Practical Data Science for Information Professionals

Jul 2020 | 200pp

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Practical Data Science for Information Professionals

David Stuart

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
  • 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.



1 What is data science? 
Data, information, knowledge, wisdom 
Data everywhere 
The data deserts 
Data science 
The potential of data science 
From research data services to data science in libraries 
Programming in libraries 
Programming in this book 
The structure of this book 

2 Little data, big data 
Big data 
Data formats 
Standalone files 
Application programming interfaces 
Unstructured data 
Data sources 
Data licences 

3 The process of data science 
Modelling the data science process 
Frame the problem 
Collect data 
Transform and clean data 
Analyse data 
Visualise and communicate data 
Frame a new problem 

4 Tools for data analysis 
Finding tools 
Software for data science 
Programming for data science 

5 Clustering and social network analysis 
Network graphs 
Graph terminology 
Network matrix 
Network analysis 

6 Predictions and forecasts 
Predictions and forecasts beyond data science 
Predictions in a world of (limited) data 
Predicting and forecasting for information professionals 
Statistical methodologies 

7 Text analysis and mining 
Text analysis and mining, and information professionals 
Natural language processing 
Keywords and n-grams 

8 The future of data science and information 

Eight challenges to data science
Ten steps to data science librarianship 
The final word: play


Appendix – Programming concepts for data science 
Variables, data types and other classes 
Import libraries 
Functions and methods 
Loops and conditionals 
Final words of advice 
Further reading 


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).