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Affordances of Data Science in Agriculture, Manufacturing, and Education

Affordances of Data Science in Agriculture, Manufacturing, and Education
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Author(s): Krishnan Umachandran (Nelcast Ltd., India)and Debra Sharon Ferdinand-James (The University of the West Indies, Trinidad and Tobago)
Copyright: 2019
Pages: 26
Source title: Web Services: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-5225-7501-6.ch052

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Abstract

Continued technological advancements of the 21st Century afford massive data generation in sectors of our economy to include the domains of agriculture, manufacturing, and education. However, harnessing such large-scale data, using modern technologies for effective decision-making appears to be an evolving science that requires knowledge of Big Data management and analytics. Big data in agriculture, manufacturing, and education are varied such as voluminous text, images, and graphs. Applying Big data science techniques (e.g., functional algorithms) for extracting intelligence data affords decision markers quick response to productivity, market resilience, and student enrollment challenges in today's unpredictable markets. This chapter serves to employ data science for potential solutions to Big Data applications in the sectors of agriculture, manufacturing and education to a lesser extent, using modern technological tools such as Hadoop, Hive, Sqoop, and MongoDB.

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