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Practical Data Science for Information Professionals [Pehme köide]

  • Formaat: Paperback / softback, 208 pages, kõrgus x laius: 234x156 mm
  • Ilmumisaeg: 24-Jul-2020
  • Kirjastus: Facet Publishing
  • ISBN-10: 1783303441
  • ISBN-13: 9781783303441
  • Formaat: Paperback / softback, 208 pages, kõrgus x laius: 234x156 mm
  • Ilmumisaeg: 24-Jul-2020
  • Kirjastus: Facet Publishing
  • ISBN-10: 1783303441
  • ISBN-13: 9781783303441

The growing importance of data science, and the increasing role of information professionals in the management and use of data, are brought together in Practical Data Science for Information Professionals to provide a practical introduction specifically designed for information professionals.

Data science has a wide range of applications within the information profession, from working alongside researchers in the discovery of new knowledge, to the application of business analytics for the smoother running of a library or library services. Practical Data Science for Information Professionals provides an accessible introduction to data science, using detailed examples and analysis on real data sets to explore the basics of the subject. Content covered includes

  • the growing importance of data science;
  • the role of the information professional in data science;
  • some of the most important tools and methods that information professionals may use; and
  • an analysis of the future of data science and the role of the information professional.

This book will be of interest to all types of libraries around the world, from large academic libraries to small research libraries. By focusing on the application of open source software, the book aims to reduce barriers for readers to use the lessons learned within.



This book will be of interest to all types of libraries around the world, from large academic libraries to small research libraries. By focusing on the application of open source software, the book aims to reduce barriers for readers to use the lessons learned within.

Arvustused

'If libraries and librarians are to be serious about the I in LIS, then analysing data to find meaning for our customers will be a core component of the service offering. David Stuarts book is an excellent entry point to the discipline.' -- Ian McCallum * Journal of the Australian Library and Information Association *

Figures
ix
Tables
xi
Boxes xiii
Preface xv
1 What is data science?
1(16)
Data, information, knowledge, wisdom
1(2)
Data everywhere
3(1)
The data deserts
4(1)
Data science
5(3)
The potential of data science
8(2)
From research data services to data science in libraries
10(4)
Programming in libraries
14(1)
Programming in this book
15(1)
The structure of this book
16(1)
2 Little data, big data
17(22)
Big data
17(2)
Data formats
19(1)
Standalone files
20(1)
Application programming interfaces
21(4)
Unstructured data
25(2)
Data sources
27(10)
Data licences
37(2)
3 The process of data science
39(16)
Modelling the data science process
39(2)
Frame the problem
41(3)
Collect data
44(2)
Transform and clean data
46(2)
Analyse data
48(2)
Visualise and communicate data
50(4)
Frame a new problem
54(1)
4 Tools for data analysis
55(22)
Finding tools
55(1)
Software for data science
56(13)
Programming for data science
69(8)
5 Clustering and social network analysis
77(20)
Network graphs
77(2)
Graph terminology
79(1)
Network matrix
80(2)
Visualisation
82(3)
Network analysis
85(12)
6 Predictions and forecasts
97(16)
Predictions and forecasts beyond data science
97(2)
Predictions in a world of (limited) data
99(2)
Predicting and forecasting for information professionals
101(1)
Statistical methodologies
102(11)
7 Text analysis and mining
113(20)
Text analysis and mining, and information professionals
113(2)
Natural language processing
115(10)
Keywords and n-grams
125(8)
8 The future of data science and information professionals
133(14)
Eight challenges to data science
134(5)
Ten steps to data science librarianship
139(5)
The final word: play
144(3)
References 147(18)
Appendix - Programming concepts for data science 165(1)
Variables, data types and other classes 165(2)
Import libraries 167(1)
Functions and methods 168(2)
Loops and conditionals 170(1)
Final words of advice 171(1)
Further reading 172(1)
Index 173
David Stuart is an independent information professional and an honorary research fellow at the University of Wolverhampton, and was previously a research fellow at King's College London and the University of Wolverhampton. He regularly publishes in peer-reviewed academic journals and professional journals on information science, metrics, and semantic web technologies. He is author of Practical Ontologies for Information Professionals (2016), Web Metrics for Library and Information Professionals (2014), and Facilitating Access to the Web of Data (2011).