crlee

Thank y Yea, thank you for the tip on the need to mention Bayesian for Duke's application. Gonna work on getting some research projects in stats/biostats area!

Does anyone know what biostats/statistics departments, besides Berkeley and Stanford, send more alumni to the tech industry?

I would apply this year too. If things do not go well, you'll have most things ready by next cycle.

I plan to apply for PhD in statistics and some masters, including Duke's Master's in Statistical & Economic Modeling, for Fall 2016. I become interested in statistics after I began to write for a tech blog and understood the application for stats/ML. Watching some of the conferences in open data, computation & journalism also reinforced my interest in learning more statistics and programming. I want to get advice on how to prepare myself this year, whether to take more math classes, do some research project, or work as RA or industry. School - Top 25 National, top 5 Public University Major - BA in International Relations and Economics, Minor in Statistics (Graduated June 2014) GRE - Q 168, V 166, AWA 4.5 GPA - 3.54/4.00 Demo - Asian, Domestic, Male Math/Stats Courses Multivariable 1 - A Multivariable 2 - B+ Linear Algebra - A Probabilities - C Mathematical Stats - A Intro to R programming - A- Regression Analysis - B- Experiment Design - B- Computation&Optimization - B+ Data Mining - B I took the practice test for GRE Math subject test, scored around 50 percentile. Yea.. my grades are not that great. Since I graduated this June, I've put more time and efforts into learning math and stats programming. I am self-learning Real Analysis with Harvey Mudd's youtube videos. I find it challenging, but do enjoy it - which made me think I should do stats despite mediocre grades. I've completed half of the JHU Data Science Specialization via coursera. I'm doing distance learning from Harvard Data Science course. I'm also sitting in a numerical analysis class at a public university, not my alma mater, near my home. Research Experience - Not sure if these count ? Senior thesis on economics - I used some survey analysis, and collected&analyzed firm-level dataset. It's not very quantitative oriented. Research assistant for an education/statistics project - I was mainly responsible for data cleaning and producing statistical graphs (STATA and R). Letter of Rec My senior thesis advisor would likely write a strong one, but my thesis is not heavily econometric. Stats/Education Professor I worked for as a research assistant - decent letter, she was quite satisfied with the work I've done. But it was mostly following instruction - little independent work there. Political Economy professor - I got an A+ and went to his office hours quite a few times. Again, not math or stats. Stats Professor - I often seek advice from one professor, but didn't really do well in his class. I wonder if it's a good idea to ask for a letter from him. Research Interests Methodologies I'm interested in include bayesian, spatial, visualization, machine learning Application areas highly interested in - open data, data journalism, economic modeling, behavioral economics, social network analysis, business operation mildly interested in - genomics, medical diagnostic (my brother works in this area, and I got exposed to this a lot at dinner table talks), financial modeling, natural language processing Programming Experiences Stats program - R and STATA I'm learning python through Harvarvd CS 109 Data Science course Questions I have a year between now and Fall 2015. How should I spend my time learning and working on to improve my application and validate my interests in grad school? My current plan is to complete JHU and Harvard's Data Science courses by Dec, and to take two math classes, real analysis and DE at a public university next Jan. I think those two math classes would help me get good letter of recommendation and help me prepare mathematically. 1. What are other classes I should consider taking besides RA? 2. Would it help for grad school application to do side projects and put it on github? Is it real research experience? 3. And, what's the best way to learn about subfields, going to R or python meetup, watching conference talks? Thank you!

Would this also apply to statistics PhD? I took Multivariable Calculus, and Linear Algebra, but not DEs. I plan to take real analysis and DE. Should I take numerical analysis or other math course instead of DE?