• Elixir

defmodule SD do
  import Enum, only: [map: 2, sum: 1]
  import :math, only: [sqrt: 1, pow: 2]

  def standard_deviation(data) do
    m = mean(data)
    data |> variance(m) |> mean |> sqrt
  end

  def mean(data) do
    sum(data) / length(data)
  end

  def variance(data, mean) do
    for n <- data, do: pow(n - mean, 2)
  end
end

# usage
data = [1,2,3,4]
m = SD.mean(data) # => 2.5
sd = SD.standard_deviation(data) # => 1.118033988749895

• C#

var m = data.Average();
var s = CalculateStdDev(data);

float CalculateStdDev(IEnumerable<float> values)
{
	double ret = 0;

	if (values.Count() > 0)
	{
		double avg = values.Average();
	      	double sum = values.Sum(d => Math.Pow(d - avg, 2));
	      	ret = Math.Sqrt((sum) / values.Count()-1);
	}
	return (float)ret;
}

• Fortran

real, allocatable :: data(:)
real :: m, s
...
m = sum( data ) / size( data )
s = sqrt( sum( data**2 ) / size( data ) - m**2 )

• Go

m, s := stat.MeanStdDev(data, nil)

• Haskell

mean dat = sum dat / (fromIntegral $ length dat)

stddev dat = sqrt . mean $ map ((**2) . (m -)) dat
  where
    m = mean dat

• Java

int n = data.length;
double m = of(data).sum() / n,
       s = of(data).map(x -> pow(x - m, 2)).sum();
s = sqrt(s / n);

• Pascal

var
  m, s: double;
  data: array of double;
...
  MeanAndStdDev(data, m, s);
...

• Perl

my $m = mean @data;
my $s = stddev @data;

• Python

m = statistics.mean(data)
sd = statistics.stdev(data)

• Ruby

m  = data.sum / data.length.to_f
sd = Math.sqrt data.sum { |n| (m-n)**2 } / data.length.to_f 

• Rust

let sum: f64 = data.iter().sum();
let m = sum / (data.len() as f64);
let sum_of_squares: f64 = data.iter().map(|item| (item - m) * (item - m)).sum();
let s = (sum_of_squares / (list.len() as f64)).sqrt();