Hands-on training about data clustering with orange data mining toolbox.

Abstract

Data clustering is a core data science approach widely used and referenced in the scientific literature. Its algorithms are often intuitive and can lead to exciting, insightful results that are easy to interpret. For these reasons, data clustering techniques could be the first method encountered in data science training. This paper proposes a hands-on approach to data clustering training suitable for introductory courses. The education approach features problem-based training that starts with the data and gradually introduces various data processing and analysis methods, illustrating them through visual representations of data and models. The proposed training is suitable for a general audience, does not require a background in statistics, mathematics, or computer science, and aims to engage the audience through practical examples, an exploratory approach to data analysis with visual analysis, experimentation, and a gentle learning curve. The manuscript details the pedagogical units of the training, motivates them through the sequence of methods introduced, and proposes data sets and data analysis workflows to be explored in the class.