I will also disagree with your statement about Julia not catering to statisticians. Especially as datasets grow, performance keeps becoming more and more important. And this essentially means that statisticians implementing new methods in R packages have to drop down to C++ via Rcpp anyway. And this is exactly the problem Julia tackles. Of course, you could argue that practitioners do not care about the trouble the method developer went through in order to optimize the code via Rcpp. Yet, the practitioner will just use the language which offers the statistical tools he needs, thus Julia will draw him in, if it can first attract method developers.

There's also the people working on systems biology. People there usually use Matlab to do all of their simulations, modelling, fitting the data, etc. Yet, for some of the data, such as gene expression data, which is fed into the models, it is a lot easier to analyze/normalize it with packages available in R/BioConductor. Here Julia could also provide a unifying interface, so you won't need to be constantly switching languages.

Also, just like you chose Numpy, someone else might choose Julia. So this turns into a Numpy versus Julia comparison. And here I feel like (and this is not really an argument, just my gut feeling) Julia might be better at attracting people with a non-CS background, who want to implement some statistical methods or analyze their biological datasets.

    z = x*(v+w)

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

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

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

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

De-vectorization in code is like embedding ASM code: you had to write it out because the compiler sucks. Good language design should favor lucid and concise syntax, and good compiler implementation should make it not necessary to circumvent it for performance. In this case, the compiler should be implementing those vector expressions without allocating unnecessary memory.

EDIT: Not yet, I guess. http://pastebin.com/Tw5PuCcJ

While julia 'prefers' for loops according to the docs, I tend to write things in vectorized form.... I have a toy neural net library I play around with (https://github.com/ityonemo/julia-ann/blob/master/NN.jl) - you'll note that the "nnlog" function is one line, vectorized, and the meat of the evaluated is a dot product. I did once write a benchmark comparing the unvectorized, for loop form of these matrix multiplications, and it was slower than the vectorized form (I didn't bother trying to figure out why).