Julia does vectors well, but scalar computations are very important. The problem with vector computations:

    z = x*(v+w)

This involves a memory allocation for v+w, and a second one for the product x times result. One way to avoid this in numpy is careful use of out:

    z = zeros(shape=x.shape, dtype=float)
    add(v,w,out=z)
    multiply(x,z,out=z)

An even faster way, which only requires traversing the arrays once, and is more readable in my opinion, is a simple for loop:

    for i=1:len(x)
      z[i] = x[i]*(v[i]+w[i])
    end

This also lets you more easily put if statements inside loops, etc. You can also do accumulative calculations without creating intermediate arrays at all. I.e. one way to create a random walk is:

    walk = cumsum(bernoulli(p).rvs(1024))
    end_result = walk[-1]

Another way is:

    end_result = 0
    for i=1:1024
      if rand() < p
        end_result += 1
      end
    end