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.
TopIntroduction
Data science as a new field of endeavor is increasingly in top demand (American Statistical Association, 2016) as technological advancements afford the availability of an abundance of real-time information (e.g., via social media or global positioning system) to users. Organisations and institutions face operational challenges such as fierce price wars, stronger competition, overhead controls, waste reduction, operational excellence, stressed customer demands, and reduced buying power. Such challenges may prompt “panic” decision-making in the absence of data science wherewithal by institutions/organisations in key sectors like manufacturing, agriculture, and education. Moreover, the education sector, unlike other service sectors, is not always prompt in responding to changing needs of the education market. Responsively, businesses and institutions can now tap into data they never knew earlier existed, making customized dynamic decisions with intelligence of no perceived boundaries. Data science as an information work space for data analytics affords in-depth processes such as scoring, predicting cross- field inputs, and functional algorithms, resulting in data warehousing and intelligence tools used appropriately for decision-making. Artificial intelligence plays a critical role in data analytics for decision making as it allows the computer system to almost think and find, while correlating varied pieces of information, producing meaningful results for analysts such as those in the agricultural sector (Roy, 2013, May 22; Tapia & Corchado, 2010). The challenge in this process is the evolution of data cognitively engaged and skimmed for decisions. The dawning of the field of data science enables market testing for possible products and services that are the real frameworks for revenue. Employing data science techniques in creating and analysing Big Data for supporting critical decision-making can enhance an institution/organisation’s ability to satisfy customers’ changing needs and build market resilience in key sectors such as agriculture, manufacturing, and education. To this end, this chapter specifically aims to achieve the following objectives:
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Explain the need for data science wherewithal.
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Analyze the use of data science in agriculture.
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Describe the use of analytical tools in data science.
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Analyze the use of data science in manufacturing.
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Describe uses of data science in education.