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.

Ever found it challenging to answer the critical “So what” question in your research? Struggle no more!

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.

Join our online workshop for a comprehensive overview of research proposal writing, applicable across various disciplines.

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.

Whether you're gearing up for graduate school applications, navigating the academic realm, or preparing for the job market, this online workshop is your key to building a successful CV.

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.

Whether you're on the path to graduate school, working in academia, or venturing into the academic job market, our online workshop will help you compose a compelling cover letter.

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.