[polyfractal]

Very helpful, thanks!

2) This is really my weakness since my stats and linear algebra are passable but not great. There are several free datasets (mostly from data.gov) that I've been playing around with. Should I "publish" the results of my practice studies on a portfolio-esque site? Or is it sufficient to just know the techniques well enough to answer interview questions?

3) I'm fairly well acquainted with machine learning - my interest in machine learning is one of the driving forces for me to take up neuroscience as a career.

4) Great information, thanks. I'm glad to see people use technology more as a scratchpad and less as a regimented "You must know XYZ tech stack". R and SciPy are on my to-do list, I'll add Map Reduce.

As a machine learning guy in an analytics department, are you hunting through internally generated numbers to find trends (like sales, ad placement, etc?) Or are you hunting through externally generated data to find new trends/products/markets?

[dvcat]

2) "Publishing" (Blogging about this) will help. I am not too sure what you mean by "know the techniques well enough". In my case, I cracked a few books on LA/Stat and worked through them and encountered situations where I had to use those skills repeatedly as part of courses I had taken in School. Doing tiny projects where you are able to acquire mathematical intuition for certain concepts will be useful.

4) So the fact of the matter is that you should be versatile enough to absorb their tech stack as soon as possible. That is possible only if you have worked in depth in at least one tech stack. This is especially true for Map Reduce based technologies.

Its hard to be specific: All those things are doing in analytics departments. There are not many machine learning people in my place of work so I mostly find problems that require algorithmic solutions and try to see if I can solve them. Things probably are more structured in larger companies..