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Hands-On Data Science for Librarians

Written by Sarah Lin,Dorris Scott

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199 pages, about 4 hours of reading

Business & Economics

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About this book

Librarians understand the need to store, use and analyze data related to their collection, patrons and institution, and there has been consistent interest over the last 10 years to improve data management, analysis, and visualization skills within the profession. However, librarians find it difficult to move from out-of-the-box proprietary software applications to the skills necessary to perform the range of data science actions in code. This book will focus on teaching R through relevant examples and skills that librarians need in their day-to-day lives that includes visualizations but goes much further to include web scraping, working with maps, creating interactive reports, machine learning, and others. While there’s a place for theory, ethics, and statistical methods, librarians need a tool to help them acquire enough facility with R to utilize data science skills in their daily work, no matter what type of library they work at (academic, public or special). By walking through each skill and its application to library work before walking the reader through each line of code, this book will support librarians who want to apply data science in their daily work. Hands-On Data Science for Librarians is intended for librarians (and other information professionals) in any library type (public, academic or special) as well as graduate students in library and information science (LIS). Key Features: Only data science book available geared toward librarians that includes step-by-step code examples Examples include all library types (public, academic, special) Relevant datasets Accessible to non-technical professionals Focused on job skills and their applications

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Reading guide

Themes, characters and key ideas in Hands-On Data Science for Librarians, written by Chaptra AI.

  • about 8 hours
  • beginner
  • Instructional
  • Practical
  • Empowering

Hands-On Data Science for Librarians is a practical guide designed to bridge the skill gap for librarians seeking to move beyond proprietary software to open-source coding for data analysis. It focuses on teaching the R programming language through step-by-step, relevant examples directly applicable to various library contexts, including academic, public, and special libraries. The book covers a wide array of data science skills, from foundational visualizations and web scraping to more advanced techniques like mapping, interactive report creation, and machine learning. Its primary goal is to empower librarians with the necessary tools to integrate data science into their daily work, making data management, analysis, and utilization more efficient and insightful.

“Librarians understand the need to store, use and analyze data related to their collection, patrons and institution, and there has been consistent interest over the last 10 years to improve data management, analysis, and visualization skills within the profession.”

Key themes

Data Literacy and Empowerment
The book directly addresses the critical need for librarians to acquire the skills to manage, analyze, and visualize data effectively. By teaching R programming and data science techniques, it aims to empower librarians to move beyond intuitive decision-making to data-driven insights, thereby enhancing their professional capabilities and the services their libraries provide. It bridges the gap between acknowledging the importance of data and possessing the practical tools to utilize it.
Practical Application and Skill Acquisition
A cornerstone of the book's philosophy is its unwavering focus on hands-on skill development and immediate applicability. It prioritizes teaching 'what librarians need in their day-to-day lives,' delivering practical, job-oriented skills rather than extensive theoretical discourse. This approach ensures that learners can quickly translate their acquired knowledge into tangible improvements in their professional roles, making data science actionable and less abstract.
Accessibility and Inclusivity for Non-Technical Professionals
The book is consciously designed to be accessible and supportive for librarians who may lack a traditional technical background or prior coding experience. It aims to overcome the perceived difficulty of moving from proprietary software to code by offering a gentle, guided introduction to R. This focus on inclusivity ensures that a broader range of information professionals can engage with and benefit from data science, democratizing access to these powerful tools.

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