 Exploratory Data Analysis (EDA) Exploiting the human visual system to extract information from data. Provides an overview of complex data sets. Identifies structure, patterns, trends, anomalies, and relationships in data. Assists in identifying the areas of interest.  Why Data Visualization?  Which Visualization Tools? R is a high-quality, cross-platform, flexible, widely used open source, free language for statistics, graphics, mathematics, and data science. R contains more than 5,000 algorithms (>10,000 packages) and millions of users with domain knowledge worldwide.  Heatmaps (i.e., Matrix Visualization)  Two Applications (Microarray Data, Mobile Data) (1) Microarray Data (2) Mobile Data  Generalized Association Plots (GAP)  Recent Advances in GAP  Big Data Big Data: The Era of 9 Vs Visualization will be key to making big data an integral part of decision making. Visualization will be the only way to make big data accessible to a large audience. Visualization will be essential to the analysis of big data so it can be of highest value.  Symbolic Data Analysis (SDA)

Implemented by Department of Mathematics
Date: 2024/10/22