buccsbandwagon

Thanks @bayessays and @StatsG0d for the responses. Totally makes sense! I was more so asking because my plan over the next couple years was to work in industry and take one advanced math class per semester since my school is good about offering those classes in the evening/late afternoon. Hoping to complete about at least 4 classes and at a minimum two semesters of real analysis. Was just curious about, in addition to taking those advanced math classes, whether shooting for big tech research or additional stat theory courses would have a significant impact on my applications. I'm doing some more research and hoping to publish this summer while I apply for jobs and was just wondering how I should rate industry research positions compared to other data related opportunities I may have. And yeah, unfortunately the for-profit thing already sticks out, but that's to be expected. I was at a top 20 engineering school before dropping out, so I probably should have stuck it out and retaken classes, but that's water under the bridge at this point. Thank you for the encouraging words!

Thank you @StatsG0d for the reply! I've had trouble getting good intel on target schools and even the professors I have talked to have said it may just be a free-for-all when it comes to my applications. That's great info about the NSF GRFP! I hadn't really considered it but I definitely will now. That's good to hear about the Biostats programs. My only hesitation about applying to those was a lack of relevant experience/research interests but you cleared up that concern for me. There are even some schools that I really like (Georgetown) that I wasn't sure I could apply to since they have only Biostats programs. More than anything its very encouraging that you think I have a really good chance at some top 50 schools. There were times (e.g. applying to M.S. programs and the initial "out-of-my-depth" sensation of reading Casella & Berger) where I doubted if I would ever be in a position to have a really good shot at PhD programs. And yes, I was planning on taking a full two-semester sequence of Real Analysis and possibly Measure Theory if I have the time. I have a follow-up question for anyone interested in answering: How would you rate the importance of me doing research in industry (past RA's for my professor have gotten FAANG research positions) vs. taking PhD level stat theory classes at my current school, in terms of bolstering my application? I'm giving my self some time to try and improve my profile over the next couple years before applying and was wondering if I should prioritize either of these things.

Long story short: I started at a UC school as an engineering student and got failing grades my first year and dropped out. Eventually went back to college and got a BS from a for-profit school ("billboard" school; albeit getting good grades). However I got really into stats and data science on my own, and eventually applied and got into an MS in Statistics at a decent program. I was surprised by this but attributed it to my GRE scores and projects. I ended up doing very well (straight A's), got some research experience, and took extra courses in Data Science. I now want to apply to a PhD program but am unsure where to aim. I am assuming rank 1-20 programs are out of the question given the sheer number superior candidates, but was wondering if schools in the 20-50 schools are attainable given that I do well in Real Analysis. Just looking to optimize my target school list. Not that I have any room to be picky but my wife would probably veto any school not in a big city/coastal. Student Type: Domestic White Male Undergrad: For-Profit University Grad School: US News Rank 45-65 private school (East Coast). Stats program ranked similarly. Major: Environmental Science (undergrad), Statistics (grad) GPA: 3.91 (undergrad, non-cumulative), 4.0 (grad) Undergrad: Linear Algebra (A), Multivariable Calc (B+), Intro Stats (A), 5 Programming courses (A's) Grad Level: Probability Theory (A), Mathematical Statistics (A), Linear Models (A), Categorical Data Analysis (A), Applied Multivariate Analysis (A), Bayesian Analysis (A), Machine Learning (A), Computational Statistics (A), Data Mining (A), Time Series Analysis (A), Data Science (A), Statistical Deep Learning (PhD Level, A). Planning on taking a Real Analysis sequence next year. My guess that other than not going to a respectable undergrad and poor initial grades, my weakness is lack of high-level mathematics. GRE: 168 Q/ 168 V/ 5 W Research: 1 year of graduate level research in the Stochastic Modeling/Operations Research domain. Publications pending. Other Experience: DS/Stats internship with Government Agency, TA at my university. Lots of hands on experience with Python, R, and SAS. Letters of Recommendation: One from research advisor and two from Stats professors. One from research advisor will be very good. I would like to apply to Stats programs. I am not opposed to Biostatistics per-say, but I just don't have a lot of experience in that domain. My main areas of interest are in Statistical Learning, Operations Research, High Dimensional Data Analysis, and Outlier Analysis. I'm assuming that these are more suited for Stats programs, but not opposed to suggestions that are not strictly Statistics PhD programs.

When it comes to applying to graduate schools and jobs, I always struggle to get advice tailored to my situation, which is admittedly uncommon (at least from what I have gathered). Background: I originally flunked out of a UC school after my freshman year. I was an electrical engineering major and in ROTC, and was in over my head. Not knowing what to do with my life, I ended up getting a degree from a diploma mill for-profit university while I worked full time (albeit doing well, but let's just say I wasn't challenging myself the way I should have). Since then, I got got committed to academics again and began re-teaching myself a lot of the math and programming that I once new, as well well as working on data-related projects. Eventually I studied for the GRE and applied to Data Science and Statistics masters degrees, and was fairly surprised when I got in to most of them. Since attending, I have developed a really keen interest in statistical theory, especially applications to outlier analysis and anomaly detection. I have completed projects and independent research related to these subjects, and I would like to do more research in these subjects through a PhD. However, despite having good relationships with my professors, I feel like they don't take me seriously when it comes to considering me as someone on the "phd track". To be honest I don't blame them-- I don't have an academic remotely similar to any of them, let alone the students in my program. I feel like I was able to compensate for my prior faults enough to get into a Masters program, but is that the ceiling for me? I guess what I am wondering is: what more can I do to make myself a more competitive potential PhD candidate? Should I take Real Analysis as a non-degree student despite doing well in analysis-heavy stats classes? Should I hunker down and study for the GRE math subject test? I know top 20 schools are out of the question, but what about the 50-100? Any advice would be greatly appreciated. More specific details below: Degrees: BS Environmental Science at Online for-profit university, MS in Statistics East Coast private school (US news rank ~50, Stats Department ranked similarly) Type of student: American (non-international) GRE: 168 Verbal, 168 Quant, 5 Analytical Writing. Programs applying: Statistics Graduate Level Stats Coursework: Probability and Statistical Theory (2 semesters), Linear Models, Categorical Data Analysis, Time Series Analysis, Machine Learning, Data Science, Data Mining, Applied Multivariate Analysis, Bayesian Stats, Statistical Deep Learning, Computational Stats. Other Coursework: Multivariable Calculus, Linear Algebra, Undergrad Stats, about 5 computer science courses. Graduate GPA: 3.92 -All As and 1 B Jobs and Research: Machine Learning and Data Science internships TA for Data Science courses Applied Stats Research (Econ and Operations Research oriented)