By Philipp K. Janert

Gathering information is comparatively effortless, yet turning uncooked info into whatever helpful calls for that you just understand how to extract accurately what you would like. With this insightful publication, intermediate to skilled programmers attracted to information research will research ideas for operating with facts in a company setting. you are going to examine info to find what it comprises, find out how to catch these principles in conceptual versions, after which feed your realizing again into the association via enterprise plans, metrics dashboards, and different purposes. alongside the way in which, you will test with recommendations via hands-on workshops on the finish of every bankruptcy. specifically, you are going to methods to take into consideration the implications you must in attaining - instead of depend upon instruments to imagine for you.

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Extra info for Data Analysis with Open Source Tools: A hands-on guide for programmers and data scientists

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Finally, if you want to compare several data sets in the same graph, always use a frequency polygon, and stay away from stacked or clustered bar graphs, since these are particularly hard to read. ) Histograms are very common and have a nice, intuitive interpretation. They are also easy to generate: for a moderately sized data set, it can even be done by hand, if necessary. That being said, histograms have some serious problems. The most important ones are as follows. 18 • The binning process required by all histograms loses information (by replacing the location of individual data points with a bin of finite width).

It is the possibility of outliers that makes it difficult to find an acceptable bin width in an automated fashion. 8 14 12 10 8 6 4 2 0 0 20 40 60 80 100 120 140 Months in Office F I G U R E 2-4. S. presidents have spent in office. Fortunately, there is an alternative to classical histograms that has none of these problems. It is called a kernel density estimate. Kernel Density Estimates Kernel density estimates (KDEs) are a relatively new technique. In contrast to histograms, and to many other classical methods of data analysis, they pretty much require the calculational power of a reasonably modern computer to be effective.

Info T A B L E 2 -1. The first 30 job titles and their relative frequencies. 749 This trick of sorting by the dependent variable is useful whenever the independent variable does not have a meaningful ordering relation; it is not limited to situations where we count events per bin. Figures 2-12 and 2-13 show two typical examples. Figure 2-12 shows the sales by a certain company to different countries. Not only the sales to each country but also the cumulative sales are shown, which allows us to assess the importance of the remaining “tail” of the distribution of sales.

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