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Yann LeCun mentioned in his AMA that he considers having a PhD very important in order to get a job at a top company.

I have a masters in statistics and my undergrad was in economics and applied math, but I am now looking into ML PhD programs. Most programs say there are no absolutely necessary CS courses; however I tend to think most accepted students have at least a very strong CS background. I am currently working as a data scientist/statistician but my company will pay for courses. Should I take some intro software engineering courses at my local University to make myself a stronger candidate? What other advice you have for someone applying to PhD programs from outside the CS field?

edit: I have taken a few MOOCs (Machine Learning, Recommender Systems, NLP) and code R/python on a daily basis. I have a lot of coding experience with statistical languages and implement ML algorithms daily. I am more concerned with things that I can put on applications.

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    $\begingroup$ He said it specifically about research job though. $\endgroup$ – Arty Jun 10 '14 at 23:58
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If I were you I would take a MOOC or two (e.g., Algorithms, Part I, Algorithms, Part II, Functional Programming Principles in Scala), a good book on data structures and algorithms, then just code as much as possible. You could implement some statistics or ML algorithms, for example; that would be good practice for you and useful to the community.

For a PhD program, however, I would also make sure I were familiar with the type of maths they use. If you want to see what it's like at the deep end, browse the papers at the JMLR. That will let you calibrate yourself in regards to theory; can you sort of follow the maths?

Oh, and you don't need a PhD to work at top companies, unless you want to join research departments like his. But then you'll spend more time doing development, and you'll need good coding skills...

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  • $\begingroup$ Thanks, see my edit. I have a lot of coding experience and have taken MOOCs. I have a masters in Statistics and a minor in applied mathematics, I would consider math my biggest strength. I am really looking for things to put on a PhD application. $\endgroup$ – bstockton Jun 10 '14 at 19:23
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    $\begingroup$ Then write some papers and get them published in a good conference: that's the best signal that you are fit for research--and a PhD program. Maybe you can use your economics background to write a paper on multi-agent learning. You don't have to stick to the same subject once you get accepted; it's just to demonstrate your ability. $\endgroup$ – Emre Jun 10 '14 at 20:59
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Your time would probably be better spent on Kaggle than in a PhD program. When you read the stories by winners (Kaggle blog) you'll see that it takes a large amount of practice and the winners are not just experts of one single method.

On the other hand, being active and having a plan in a PhD program can get you connections that you otherwise would probably not get.

I guess the real question is for you - what are the reasons for wanting a job at a top company?

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You already have a Masters in Statistics, which is great! In general, I'd suggest to people to take as much statistics as they can, especially Bayesian Data Analysis.

Depending on what you want to do with your PhD, you would benefit from foundational courses in the discipline(s) in your application area. You already have Economics but if you want to do Data Science on social behavior, then courses in Sociology would be valuable. If you want to work in fraud prevention, then a courses in banking and financial transactions would be good. If you want to work in information security, then taking a few security courses would be good.

There are people who argue that it's not valuable for Data Scientists to spend time on courses in sociology or other disciplines. But consider the recent case of the Google Flu Trends project. In this article their methods were strongly criticized for making avoidable mistakes. The critics call it "Big Data hubris".

There's another reason for building strength in social science disciplines: personal competitive advantage. With the rush of academic degree programs, certificate programs, and MOOCs, there is a mad rush of students into the Data Science field. Most will come out with capabilities for core Machine Learning methods and tools. PhD graduates will have more depth and more theoretical knowledge, but they are all competing for the same sorts of jobs, delivering the same sorts of value. With this flood of graduates, I expect that they won't be able to command premium salaries.

But if you can differentiate yourself with a combination of formal education and practical experience in a particular domain and application area, then you should be able to set yourself apart from the crowd.

(Context: I'm in a PhD program in Computational Social Science, which has a heavy focus on modeling, evolutionary computation, and social science disciplines, and less emphasis on ML and other empirical data analysis topics).

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I am glad you also found Yann LeCun's AMA page, it's very useful.

Here are my opinions
Q: Should I take some intro software engineering courses at my local University to make myself a stronger candidate?
A: No, you need to take more math courses. It's not the applied stuff that's hard, it's the theory stuff. I don't know what your school offers. Take theoretical math courses, along with some computer science courses.

Q:What other advice you have for someone applying to PhD programs from outside the CS field?
A: How closely related are you looking for. Without a specific question, it's hard to give a specific answer.

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  • $\begingroup$ Thanks for the answer. I have a minor in applied mathematics and a masters in statistics. I have been taking graduate math courses for the last two years, as I did my masters in statistics. Are there any specific classes I should take? I have taken my calc sequence, linear algebra, differential equations, fourier analysis, stochastic processes, advanced probability, statstical inference, bayesian analysis, time series and a few others. Any others in particular $\endgroup$ – bstockton Jun 10 '14 at 21:42
  • $\begingroup$ Statistics MS/MA is offered everywhere these days, they don't help you get into a stat PhD. Stat PhD is looking for solid math undergrads: real analysis, optimization, numerical analysis. CS PhD is looking for cs and math undergrad. Why don't you continue on economics? $\endgroup$ – user13985 Jun 11 '14 at 0:12
  • $\begingroup$ When I left undergrad I was 12 credit hours short of a math major. After I finished my MS in stats I could have pursued a PhD where I got my MS(top 30 school), however I am more interested in ML. I really don't think my math background will be a problem, as I feel it is very strong. I left economics and went to pure statistics in graduate school because economics no longer interested me, so that is definitely out. So do you think I should try to finish a math undergrad? It would take less than two semesters $\endgroup$ – bstockton Jun 11 '14 at 16:15
  • $\begingroup$ No, you should not back dig for that math major, but take courses you need like real analysis, and optimization. I know these courses sound irrelevant, but PhD programs want to see it, please them. They want to know do you have the theories down. They don't worry if you don't understand neural network well. As Prof. LeCun said, take as many math courses as you can. $\endgroup$ – user13985 Jun 12 '14 at 20:46
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You have the option of joining a PhD program in business school and information school as well. There are quantitative professors and data scientists in business schools and information schools as well (About US, I am sure there are a lot of schools). This way you are qualified or even over-qualified in terms of quantitative and technical skills and you can spend your time on reinforcing other skills.

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