizing their curricula has helped accelerate the dissemination of Scientific Python, empowering instructors and learners far beyond the original data science classroom

"Accelerating the Advancement of Data Science Education"
Eric Van Dusen, Anthony Suen, Alan Liang, Amal Bhatnagar
SciPy July 2019
https://doi.org/10.25080/Majora-7ddc1dd1-000

[ericvd-ucb]

Berkeley’s commitment to open-source curriculum and pedagogy, centered around the Scientific Python ecosystem, has extended far beyond its own campus. More than 50 universities and colleges across the country have adopted components of this teaching stack, with some institutions fully integrating Berkeley’s Data 8 curriculum as a complete, foundational course in data science education. Others have adapted individual elements to suit their disciplinary needs, embedding Scientific Python tools into courses with relevant scope. Through partnerships with the National Science Foundation’s CloudBank project, Berkeley has provided targeted support to community colleges, California State Universities (CSUs), and other University of California campuses, helping these institutions adopt scalable, cloud-based infrastructure and open-source educational resources. Berkeley also hosts a recurring workshops to equip faculty with the skills and materials needed to bring these resources into their classrooms, in collaboration with the California Learning Lab and the statewide Zero Textbook Cost Initiative. These efforts continue to expand access to modern, affordable, and effective data science education, grounded in the power of the Scientific Python ecosystem.

https://cdss.berkeley.edu/dsus/nwdse