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

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9780596802356: Data Analysis with Open Source Tools: A Hands-On Guide for Programmers and Data Scientists

These days it seems like everyone is collecting data. But all of that data is just raw information -- to make that information meaningful, it has to be organized, filtered, and analyzed. Anyone can apply data analysis tools and get results, but without the right approach those results may be useless.

Author Philipp Janert teaches you how to think about data: how to effectively approach data analysis problems, and how to extract all of the available information from your data. Janert covers univariate data, data in multiple dimensions, time series data, graphical techniques, data mining, machine learning, and many other topics. He also reveals how seat-of-the-pants knowledge can lead you to the best approach right from the start, and how to assess results to determine if they're meaningful.

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Book Description:

A hands-on guide for programmers and data scientists

About the Author:

After previous careers in physics and softwaredevelopment, Philipp K. Janert currentlyprovides consulting services for data analysis,algorithm development, and mathematical modeling.He has worked for small start-ups and in largecorporate environments, both in the U.S. andoverseas. He prefers simple solutions that workto complicated ones that don't, and thinks thatpurpose is more important than process. Philippis the author of "Gnuplot in Action - UnderstandingData with Graphs" (Manning Publications), and haswritten for the O'Reilly Network, IBM developerWorks,and IEEE Software. He is named inventor on a handfulof patents, and is an occasional contributor to CPAN.He holds a Ph.D. in theoretical physics from theUniversity of Washington. Visit his company websiteat www.principal-value.com.

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Descrizione libro O Reilly Media, Inc, USA, United States, 2010. Paperback. Condizione libro: New. Language: English . Brand New Book. 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. Codice libro della libreria AAS9780596802356

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Descrizione libro O Reilly Media, Inc, USA, United States, 2010. Paperback. Condizione libro: New. Language: English . Brand New Book. 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. Codice libro della libreria AAS9780596802356

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