Basic Examples (2) 

Find the derivative of data sampled from a function:

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Find the derivative of {x,y} data sampled from a function:

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Scope (4) 

The default method, "Center" generates new points between each of the original x-values using values from both sides to approximate the derivative:

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The method, "Forward" generates new points at each of the original x-values using the next values to determine the derivative:

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The method, "Backward" generates new points at each of the original x-values using the previous values to determine the derivative:

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The method, "Fourier" is appropriate for larger sets of oscillatory data:

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Options (3) 

A small amount of noise in data can create significant errors in ListD:

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By increasing the "WindowSize" the derivative is established by from the best local fit to the points:

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Smoothing the data first to remove noise has a similar effect but often produces less smooth results for the same amount of smoothing:

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Possible Issues (1) 

The "Fourier" method is only supported for regularly sampled data:

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