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:

· 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 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.