Data Science for Librarians

by Yunfei Du, Hammad Rauf Khan

Overview

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.

Product Details

ISBN-13:	9781440871214
Publisher:	Bloomsbury Academic
Publication date:	03/26/2020
Series:	Library and Information Science Text Series
Pages:	180
Product dimensions:	7.00(w) x 10.00(h) x 0.45(d)

About the Author

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.

Table of Contents

1 More Data, More Problems 1

What Is Data? 2

Quantitative vs. Qualitative Data 3

Digital vs. Nondigital Data 3

What Is Big Data? 4

How Big Data Works 5

Problems with Having Too Much Data 5

Data and Information Are Different 5

Data Saturation 6

Confirmation Bias and Signal Error 6

Effects on Society 6

Impact on Health Services 6

Government Planning 7

News and Media Consumption 7

Sports 7

Big Data and the Data Deluge 7

Open Data 8

Open Government Data 9

Data.gov 9

Principles of Open Government Data 10

Research Data in Academic Libraries 10

Data Literacy Concepts 11

Data Life Cycle 11

Era of Big Data 12

Looking Ahead 14

References 14

2 A New Strand of Librarianship 17

Data-Driven Decision Making 17

History of Data in Academic Libraries 19

What Does Big Data Mean for Libraries? 20

Data Librarianship 21

Research Data Services 23

Data Management Plans 24

Management: GIS 26

Conclusion 28

References 28

3 Data Creation and Collection 31

Surveys 31

Online Tools 32

Social Media Data 33

Data Noise 33

Data Acquisitions 35

Disadvantages of Big Data Collection 36

Big Data Analytics 37

Conclusion 39

References 40

4 Data for the Academic Librarian 43

E-Science and E-Research 44

Data Reference Interview 45

Data Storage and Archiving 48

Data Repositories 49

References 51

5 Research Data Services and the Library Ecosystem 55

What Is RDS? 56

How Much of the Research Data Life Cycle is Represented within RDS? 56

Who Works in RDS? 59

Data Literacy 60

References 63

6 Data Sources 65

Data and the Library Professional 65

Open Government Data 66

Data Repositories 67

Metadata 68

Data Citation 69

Data Collection and Harvesting 70

Data Extraction, Transformation, and leading 71

Data Mining 72

Data Cleaning 72

Data Mining and Analysis for Librarians 73

Data Mining: Techniques 75

Data Mining: Advantages and Disadvantages 75

Data Analysis and Librarians: An Overview 76

Conclusion 77

References 78

7 Data Curation (Archiving/Preservation) 81

Data Curation Process 81

Data Stewardship 82

Metadata 83

Data Access and Reuse 85

Data Sharing 86

Data Quality 86

Conclusion 88

References 88

8 Data Storage, Management, and Retrieval 91

Big Data Storage Solutions 91

High-Performance Computing 92

Variety of Big Data Storage Patterns 93

Social Networking Data 94

Cloud Computing 95

Apache Hadoop 96

Common Cloud Storage Solutions 96

Privacy Concerns on Cloud Computing 97

Big Data Management 98

Data Cleaning 98

Big Data Security and Policies 99

Managing the Velocity of Big Data 100

Conclusion 100

References 101

9 Data Analysis and Visualization 105

Big Data Analysis 105

Descriptive Analytics 105

Diagnostic Analytics 106

Predictive Analytics 106

Prescriptive Analytics 107

Statistics for Data Science 107

Hypothesis Testing and Statistical Significance 107

Probability Distributions 108

Correlation 109

Regression 110

Data Visualization 111

Brief History of Data Visualization 111

Data Visualization Methods and Tools 112

Text Visualization 114

Data Visualization Applications 115

Conclusion 117

References 117

10 Data Ethics and Policies 121

Data Security 122

User Privacy and Data Retention 123

Data Privacy 125

Data Ethics 126

Copyright and Ownership 127

Personal Information Data in Libraries 128

Conclusion 129

References 130

11 Data for Public Libraries and Special Libraries 131

Smart Cities Initiatives 131

Open Government Initiatives 133

Internet of Things and Privacy Concerns 134

Internet of Things 134

Challenges 135

Census Data 135

Role of Public Libraries in the Era of Big Data 136

Public Libraries Can Use Big Data to Address Local Needs 136

Librarians Are Advocates for Privacy of Citizens 137

Data Librarians in Public Libraries 137

Public Libraries as Learning Centers for Teens 137

Role of Special Libraries in the Era of Big Data 138

Law Librarians 139

Corporate Libraries 140

Medical Librarians 140

Conclusion 141

References 141

12 Conclusion: Library, Information, and Data Science 145

Data as an Infrastructure for Society 145

Data and Information 146

Data as Public Good 146

Data as the Driving Force for the Economy 146

Data for Governance 147

Librarians and Data Life Cycle 147

New Job Titles for Librarians 148

Librarians in Data Life Cycle 148

Data Analysis Skill Sets for Librarians 149

Data Ingestion 149

Data Curation 149

Data Visualization 149

Data Analytics 150

Data Literacy for Library Users 151

Data Literacy in Academic Settings 151

Data Literacy for Public Library Users 151

Conclusion 153

References 153

Glossary 157

Index 165

What People are Saying About This

Junhua Ding

"Yunfei Du and Hammad Rauf Khan are true experts in both library science and data science. They have written the best primer for every librarian interested in data science."

Editorial Reviews

A worthy contribution to the dynamic field of data science, suited for library professionals in all types of libraries.” —Library Journal

From the Publisher