Data Analysis with Open Source Tools pdf epub mobi txt 電子書下載2025

☆☆☆☆☆

Philipp K. Janert

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.

出版者:O'Reilly Media

作者:Philipp K. Janert

出品人:

頁數:540

译者:

出版時間:2010-11-25

價格:USD 39.99

裝幀:Paperback

isbn號碼:9780596802356

叢書系列:

圖書標籤:

數據分析
數據挖掘
O'Reilly
Data-Analysis
Python
opensource
data
計算機

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Description

Real World Data Analysis shows you how you think about data and the results you want to achieve with it. Author Philipp Janert teaches you how to effectively approach data analysis problems, and how to extract all the available information from your data. Many people can apply a data analysis formula. This book shows you how to look at the results and know whether they're meaningful.

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.

In Real World Data Analysis, 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.