Download PDF by Philipp K. Janert: Data Analysis with Open Source Tools

By Philipp K. Janert

ISBN-10: 1449301851

ISBN-13: 9781449301859

Accumulating facts is comparatively effortless, yet turning uncooked details into whatever helpful calls for that you just understand how to extract accurately what you wish. With this insightful publication, intermediate to skilled programmers attracted to info research will study recommendations for operating with facts in a enterprise setting. You'll how you can examine information to find what it includes, tips to seize these principles in conceptual versions, after which feed your realizing again into the association via enterprise plans, metrics dashboards, and different applications.

Along the way in which, you'll test with techniques via hands-on workshops on the finish of every bankruptcy. exceptionally, you'll the right way to take into consideration the implications you need to achieve—rather than depend on instruments to imagine for you.
• Use photos to explain information with one, , or dozens of variables
• advance conceptual types utilizing back-of-the-envelope calculations, in addition to scaling and chance arguments
• Mine info with computationally in depth tools resembling simulation and clustering
• Make your conclusions comprehensible via experiences, dashboards, and different metrics programs
• comprehend monetary calculations, together with the time-value of money
• Use dimensionality relief suggestions or predictive analytics to beat demanding info research situations
• get to grips with various open resource programming environments for info research

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Comparing the corresponding CDFs is usually much more conclusive. One last remark, before leaving this section: in the literature, you may find the term quantile plot. A quantile plot is just the plot of a CDF in which the x and y axes have been switched. Figure 2-8 shows an example using once again the server response time data set. ” But the information contained in this graph is of course exactly the same as in a graph of the CDF. Optional: Comparing Distributions with Probability Plots and QQ Plots Occasionally you might want to confirm that a given set of points is distributed according to some specific, known distribution.

Figures 2-14 and 2-15 are two representations of the same data, the former as a kernel density estimate and the latter as a box plot. The box plot emphasizes the overall structure of the data sets and makes it easy to compare the data sets based on their location and width. At the same time, it also loses much information. The KDE gives a more detailed view of the data—in particular showing the occurrence of multiple peaks in the distribution functions—but makes it more difficult to quickly sort and classify the data sets.

Comparing the corresponding CDFs is usually much more conclusive. One last remark, before leaving this section: in the literature, you may find the term quantile plot. A quantile plot is just the plot of a CDF in which the x and y axes have been switched. Figure 2-8 shows an example using once again the server response time data set. ” But the information contained in this graph is of course exactly the same as in a graph of the CDF. Optional: Comparing Distributions with Probability Plots and QQ Plots Occasionally you might want to confirm that a given set of points is distributed according to some specific, known distribution.

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Data Analysis with Open Source Tools by Philipp K. Janert


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