New case studies include expanded coverage of textual management and analytics New chapters on visualization and big data Discussion of new visualizations of the end-state architecture
Teaches you the basic building blocks of programming needed to create advanced graphics applications including interactive art, live video processing, and data visualization. This title offers graphic and web designers working with the Processing programming environment instructions on the basic principles of the language.
If you´re a data scientist trying to navigate the murky space between data and insight, this practical book shows you how to make sense of your data through high-level questions, well-defined data analysis tasks, and visualizations to clarify understanding and gain insights along the way.
Based on course notes of SIGGRAPH course teaching techniques for real-time rendering of volumetric data and effects; covers both applications in scientific visualization and real-time rendering. Starts with the basics (texture-based ray casting) and then improves and expands the algorithms incrementally. Book includes source code, algorithms, diagrams, and rendered graphics.
If you?re considering R for statistical computing and data visualization, this book provides a quick and practical guide to just about everything you can do with the open source R language and software environment. You?ll learn how to write R functions and use R packages to help you prepare, visualize, and analyze data. Author Joseph Adler illustrates each process with a wealth of examples from medicine, business, and sports. Updated for R 2.14 and 2.15, this second edition includes new and expanded chapters on R performance, the ggplot2 data visualization package, and parallel R computing with Hadoop. * Get started quickly with an R tutorial and hundreds of examples * Explore R syntax, objects, and other language details * Find thousands of user-contributed R packages online, including Bioconductor * Learn how to use R to prepare data for analysis * Visualize your data with R?s graphics, lattice, and ggplot2 packages * Use R to calculate statistical fests, fit models, and compute probability distributions * Speed up intensive computations by writing parallel R programs for Hadoop * Get a complete desktop reference to R