Data science skills: Building partnership for efficient school curriculum delivery in Africa

Nureni, Olawale Adeboye and Peter, Osuolale Popoola and Ogunnusi, O.N (2020) Data science skills: Building partnership for efficient school curriculum delivery in Africa. Statistical Journal of the IAOS. pp. 49-62. ISSN 200693

[img] Text
sji_2020_36-S1_sji-36-S1-sji200693_sji-36-sji200693.pdf

Download (944kB)

Abstract

Data science is a concept to unify statistics, data analysis, machine learning and their related methods in order to analyze actual phenomena with data to provide better understanding. This article focused its investigation on acquisition of data science skills in building partnership for efficient school curriculum delivery in Africa, especially in the area of teaching statistics courses at the beginners’ level in tertiary institutions. Illustrations were made using Big data of selected 18 African countries sourced from United Nations Educational, Scientific and Cultural Organization (UNESCO) with special focus on some macro-economic variables that drives economic policy. Data description techniques were adopted in the analysis of the sourced open data with the aid of R analytics software for data science, as improvement on the traditional methods of data description for learning and thus open a new charter of education curriculum delivery in African schools. Though, the collaboration is not without its own challenges, its prospects in creating self-driven learning culture among students of tertiary institutions has greatly enhanced the quality of teaching, advancing students skills in machine learning, improved understanding of the role of data in global perspective and being able to critique claims based on data.

Item Type: Article
Subjects: Q Science > QA Mathematics
Divisions: Faculty of Engineering, Science and Mathematics > School of Mathematics
Depositing User: Mr Taiwo Egbeyemi
Date Deposited: 10 Feb 2022 09:45
Last Modified: 10 Feb 2022 09:45
URI: http://eprints.federalpolyilaro.edu.ng/id/eprint/1852

Actions (login required)

View Item View Item