Data Analysis with Open Source Tools 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 know the way to extract accurately what you would like. With this insightful booklet, intermediate to skilled programmers attracted to info research will examine options for operating with facts in a enterprise surroundings. You'll the right way to examine info to find what it includes, the way to trap these principles in conceptual versions, after which feed your knowing again into the association via company plans, metrics dashboards, and different applications.

Along the way in which, you'll scan with strategies via hands-on workshops on the finish of every bankruptcy. mainly, you'll find out how to take into consideration the consequences you must achieve—rather than depend on instruments to imagine for you.
• Use pix to explain information with one, , or dozens of variables
• boost conceptual types utilizing back-of-the-envelope calculations, in addition to scaling and likelihood arguments
• Mine facts with computationally extensive tools equivalent 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 aid thoughts or predictive analytics to beat difficult facts research situations
• familiarize yourself with diverse open resource programming environments for information research

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Cumulative distribution functions have a number of important properties that follow directly from how they are calculated. 24 • Because the value of the CDF at position x is the fraction of points to the left of x, a CDF is always monotonically increasing with x. • CDFs are less wiggly than a histogram (or KDE) but contain the same information in a representation that is inherently less noisy. • Because CDFs do not involve any binning, they do not lose information and are therefore a more faithful representation of the data than a histogram.

If median and percentiles are so great, then why don’t we always use them? A large part of the preference for mean and variance is historical. In the days before readily available computing power, percentiles were simply not practical to calculate. Keep in mind that finding percentiles requires to sort the data set whereas to find the mean requires only to add up all elements in any order. The latter is an O(n) process, but the former is an O(n 2 ) process, since humans—being nonrecursive—cannot be taught Quicksort and therefore need to resort to much less efficient sorting algorithms.

Cumulative distribution functions have a number of important properties that follow directly from how they are calculated. 24 • Because the value of the CDF at position x is the fraction of points to the left of x, a CDF is always monotonically increasing with x. • CDFs are less wiggly than a histogram (or KDE) but contain the same information in a representation that is inherently less noisy. • Because CDFs do not involve any binning, they do not lose information and are therefore a more faithful representation of the data than a histogram.

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