EAGER: Learning Big Data Analytic Skills through Scientific Workflows

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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.