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Practical Data Science for Information Professionals

Book Hero Magic crafted this summary to help describe this book. While it's new and still learning, it may not be perfect - your feedback is welcome! Summary
Practical Data Science for Information Professionals offers a clear and accessible introduction to data science, tailored for information professionals. It covers key areas including clustering and social network analysis, predictions and forecasts, and text analysis and mining, using real data sets to illustrate concepts. The book also highlights user-friendly tools and includes example code in R and Python, helping readers move beyond graphical interfaces to perform meaningful data analysis with minimal coding.
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Format: Paperback / softback
$13000
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Book Hero Magic created this recommendation. While it's new and still learning, it may not be perfect - your feedback is welcome! IS THIS YOUR NEXT READ?

This book is ideal for librarians and information professionals in academic, research, and other library settings who seek a practical foundation in data science. It is especially suitable for those interested in applying open source software tools to enhance their data management and analysis skills.

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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.

Book Hero Magic formatted this description to make it easier to read. While it's new and still learning, it may not be perfect - your feedback is welcome! Description

Practical Data Science for Information Professionals provides an accessible introduction to a potentially complex field, giving readers an overview of data science and a framework for its application. It uses detailed examples and analysis of real data sets to explore the basics of the subject in three principal areas: clustering and social network analysis; predictions and forecasts; and text analysis and mining.

In addition to highlighting a wealth of user-friendly data science tools, the book includes example code in two of the most popular programming languages: R and Python. This demonstrates how easily information professionals can move beyond the graphical user interface to achieve significant analysis with just a few lines of code.

After reading, readers will understand:

Β· The growing importance of data science

Β· The role of the information professional in data science

Β· Some of the most important tools and methods that information professionals can use.

Bringing together the growing importance of data science and the increasing role of information professionals in the management and use of data, Practical Data Science for Information Professionals provides a practical introduction to the topic, specifically designed for the information community. It will appeal to librarians and information professionals worldwide, from large academic libraries to small research libraries. By focusing on the application of open source software, it aims to reduce barriers for readers to utilise the lessons learned within.

Book Details

INFORMATION

ISBN: 9781783303441

Publisher: Facet Publishing

Format: Paperback / softback

Date Published: 24 July 2020

Country: United Kingdom

Imprint: Facet Publishing

Audience: Professional and scholarly

DIMENSIONS

Width: 156.0mm

Height: 234.0mm

Weight: 250g

Pages: 208

About the Author

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).

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