Affordances of Data Science in Agriculture, Manufacturing, and Education

Introduction

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.