| NSF Org: |
IIS Division of Information & Intelligent Systems |
| Recipient: |
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| Initial Amendment Date: | September 19, 2019 |
| Latest Amendment Date: | October 15, 2020 |
| Award Number: | 1923982 |
| Award Instrument: | Continuing 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, 2024 (Estimated) |
| Total Intended Award Amount: | $614,955.00 |
| Total Awarded Amount to Date: | $858,229.00 |
| Funds Obligated to Date: |
FY 2020 = $256,257.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
1000 HILLTOP CIR BALTIMORE MD US 21250-0001 (410)455-3140 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
1000 Hilltop Circle Baltimore MD US 21250-0001 |
| 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): |
HDR-Harnessing the Data Revolu, Special Projects - CNS, Special Projects - CCF, IIS Special Projects |
| Primary Program Source: |
01002021DB NSF RESEARCH & RELATED ACTIVIT |
| 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
The goal of this project is to develop a team-based data science corps program for undergraduate students from Computer Science, Information Systems, and Business integrating both academic training as well as hands-on experience through real-world data science projects. This project is a collaborative effort with the University of Maryland Baltimore County as the coordinating as well as an implementing organization, and the University of Baltimore, Towson University, and Bowie State University as implementing organizations. This project focuses on the city of Baltimore as an exemplar for other cities in the US and across the globe. The project team will collaborate with a number of communities in the city of Baltimore to integrate real-world data science projects into classroom instruction in data science. The specific objectives of this project are as follows: (i) Develop the technical, analytical, modeling, and critical thinking skills that are key to success as a data science professional; (ii) Connect a cohort of students to communities, organizations, and projects that can benefit from the power of data science; (iii) Nurture and support innovative thinking in solving some of the key challenges facing the real world; (iv) Promote a better understanding of the power and pitfalls of data-driven discoveries to improve the quality of life in urban communities; (v) Increase the data science workforce capacity to support this critical area that is of growing importance in society; and finally, (vi) Evaluate the effect of the proposed data science corps on student learning.
This project will create a core set of knowledge that will be valuable in developing solutions for real-world urban settings with the understanding that not all projects will require the application or use of every topic covered in the data science corps program. The core set of knowledge includes data collection and cleaning, data analysis using machine learning and deep learning techniques, data visualization including geospatial data and virtual reality, data privacy and security, and infrastructure for smart cities including IoT-based sensor networks. The proposed data science corps program will have two main phases: instructional phase (10 modules in total) and real-world team projects (5 modules in total). The project teams consist of students who have taken a course in at least one of the following areas: data collection and analysis, big data, machine learning including deep learning, smart cities, cybersecurity, geospatial data analysis and visualization, and virtual reality. Examples of team projects include: (i) developing community-based indicators that are compiled from open data portals and parametric and non-parametric statistical techniques to understand the relationship between urban sustainability and a range of factors including cleanliness and environment, crime and safety, business and economics, social and political, housing, health, and education; (ii) combining deep learning models such as convolutional neural networks (CNN) and long term short term memory recurrent neural networks (LSTM-RNN) to develop prediction models for derelict buildings that are likely to become vacant; (iii) combining sensor data and social media for automated information extraction, validation, and quality checks that can be beneficial to both citizens and emergency managers in crisis situations such as flash floods; (iv) developing smart streetlights that are networked LED systems that can be adjusted based on time of day and motion and can report outages back to central operations; and (v) developing augmented reality-based systems that leverage systems such as Microsoft HoloLens and mobile devices for building evacuation.
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.
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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.
Project Outcomes Report
Project Overview
From 2019 to 2024, the University of Maryland Baltimore County (UMBC) served as the coordinating and implementing institution for the grant titled “HDR DSC: Collaborative Research: Creating and Integrating Data Science Corps to Improve the Quality of Life in Urban Areas”, funded by the National Science Foundation. The program was implemented in partnership with Towson University, Bowie State University (later transferred to the University of North Texas), and the University of Baltimore. The data science corps program aimed to enhance data science education through experiential learning while addressing urban challenges in cities like Baltimore. The initiative focused on building technical, analytical, and critical thinking skills while promoting civic engagement and awareness of the limitations of data-driven insights. It sought to expand the data science workforce and evaluate the impact of experiential learning on student development.
Student Participation
During the grant period, 84 undergraduate students participated in the data science corps, including 32 from UMBC, 24 from Towson University, 13 from Bowie State University, 8 from the University of North Texas, and 7 from the University of Baltimore, representing a diverse and vibrant cohort. These students learned key data science concepts designed by the project team, participated in research with faculty members, and engaged in community-focused projects that applied data science to real-world problems, including analyzing crime patterns, improving public safety, and addressing service needs. Through these projects, students integrated academic knowledge with practical applications, gaining skills in system administration, spatial databases, data preprocessing, spatio-temporal analysis, machine learning, and data visualization.
Intellectual Merit
The data science corps provided students with a multidisciplinary instructional approach, consisting of 8 modules designed to address gaps in traditional data science curricula. These modules, available at https://datasciencecorps.umbc.edu/training-modules, were carefully curated to enhance technical and analytical skills. Students participated in team-based projects that emphasized collaboration, hands-on learning, and interdisciplinary application. Through the program, students worked on a variety of impactful projects, including the geospatial analysis of urban crime patterns, the prediction of vacant housing using deep learning models, and the development of tools to assess the relationship between crime hotspots and transportation networks. They also created interactive dashboards for visualizing air quality and environmental health data, applied machine learning techniques to identify gaps in healthcare access, and conducted community sentiment analyses using social media data to evaluate public response to government initiatives.
These projects enabled students to develop and apply technical skills through initiatives enhancing urban environments. Technical areas included IoT, cybersecurity in federated learning, deep learning in resource-constrained settings, geospatial analysis, data preprocessing, clustering techniques, and predictive modeling. Students gained hands-on experience with hardware like Raspberry Pi, NVIDIA Jetson Nano, Lidars, mm-wave radars, real-sense and thermal cameras, Microsoft HoloLens, and robotic platforms. They mastered software tools such as Tableau, Power BI, R Shiny, and Python libraries like Pandas, SciKit Learn, PyTorch, and TensorFlow. Cloud computing platforms and collaborative tools, including Google Colab Pro+, GitHub, and Slack, enhanced teamwork and project management skills. By combining technical training with real-world applications, the data science corps created an experiential learning environment equipping students to tackle complex societal issues.
Broader Impacts
The Data Science Corps program provided students a platform to showcase their work during Baltimore Data Week, a series of interactive workshops demonstrating the availability, accessibility, and actionability of data for community use. The event attracted a diverse audience, including community residents, leaders, volunteers, university faculty, non-profit representatives, government officials, businesses, teachers, journalists, civic technologists, and healthcare workers. Many students collaborated with community partners such as the Baltimore City Department of Public Works, Chesapeake Conservancy, and the Baltimore City Council President's Office, applying their skills to real-world challenges. By presenting to this varied audience, students shared insights that improved resource allocation and urban planning for Baltimore officials. Their efforts demonstrated scalable methods applicable to other cities, enhancing public safety, healthcare access, and urban efficiency. The initiative highlighted urban design’s influence on crime patterns, service needs, and resource accessibility, offering a framework for improving diverse urban environments. Through these efforts, students developed essential communication skills, fostered collaboration, and gained a deeper understanding of the transformative power of data-driven solutions. They bridged data science education with impactful solutions while advancing their careers, securing positions at companies like BG&E, Amazon, and SCCI, either full-time or as interns.
Summary and Conclusions
The data science corps demonstrated the potential of experiential learning to advance data science education and address societal challenges. Over the 2019–2024 grant period, the program integrated students into community-driven projects, creating a replicable model for combining technical education with impactful civic engagement. Many participants transitioned into successful careers in data science, underscoring the program’s success in preparing a skilled and engaged workforce. This initiative serves as a benchmark for future projects aiming to merge education, technical skill development, and community impact in meaningful ways.
Last Modified: 11/29/2024
Modified by: Aryya Gangopadhyay
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