Visualization for Data Science - Filtering, Aggregation, and Clustering

Learn essential data science visualization techniques in this comprehensive lecture covering filtering, aggregation, and interactive visualization methods. Explore centered widgets, dynamic queries, and sketch-based queries while understanding their practical applications in data analysis. Dive into clustering algorithms including K-means, DBSCAN, and hierarchical clustering, with special attention to their properties and comparative advantages. Examine real-world applications through a case study on gerrymandering, understanding both its concept and examples. Master interactive legends and visualization techniques that enhance data exploration and presentation. Conclude with a design critique session to develop critical evaluation skills for creating effective data visualizations.