
NSF Org: |
IIS Division of Information & Intelligent Systems |
Recipient: |
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Initial Amendment Date: | September 17, 2019 |
Latest Amendment Date: | July 21, 2022 |
Award Number: | 1924017 |
Award Instrument: | Standard Grant |
Program Manager: |
Sylvia Spengler
sspengle@nsf.gov (703)292-7347 IIS Division of Information & Intelligent Systems CSE Directorate for Computer and Information Science and Engineering |
Start Date: | October 1, 2019 |
End Date: | September 30, 2023 (Estimated) |
Total Intended Award Amount: | $581,902.00 |
Total Awarded Amount to Date: | $581,902.00 |
Funds Obligated to Date: |
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History of Investigator: |
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Recipient Sponsored Research Office: |
10 ELM ST NORTHAMPTON MA US 01063-6304 (413)584-2700 |
Sponsor Congressional District: |
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Primary Place of Performance: |
Northampton MA US 01063-6304 |
Primary Place of
Performance Congressional District: |
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Unique Entity Identifier (UEI): |
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Parent UEI: |
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NSF Program(s): | IIS Special Projects |
Primary Program Source: |
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Program Reference Code(s): |
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Program Element Code(s): |
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Award Agency Code: | 4900 |
Fund Agency Code: | 4900 |
Assistance Listing Number(s): | 47.070 |
ABSTRACT
This project simultaneously addresses two problems: 1) the inability of community-based and non-profit organizations to tackle data science problems; and 2) the lack of real world experience gained by students studying data science. The increased availability of data, combined with increased computing power at lower costs, has brought to the desktop tremendous analytical and problem solving capabilities. Yet many organizations are not able to take advantage of these developments because they often lack appropriate staffing to wrestle with complex data science problems. Meanwhile, as students increasingly gravitate toward data science programs, much of their course-based problem solving experience focuses on clean problems with simple data sets. This leaves them unprepared for the reality of the data science applications they will face in professional settings. This project addresses both issues by deploying teams of data science students to assist local organizations, thereby increasing the long-term capacity of the data science workforce.
This is a multifaceted project that will provide immediate impact to local organizations and long-term benefit for students through valuable hands-on data science experience. There are two major components of the proposed project. First, Data Science WAV teams of four specially-trained undergraduate students will be deployed to community-based organizations to Wrangle, Analyze, and Visualize their data. Second, this project will offer summer faculty development workshops designed to help new instructors, especially those at community colleges, teach data science at their institutions. Curricular innovations that bring experiential data science learning into the curriculum will lead to sustained impact at the partnering academic institutions and in the larger Pioneer Valley region. This proposal is diverse across both institutions and student populations. It comprises one major research university (The University of Massachusetts, Amherst), four liberal arts colleges (Amherst, Hampshire, Mount Holyoke, and Smith), and three local community colleges (Greenfield, Holyoke, and Springfield Technical). The inclusion of two women's colleges (Smith and Mount Holyoke) and two Hispanic-serving institutions (Holyoke and Springfield Technical) will help ensure that a diverse student population is engaged in the project.
NSF's Harnessing the Data Revolution Data Science Corps program focuses on building capacity for harnessing the data revolution at the local, state, national, and international levels to help unleash the power of data in the service of science and society. Projects in this program are being jointly funded by the NSF's Harnessing the Data Revolution Big Idea; the Directorate for Computer and Information Science and Engineering, Division of Information and Intelligent Systems; the Directorate for Education and Human Resources, Division of Undergraduate Education; the Directorate for Mathematical and Physical Sciences, Division of Mathematical Sciences; and the Directorate for Social, Behavioral and Economic Sciences, Office of Multidisciplinary Activities and Division of Behavioral and Cognitive Sciences.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
We worked as a collaborative group of nine institutions (Smith College, Amherst College, Hampshire College, Mount Holyoke College, the University of Massachusetts-Amherst, and the University of Minnesota to increase long-term data science capacity in the Connecticut River Valley area of Western Massachusetts while also providing students with valuable hands-on data science experience. This project has now been expanded to include Bard College in place of Mount Holyoke College.
The two main components of the project were:
- 1. Data Science WAV teams: specially-trained teams of four or five undergraduate students who are deployed to community-based organizations to Wrangle, Analyze, and Visualize their data.
- 2. Summer Faculty Development Workshops: designed to help new instructors (especially those at two-year (community) colleges) teach data science at their institutions, using curricular innovations that bring experiential data science learning into the two-year college curriculum.
We organized and implemented faculty development workshops in 2020 (pivoted to remote due to the pandemic), 2021, and 2022 along with a Symposium on Data Science at Massachusetts Community Colleges (also in 2022). These were well-attended and fostered deeper connections between four- and two-year faculty members in Massachusetts.
Four papers have been published to disseminate results of the project:
- 1. Horton, N.J., Baumer, B. S., Zieffler, A., and Barr, V. (2021) The Data Science Corps Wrangle-Analyze-Visualize Program: Building data acumen for undergraduate students. Harvard Data Science Review, https://doi.org/10.1162/99608f92.8233428d
- 2. Legacy, C., Zieffler, A., Baumer, B.S., Barr, V., & Horton, N.J. (2023). Facilitating team-based data science: Lessons learned from the DSC-WAV project. Data Science Education Research (special issue) Foundations of Data Science, 5(2):244-265, https:://doi.org/10.3934/fods.2022003.
- 3. Baumer, B. S., & Horton, N. J. (2023). Fostering and simplifying data science transfer pathways in Massachusetts. Harvard Data Science Review, 5(1), https://doi.org/10.1162/99608f92.e2720e81
- 4. Pruim R., Girjau, M.C, and Horton, N. J. (2023). Fostering Better Coding Practices for Data Scientists. Harvard Data Science Review, 5(3), https://doi.org/10.1162/99608f92.97c9f60f
More than 20 presentations by faculty and students were given over the project period. These materials and related resources are available at the DSC-WAV website: https://dsc-wav.github.io/www/index.html
Last Modified: 10/16/2023
Modified by: Benjamin Baumer
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