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Sebastopol, CA—Collecting data is relatively easy, but turning raw information into something useful requires that you know how to extract precisely what you need. With Data Analysis with Open Source Tools (O'Reilly Media, $39.99 USD), intermediate to experienced programmers interested in data analysis will learn techniques for working with data in a business environment.

"Every business is collecting data, and at a tremendous rate. The challenge is how to make it useful," says data consultant and author Philipp K. Janert. "As corporations get more data-driven, it is important to understand what you are doing with data. Otherwise, you are just adding to the confusion. This book is the first on data analysis that was written for programmers, taking a hands-on approach that is accessible for anyone with some software development skills."

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.

Data Analysis with Open Source Tools will show you how to:

• Use graphics to describe data with one, two, or dozens of variables
• Develop conceptual models using back-of-the-envelope calculations, as well as scaling and probability arguments
• Mine data with computationally intensive methods such as simulation and clustering
• Make your conclusions understandable through reports, dashboards, and other metrics programs
• Understand financial calculations, including the time-value of money
• Use dimensionality reduction techniques or predictive analytics to conquer challenging data analysis situations
• Become familiar with different open source programming environments for data analysis

Advance Praise
"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