Data Science for Librarians

This unique textbook intersects traditional library science with data science principles that readers will find useful in implementing or improving data services within their libraries. Data Science for Librarians introduces data science to students and practitioners in library services. Writing for academic, public, and school library managers; library science students; and library and information science educators, authors Yunfei Du and Hammad Rauf Khan provide a thorough overview of conceptual and practical tools for data librarian practice. Partially due to how quickly data science evolves, libraries have yet to recognize core competencies and skills required to perform the job duties of a data librarian. As society transitions from the information age into the era of big data, librarians and information professionals require new knowledge and skills to stay current and take on new job roles, such as data librarianship. Such skills as data curation, research data management, statistical analysis, business analytics, visualization, smart city data, and learning analytics are relevant in library services today and will become increasingly so in the near future. This text serves as a tool for library and information science students and educators working on data science curriculum design.

Yunfei Du is professor of library science and associate dean in the College of Information at the University of North Texas. He has worked as a systems librarian and published in many academic journals. Hammad Rauf Khan is the director of library services at the Columbus College of Art & Design.

1 More Data, More Problems What Is Data? Quantitative vs. Qualitative Data Digital vs. Nondigital Data What Is Big Data? How Big Data Works Problems with Having Too Much Data Data and Information Are Different Data Saturation Confirmation Bias and Signal Error Effects on Society Impact on Health Services Government Planning News and Media Consumption Sports Big Data and the Data Deluge Open Data Open Government Data Principles of Open Government Data Research Data in Academic Libraries Data Literacy Concepts Data Life Cycle Era of Big Data Looking Ahead References 2 A New Strand of Librarianship Data-Driven Decision Making History of Data in Academic Libraries What Does Big Data Mean for Libraries? Data Librarianship Research Data Services Data Management Plans Management: GIS Conclusion References 3 Data Creation and Collection Surveys Online Tools Social Media Data Data Noise Data Acquisitions Disadvantages of Big Data Collection Big Data Analytics Conclusion References 4 Data for the Academic Librarian E-Science and E-Research Data Reference Interview Data Storage and Archiving Data Repositories References 5 Research Data Services and the Library Ecosystem What Is RDS? How Much of the Research Data Life Cycle is Represented within RDS? Who Works in RDS? Data Literacy References 6 Data Sources Data and the Library Professional Open Government Data Data Repositories Metadata Data Citation Data Collection and Harvesting Data Extraction, Transformation, and Loading Data Mining Data Cleaning Data Mining and Analysis for Librarians Data Mining: Techniques Data Mining: Advantages and Disadvantages Data Analysis and Librarians: An Overview Conclusion References 7 Data Curation (Archiving/Preservation) Data Curation Process Data Stewardship Metadata Data Access and Reuse Data Sharing Data Quality Conclusion References 8 Data Storage, Management, and Retrieval Big Data Storage Solutions High-Performance Computing Variety of Big Data Storage Patterns Social Networking Data Cloud Computing Apache Hadoop Common Cloud Storage Solutions Privacy Concerns on Cloud Computing Big Data Management Data Cleaning Big Data Security and Policies Managing the Velocity of Big Data Conclusion References 9 Data Analysis and Visualization Big Data Analysis Descriptive Analytics Diagnostic Analytics Predictive Analytics Prescriptive Analytics Statistics for Data Science Hypothesis Testing and Statistical Significance Probability Distributions Correlation Regression Data Visualization Brief History of Data Visualization Data Visualization Methods and Tools Text Visualization Data Visualization Applications Conclusion References 10 Data Ethics and Policies Data Security User Privacy and Data Retention Data Privacy Data Ethics Copyright and Ownership Personal Information Data in Libraries Conclusion References 11 Data for Public Libraries and Special Libraries Smart Cities Initiatives Open Government Initiatives Internet of Things and Privacy Concerns Internet of Things Challenges Census Data Role of Public Libraries in the Era of Big Data Public Libraries Can Use Big Data to Address Local Needs Librarians Are Advocates for Privacy of Citizens Data Librarians in Public Libraries Public Libraries as Learning Centers for Teens Role of Special Libraries in the Era of Big Data Law Librarians Corporate Libraries Medical Librarians Conclusion References 12 Conclusion: Library, Information, and Data Science Data as an Infrastructure for Society Data and Information Data as Public Good Data as the Driving Force for the Economy Data for Governance Librarians and Data Life Cycle New Job Titles for Librarians Librarians in Data Life Cycle Data Analysis Skill Sets for Librarians Data Ingestion Data Curation Data Visualization Data Analytics Data Literacy for Library Users Data Literacy in Academic Settings Data Literacy for Public Library Users Conclusion References Glossary Index