Название: Practical Data Science for Information Professionals
Автор: David Stuart
Издательство: Facet Publishing
Год: 2020
Страниц: 201
Язык: английский
Формат: pdf (true)
Размер: 10.2 MB

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.

It is well known that we live in an age of unprecedented access to vast quantities of data, and that analysis of this data can produce insights that may be extremely valuable. Less well known is how to analyse the data to gain those insights. That is the topic of data science and this book. The first chapter explores what Data Science is, some of the drivers behind the rapid increase in data, and how it can be applied within the library and beyond. By the end of this chapter the reader will be able to see the value of Data Science beyond the hype, and its widespread applicability within the library and information sector.

To a certain extent the distinction between software applications for data science and programming for data science is a false one, with diverse types of software being accessible through a graphical user interface (GUI) and programmatically via an API, though some applications (e.g. Microsoft Office applications) have an extensive programming language already built in (e.g. VBA). Whether you decide to interact one way or another with an application may depend on whether the particular functionality already exists via the GUI or how many times a particular task has to be repeated.

This book is not designed as an introduction to programming; it is an introduction to data science, for which programming is an extremely useful skill. As can be seen from the examples throughout the book, quite a lot can be achieved with relatively little programming knowledge by building on the work of others. All that is required is an understanding of a few basic concepts (variables, functions, loops). These are briefly covered in the Appendix, which gives simple examples for R and Python. R and Python are only two of the many hundreds of computer languages available. This book uses Python and R: Python because of its versatility and widespread use, and R because of the statistical packages that have been built for it. R and Python are very powerful, extensive languages with active sets of users, and the web is filled with tutorials for them.