Hands-on Workshop: Applying AI Tools for Quantitative Data Analysis – Mar. 24 2026
Session Description
March 24 2026 @ 1:00 pm - 4:00 pm
Generative AI tools are increasingly being used to support quantitative research tasks such as data cleaning, exploratory analysis, and code drafting. When used carefully, these tools can reduce friction in common workflows; when used uncritically, they can introduce subtle errors, weaken methodological rigor, or obscure analytical decisions.
This in-person hands-on workshop focuses on how researchers can reason about and experiment with AI-assisted analysis workflows. The emphasis is on developing sound judgment: understanding where AI support can be useful, how to structure requests effectively, and how to interpret and verify outputs in the context of real research problems.
The session will demonstrate how AI tools can assist with data preparation, analysis planning, and code interpretation. A dedicated section addresses responsible use in an academic context, including data sensitivity and documentation practices, followed by a guided hands-on block where participants apply these ideas themselves.
This workshop builds on the AI-Assisted Quantitative Data Analysis webinar and is intended for researchers who regularly work with quantitative data and want a clearer, more grounded understanding of how AI can (and cannot!) support their analytical workflows.
Intended Audience:
- This workshop is designed for faculty researchers and their research teams who work regularly with quantitative data. Participants should be comfortable working with datasets and conducting quantitative analyses; basic coding experience is strongly preferred (any data analysis environment/programming language is acceptable).
- Researchers from any discipline are welcome.
- This is a limited enrollment workshop. Preference will be given to faculty researchers.
Presenter: Alex Olson, Acting Head, Centre for Analytics & AI Engineering (CARTE), University of Toronto
Deadline to apply: Wednesday, Feb 25th, 2026
Participants will be notified by early March
Learning Objectives
After this session, attendees will be able to:
- Articulate where AI assistance is most and least appropriate within quantitative research workflows
- Structure prompts that support data cleaning, analysis planning, and code interpretation
- Critically assess AI-generated outputs and identify common failure modes
- Apply a basic, documented AI-assisted workflow to a realistic quantitative dataset
- Recognize data sensitivity and integrity considerations relevant to academic research contexts
Additional Information
Participants are required to have attended or watched the recording of the Jan 22nd webinar on AI-Assisted Quantitative Data Analysis (recording will be available after Jan 22nd).
Technology requirements:
- Participants are required to bring a laptop and charger.