playingstats

This is all helpful. Do either of you (or anyone else reviewing this later) think I'd have a shot at admission into a Master's program at one of the schools I mentioned? There are a few points that I think are lost in translation at the moment which I want to address, because I believe my understanding of Statistics is better than you may perceive it to be. For slightly more context, I am working in an engineering science research lab now, and I am very aware of what goes into conducting top-tier research in science. Statistics and mathematics is more guess work for me because it is the area I'm weakest in. But statistics is the field I want to work in. To both of your points, I think I do understand what a PhD in Statistics signifies, and why there is a distinction between Statistics and Applied Statistics among departments. In my mind a PhD statistician from a top university is someone who has expertise in core statistical concepts, such as stochastic processes, sampling theory, estimation theory, and decision theory from both a frequentist and a Bayesian perspective; has graduate level exposure to linear algebra, analysis, and mathematical probability; and has advanced exposure to algorithms and data structures for scientific computing. (I think the quality of training in algorithms and scientific computing is the hardest to estimate among these categories, but it is clear that it is advanced.) Then of course there is the actual subfield that a candidate studied in their dissertation or presently pursues. Imaginably, a PhD will combine their statistical expertise in their area with practical knowledge of how applied research is conducted and what policies and regulations govern the same. My goals for obtaining a PhD in Statistics are two-fold: one is to learn the specific area I'm interested in for a long-term career, and the second is to develop expertise in the topics mentioned before. The area I want to study is relatively old, but has rapidly expanded due to advances in computing technology. I suspect that it will become much more significant as we continue developing new sensors for measuring and logging data that the field addresses, especially as the market for those technologies grows in private industry. In that time, I think there will be an abundant need for people with expertise in that area in academic, government, and industrial settings, and I would be interested in making a career collaborating with people and organizations in each of those respective settings. My greatest preference would probably be to continue to a Postdoc or Assistant Professor position, but I would not turn away from the right industry or government position. The place I'm at now is a dead end for the career I want to have. It is a top-tier research environment in an area that I have no interest in pursuing, and there are few perceivable opportunities to transition into a better place from here. I don't want to become a business analyst or a data scientist or a machine learning guru—I want to become a statistician. Realizing how bad the circumstances I'm in now are, I am frustrated that I allowed myself to get here. By next year I want to correct course and start in a program that has higher odds of leading into the area I want to research in.

Graduated undergrad in May 2018 and took a staff job as an RA at an Ivy campus. I passed on applying last year in part because I didn't know much about stat departments or statistics research, but mostly because my grades are a huge anchor to deal with. Since then I have taken additional courses and made a habit of reading 1-2 articles a week to learn what statistics literature looks like. My information: Demographic: Domestic White Male, late 20's, US Military Veteran Undergrad Institution: Small state school that no one's heard of Major: BS Math 3.33/4.00, BS Applied Science 3.60/4.00 CGPA: 3.29 Minor: Business Administration Awards: Outstanding graduating math student Outstanding graduating applied science student Service award Who's Who award twice Retention and athletic scholarships Math courses and grades: Calc 1,2,3 (C, A, A) Intro stat (C) Diff Eq (A) Intro to proofs (B) Linear Algebra (B) Numerical Analysis (B) Mathematical Modeling (B) Complex Analysis (A) Linear Programming (A) Abstract Algebra (B) Real Analysis (B) Mathematical Probability and Statistics (A) Topology (B) Grades are similar in applied science. GRE: 162V -- 164Q -- 4 AW (91% -- 86% -- 59%) Graduate coursework: Data science classes at Harvard Extension School Mathematical Statistics (A) Intro to Statistical Modelling (A-) Deep Learning with Natural Language Processing (A) Bayesian Inference (In progress) Stochastic Methods for Data Analysis, Inference and Optimization (In progress) Research Experience: 1 science REU, senior thesis in applied science, assisted with projects in my (applied science) department as an undergrad. No research experience in math or stats. Letters: Undergrad advisors from applied science and math. Probably one from the PI I work for now or from a professor at the Extension School. Each professor has assured me they'd write great letters for me. I'm looking to apply to PhD programs this year or next year, and selecting schools to work with specific faculty. I thought I knew the subject I'd want to research after doing an undergrad thesis but I didn't feel ready to apply yet. Now I broadly know the area I want to study, I've identified great faculty in that subject, and I am pretty confident in what I want to do as a career after graduation. Now I need to convince the adcoms to look past my undergrad GPA, but I have no idea what I look like relative to other applicants. Biostatistics PhD UCLA PhD Statistics PhD (Sorted by interest!) Texas A&M Purdue Michigan State University University of Missouri Ohio State University U Florida (safety) Florida State (safety) Bowling Green State (safety) How bad do I look relative to other applicants at this schools?