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AI-Assisted Quantitative Data Analysis – Jan. 22, 2026
Session Description
January 22 2026 @ 10:30 am - 12:00 pm
This webinar introduces faculty and researchers to practical methods for using generative AI tools to support quantitative data analysis. We will look at how AI systems can assist with data cleaning, transformation, exploratory analysis, statistical reasoning, and coding tasks, while also highlighting key limitations related to accuracy, reproducibility, and data privacy. Participants will learn strategies for designing effective prompts, evaluating AI-generated outputs, and integrating AI-assisted steps into rigorous research workflows. The session will also outline responsible-use considerations relevant to academic research.
Intended Audience:
- U of T faculty members, research staff, and research trainees who work with quantitative data.
Speaker:
- Alex Olson, Acting Head, Centre for Analytics & AI Engineering (CARTE), University of Toronto
Technology Requirements:
No accounts are required. Participants may optionally log into their preferred GenAI platform if they wish to follow along.
Learning Objectives
After this session, attendees will be able to:
- Identify appropriate uses of generative AI tools in quantitative workflows
- Apply foundational prompting strategies for data cleaning, exploratory data analysis (EDA), and analysis
- Evaluate limitations and risks of AI-generated analytical outputs
- Integrate AI-assisted steps into transparent and reproducible workflows
Additional Information
Post-session materials can be found below (links open in new window):
- Webinar Recording (UTORID required)
- Presenter Slides (UTORID required)