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

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

这本书的主要内容是“数据分析”，而且讲解明显不够透彻，有泛泛之嫌，总评B+，难度A，推荐指数B(SABC分级) 内容尚且如此，翻译啥的就不重要了，不过倒不是翻译的特别烂，应该说不会有明显倒胃口的情况  

我统计学没学扎实的还有点搞不懂里面的说的那些理论，上网搜索英文的的更是很难搞懂了，加上里面的里面例子有没有提供数据来源，没有告诉图形是怎么做出来的，所以书的内容和标题有点南辕北辙啊。 但是作者提供了一种系统的思路的做数据分析，这可以提供一些思路去学习更细节的...

对于有一些数据分析经验的人来说，这本书读起来饶有风趣。 作者主要通过实例展示通过分析数据我们可以了解什么信息，如何解释分析结果，以及在这过程之中会有什么陷阱，重点关注的是分析数据时的思想方法，但是对于实际操作的具体方法以及其深层的理论基础则只是简单带...  

我统计学没学扎实的还有点搞不懂里面的说的那些理论，上网搜索英文的的更是很难搞懂了，加上里面的里面例子有没有提供数据来源，没有告诉图形是怎么做出来的，所以书的内容和标题有点南辕北辙啊。 但是作者提供了一种系统的思路的做数据分析，这可以提供一些思路去学习更细节的...

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……)

Author keeps placing emphasis on insights instead of numbers while working with data. The ultimate goal of data analysis is to understand how the system works, not to show off how proficient you are at Math. That's the true spirit of professionalism. Some annoying jargon are well explained in a plain manner. Little sections on R.

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……)

这本书都是在介绍经验，虽然有时候有些偏激但总体来说真的不错。适合有统计基础的人看，不适合新手。

适合新手数据分析