Data Analysis with Open Source Tools by Philipp K. Janert

By Philipp K. Janert

Amassing facts is comparatively effortless, yet turning uncooked details into whatever invaluable calls for that you simply know the way to extract accurately what you would like. With this insightful e-book, intermediate to skilled programmers attracted to information research will study innovations for operating with information in a company setting. You'll the best way to examine facts to find what it includes, how you can catch these rules in conceptual types, 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 ideas via hands-on workshops on the finish of every bankruptcy. in particular, you'll take into consideration the consequences you must achieve—rather than depend upon instruments to imagine for you.
• Use portraits to explain facts with one, , or dozens of variables
• advance conceptual versions utilizing back-of-the-envelope calculations, in addition to scaling and chance arguments
• Mine facts with computationally in depth equipment similar to simulation and clustering
• Make your conclusions comprehensible via stories, dashboards, and different metrics programs
• comprehend monetary calculations, together with the time-value of money
• Use dimensionality relief ideas or predictive analytics to overcome not easy info research situations
• get to grips with varied open resource programming environments for info research

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Whenever you suspect that your distribution is not symmetric or has important outliers. If a distribution is symmetric and well behaved, then mean and median will be quite close together, and there is little difference in using either. Once the distribution becomes skewed, however, the basic assumption that underlies the mean as a measure for the location of the distribution is no longer fulfilled, and so you are better off using the median. ) Furthermore, the moment you have outliers, the assumptions behind the standard deviation as a measure of the width of the distribution are violated; in this case you should favor the IQR (recall our shopping basket example earlier).

That is a question that is much harder to answer by looking at the histogram in Figure 2-2. Besides wanting to know how much weight is in the tail, we might also be interested to know what fraction of requests completes in the typical band between 150 and 350 milliseconds. It’s certainly the majority of events, but if we want to know exactly how many, then we need to sum up the contributions from all bins in that region. The cumulative distribution function (CDF) does just that. The CDF at point x tells us what fraction of events has occurred “to the left” of x.

Compare to Figure 2-7. 5 3 F I G U R E 2-9. Jitter plot, histogram, and cumulative distribution function for a Gaussian data set. As it turns out, there is. The method is technically easy to do, but the underlying logic is a bit convoluted and tends to trip up even experienced practitioners. Here is how it works. Consider a set of points {xi } that we suspect are distributed according to the Gaussian distribution. 5 -3 -2 -1 0 1 2 3 Multiples of Standard Deviation F I G U R E 2-10. Probability plot for the data set shown in Figure 2-9.

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