Teaching Machine Learning with R in Nigeria: Challenges and Prospects

By Abimbola Aderinola

The field of Data Science and Machine Learning is young in Nigeria, as most universities, colleges, and polytechnics do not offer Data Science at undergraduate level. This means potential data scientists come from Statistics, Mathematics, and Computer Science fields. One of the major challenges we have seen from experience of teaching Machine Learning with statistical computing (such as R) is that students and learners in Nigeria lack basic skills in statistical computing using R.

R is an open-source software with many advantages which include: R has many functions and packages for statistical analyses and graphics; R language allows the user to program loops to successively analyze several data sets; it is possible in R to combine in a single program different statistical functions to perform more complex analysis.

R is very suitable for Machine Learning analysis. Machine Learning is a collection of algorithms that generate insight from data. That insight might be used by humans or other machines to make decisions. Machine Learning is classified into supervised, unsupervised, and Reinforced Machine Learning.

Teaching Machine Learning with R in Nigeria is faced with the following challenges such as the problem of integrating Machine Learning activities into the tertiary education curriculum; lack of basic skills in using R language and other languages; few numbers of universities in Nigeria running BSc in Data Science.

To overcome these challenges, Dr. Monday Osagie Adenomon, Associate Professor of Statistics with research interest in Econometrics, Financial Time Series, Data Science and Machine Learning of the Department of Statistics, Faculty of Natural and Applied Sciences, proposed the TEAM-R framework for Teaching Machine Learning in Nigeria. TEAM-R stands for T – Teach (teaching machine learning through seminar, webinar, workshop, and short course, etc.); E – Examples with real-world data such as data from National Bureau of Statistics (NBS), World Bank, International Monetary Fund, Financial market data, and so on. A – Attitudinal influence which has to do with influencing the attitude of learners using the concept of “it can be done”, “learning by doing”. M – Mentoring is a process where experienced machine learning experts try to teach and mentor newcomers to Machine Learning; and R – Relationship which includes mutual respect between teacher and learner of Machine Learning.

Furthermore, Dr. Monday Osagie is not just an expert in Data Science, Machine Learning and M&E, but also a Director, ADECLAR Research Consult; Founder, FOUND-LEAS-IN-NIGERIA; Associate Professor of Statistics and Former Director of Centre for Cyberspace Studies, Nasarawa State University, Keffi, Nigeria; Elected member, ISI; Chair, IASC-AMG; Coordinator, ISLP-Nigeria; Geographic Centre Lead for Africa (RoSE). He has over a decade of experience in research and teaching at the university level. He has supervised over seventeen (17) candidates, twenty-seven (27), many PGD and BSc students. He has over fifty (50) publications with some indexed in Scopus and Web of Science journals. He has 559 citations in Google Scholar.

Dr. Adenomon presented the following as prospects of the application of Machine Learning in Nigeria which include fraud detection, monitoring of electoral processes in Nigeria, agriculture, climate change, investigating and providing solutions to security problems in Nigeria, application in medicine, and usefulness in education and research in Nigeria.

Dr. Adenomon recommended that there is need to integrate Data Science components across all courses of study in the Nigerian tertiary education since Data Science is multidisciplinary in nature, while undergraduate programmes in Data Science should be run in Nigerian tertiary institutions in order to achieve the prospects of Machine Learning, Big Data, and Artificial Intelligence (AI) in Nigeria.