Buch, Englisch, 200 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 316 g
Buch, Englisch, 200 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 316 g
Reihe: Chapman & Hall/CRC Data Science Series
ISBN: 978-1-032-08082-6
Verlag: Chapman and Hall/CRC
Librarians understand the need to store, use and analyze data related to their collection, patrons and institution, and there has been consistent interest over the last 10 years to improve data management, analysis, and visualization skills within the profession. However, librarians find it difficult to move from out-of-the-box proprietary software applications to the skills necessary to perform the range of data science actions in code. This book will focus on teaching R through relevant examples and skills that librarians need in their day-to-day lives that includes visualizations but goes much further to include web scraping, working with maps, creating interactive reports, machine learning, and others. While there’s a place for theory, ethics, and statistical methods, librarians need a tool to help them acquire enough facility with R to utilize data science skills in their daily work, no matter what type of library they work at (academic, public or special). By walking through each skill and its application to library work before walking the reader through each line of code, this book will support librarians who want to apply data science in their daily work. Hands-On Data Science for Librarians is intended for librarians (and other information professionals) in any library type (public, academic or special) as well as graduate students in library and information science (LIS).
Key Features:
- Only data science book available geared toward librarians that includes step-by-step code examples
- Examples include all library types (public, academic, special)
- Relevant datasets
- Accessible to non-technical professionals
- Focused on job skills and their applications
Zielgruppe
Academic and Professional Practice & Development
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
1. Introduction 2. Using RStudio’s IDE 3. Tidying data with dplyr 4. Visualizing your project with ggplot2 5. Webscraping with rvest 6. Mapping with tmap 7. Textual Analysis with tidytext 8. Creating Dynamic Documents with rmarkdown 9. Creating a flexdashboard 10. Creating an interactive dashboard with shiny 11. Using tidymodels to Understand Machine Learning 12. Conclusion Appendix A. Dependencies Appendix B. Additional Skills
