Spring 2026
Introduction to Python for Data Analysis
Instructors: Deborah Laze (laze@yorku.ca) & Gavin Klorfine (gklorfin@yorku.ca)
Description: Starting with the installation of Python and relevant packages, attendees are then guided through basic programming structures/syntax, culminating in the manipulation and analysis of data. The Python packages of interest for this course include, but are not limited to: numpy, Pandas, and matplotlib. Interactive exercises will be available throughout the workshop to practice applying Python and data analysis skills and will serve as the building blocks for a short, end-of-workshop project. Successful completion of this project will earn you a digital credential. This workshop is meant for those with no prior experience or exposure to Python, although all levels are welcome.
Dates/Times: June 26, June 27, & June 28 (11:30 am - 2:30 pm)
Format: Online (Zoom)
Cost: $45 + tax
Eligible for a Digital Credential: Yes
Registration: Click Here to Register
Introduction to Multilevel Modeling
Instructor: Miranda Too (mtoo@yorku.ca)
Description:
Dates/Times:
Format: Online (Zoom)
Cost: $ + tax
Eligible for a Digital Credential: Yes
Registration: Coming soon ...
Introduction to Bayesian Modeling
Instructor: Saurabh Panchasara (saupan@yorku.ca)
Description: Bayesian methods are reshaping how scientists reason about uncertainty, with applications spanning biostatistics, social/behavioral science, deep learning, and many more. This workshop offers an in-depth, hands-on introduction to the Bayesian modeling. Prior knowledge of R will be assumed and RStan will be introduced in this workshop. Over three focused sessions, we will discuss the comparison of Bayesian and frequentist paradigms through engaging real-world examples, then build up the core machinery you need to model your own data. Topics include the choice of priors, posterior inference, a brief introduction to MCMC, model checking, linear regression & generalized linear models in a Bayesian framework (logistic regression), and hierarchical models (these kinds of models are where Bayesian methods and MCMC shine). Every concept is paired with practical implementation in R and RStan. We'll end this workshop with a discussion of the pitfalls of current MCMC methods and a brief introduction at variational inference as a rising, scalable alternative and its applications.
Dates/Times: June 15, June 16, & June 17 (11:30 am - 2:30 pm)
Format: Online (via Zoom)
Cost: $45 + tax
Eligible for a Digital Credential/Badge: Yes
Registration: Click Here to Register
Beyond p-values: Modern Statistical Thinking in Psychology
Instructor: Katherine Newman (kmnewman@yorku.ca)
Description: This three-part workshop builds on students' existing statistical training by introducing a complete, start-to-finish research pipeline: from raw data and assumption-checking to analysis, visualization, interpretation, and final write-up. Using JASP statistical software (free, open-source, no coding required), participants work hands-on with a dataset across all three sessions, applying frequentist and Bayesian approaches side by side and learning to interpret what each framework reveals about their data. Core topics include data visualization, effect sizes, confidence intervals, and evidence evaluation, with a strong emphasis on interpretation and critical thinking rather than memorization of procedures. The workshop concludes with a guided exercise in which participants write, peer-review, and revise a complete APA-style results section, a skill directly transferable to honours theses, graduate research, or any context where data needs to be communicated clearly. Participants will receive take-home guides after each session, including curated resources discovered throughout my PhD (e.g., data visualization tools, statistical references, and writing support). No prior JASP experience required. Introductory statistics or research methods is recommended.
Dates/Times: June 9, June 11, June 16 (11:30 am - 1:30 pm)
Format: Online (via Zoom)
Cost: $30 + tax
Eligible for a Digital Credential: Yes
Registration: Click Here to Register
Introduction to R
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.
Dates/Times: May 27, May 28, May 29 (11:30-2:30 PM)
Format: Online (via Zoom)
Cost: $45
Eligible for a Digital Credential: Yes
Registration: Click Here to Register
Introduction to Simple and Multiple Linear Regression in R
Instructor: Veerpal Bambrah, M.A. (bambrahv@yorku.ca)
Course Description: This 3-week workshop will focus on conducting simple linear regressions and multiple linear regressions, including moderation analyses, in R, with an additional focus on how to write up these results in a research paper. In this workshop, participants will learn when and how to conduct, interpret, visualize the data, and write up the results of: 1) A simple linear regression and multiple linear regression, including performing regression diagnostics; and 2) Moderation analyses, specifically 2-way interactions. This course is intended for participants who possess little-to-no experience with conducting statistical analyses and presenting statistical results, or who possess an elementary understanding of linear regression but would like to build on this understanding by conducting and interpreting moderation analyses. This course will be taught in R, so participants should have introductory experience with this software.
Note: The use of your own laptop or computer will be necessary for this course. It is also necessary to have R and RStudio installed on your laptop or computer. These software are free and can be found at:
- How to Download and Install R/RStudio: https://www.youtube.com/watch?v=YrEe2TLr3MI
- R for macOS: https://cran.r-project.org/bin/macosx/
- R for Windows: https://cran.r-project.org/bin/windows/base/
- RStudio: https://posit.co/download/rstudio-desktop/
Dates/Times: June 10th, June 17th, June 24th (2:30 pm to 5:30 pm)
Format: Online (via Zoom)
Cost: $45
Eligible for a Digital Credential/Badge: No
Registration: Click Here to Register
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
