FORC: J-Term Data Skills Immersion Training for Graduate Students Track 1 In-Person
Description
NYU Data Services (NYU Libraries and IT), the Graduate School of Arts & Science Master’s College, and the Arts & Sciences Office of Teaching Excellence and Innovation are excited to launch a new extension of the Foundations of Research Computing (FORC) Camp training opportunity for graduate students! Given the strong interest in data skills development on the part of our students and the success of the summer FORC Camp offerings, we will pilot a JTerm offering of two new tracks. Grounded in the principle that foundational data skills are essential for early career researchers, these two new tracks will explore the potential of generative AI as a research tool.
During this one day in-person learning opportunity, taking place January 16, 2025, interested students can select one of two tracks.
The day’s schedule will include four hours of interactive instruction. Participants who complete all hours of the FORC curriculum will receive a letter of completion for their portfolio detailing the skills covered in their track.
- Date:
- Thursday, January 16, 2025
- Time:
- 10:00am - 3:00pm
- Time Zone:
- Eastern Time - US & Canada (change)
- Location:
- Bobst Library, 7th Floor, Rm. 745
- Libraries:
- Bobst Library
- Type:
- Data Services
Track 1:
Schedule
| Date | Time | Room |
| January 16 | 10am - 12pm | Bobst 745 |
| 1pm - 3pm | Bobst 745 |
Description
Generative AI offers an exciting opportunity to interact with large amounts of data and discover connections in ways that were not previously possible with traditional research methods. However, commercial Generative AI products offer a “black box” for users, who may not know what Large Language Model (LLM) is being used or how queries are being modulated, all factors that can affect the quality and usefulness of outputs. In this track, you will learn about differences in LLM offerings and their applications, standard methods for modulating outputs, and basic contours for narrowing and improving outputs using a retrieval-augmented generation (RAG) workflow. No coding skills are required.
