Enhancing data science learning through the use of images
DOI:
https://doi.org/10.52041/iase2023.112Abstract
Image analysis represents a crucial and dynamic field within the realm of data science and machine learning, enabling automated interpretation and analysis of images through statistical methods. Beyond its practical applications, such as image recognition, images offer a valuable pedagogical tool for teaching various multivariate analysis techniques, including cluster analysis, principal component analysis, and k nearest neighbors. By employing straightforward R code, images can be transformed into tidy data formats, primed for multivariate analysis. The resultant analysis outcomes can then be translated back into images, affording students the opportunity to visually comprehend the impact of techniques like cluster analysis, principal component analysis or k nearest neighbors. This approach effectively bridges the gap between abstract multivariate analysis concepts and concrete understanding, as students can visually perceive how cluster centroids or principal components reduce the complexity of the original (image) data.References
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