Fall 2026
Instructor: Hannah Tran (tranhan@yorku.ca)
Description: This workshop is designed for new users with a basic understanding of statistics who want to start working with R. You'll begin by getting comfortable with the RStudio interface, learning basic commands, working with R packages, and importing and exporting datasets. From there, you'll use R to explore, manipulate, and clean data, then run descriptive statistics and learn to interpret the output directly in R. Finally, you'll create visualizations and use R Markdown to present your findings. By the end, you'll have the core R skills needed to start tackling your own data projects.
Cost: $45 + tax
Eligible for a Digital Credential: Yes
Registration:
Intermediate R
Instructor: Gabriel Crone, MA (gc2001@yorku.ca)
Description: Many introductory R courses focus on the fundamentals and often overlook important concepts that help R users become better coders. This workshop empowers participants to expand their R programming skills and develop stronger coding workflows. Over three days, participants will build fluency in core R programming and data manipulation using the Tidyverse (Day 1); learn to write functions and apply iteration techniques (Day 2); and master reproducible workflows with RMarkdown, GitHub, and if time permits, RProjects (Day 3). This workshop is designed for intermediate R users: those with roughly six months to two years of experience or who have completed an introductory R course. Advanced users with extensive programming or package development experience may find the content more of a refresher. By the end, participants will be equipped with a suite of tools to become more versatile, knowledgeable, and confident R coders.
Dates/Times: November 12, 19, & 26, 2:30 - 5:30 PM
Format: Online (Zoom)
Cost: $45
Eligible for a Digital Credential: Yes
Introduction to Python for Data Analysis
Instructors: Gavin Klorfine (gklorfin@yorku.ca) & Deborah Laze (laze@yorku.ca)
Description: This workshop guides attendees from basic programming structures/syntax to the manipulation and analysis of data in Python. It is designed for those with minimal or no prior exposure to Python or programming in general, though all levels are welcome. Day one focuses on foundational knowledge, including variable types and their usage, conditional statements, and loops. Day two extends this knowledge to the manipulation of data using Python packages NumPy and pandas. Last, day three applies knowledge from previous days to data visualization (via the matplotlib package) and fitting statistical models (via the statsmodels package). The statistical analyses covered include independent samples t-tests, correlation, and linear regression. A basic working knowledge of these statistical methods is assumed. Short exercises will be provided after each day, serving as building blocks for a final project. A digital credential is provided upon the successful completion of this project. Detailed instructions on how to install Python and required software will be sent to attendees in advance.
Dates/Times: Nov. 9, 16, & 23, 2:30 - 5:30 PM
Format: Online (Zoom)
Cost: $45 + tax
Eligible for a Digital Credential: Yes
Registration: Coming soon ...
How to Leverage AI to Assist with Data Analysis
Instructor: Carmel Camilleri (carmel01@yorku.ca)
Description: This hands-on workshop explores how AI can help you become a more effective, confident, and productive data analyst while leveraging your ability to improve statistical literacy. Through small-group activities and practical exercises, you’ll explore AI tools for common analytical tasks, including planning analyses, cleaning data, writing and troubleshooting code, creating visualizations, and interpreting results. Working collaboratively, you’ll practise crafting useful prompts, evaluating AI-generated responses, and using AI as a guide to deepen your understanding of statistical methods and analytical decisions. Particular attention will be given to when and how not to use AI, including recognizing misleading output, protecting sensitive data, and avoiding shortcuts that undermine learning or sound analysis. By the end, you’ll have practical strategies for incorporating AI into your workflow while maintaining critical judgment, transparency, and reproducibility. A basic understanding of statistics is recommended; no prior experience with AI tools is required.
Dates/Times: Nov 18 & 25, 2:30 - 4:30 PM
Format: TBA
Cost: $20 + tax
Eligible for a Digital Credential: Yes
Registration: Coming soon
AI Workshop
Instructor: Xijuan (Cathy) Zhang (xijuan@yorku.ca)
Description: TBA
Dates/Times: TBA
Format: Online (Zoom)
Cost: $TBA + tax
Eligible for a Digital Credential: Yes
Registration:
Some courses offered through the QMWS permit students to receive a Digital Credential/Badge for completing the course. There is no cost to you in order to receive the Digital Credential/Badge. For more details please see Digital Credentials.
To see a list of past QMWS workshops, visit our Archives Page
