Schubert, K. D., & Romer, S., & Addison, S. R., & Moore, T. D., & Berry, L. J., & Fowler, J. M., & Shoultz, L., & Davis, C. C. (2024, June), Envisioning and Realizing a Statewide Data Science Ecosystem  Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. https://peer.asee.org/47325

\bibitem{asee_peer_47325}
  Schubert, K. D., \& Romer, S., \& Addison, S. R., \& Moore, T. D., \& Berry, L. J., \& Fowler, J. M., \& Shoultz, L., \& Davis, C. C. (2024, June), \emph{Envisioning and Realizing a Statewide Data Science Ecosystem}
  Paper presented at 2024 ASEE Annual Conference \& Exposition, Portland, Oregon.
  https://peer.asee.org/47325

Karl D. Schubert FIET, Shantel Romer, Stephen R. Addison, Tina D Moore, Laura J Berry, Jennifer Marie Fowler, Lee Shoultz, and Christine C Davis.  "Envisioning and Realizing a Statewide Data Science Ecosystem".  2024 ASEE Annual Conference & Exposition, Portland, Oregon, 2024, June.  ASEE Conferences, 2024.  https://peer.asee.org/47325 Internet.  30 Jul, 2024

\bibitem{asee_peer_47325}
  Karl D. Schubert FIET, Shantel Romer, Stephen R. Addison, Tina D Moore, Laura J Berry, Jennifer Marie Fowler, Lee Shoultz, and Christine C Davis.
  "Envisioning and Realizing a Statewide Data Science Ecosystem".
  \emph{2024 ASEE Annual Conference \& Exposition, Portland, Oregon, 2024, June}.
  ASEE Conferences, 2024.
  https://peer.asee.org/47325 Internet.  30 Jul, 2024

@INPROCEEDINGS{asee_peer_47325
author = "Karl D. Schubert FIET, Shantel Romer, Stephen R. Addison, Tina D Moore, Laura J Berry, Jennifer Marie Fowler, Lee Shoultz, and Christine C Davis"
title = "Envisioning and Realizing a Statewide Data Science Ecosystem"
booktitle = "2024 ASEE Annual Conference \& Exposition"
year = "2024"
month = "June"
address = "Portland, Oregon"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/47325}
}

TY  - CPAPER
AB  - This paper describes the vision, strategy, plan, and realization of a state-wide rigorous data science educational ecosystem.  The need for developing data science degree programs and education has been well-established and, in our state, a blue-ribbon panel with industry, academic, and government representatives defined the needs of the state.  Additionally, a well-established “think and do tank” published several reports on the importance of data science education and graduates.  As we began to develop our programs separately, it occurred to us that we were a small enough state that, if we chose to do so, we could work together for consistent degree programs. Because we were a small state, we needed to work together to develop quality data science education at all levels.  In late 2019, we co-hosted a workshop with representatives from industry, academia, state government, and students at all levels, to explore the potential for developing a state-wide data science educational ecosystem.  The response was overwhelmingly “yes.”  As a result, and with the collaboration of our state’s division of higher education, we developed a vision, strategy, and plan to do so with an “opt-in” approach and this paper presents the vision, strategy, plans, results, and experiences and continuous improvement over more than four years of collaboration.  These include the development of AS degrees in Data Science. BS degrees in Data Science, 2+2 programs in data science (AS Data Science in a two-year college and transfer to a 4-year university for the +2 BS Data Science with no loss of credits), proposed certificates in data science, a common curriculum state-wide, a high-school data science track based on the common curriculum, and a vision realized of “start anywhere, finish anywhere.”   Finally, we look to the future in expanding the “opt-in” academic institutions and significantly increasing the number of data science graduates at all levels.
AU  - Karl D. Schubert FIET
AU  - Shantel Romer
AU  - Stephen R. Addison
AU  - Tina D Moore
AU  - Laura J Berry
AU  - Jennifer Marie Fowler
AU  - Lee Shoultz
AU  - Christine C Davis
CY  - Portland, Oregon
DA  - 2024/06/23
PB  - ASEE Conferences
TI  - Envisioning and Realizing a Statewide Data Science Ecosystem
UR  - https://peer.asee.org/47325
ER  -