I see it almost the other way around: Companies strictly demand PhD's for Big Data jobs and can't find this unicorn. Yet we live in a time where we don't need a PhD program to receive education from the likes of Ng, LeCun and Langford. We live in a time where curiosity and dedication can net you valuable results. Where CUDA-hackers can beat university teams. The entire field of big data visualization requires innate aptitude and creativity, not so much an expensive PhD program. I suspect Paul Graham, when solving his spam problem with ML, benefited more from his philosophy education than his computer science education.
Of course, having a PhD. still shows dedication and talent. But it is no guarantee for practical ML skills, it can even hamper research and results, when too much power is given to theory and reputation is at stake.
In my experience Machine Learning was locked up in academics, and even in academics it was subdivided. The idea that "you need to be an ML expert, before you can run an algo" is detrimental to the field, not helping so much in adopting a wider industry use of ML. Those ML experts set the academic benchmarks that amateurs were able to beat by trying out Random Forests and Gradient Boosting.
I predict that ML will become part of the IT-stack, as much as databases have. Nowadays, you do not need to be a certified DBA to set up a database. It is helpful and in some cases heavily advisable, but databases now see a much wider adoption by laypeople. This is starting to happen in ML. I think more hobbyists are right now toying with convolutional neural networks, than there are serious researchers in this area. These hobbyists can surely find and contribute valuable practical insights.
Tuning parameters is basically a gridsearch. You can bruteforce this. In goes some ranges of parameters, out come the best params found. Fairly easy to explain to a programmer.
Adapting existing algorithms is ML researcher territory. That is a few miles above the business people extracting valuable/actionable insight from (big or small or tedious) data. Also there is a wide range of big data engineers making it physically possible to have the "necessary" PhD's extract value from Big Data.
> Tuning parameters is basically a gridsearch. You can bruteforce this. In goes some ranges of parameters, out come the best params found.
This sounds so simple. However, if you just do a bruteforce grid search and call it a day, you're most likely going to overfit your model to the data. This is what I've seen happen when amateurs (for lack of a better word) build ML systems:
(1) You'll get tremendously good accuracies on your training dataset with grid search (2) Business decisions will be made based on the high accuracy numbers you're seeing (90%? wow! we've got a helluva product here!) (3) The model will be deployed to production. (4) Accuracies will be much lower, perhaps 5-10% lower if you're lucky, perhaps a lot more. (5) Scramble to explain low accuracies, various heuristics put in place, ad-hoc data transforms, retrain models on new data -- all essentially groping in the dark, because now there's a fire and you can't afford the time to learn about model regularization and cross-validation techniques.
And eventually you'll have a patchwork of spaghetti that is perhaps ML, perhaps just heuristics mashed together. So while there's value in being practical, when ML becomes a commodity enough to be in an IT stack, it is likely no longer considered ML.
What scares me is that MOOCs are really pushing the data scientist field -- see Udacity and Coursera -- but giving people only enough knowledge to be dangerous. It's tough because data science is a fascinating field and many people who have the interest and aptitude don't have the means or life situation to go to grad school for it. These MOOCs are trying to appeal to such people, but they're not nearly rigorous enough.
The popular MOOCs don't take you far enough to start doing serious machine learning, but you don't need a PhD to be ready to solve those problems.
It takes work. Lots of work. Re-learn linear algebra until you know why "eigenvectors" are so important. Know what the most important matrix factorizations (LU, QR, SVD, Eigen, Cholesky) do. Read the papers until the math becomes "no big deal". Pick up a probability textbook and read the whole thing; also, get a working knowledge of real analysis. It won't happen quickly.
The PhD is some classes, plus 3-7 years of focused work. Some of that's compressible and unnecessary to becoming a data scientist. Some of it isn't. The Coursera courses are great for getting you started; they're entry-level college courses, and if you read the papers and the seminal textbooks (e.g. Elements of Statistical Learning by Hastie et al) you can get into the intermediate territory in a couple years or so. It's not easy, but it can definitely be done. Getting to the expert level, I think, just requires real-world experience on real-world problems... but, one hopes, you can start attacking such problems once you're at the intermediate level.
From a level of the mathematical difficulty, Elements etc. (but also current-level research papers) are all readable by anyone with a solid understanding of undergraduate mathematics (which is essentially a decent Linear Algebra course, multivariate analysis, a probability course, and a numerical computing course).
I think the reason why employers look for candidates with a PhD is that too many people "scrape by" when getting their CS degree -- e.g. they somehow fulfilled the required coursework, and somehow got their degree. The PhD requirement is essentially a bureaucratic substitute for answering the question "has this person understood math in sufficient depth to be able to do independent work with it".
That being said, I haven't given up completely. I'm starting to read "The Haskell Road to Logic, Maths, and Programming" in the hopes of finally being able to grok proofs. At the very least, I feel that learning more math can only help me as a developer.
For others reading this, this edx course on Probability seemed like it was really good, until my lack of maths background caught up to me: https://www.edx.org/course/mitx/mitx-6-041x-introduction-pro... For Linear Algebra, check out http://www.ulaff.net/
Trying to emulate biological brains might not be the way forward. People tried to fly by constructing bird-like feathers and wings - it obviously didn't work. We had to understand the underlying principles governing flight. The same applies to creating neural networks.
There's some underlying principle the brain uses. It doesn't mean we have to crack brain structure to achieve strong intelligence.
We should look for inspiration in biological systems but we should not try to copy them.
Experiments such as this one http://web.mit.edu/msur/www/publications/Newton_Sur04.pdf
The plasticity of the brain is phenomenal.
The fact that you think it takes a PhD to be a decent data scientist indicates that you're out of your depth on this one.
You don't need a PhD to get useful work done in these fields. You need to work hard and tackle difficult math. It takes years, but it can be done if you have the talent and drive. A prestigious (top-10) PhD certainly makes your life easier in getting the top jobs, but it doesn't really make you more (or less) able to fulfill them. I don't have a PhD and can do what 95+ percent of "PhD Data Scientists" do for work.
PhD is (a) focused work on a specific, usually narrow problem, and (b) years of self-study that required to know enough to attack said problem. For real-world data science, (a) only matters in the ~1% chance of overlap between your dissertation and the needs of your employer, and (b) doesn't require five years in an academic institution (although it probably does require about that much time, if you study on your own, since you're likely to be doing much of the work on your own time).
The PhD is a valuable experience and I don't mean to denigrate it. I often wish I had gotten one, in my 20s, instead of becoming a world-class expert on software office politics and "only" an intermediate-plus Haskell/Clojure/machine-learning guy. The PhD is a great experience for many people, but I don't think it belongs on a pedestal.
You can look at some of the modern ML algorithms and see what I mean; many people that I know have worked with Latent Dirichlet Allocation, but they have no idea how the model works, and there's no way they could extend to work online or under certain performance or storage constraints without havin spent months and years working on that problem.
That's not a realistic expectation for anyone in the field. Yes, the algorithms you find in Weka and other ML toolkits are useful, but the actual "Big Data" problems have their own performance and algorithmic constraints that are far, far beyond dedicated self-learners.
In my experience there are very few domains within Machine Learning where you don't need to be an expert in the field to yield useful conclusions out of the data.
Even if you have a high-level conceptual understanding of the statistical methods, tuning the parameters to yield something relevant, or much more so, adapting existing algorithms to meet your needs requires some pretty serious dedication to the field.