Data Analysis with Open Source Tools

Philipp K. JanertAfter 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

页数:540

出版时间:2010-11-25

价格:USD 39.99

装帧:Paperback

isbn号码:9780596802356

图书标签:数据分析数据挖掘O'ReillyData-AnalysisPythonopensourcedata计算机

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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.

读后感

书的理论性较强 至少对我我这种不是学统计和学数学出身的人来讲 很多分析和图例没有给出实际的操作过程。 不是很推荐。 感觉作者很专业，讲的也很系统，但是觉得并不是一个入门级的书 要我写多少字才可以啊？  

书的理论性较强 至少对我我这种不是学统计和学数学出身的人来讲 很多分析和图例没有给出实际的操作过程。 不是很推荐。 感觉作者很专业，讲的也很系统，但是觉得并不是一个入门级的书 要我写多少字才可以啊？  

1. 30页起Rank-Order Plots, Pareto Chart。由于引入了dependent variable，个人认为这种解决方案已经不属于单变量数据的可视化，应当放在第三章(双变量数据)中加以叙述。 2. 34页，关于标准差的定义公式有2个，其中第一个是正确的，而第二个则是错误的。  

用户评价

其实我觉得70%都是在讲概率和应用数学……我是走错片场了么？(Update: 我的确走错片场了，看完了发现它想要告诉我全部细节，结果就是神马都是重点，抓狂了……)

其实我觉得70%都是在讲概率和应用数学……我是走错片场了么？(Update: 我的确走错片场了，看完了发现它想要告诉我全部细节，结果就是神马都是重点，抓狂了……)

比较high-level的入门书，很好懂，理论以“都介绍一点”为主，每章也列出可以用来做这章里讲到的东西的python和R的libraries。缺点是实战例子不多。

相当好的统计入门书，适应目前数据科学的变化。缺点是没有数据源，例子没法操作，效果很打折扣啊

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