Data Analysis and Visualisation

Data and visual analytics is an emerging field concerned with the analysis, modeling and visualisation of complex high-dimensional data. This course will introduce students to the field by covering state of the art modeling, analysis and visualisation techniques. Focus is on practical challenges involving complex real-world data and including case studies and hands on work with a high level programming language using either Python or the R programming language for the purpose of communicating relevant data patterns in the form of graphical displays. The course covers classical descriptive statistics and data visualisation. Classical descriptive statistics includes how to draw conclusions about the distribution of data using graphs and covers how to summarise data using measures of centre, spread, and association, and how to analyse data using normal distribution. The course covers the fundamentals of data driven visualisation of information. It introduces how graphical elements and colour theory, design aspects, and perception affect how figures are perceived. The term visual literacy is introduced together with Tufte’s principles for scientific graphics. The visualisation of different data types, including categorical and textual data, is also covered in the course.