Data Analysis with Open Source

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 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

"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

After previous careers in physics and software development, Philipp K. Janert currently provides consulting services for data analysis, algorithm development, and mathematical modeling. He has worked for small start-ups and in large corporate environments, both in the U.S. and overseas. He prefers simple solutions that work to complicated ones that don't, and thinks that purpose is more important than process. Philipp is the author of "Gnuplot in Action - Understanding Data with Graphs" (Manning Publications), and has written for the O'Reilly Network, IBM developerWorks, and IEEE Software. He is named inventor on a handful of patents, and is an occasional contributor to CPAN. He holds a Ph.D. in theoretical physics from the University of Washington. Visit his company website at www.principal-value.com.