Reposted with permission from AALL Spectrum, Volume 25, Number 5 (May/June 2021), pgs. 16-19.
By Sarah Lin, Information Architect & Digital Librarian at RStudio, PBC
As law librarians, many of us scrutinize the data we have access to with Excel and out-of-the-box visualization tools. Whether that data is from docket activity, research databases, websites, or online catalogs, what we have can generally be described as “usage data.” But what one skill set would allow us to do so much more with that data, to better understand and communicate what our users are doing and what they need? Enter, data science.
Broadly speaking, data science brings opportunities to work more quickly and easily with data. It provides better reporting formats by incorporating outside data from various sources, and can even turn text into data that can be displayed visually. Even though legal information isn’t always associated with data, science, or data science, data science skills enable law librarians to do their jobs with greater efficiency. With data science skills, we are able to show new value for our teams and organizations, so it is definitely worth the time invested.
Even in a year when time has been both condensed and stretched (when many of us picked up new hobbies, such as baking), learning to code for just one use case, such as replacing Excel as a data analysis tool, doesn’t make sense. Luckily, data science skills are useful for more than just data manipulation, and learning to code allows you to provide many more use cases than just creating better data visualizations for management. Cooking is a useful metaphor for data science: while it’s completely possible to eat take-out, frozen food, box mixes, and cereal for dinner, you can actually create healthier meals with the right tools, enhanced cooking skills, and a better understanding of ingredients. For example, pre-cut vegetables are available in grocery stores, but a chef ’s knife and some practice allow you to customize any meal you make as well as lower costs. Similarly, while you can do your job with Excel and a commercial tool such as Tableau or PowerBI, learning to do data science opens a window of opportunities to new and improved skills that do more than just create improved graphics for reports or budget projections.
The following 10 data science skills and techniques, along with descriptions of the amazing deliverables that are associated with them, are listed in a progressive skill-building sequence, and they will provide you with a fully stocked data science kitchen. Keep in mind that the examples in this article focus on the R programming language, even though data science can also be done in Python (which has similar and sometimes compatible resources for you to use). The power of data science using R or Python comes from the powerful skills and techniques they enable you to use to transform how you work with data in your day to-day job. It’s time to graduate from Excel and start cooking with gas!