Data Analysis with Open Source Tools: A hands-on guide for by Philipp K. Janert
By Philipp K. Janert
Amassing facts is comparatively effortless, yet turning uncooked details into anything worthy calls for that you just understand how to extract accurately what you would like. With this insightful e-book, intermediate to skilled programmers attracted to facts research will research ideas for operating with information in a enterprise atmosphere. you are going to how to examine info to find what it comprises, how one can trap these principles in conceptual types, after which feed your figuring out again into the association via company plans, metrics dashboards, and different functions. alongside the way in which, you will scan with suggestions via hands-on workshops on the finish of every bankruptcy. specially, you are going to easy methods to take into consideration the consequences you must in achieving - instead of depend upon instruments to imagine for you.
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Additional resources for Data Analysis with Open Source Tools: A hands-on guide for programmers and data scientists
It may appear otherwise since we can sort the job titles alphabetically, but realize that this ordering is entirely arbitrary! There is nothing “fundamental” about it. If we choose a different font encoding or locale, the order will change. Contrast this with the ordering relationship on numbers—there are no two ways about it: 1 is always less than 2. In cases like this, where the independent variable does not have an intrinsic ordering, it is often a good idea to sort entries by the dependent variable.
Distributions depend on location and scale parameters in exactly the same way as the Gaussian distribution, so all the arguments discussed earlier go through as before. • So far, I have always assumed that we want to compare an empirical data set against a theoretical distribution. But there may also be situations where we want to compare two empirical data sets against each other—for example, to find out whether they were drawn from the same family of distributions (without having to specify the family explicitly).
The quality of any histogram hinges on the proper choice of bin width. If you make the width too large, then you lose too much detailed information about the data set. Make it too small and you will have few or no events in most of the bins, and the shape of the distribution does not become apparent. Unfortunately, there is no simple rule of thumb that can predict a good bin width for a given data set; typically you have to try out several different values for the bin width until you obtain a satisfactory result.