Unveiling Library Usage Patterns: A Python-based Descriptive Analysis of Big Data in Librarianship

Year : 2024 | Volume : | : | Page : –
By

Neeraj

  1. Research Scholar Department of Library and Information Science, Maharshi Dayanand University, Rohtak Haryana India

Abstract

This study analyses circulation data from Vivekananda Library, Maharshi Dayanand University, Rohtak, Haryana, India, for the academic year 2022-23. The analysis is conducted using descriptive statistics and Python programming to understand library resource utilisation patterns, temporal trends, and user behaviour. The findings reveal distinct usage patterns across the four quarters, with variations in the number and types of transactions. Temporal trends show fluctuations in library usage based on academic calendars and semester schedules. Popular materials are identified, providing insights into user preferences and needs. The study demonstrates the value of data analytics in informing decision-making processes in librarianship and highlights the practical application of data-driven approaches in improving library services. The findings have implications for resource allocation, collection development, and service provision, emphasising the importance of library usage analytics in enhancing the user experience and demonstrating the value of libraries in academic settings.

Keywords: Library usage analytics, Circulation data analysis, Academic libraries, Descriptive statistics, Python programming, User behaviour, Resource allocation, Collection development, Service provision, User experience

How to cite this article: Neeraj. Unveiling Library Usage Patterns: A Python-based Descriptive Analysis of Big Data in Librarianship. Journal of Advancements in Library Sciences. 2024; ():-.
How to cite this URL: Neeraj. Unveiling Library Usage Patterns: A Python-based Descriptive Analysis of Big Data in Librarianship. Journal of Advancements in Library Sciences. 2024; ():-. Available from: https://journals.stmjournals.com/joals/article=2024/view=145599


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Ahead of Print Subscription Original Research
Volume
Received April 6, 2024
Accepted April 24, 2024
Published May 8, 2024