This research was set to examine the effect Multicollinearity has, on the standard error for regression coefficients when it is present in a Classical Linear Regression model (CLRM 84.472, 191.715,502.179 and 675.633

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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.

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Machine learning is a branch of artificial intelligence that helps machines learn from observational data without being explicitly programmed and its methods have been found to be very useful in the modern age for medical diagnosis and for early detection of diseases. According to the World Health Organization, 12 million deaths occur annually due to heart-related diseases. Thus, its early detection and treatment are of interest. This research introduces a better way of improving the timely prediction of cardiovascular diseases in suspected patients by comparing the efficiency of two boosting algorithms with four (4) other single based classifiers on cardiovascular official data. The best model was selected based on performances of 5 different evaluation metrics. From the results, Adaptive boosting is seen to outperform all other algorithms with a classification accuracy of 74.2%, closely followed by gradient boosting. However, gradient boosting was chosen as an acceptable technique because it trains faster than Adaboost with a better precision of 74.9% compared to 74.7% exhibited by Adaboost. Thus boosting algorithms are better predictors compared to single based classifiers with factors of age, systolic blood pressure, weight, cholesterol, height, and diastolic blood pressure as the major contributors to the model building.