The Role of Data Science in Library and Information Science Education

Abstract

This article explores the evolution of Library and Information Science (LIS) education in relation to data science, examining how curriculum frameworks have adapted to incorporate data-driven methodologies and tools. It delves into the symbiotic relationship between data science and LIS education, highlighting core competencies required for effective data analysis, including statistical methods, data visualisation and machine learning techniques. The study assesses the role of technological tools, such as advanced analytics platforms and programming languages, in enhancing data analysis capabilities within LIS programs. Information management skills are emphasised, focusing on how data science methodologies improve user behaviour studies and research methodologies. This article reviews the development of data science within LIS education, identifying intersecting competencies that bridge the gap between data science and traditional LIS roles. Opportunities for leveraging data science in LIS education are discussed alongside the challenges, including curriculum development, faculty expertise and resource constraints. Future directions are proposed, emphasising the need for continuous adaptation and interdisciplinary collaboration. This study addresses the critical role of LIS professionals in the effective implementation of data science within LIS education, advocating for professional development and strategic partnerships to bridge the skills gap. The study provides a comprehensive overview of how data science can transform LIS education, offering insights into overcoming challenges and seizing emerging opportunities to enhance the relevance and impact of LIS programs in the modern era.

Keywords: Data Science, LIS Education, LIS Profession, Data Management, Information Technology, Data Analytics

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