Data Computing introduces wrangling and visualization, the techniques for turning data into information. Ideal for self-study or as a classroom text, Data Computing shows how to condense and combine data from multiple sources to present them in a way that informs discovery and decision making. An accessible introduction to technical computing for those whose primary interest is with data, Data Computing builds on breakthrough software developments that let even beginners exploit the power of professional-level tools. Based on a no-prerequisite short course the author developed at Macalester College, Data Computing uses R, a leading software application for statistics and data analysis which is freely available for all widely-used platforms. The book's short chapters and the clarity of notation supported by Hadley Wickham's hugely popular dplyr and ggplot2 packages help the reader to develop skills at a measured pace. Visit Data-Computing dot org

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Daniel Kaplan is a Harvard-trained, award-winning teacher. He has two decades of experience teaching statistics and modeling, computing, and applied mathematics. His graduate training in biomedical engineering and economics, as well as extensive consulting, give him an applied perspective: using data to serve a purpose.

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