Hands-On Data Science for Librarians

Abstract

The article reviews Hands-On Data Science for Librarians by Sarah Lin and Dorris Scott as an applied work that connects data science with library and information science. It begins by explaining the interdisciplinary nature of data science and its relationship with statistics, computer science, and mathematics, then highlights its relevance to librarians and information professionals in handling data related to collections, users, and information institutions, including storage, analysis, and visualization. The review shows that the book targets professionals working in different types of libraries as well as graduate students in library and information science, with a practical focus on developing skills in the R statistical programming language and the RStudio environment. It summarizes the book’s twelve chapters, covering topics such as data tidying with dplyr, data visualization with ggplot2, web scraping with rvest, mapping with tmap, text analysis with tidytext, creating dynamic documents and dashboards with rmarkdown, Flexdashboard, and Shiny, and introducing machine learning through tidymodels. The review concludes that the book offers a useful practical guide for information professionals and recommends making its content available to Arab researchers and practitioners, including through scholarly Arabic translation, to strengthen the use of data science tools and applications in library contexts.