Abstract

The global statistics community has witnessed the emergence of data science over the last decade. While some academic institutions have been able to work out an amicable co-existence of both disciplines, some unique challenges present themselves in the developing world in pioneering data science education and practice. These challenges and strategies undertaken by three such academic institutions based in India, Kenya, Egypt and Nigeria will be presented in this session and the discussion section will focus on developing a roadmap for the way ahead. While the emergence of data science with its focus on a substantive domain of interest has the potential to boost the scope for data driven decision making, programme implementation and monitoring in developing countries, academic institutes based in developing countries face some unique challenges. In the case of India, mathematical and technical skills are generally high but creation of an interdisciplinary environment has proved to be a challenge as has the development of accompanying soft skills such as visualisation and communication. The enormous demand for data scientists has however led to the proliferation of many training programmes of questionable quality. In case of Kenya, the challenge is of making the transition from traditional programmes focusing on classical statistics while in Egypt, an additional consideration is the lack of trained faculty and the absence of a business community driving the demand. In all three cases, the lack of public domain data suitable for academic modelling has been an additional challenge. Some strategies which have been considered and will be discussed include creation of south-south academic partnerships, using online meeting forums for long distance training within such networks, launching joint training programmes with industry, national institutes and non-profit organisations and exploring public domain resources such as R. Pros and cons of each will be discussed and a workable strategy presented.