HDR DSC: Collaborative Research: Creating and Integrating Data Science Corps to Improve the Quality of Life in Urban Areas

该项目的目标是为来自计算机科学，信息系统和商业的本科生开发一个基于团队的数据科学团队计划，通过真实世界的数据科学项目整合学术培训和实践经验。该项目是与马里兰州巴尔的摩县大学作为协调和执行组织，以及巴尔的摩大学，陶森大学和鲍伊州立大学作为执行组织的合作努力。 这个项目的重点是巴尔的摩市，作为美国和地球仪其他城市的典范。 该项目团队将与巴尔的摩市的一些社区合作，将真实世界的数据科学项目整合到数据科学的课堂教学中。该项目的具体目标如下：（一）发展技术，分析，建模和批判性思维技能，这是成功的关键作为一个数据科学专业;（二）连接一批学生的社区，组织和项目，可以受益于数据科学的力量;（三）培养和支持创新思维，解决真实的世界面临的一些关键挑战;（iv）促进更好地了解数据驱动发现的力量和陷阱，以改善城市社区的生活质量;（v）提高数据科学工作者的能力，以支持这一在社会中日益重要的关键领域;最后，（vi）评估拟议的数据科学团队对学生学习的影响。该项目将创建一组核心知识，这些知识在为现实世界的城市环境开发解决方案时将是有价值的，并不是所有项目都需要应用或使用数据科学团计划中涵盖的每个主题。核心知识集包括数据收集和清理、使用机器学习和深度学习技术进行数据分析、数据可视化（包括地理空间数据和虚拟现实）、数据隐私和安全，以及智能城市的基础设施（包括基于物联网的传感器网络）。 拟议的数据科学团队计划将有两个主要阶段：教学阶段（共10个模块）和现实世界的团队项目（共5个模块）。项目团队由至少在以下领域之一学习过课程的学生组成：数据收集和分析，大数据，包括深度学习在内的机器学习，智能城市，网络安全，地理空间数据分析和可视化以及虚拟现实。团队项目的例子包括：（一）制定基于社区的指标，这些指标是从开放的数据门户网站和参数和非参数统计技术汇编的，以了解城市可持续性与一系列因素之间的关系，包括清洁和环境、犯罪和安全、商业和经济、社会和政治、住房、卫生和教育;（ii）组合深度学习模型，例如卷积神经网络（CNN）和长期短期记忆递归神经网络（LSTM-RNN）为可能空置的空置建筑物开发预测模型; ㈢将传感器数据和社交媒体相结合，以实现自动信息提取、验证和质量检查，这在山洪暴发等危机情况下对公民和应急管理人员都有好处;（iv）开发智能路灯，这些智能路灯是联网的LED系统，可以根据一天中的时间和运动进行调整，并可以向中央操作报告停电情况;（五）发展增强现实--利用微软HoloLens和移动的设备等系统进行建筑物疏散。NSF的利用数据革命数据科学团计划侧重于建立以下能力：在地方、州、国家和国际层面利用数据革命，帮助释放数据的力量，为科学和社会服务。该计划中的项目由NSF的利用数据革命大想法;信息和智能系统部计算机和信息科学与工程局;本科教育部教育和人力资源局;数学科学部数学和物理科学局;该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.