This is a how to book for scientific visualization. An introduction on the philosophy of the subject sets the scene, showing why visualization works and what to aim for when presenting scientific data visually - different software architectures for visualization are covered briefly. The theory of colour perception and its value and use for conveying information about data is introduced. Next, using Brodlies taxonomy to underpin its core chapters, it is shown how to classify data, choose a technique thats appropriate for its visualization, and alerts the reader to some of the pitfalls. Worked examples are given throughout the text and there are practical sidebars - virtual laboratory classes for those readers with access to the IRIS Explorer software who can try out the demonstrations on an accompanying website. The book concludes with a taster of ongoing research, including the move towards large-scale displays, visualization for computational steering and collaborative visualization.