Data Analysis with Open Source Tools: A Hands-On Guide for Programmers and Data Scientists

Philipp K. Janert

Nov 2010 · "O'Reilly Media, Inc."

About this ebook

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 asscaling 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

Ratings and reviews

This book discusses how to make models and mine data. The author provides caveats that that appearances often override data, decision makers use data for support rather than reasoning, ethics outweigh data, and many things cannot be measured yet. Realtime means right this minute rather than up to date. Data is cleaned prior to analysis. There are a couple of dozen software tools discussed. It uses math examples rather than code, for data analysis and calculus, and has a statistics refresher. There are interesting styles of plots. Some case studies are detailed. Each chapter has workshop exercises, an intermezzo for related topics, and further reading. There are four parts, eighteen chapters and three appendices. The reader interested in data filtering might need additional sources beyond the time series presented here.

Excellent