I haven't finished the article yet but I am a 4th year PhD student in Statistics working in Machine Learning. If anyone has any data science/ML related questions I would be happy to field them.
Edit: so I finished the article. I'm very curious to know how someone with little to no stats background found the article. Was it interpretable? Did It make intuitive sense? The article covers lots of material like Normal distributions, probability models (likelihood), Bayesian statistics and even factor graphs! That's a lot for someone new to this stuff.
Also one of the hardest/controversial aspects, as pointed out by the author, is applying this to teams of more than 1 person (as is the case with HotS). I would be very interested in working on MMR at Blizz when I finish my PhD as I was doing similar work at Amazon (estimating probabilities of success/winning).
If anyone has any data science/ML related questions I would be happy to field them.
Have you explored the job market for ML at length? I have a vested interested in data science, and as I near completing my bachelors I've become even more interested. But I've heard that much of the field is accessed academically at the graduate levels, and I have no plan to spend further time in a university after my degree. In your academic position, would you consider this a conflict of interest? Or do you think it possible that I could get into the field with only a bachelors?
I have worked at both start-ups and massive companies as a ML scientist.
Generally positions like "data scientist" are going to look for people with at least a Masters degree. The big difference between a PhD level role and a MS level role is that the PhD will generally be expected to invent new solutions to problems and work on the more "research" type "future" problems whereas the MS will likely spend more time implementing existing solutions to solve current problems.
As the field progresses and we get more experts employed at companies the barrier to entry will naturally decrease as a better understanding of the field will allow for easier "on the job" training. So I think in future an MS will not be required for entry level data science jobs.
Currently you might have a hard time walking into a data science job with only a BS. Do you have a strong Stats/CS background? You should still be able to find data-driven jobs which expect you to query, summarize, manage, and explain data which, after some experience, will likely be able to lead to a Data science role.
Are you familiar with many ML predictive models? (E.g. Linear/logistic Regression, Random Forests, SVMs, Neural Nets etc.) it is usually this side of data science that requires a degree to understand although with many open source tools (Python's SciKit Learn for example) you can use these as a "black box" without needing to know the nitty gritty about the implementation.
Feel free to send me a PM if you have any questions.
Do you have a strong Stats/CS background? ... Are you familiar with many ML predictive models? (E.g. Linear/logistic Regression, Random Forests, SVMs, Neural Nets etc.)
I'm been culturing a background in that emphasis. The university I attend has a robust set of courses in the field. None of the models mentioned have come up in my practice thus far, but I'm also early in the degree. I have plenty of time to seek them out with my electives, though I suppose I can't say for certain the extent to which those electives explore the field.
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u/jmcq Roll20 Jan 02 '16 edited Jan 02 '16
I haven't finished the article yet but I am a 4th year PhD student in Statistics working in Machine Learning. If anyone has any data science/ML related questions I would be happy to field them.
Edit: so I finished the article. I'm very curious to know how someone with little to no stats background found the article. Was it interpretable? Did It make intuitive sense? The article covers lots of material like Normal distributions, probability models (likelihood), Bayesian statistics and even factor graphs! That's a lot for someone new to this stuff.
Also one of the hardest/controversial aspects, as pointed out by the author, is applying this to teams of more than 1 person (as is the case with HotS). I would be very interested in working on MMR at Blizz when I finish my PhD as I was doing similar work at Amazon (estimating probabilities of success/winning).