This workshop introduces graph-based clustering, including community-detection approaches such as spectral clustering and modularity-based methods. We examine how similarity networks are constructed and how graph topology guides cluster formation.

This session will introduce participants to non-parametric tests, which are useful when data distributions do not meet the assumptions of parametric tests.

In this workshop, participants are invited to explore how to practically and creatively incorporate disability justice principles in participatory and community-engaged research.

This session focuses on preparing mixed-type datasets—categorical, ordinal, and continuous variables—for clustering. We cover distance measures, data transformations, and quantification techniques that preserve variable meaning.

This session will introduce participants to the Analysis of Variance (ANOVA), a statistical method used for comparing the means of three or more groups.

In this workshop, participants are invited to explore how to practically and creatively incorporate disability justice principles in participatory and community-engaged research.

This session will address the concepts of correlation, causation, and association in data. Participants will learn to differentiate between these concepts and to recognize and interpret various types of correlations.

Building on the previous workshop, this session applies clustering methods designed for mixed-type data. We introduce algorithms such as k-prototypes and integrated similarity-based approaches.

This session will introduce participants to fundamental computation concepts in Python, laying the foundation for more advanced topics.

This session will address importing Excel and comma separated value files into Python for analysis and address initial data exploration.

This workshop addresses fairness considerations in clustering, including sources of bias, representation issues, and evaluation metrics for equitable outcomes. We discuss practical tools for mitigating unfair clustering behaviour and ensuring responsible use of unsupervised methods.

Part one introduces key dimensionality-reduction techniques such as PCA and factor models. We discuss variance decomposition, latent structure, and interpretability considerations.

Do you want to learn how to use copyrighted material in your research, and how to protect your own rights as an author? If so, this workshop is for you!

This session will explore the variety of ways that data can be filtered and subset in Python.

This session explores nonlinear and manifold-based methods, including t-SNE and UMAP. We examine their assumptions, visualization strengths, and implications for downstream clustering.

This session will introduce logical conditions and iteration in Python. Participants will revisit the vector underpinnings of pandas, learn functions for iterating or looping over objects, and be introduced to for loops and if/else constructs.

Do you want to learn how to use copyrighted material in your research, and how to protect your own rights as an author? If so, this workshop is for you!

This session will introduce data visualization in Python and methods for creating reproducible reports.