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.
Rezensionen / Stimmen
A worthy contribution to the dynamic field of data science, suited for library professionals in all types of libraries. * Library Journal *
Reihe
Sprache
Verlagsort
Verlagsgruppe
Bloomsbury Publishing Plc
Zielgruppe
Für höhere Schule und Studium
Für Beruf und Forschung
Illustrationen
Maße
Höhe: 254 mm
Breite: 178 mm
Dicke: 11 mm
Gewicht
ISBN-13
978-1-4408-7121-4 (9781440871214)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
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
