vital_green

Undergrad Institution: US News Top 100 Major: Math, graduated 2017 Minor: Computer Science GPA: ~3.80 GRE: 158V, 168Q, 4.0 W Type of Student: Domestic White Male Math Courses: (all undergrad level) Calc I A, Calc II A, Multivariable Calc B+, ODE A, Linear Algebra A, Analysis I B+, Analysis II A, Probability Theory A, Mathematical Biology A, Statistical Methods B. I also took a lot of other math classes that wouldn't be useful to list out - the grades range from B's at the lowest to mostly A's. My Analysis II course briefly covered Measure Theory but was mainly just the multivariable extension of Analysis I. Research Experience: Senior year project for two semesters using functional data analysis. No publications Work Experience: F500 consumer packaged goods firm for three years as a Data Analyst. Letters of Recommendation: One from a Math professor that I took a few upper level courses with, one from my current manager and one from another Data Science manager at my firm. Application Concerns: I think I check off the major items without too many issues (168 Q GRE, mostly As in the required Math courses) that are needed for a lot of Biostat/Stat PhD programs but obviously there are a few concerns with my profile. I did end up getting a few B's in my upper level math/stat courses (such as Statistical Methods listed above) - getting these grades from a university that isn't well ranked is probably concerning. All of these B's were in my senior year - I had taken the majority of my Math courses including the Analysis sequence by the end of my junior year. I also don't have much research experience apart from my senior year project. In terms of industry work that may be relevant, I've taken a few data science projects from start to finish at my current job - starting with collecting data, finding ways to analyze the data (since standard methods would not work) and defending my results to upper level management (presentations that involved the data being questioned to every specific detail). A lot of this also involved writing efficient code to reduce computational complexity. My industry recommendations will speak about this and I'd like to believe that these skills overlap somewhat with what academic Admissions Committees are looking for. In reality though, how much weight will this experience carry? For what it's worth, both industry recommenders have a somewhat high title/rank at the firm. Programs Applying to: PhD Biostats mainly, with a few Stat programs thrown in. Computational Statistics and Genetics interest me the most but I know that's likely to change as I go through graduate courses. Brief list so far: UMich Biostat, Minnesota Biostat, UNC Biostat, Iowa Stats. Preference to the midwest but willing to go anywhere. Few questions: Do I have the right idea in what kind of programs I am applying to? If I should be aiming lower, what schools could I go with? From what I've seen on online forums and admissions data available through universities, a lot of the smaller departments tend to be more selective than larger programs so I think I'd have the most success with the type of programs within large departments such as the ones listed above. How much will my work experience matter in the application process? Would it help to take a graduate level Stat Theory or Analysis class in the Fall to rectify any concerns about my mathematical background? Is it likely that the 2021-22 application cycle will be more competitive than usual in Biostats/Stats? I greatly appreciate any input, thanks!