Module focused on reading, organizing, and analytically exploring data.
The second module deepens analytical reading of data, enabling students to describe, visualize, prepare, and synthesize complex datasets in order to generate insight, understanding, and value before predictive modeling.
Covers variable types and charts, fundamentals of visual perception, quantitative relationships, visual patterns, analytical interaction techniques, dashboards, color theory, storytelling, and visualization assessment.
Works with data types and quality, preprocessing, similarity and dissimilarity measures, formats and storage, cleaning, preparation, aggregation, dimensionality reduction, and feature selection.
Studies techniques for synthesizing multivariate data, including principal components, factor analysis, discriminant analysis, multivariate analysis of variance, and clustering methods such as K-means, DBSCAN, Gaussian Mixture Models, and hierarchical clustering.