Application of Statistical and Mathematical Algorithms to Data Analytics and Job Creation in Nigeria

David E. Omoregie, Moses K. Tule


In this paper, we examine the use of statistical and mathematical algorithms in data analytics and their application in business intelligence, insights and collective intelligence, for enhanced job creation interventions in Nigeria. The paper argues that the demand-driven job creation, involving developing skills for existing vacancies or opportunities is no longer sustainable in the current challenging economic conditions. Rather it makes a case for supply-driven job creation, where skills are developed in technology and data analytics (with strong reliance on statistics and mathematics), with a view to solving business and corporate problems, thereby enhancing job creation in those businesses and corporations, which hitherto had no vacancies. The paper surveys statistical and mathematical algorithms, categorized as supervised and unsupervised learning techniques, applied in data analytics, and discusses the emerging requirements for data analytics in modern business and corporations. It further discusses modern application of data analytics in a number of business areas such as marketing, customer management, finances, data mining, web and learning, highlighting a number of metrics specific to each sector. The paper also identifies the specialized skills required to create job opportunities in key sectors in Nigeria. Drawing extensively from the lead author’s experience in the UK, the paper presents how skills in modern data analytics can lead in creating job opportunities, a major lesson for Nigeria.

Keywords: Job Creation, Data Analytics, Data Science, Business intelligence, Insights, Algorithms

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ISSN (Paper)2224-5758 ISSN (Online)2224-896X

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