Information Visualisation

InfoVis 1

Conventional inferential statistics are typically employed to confirm hypotheses. These hypotheses are derived from established theories and are then tested through experiments to determine whether they should be accepted or rejected. Conversely, Exploratory Data Analysis (EDA) takes an antagonistic approach, by first seeking out patterns and relationships within the data, which can subsequently inform the development of hypotheses for testing. This module presents the traditional five-step process of Exploratory Data Analysis as established by Tukey in 1980, culminating in a transition to its contemporary application through Visual Analytics.

InfoVis 2

Information visualisation stands out as a flexible, powerful, and efficient tool for exploratory data analysis. Beyond the well-known scatter plots and histograms, there are innovative visualisation techniques like parallel coordinate plots, tree maps, and chord diagrams that provide unique perspectives for analysing increasingly large and complex datasets. In this lesson, students get to know a number of information visualisation types, learn to design them in a targeted manner and to create them themselves.