EAGER: Learning Big Data Analytic Skills through Scientific Workflows

大数据分析已成为许多领域广泛需要的技能。虽然现在有关于大数据各个方面的课程，但缺乏一个广泛且可访问的课程，涵盖与大数据分析有关的各种主题。 因此，对于许多学生和专业人士来说，获取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我们庞大的数字数据资源的能力。这项工作的目标是开发大数据分析的课程材料，在真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而实用的数据分析培训。 该方法的一个关键技术基础是使用工作流，这些工作流捕获专家分析方法，并将这些方法提供给用户，供用户在预定义的课程单元内使用真实世界的数据集进行练习。这项工作的成果包括学习大数据分析专家级技能的课程单元，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探索科学数据进行学习的实践工作流程框架。这项工作侧重于与地球科学相关的大数据问题，如水质分析，水文学，湖泊生态系统可持续性，气候科学，该项目将补充现有的大数据学术培训材料。 PI将使用真实世界的地球科学数据和领域任务。 所有这些材料都将在开源许可证下提供。 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。该框架将提供给缺乏编程技能的学生，他们需要组装自己的端到端数据分析系统进行实验和实践学习。广泛采用所提出的方法最终可能会通过改变人们与数据交互的方式，从使用科学数据中学习以及他们参与大数据分析的能力来产生广泛的社会影响。

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.