Yunfei Du, Hammad Rauf Khan

Data Science for Librarians (eBook, PDF)

• Format: PDF

Produktbeschreibung

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.

Produktdetails

• Verlag: Bloomsbury Publishing Inc
• Seitenzahl: 180
• Altersempfehlung: ab 7 Jahre
• Erscheinungstermin: 26. März 2020
• Englisch
• ISBN-13: 9781440871221
• Artikelnr.: 68255596

Autorenporträt

Yunfei Du is associate professor at the University of North Texas (UNT), Denton, TX.

Inhaltsangabe

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