JTTCOTE

Hi guys - admitted to Northwestern's MS in AI, NYU's MSDS, and waitlisted for Columbia's MSDS. I don't count chickens till they hatch, so I'm debating Northwestern and NYU. What are your thoughts on them? They seem very different programs - 5 quarters vs 2 years, 40 students vs 150 students. What would you value in each of these? Additionally - I don't pay much attention to brand names of colleges but if one of you knows which one is better regarded I'm curious.

Hello all! Happy Holidays! Although I have a bad GPA, I feel that the other factors of my application are far better than my GPA would suggest. I'm wondering what sorts of Masters in Data Science programs I would stand a chance at. How much can all the other aspects of the application make up for low GPA? Right now my plan is to spray applications everywhere in hopes that one of the schools out there puts less weight on GPA, which is not a great plan. I would hugely appreciate any insight in programs to apply to - I know what the top tier schools are, but I'm very unlikely to make it into those. Ideally, I'm looking for programs with a heavier machine learning component. Undergrad: Carnegie Mellon, Major in Statistics & Machine Learning, double major in Chinese, minor in CS GPA: 3.1 (overall and major happen to be the same) GRE: 167Q / 169 V / 5.0 A No research/work experience Internships: Small (200-300 person) company where I solo built an ML model (conv. neural net) saving them 100k+ a year Microsoft data science intern (NLP work, but no results in the time I was there) Rec letters: Alg&Data Structures professor - we talked often and I nailed the course, 92 vs a class avg of 76. Stats professor - took a corporate capstone project, did good work with them and went to prof often for advice. Microsoft boss who liked me & my work.