Visual Analytics Lecture BigData Analytics Julian M. Kunkel julian.kunkel@googlemail.com University of Hamburg / German Climate Computing Center (DKRZ) 2017-10-20 Disclaimer: Big Data software is constantly updated, code samples may be outdated. Visual Data Analysis Visual Perception Designing Graphics Summary Outline 1 Visual Data Analysis 2 Visual Perception 3 Designing Graphics 4 Summary Julian M. Kunkel Lecture BigData Analytics, WiSe 17/18 2 / 28 Visual Data Analysis Visual Perception Designing Graphics Summary Statistical Graphics [44] Definition: Graphics in the field of statistics used to visualize quantitative data Objectives The exploration of the content of a data set The use to find structure in data Checking assumptions in statistical models Communicate the results of an analysis Plots (Excerpt) Scatter, box, histograms Statistical maps Probability plots Spaghetti plots Residual plots Julian M. Kunkel Source: William Playfair’s Time Series of Exports and Imports of Denmark and Norway [44] Lecture BigData Analytics, WiSe 17/18 3 / 28 Visual Data Analysis Visual Perception Designing Graphics Summary Visual Analytics [32] Definition [33] The science of analytical reasoning facilitated by interactive visual interfaces. Objective Solve complex questions/time critical problems applying the scientific method Present gained insight / communicate it visually Analytical tasks Understanding past situations; trends and events that caused current conditions Monitoring events for indicators for an emergency Identifying possible alternative future scenarios and their warning signs Determining indicators of the intent of an action or an individual Supporting decision makers in times of crisis Julian M. Kunkel Lecture BigData Analytics, WiSe 17/18 4 / 28 Visual Data Analysis Visual Perception Designing Graphics Summary Visual Analytics Workflow Data Exploration User Interaction Da ta M ini n g Visualization Data M Vis