Hello. Hope everyone's coping during this difficult covid19 period.
I come from a non-traditional background and would like to pursue Statistics, perhaps via a rigorous M.S. Statistics program as a first step. Would apply to a PhD program if my profile were competitive and if I had a fairly solid idea of the areas that interest me.
The following are questions I have on my mind. Would be grateful if you could share your thoughts/experiences regarding these:
- Will a good score on the GRE math subject test help?
- Will publishing at a machine learning venue help?
- Am I targeting the right universities?
Undergrad Institution: A non-US, top 25 in THES & QS rankings
Major: Computer Science
Minor: Mathematical Finance
GPA: 3.80/4.00
Student type: International
Relevant Courses:
Probability (year 1 course), Statistics (year 1 course), Multivariate Calculus, Linear Algebra, Discrete math, Data Structures and Algorithms, Quantitative Finance.
(Grades are A's and the occasional B.)
(Have not taken real analysis)
General GRE: Verbal 163 93%; Quant 169 95%; Ana 5 92%
GRE Mathematics Subject Test: should i take this?
Experience: Currently a machine learning research engineer with 3 years of experience. Trying hard but still struggling to publish at reputable venues.
Aspiration: Enter a PhD program, or seek a job whose functions are related to statistics preferably in tech (a familiar industry) as a Plan B out of academia.
Universities in my watchlist and thoughts about them (open to hearing your opinion):
California:
-UC Berkeley (Did not apply. My profile isn't competitive for PhD. M.A. Stats program seems more for preparing students for industry.)
-UCLA (waiting for reply)
-UC Davis (No thesis option for M.S.)
-USC (admitted for M.S.)
Canada:
University of Toronto (Should I apply?)
University of Waterloo (Should I apply?)
New York:
Columbia (M.A. Stats. Should I apply?)
NYU (M. Applied Stats. Don't think I should apply.)
Cornell (No terminal masters)
Stay safe during this period and remember that you aren't alone in this difficult time. Thanks for reading!