This class is no longer accepting registrations
Region 5 is excited to announce we will be hosting a Library Carpentry workshop in October 2023. There are 25 spots available.
The October 2023 session will be October 17-19, 12:30 p.m. - 5:00 p.m., PT. To be considered for enrollment, please register by Monday, September 18th at 6:00 p.m. PT. We will notify you of your enrollment or wait list status by Wednesday, September 20th at 6:00 p.m. PT. If we offer you enrollment, please confirm your commitment to attend by Monday, October 2nd at 6:00 p.m. PT.
Library Carpentry focuses on building software and data skills within library and information-related communities. Their hands-on, approachable workshops empower people in a variety of roles to use software and data in their own work and support effective, efficient, reproducible practices. Learn more about Library Carpentry on their website.
The Library Carpentry workshop is broken into the following lessons: Intro to Data, Unix Shell, Git, and/or OpenRefine. You can learn more about the lessons on their website.
The Library Carpentry workshops are open to all, and the training will focus on skills for library workers. We encourage anyone interested in Library Carpentry skills to register; no prior knowledge is needed. We are prioritizing equitable geographic representation of members in Region 5 (Alaska, California, Hawaii, Nevada, Oregon, Washington, and U.S. Territories and Freely Associated States); however, if we are not able to fill all 25 spots with Region 5 participants we will be accepting non-Region 5 registrants on a first come first serve basis.
Any questions about the Library Carpentry workshops can be directed to nnlm@uw.edu
- Cut through the jargon terms and phrases of software development and data science and apply concepts from these fields in library tasks;
- Identify and use best practices in data structures;
- Learn how to programmatically transform and map data from one form to another;
- Work effectively with researchers, IT, and systems colleagues;
- Automate repetitive, error prone tasks.