Teaching Data Science and Cloud Computing in Low and Middle Income Countries

Shanahan, Hugh; Harrison, Andrew; May, Sean Tobias

Abstract

Large, publicly available data sets present a challenge and an opportunity for researchers based in Low and Middle Income Countries (LMIC). The challenge for these researchers is how they can make use of such data sets given their poor connectivity and infrastructure. The opportunity is the ability to perform leading edge research using these data sets and hence avoid having to invest substantial resources in generating the data sets. The offshoot of this will be to generate solutions to the substantial local problems encountered in these countries and create an educated workforce in data science. Cloud computing in particular may well close the infrastructural gap here. In this paper we discuss our experiences of teaching a variety of summer schools on data intensive analysis in bioinformatics in China, Namibia and Malaysia. On the basis of these experiences we propose that a larger series of summer schools in data science and cloud computing in LMIC would create a cadre of data scientists to start this process. We finally discuss the possibility of the provision of cloud computing resources where the usage costs are controlled so that it is affordable for LMIC researchers.

Citation

Shanahan, H., Harrison, A., & May, S. T. (2015). Teaching Data Science and Cloud Computing in Low and Middle Income Countries. Advanced Techniques in Biology & Medicine, 3(3), Article 150. https://doi.org/10.4172/2379-1764.1000150

• Journal Article Type: Article
• Acceptance Date: Nov 16, 2015
• Publication Date: Nov 23, 2015
• Deposit Date: Nov 25, 2016
• Publicly Available Date: Nov 25, 2016
• Journal: Advanced Techniques in Biology & Medicine
• Electronic ISSN: 2379-1764
• Publisher: Longdom Publishing
• Peer Reviewed: Peer Reviewed
• Volume: 3
• Issue: 3
• Article Number: 150
• DOI: https://doi.org/10.4172/2379-1764.1000150
• Keywords: Data science; LMIC (Low and middle income countries);Cloud computing
• Public URL: https://nottingham-repository.worktribe.com/output/765653
• Publisher URL: http://dx.doi.org/10.4172/2379-1764.1000150