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permanent_gradstudent

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  1. Undergrad & Grad Institution: Top 20 Public/State School (US) Major(s): Neuroscience Minor(s): Chemistry, Stats GPA: 3.46 Grad Degree: MS, Biostatistics GPA: 3.98 Type of Student: Domestic White Male GRE General Test: Q: 168 V: 165 W: 5.5 Programs Applying: Biostatistics PhD Research Experience: Involved in research from undergrad (~2016) to present. Handful of publications (one first-author publication, two "co-first-author" publication), poster and oral presentations with a top submission award, work as a statistician within large/national collaboratives Pertinent Activities or Jobs: Three years full-time work as data analyst & statistician in gap years after undergrad and masters; Graduate and post-graduate work developing statistical/data science open education resources with professors & health science/pharma industry collaborators Awards/Honors/Recognitions: Small/partial tuition graduate scholarship Summer scholarship to attend UW's SISMID Full tuition undergraduate scholarship Two undergraduate fellowships/honors programs (mildly competitive but completely unrelated to statistics) Letters of Recommendation: MS Thesis Advisor Professor; Stat Theory Professor; Collaborative/Applied Professor Math/Statistics Grades: Undergraduate: Multivariate Calculus (A), Data Science (A), Statistical Packages/Programming (A), Experimental Design (B) Graduate: Applied Regression (A), Statistical & Probability Theory (A+), Health Data Science (A), Python for Data Analytics (A), Mathematical Methods for Statistics (A+), Machine Learning (A), Biostatistical Methods (A), Linear Models (A-), Statistical Learning (A+), Neural Data Science (A), Linear Algebra (A+) Programming/ Software skills: R, Python, SAS, SQL, Stata Applying to Where (all biostatistics PhD): Harvard University - Accepted, Attending Penn - Accepted Vanderbilt - Accepted VCU - Accepted Pitt - Accepted Emory - Accepted Florida - Accepted Yale - Rejected (no interview) Columbia - Rejected (no interview) Johns Hopkins - Rejected (no interview) Brown - Rejected (no interview) Thoughts Overall very happy with this cycle and excited to be attending Harvard! I've been considering applying to PhD programs since mid-2020 but opted to take my time in consulting my professors for guidance, crafting the materials while balancing work, and attempting to let the effects of COVID "stabilize" after the last cycle. Everyone's professional path and experience is different, but I found that I benefitted greatly (personally, professionally, and financially) by taking gap years, including both one after undergrad and two after my masters program. I was unsure of the competitiveness of my app without Real Analysis or pure math beyond Linear Algebra. Some schools (I assume) seemed more accepting of this with my masters and more extensive applied work/research background. A lot of folks on this forum have great mathematical coursework which intimidated me a bit when applying (and which I'm certain is helpful if not necessary for pure statistics programs), but contrary to what you might read it is possible (acknowledging the n=1 sample size here) to get into some great biostatistics programs without a "heavy" mathematics background but with solid stats coursework and professional/research experience. Lastly, I also believe my success was largely possible only due to the wonderful mentorship that I received from my letter writers and some grad professors, extremely thankful for their help!
  2. I appreciate the detailed feedback from both of you! I know there are opportunities to take Analysis I and a more thorough Linear Algebra course, and possibly a later Abstract Algebra course. It certainly sounds like it would be to my advantage an applicant (and statistician) to enroll in some additional theory coursework and build these foundations for a more competitive Fall 2022 application
  3. Sorry if my course section isn't very descriptive, the "Mathematical Methods for Statistics" was a combined section of some calculus and the majority of linear algebra, and the Linear Models course in which I am currently enrolled is a PhD level course of applied linear algebra
  4. Type of Student: Domestic White Male Undergrad and Graduate Institution: Large State School (~Top 20 Public, Top 60 USNews General Ranking) Undergrad Major: Neuroscience GPA: 3.45 (BSc) Grad Degree: Biostatistics (MS) GPA: 4.00 GRE General Test: 165 V/ 168 Q/ 5.5 AWA Programs Applying: Statistics/ Biostatistics PhD Research Experience: 2.5 years as data analyst/statistician in academic research lab. One first-author publication, two "co-first-author" publication (so second-author), another second-author, and a a handful of 3-4 poster presentations including one oral presentation with a "top undergraduate research" award at a local/institutional research symposium. All of this research very epidemiological/clinical (e.g. clinical prediction model of outcome, large cohort studies), no formal math or statistics work (outside of applied data analysis). In addition to statistician position, currently collaborating with a clinical lecturer to build out a Python course by aggregating python resources, simulating data and creating assessments for course projects Awards/Honors/Recognitions: Full tuition undergraduate scholarship; Two fellowships/honors programs in undergraduate (mildly competitive but completely unrelated to statistics); Small/partial tuition graduate scholarship; Summer 2020 scholarship to attend UW's SISMID; Letters of Recommendation: Collaborative clinical lecturer from Python work mentioned above (Clinician, so would it be better to identify a third writer with a quantitative PhD rather than clinical degree?) Professor with whom I've worked with in research outside of coursework Thesis supervisor and previous professor Math/Statistics Grades: Undergraduate: Multivariate Calculus (A), Data Science (A), Experimental Design (B), Applied Regression (C) Graduate: Applied Regression (A), Statistical & Probability Theory (A+), Health Data Science (A), Python for Data Analytics (A), Mathematical Methods for Statistics (A+), Machine Learning (A), Biostatistical Methods (A) and currently Linear Models, Statistical Learning, Neural Data Science Programming/ Software skills: R, SAS, Stata, Python, SQL, MatLab (basic) Note: I'm worried about my relatively poor and less-quantitative undergraduate performance (in general and in the few statistics courses I took). My recent academic performance has been much improved, and I'm hoping that some of the research experience/background might be able to moderately compensate. I'm also unsure if my graduate coursework is sufficiently rigorous wrt math/theory courses to be competitive for any statistics programs. Schools: Currently casting a wide net with applications to both Stat and Biostat programs, but mostly as I am unsure how to best evaluate how competitive my application is. I'm currently assuming that my school list may be optimistic and should include a few more realistic options, but would greatly appreciate any feedback! In no particular order: Carnegie Mellon - Stats Washington (Seattle) - Biostatistics Emory - Biostatistics Johns Hopkins - Biostatistics Yale - Biostatistics UNC - Biostatistics Brown - Biostatistics Vanderbilt - Biostatistics Virginia Commonwealth University - Biostatistics Virginia Tech - Statistics Duke - Biostatistics
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