Demonstates three basic tasks of visual data analytics  uses data of forest fires (fires.csv) begin with |N|≥500, |D|≥10)  client-server system: python for processing (server), D3 for VIS (client) Task 1: data clustering and decimation  implemented random sampling and stratified sampling  the latter includes the need for k-means clustering (optimize k using elbow) Task 2: dimension reduction (use decimated data  find the intrinsic dimensionality of the data using PCA  produce scree plot visualization and mark the intrinsic dimensionality  obtain the three attributes with highest PCA loadings Task 3: visualization (use dimension reduced data)  visualize the data projected into the top two PCA vectors via 2D scatterplot  visualize the data via MDS (Euclidian & correlation distance) in 2D scatterplots  visualize scatterplot matrix of the three highest PCA loaded attributes