If you understand the basics of HTML5, CSS, and JavaScript and want to make quick sense of the extensive but often overwhelming reference documentation on D3.js, this short book is for you. Philipp K. Janert, author of Data Analysis with Open Source Tools (O'Reilly), provides a concise roadmap to this library, including its conventions and foundational concepts. D3.js for the Impatient is concise, yet comprehensive. Janert presents an overall survey of working with D3.js, while steering clear of long-winded and meandering explanations. Right away, this first chapter shows you how to create simple graphs and tackle two of the library's more unusual aspects: selecting and binding-the way D3.js uses DOM nodes to represent data visually. From there, you can easily explore individual chapters that adhere to your particular interests.

Collecting data is relatively easy, but turning raw information into something useful requires that you know how to extract precisely what you need. With this insightful book, intermediate to experienced programmers interested in data analysis will learn techniques for working with data in a business environment. You'll learn how to look at data to discover what it contains, how to capture those ideas in conceptual models, and then feed your understanding back into the organization through business plans, metrics dashboards, and other applications.

Along the way, you'll experiment with concepts through hands-on workshops at the end of each chapter. Above all, you'll learn how to think about the results you want to achieve -- rather than rely on tools to think for you.Use graphics to describe data with one, two, or dozens of variablesDevelop conceptual models using back-of-the-envelope calculations, as well asscaling and probability argumentsMine data with computationally intensive methods such as simulation and clusteringMake your conclusions understandable through reports, dashboards, and other metrics programsUnderstand financial calculations, including the time-value of moneyUse dimensionality reduction techniques or predictive analytics to conquer challenging data analysis situationsBecome familiar with different open source programming environments for data analysis

"Finally, a concise reference for understanding how to conquer piles of data."--Austin King, Senior Web Developer, Mozilla

"An indispensable text for aspiring data scientists."--Michael E. Driscoll, CEO/Founder, Dataspora