Data Visualisation COS30045 Assignment 1
Table of Contents Introduction 3 Background 3 Key Concepts and terms 3 Aim Of the Report 4 Audience 6 In depth analysis 1 7 In depth analysis 2 9 Contrast 11 Conclusion 12 Bibliography 13 In depth analysis 3 10 2
Introduction Background Data Visualisation is "the science of visual representation of data". The main aim is communicating the meaning of data so people can explore it further, discover unknown facts, trends, visualize relationships, and make better decisions. Visualisation Critique is the detailed evaluation of some visual so as to increase the reader's understanding of the data. Key Terms and concepts As data scientist we use different principles and rules that help us identify usefulness of a visual. There are different types of visualisations such as tables, networks, fields, graphs and clusters that help interpret distribution of data, the trends, relationships between values etc. There are also different data types such as binary, discrete, continuous, nominal, categorical, etc. that can be usefully represented in a visual. Data Visual Encoding- is the process of translating the data into visual element, for example on a chart, on a map or tables. There are different visual encoding variables which are: · Position(X &Y) · Size · Value · Texture · Colour · Orientation · Shape When choosing a visual we consider the effectiveness -a visual which is said to be more effective if the information conveyed is more perceived than the information on another visual. Expressiveness is when the visual encoding only expresses all of the facts and only the facts of a set of data. Design Principles are how to apply the elements correctly. When the principles are followed a design is set to be accurately represented. . Balance- provides stability and structure to the design and how the placement of the elements balances the visual. · Proximity- is when elements are connected in some way as it creates relationship between elements. 3