silverss

So I am currently halfway through my first year in a 2 years masters in math with probability theory as my specialization. So in my academic career thus far I have focused quite a bit on the theory side, but I have recently decided I would like to move to a more applied field such as statistics. I graduate from my current masters program in Summer 2016 and am trying to figure out what my next steps should be in order to plan for the change in fields. My end goal is to do research in an industry setting, preferably outside of north america. Data science/machine learning are somethings that seem particularly interesting to me right now. I have the following questions that I would love to get some feedback on if possible: 1) With my goals and history, should I go for a masters or PhD,? 2) How difficult would it be to make the transition/what should I be doing. I am worried that because of my poor background in stats and programming will set me back. 3) If decide to go for a PhD I would like to take gap year to travel since I don't want to do 10 years of school straight. Will doing that hinder grad school prospects? 4) Realistically what kinds of schools do I have a shot at/should look into? Would I be competitive for tier 1 schools? I have tried to be as specific as possible with my background. Undergrad - University of Waterloo - Double major in Pure and Applied mathematics - cumulative GPA 3.9+ Masters - University of British Columbia - Probability theory - 4.0 gpa Courses/Math background - I have taken over 40 math/physics courses in my undergrad, with many at the graduate level. Beyond the core courses I overall have a very strong background in math, physics, especially when it come to analysis, differential equations, differential geometry, probability/stochastic processes. - During my masters I have taken courses in advance courses in pdes, multiple in probability, ergodic theory. I plan on taking some stat courses and more probability, and probably a computation methods course. - Although my probability background is very strong, my current stat/computational background is poor to say the least. I have taken the basic stat course at Waterloo as well an applied linear models course. - I have had exposure to R, mathlab, maple, and am currently learning python, but my background is poor to say the least. - I plan on taking the math gre next month. Research/Work Experience - I have done 2 research terms in pure math in fractal and differential geometry respectively. - Doing a masters thesis, probably on stochastic pdes - Did one 4 month internship as actuarial analyst (have passed first two exams). - Currently an instructor for a section of a calculus 2 course - Been private tutoring for 4 years at university level Thank you in advance, any advice would be appreciated.