Foundations of Research Computing
Foundations of Research Computing
Join us for The Foundations of Research Computing (FORC) Camp, a three-day data skills immersion program offered to all NYU graduate students through a collaboration among NYU Data Services (NYU Libraries and IT), the GSAS Master's College, and the Arts & Science Office of Teaching Excellence and Innovation. FORC will give participants a thorough grounding in the digital skills, including Gen AI, essential for their research. There is something for every disciplinary approach, from creating visualizations that require no coding skills to data harvesting and statistical analysis. FORC takes place this summer, August 25-27, 2026.
New this year: Participants will have an opportunity to apply the skills they are learning to data sets and a model project. On day three, each track will share their work with all participants during an end of program symposium.
Each day will include four hours of interactive instruction. The program includes free lunch everyday, and an invited keynote speaker to inspire your learning. Students can take advantage of 1:1 consultations, connect with subject librarians, and network with fellow graduate students.
Participants who complete the program will receive a letter of completion for their portfolio detailing the skills covered in their track.
Send inquiries to asteaching@nyu.edu.
FORC tracks are concurrent. Thus, participants may register for only one FORC track.
Room: TBD
Description: How does research data get made? What research can become "data"? In this track, we'll explore best practices for telling stories and making meaning by collecting, analyzing, and presenting research materials. You’ll learn how to structure and transform images, text, and multimedia resources for use in mapping, text analysis, data visualization, web publication, and digital exhibits. Through shared examples or your own data, this track will also introduce different tools and software platforms available to share, visualize, and publish your research for new and wider audiences. No coding skills are required for this track.
Room: TBD
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. Students will learn how to configure NotebookLM to meet their research needs. No coding skills are required.
Room: TBD
Description: In this track, participants will get an understanding of how to find, understand, and manage data as well as hands-on experience with using Python for computational research purposes. We’ll explore different libraries commonly used for data analysis and finish strong with creating visualizations in Jupyter notebooks. No prior coding knowledge needed.
Join our multi-day R workshop designed specifically for graduate students. This series will teach you the modern, highly efficient tools needed to analyze real-world datasets with confidence, while introducing Generative AI as an assistant to accelerate your workflow.
We will introduce RStudio environment and foundations of R language, explore core tidyverse packages for importing, cleaning, wrangling, and ultimately visualizing datasets. We will also explore AI assisted coding. By the end of the series, students will have explored a dataset, investigated relationships within, and presented their findings to their peers.
No prior coding knowledge is required.
JIn this track, users will learn about programmatic use of LLMs beyond the chatbot. The first day will cover the basics of LLM APIs, parameters and tools. The second day of the workshop focuses on Retrieval Augmented Generation (RAG) and how it enhances AI models by combining external data retrieval with large language models (LLMs). They will explore the steps to build a RAG pipeline, including embedding text into vector representations, retrieving relevant context from databases, and augmenting prompts to generate accurate answers. The tutorial provides practical insights into when to use RAG over fine-tuning models and how to integrate this approach in building dynamic, context-aware AI solutions.
For additional information, see Retrieval Augmented Generation (RAG) page.
Prerequisites: basic understanding of Python