This class is no longer accepting registrations
**This workshop has reached capacity. If you register you will automatically be placed on the wait list, and we will reach out if space opens up.**
Region 5 is excited to announce we will be hosting a Library Carpentry workshop in December of 2022. There are 25 spots available.
The December 2022 session will be December 13-15, 12:30 p.m. - 5:30 p.m., PT. Registration will close Monday, November 21st, 6:00 p.m. PT. Notification of your registration approval and the Zoom link will be provided by Wednesday, November 23rd, 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 three lessons: Introduction to Working with Data (Regular Expressions), OpenRefine, and Introduction to R. 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 will prioritize registration from Region 5 states (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.