Hi everyone, I'm currently an undergrad, math major (with a focus on stats) at a top 15 liberal arts college and will be applying to data science master's programs this year for fall 2021. Throughout my time at college, I have taken lots of statistics classes and classes like "Big Data and Machine Learning", "Math of Machine Learning" etc. so I've had a strong exposure to data science areas as well extensive training in programming languages like R and python in implementing machine learning techniques (like monte carlo simulations, bootstrapping, decision trees, random forest, clustering, SVM, little bit of ANN etc). I have 2 summers and 2 semesters of research experience in a computational biophysics lab. I also have an actuarial internship under my belt where I worked at the intersection of the actuarial science and data science departments at the company.
The downside is that my GPA is not so great: 3.35. My grades in stats, machine learning and CS classes have all been A/A-'s and one or two B+'s. My grade in linear algebra and multivariable calculus are B and B- respectively. Is that going to hurt my chances a lot, or will the fact that I excelled in those stats and machine learning classes compensate for the bad grade in calculus and linear algebra?
I am aiming for a GRE score of 170 in quantitative section and 165+ in the verbal section. I'm sure my recommendation letters and personal statement are all going to be stellar. Given all this information, do you think that my background is sufficient enough to make me a competitive candidate for data science programs? These are the schools whose DS programs I am considering applying to: Columbia, Harvard, Upenn, Georgetown, USC, USF, UVA, UChicago, NYU, Stanford.
Am I being too ambitious? Are there programs in other schools where I have higher chances of getting in that you would recommend?
ANY comments/insights would be HIGHLY appreciated. Thank you so much!