EAGER: Learning Big Data Analytic Skills through Scientific Workflows

EAGER：通过科学工作流程学习大数据分析技能

• 批准号: 1355475
• 负责人: Yolanda Gil
• 金额: $ 20万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-15 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1355475&HistoricalAwards=false
• 关键词: EAGER; Learning; Big; Data; Analytic

Big data analytics has emerged as a widely desirable skill in many areas. Although courses are now available on a variety of aspects of big data, there is a lack of a broad and accessible course that covers the variety of topics that concern big data analytics. As a result, acquiring practical data analytics skills is out of reach for many students and professionals, posing severe limitations to our ability as a society to take advantage of our vast digital data resources.The goal of this work is to develop curriculum materials for big data analytics to provide broad and practical training in data analytics in the context of real-world and science-grade datasets and data analytics methods. A key technical basis of the approach is the use of workflows that capture expert analytic methods that will be presented to users for practice with real-world datasets within pre-defined lesson units. The results of this work include lesson units for learning expert-level skills in big data analytics, a framework for non-programmers to understand basic concepts in big data analytics, and a hands-on workflow framework to learn by direct experimentation and exploration with scientific data.The work focuses on big data problems relevant to geosciences, such as water quality analysis, hydrology, lake ecosystem sustainability, climate science, and earth modeling.This project will supplement existing academic training materials in big data. The PIs will use real-world geosciences data and domain tasks. All the materials will be available under open source licenses. The proposed work will have great impact in the ability of students to pursue careers in big data analytics. The framework will be accessible to students who lack the programming skills required to assemble themselves end-to-end data analysis systems for experimentation and practical learning. The wide adoption of the proposed approach could ultimately lead to broad societal impact by changing the way people interact with data, learn from using scientific data, and their ability to participate in big data analysis.

大数据分析已成为许多领域广泛需要的技能。尽管现在有关于大数据各个方面的课程，但缺乏涵盖与大数据分析相关的各种主题的广泛且易于理解的课程。 因此，对于许多学生和专业人士来说，获得实用的数据分析技能是遥不可及的，这严重限制了我们作为一个社会利用庞大数字数据资源的能力。这项工作的目标是为大数据开发课程材料。数据分析，在现实世界和科学级数据集和数据分析方法的背景下提供广泛且实用的数据分析培训。 该方法的关键技术基础是使用捕获专家分析方法的工作流程，这些方法将呈现给用户，以便在预定义的课程单元中使用真实世界的数据集进行练习。这项工作的成果包括用于学习大数据分析专家级技能的课程单元、供非程序员理解大数据分析基本概念的框架，以及通过直接实验和科学探索来学习的实践工作流程框架。该工作重点关注与地球科学相关的大数据问题，例如水质分析、水文学、湖泊生态系统可持续性、气候科学和地球建模。该项目将补充现有的大数据学术培训材料。 PI 将使用现实世界的地球科学数据和领域任务。 所有材料都将在开源许可下提供。 拟议的工作将对学生从事大数据分析职业的能力产生重大影响。缺乏自行组装端到端数据分析系统以进行实验和实践学习所需的编程技能的学生可以使用该框架。该方法的广泛采用最终可能会改变人们与数据交互的方式、从使用科学数据中学习以及参与大数据分析的能力，从而产生广泛的社会影响。