The Case for Digital Skills Development for Librarians
Digital skills are increasingly in demand from the 21st century workplace while the digital divide continues to widen and this is a key concern as libraries seek to support the needs of their communities and pursue professional development. Marketplace Tech recently interviewed Brookings Institution Senior Fellow Mark Muro about this topic and the content of a 2017 report that he and his colleagues wrote called Digitalization and the American Workforce. The Marketplace segment focused on the part of the report that shows how the digital divide is widening between rural and urban areas. Librarians are always looking for ways to bridge the digital divide for their communities and one way to do this is through professional development.
The broader focus of the report analyzed how digital skill levels changed in 545 different occupations from 2002 to 2016. 517 of the 545 occupations had an increase in digital skills including Librarians (Figure 1) and Library Technicians (Figure 2) based on a digital score between 0-100. Both Librarians and Library Technicians moved from the medium range to the high range scoring between 60-100 with Librarians at 65.9 (+27%) and Library Technicians at 62 (+35%) moving up from their 2002 scores of 52 and 45.8 respectively.
The area of digital scholarship in librarianship is one manifestation of these changes in academic libraries. It is becoming more likely that a librarian will be asked to create a database in addition to being able to search them. It is increasingly likely that librarians will be asked to teach the use of digital creation tools like timelines, maps, and digital archives as well as the consumption of information from the outcomes of these tools. The Digital Scholarship resource page at the Library of Congress Labs is a great example of this trend in librarianship and is full of great resources to get started including Eileen Jakeway’s Digital Scholarship 101, that focusing on six digital tools that are relatively easy to learn.
Some more advanced tools that are worth learning that are not on the Library of Congress Digital Scholarship page are programming languages Python and the R Project for Statistical Computing, or just R, that can be used for text mining or data visualization projects. They are both a big part of data science and involved in many digital scholarship and digital humanities projects. An R user and researcher, Robert Muenchen, published a report, The Popularity of Data Science Software, tracking the most popular data science software in job postings and in scholarly literature. Figure 3 shows the results of his study of data science-related job postings on Indeed.com in February 2017 and highlights where Python and R fall on that list.
Figure 4 shows the prevalence of R and Python in the scholarly literature through the whole year of 2016.
Free Resources to Learn R and Python
With these trends in mind there are many resources a Librarian can seek out to develop skills in these areas. To learn R or Python within the context of digital scholarship and digital humanities, Programming Historian, is an excellent source of tutorials to learn some of these skills from the perspective of fields that do not typically have computer programming experience. To get a deeper understand of Python while starting out as a beginner, Automating the Boring Stuff with Python, by Al Sweigart is a great free textbook with quality exercises and explanations. Code Academy’s free Python tutorial is an easy way to get some experience with Python without having to load it onto your computer. I was also reminded by a colleague about Python Anywhere, which is a free online platform that has the environment set up with different versions of Python and a place to store your code files. It works very well for workshops, tutorials, and classwork because it is web-based and avoids some of the pitfalls of loading Python onto a personal computer or onto shared, enterprise computers.
Subscription-based Resources to Learn R and Python
Note: [post updated Jan 2, 2019 with Python Anywhere mention]