Tanul

Hi, I'm a Mechie by qualification, BA by profession looking for a ML transition. Owing to my diverse background I've realized that if I want to get close to the ML space, rooting for MS in data science is my best bet. Profile - Mech Engg; CGPA: 7.44/10 (~3.2/4) GRE: 323 (159V; 164Q; 3.5AWA) 3 years of relevant work ex in Data analytics ML research project at a leading Space Tech firm Reccos: 2 from the work and project ex (good); 1 from coll dept prof (moderate) Need help in bucketing the following universities for MS in Data Science into Safe/Moderate/Ambitious - NYU Columbia UWashington U British Columbia NW MS in Analytics Thanks! and any other university/course suggestions would be appreciated! (if I have better chances in any of those - great!)

Good point! OR will again be very different and far off from NLP. In that case, I'll just treat OR applications as the last resort. Any thoughts around this? How important would it be to get a research publication for getting into MS in Data Science programs?

Hi @compscian, Thank you for the reply! Its great to hear about living proof that such a transition is possible! The details and some follow ups - 1. Eventually I want to do PhD around NLP, and the best bet that I feel right now is to get into a MS in data science course and then collaborate with the ML Prof in that Uni to get into research and PhD later on. I wouldn't want to apply to a MS in CS/Stats/EE course because of both zero background coursework and next-to-impossible chances of getting in. (Not to mention I'm not very inclined towards doing their coursework as well). 2. I would apply to MS in Data science and also try the Operation Research courses as well (Didn't think of the OR option, thank you for suggesting this!). The Mech undergrad can actually help out in my OR applications 3. I'm not doing the online courses to increase the weight of my applications per se, but more on the lines of improving my own basic theoretical knowledge around the subject. 4. I know this is tough as hell but as long as it is worth it, I'll do it. I don't think I have enough time left to be able to build a strong online portfolio and then try and join research projects with a Prof here. Also, should this be necessary if I apply to MS in Data Science/ Operations Research programs? Also, I would like to PM you and directly hear more about your ME to ML journey. Let me know and thanks again!

Background- The Good: - The 3 yrs BA experience (working in the analytics field has actually inspired me to go one level deeper and transition into ML) The Bad: GRE: 319 (V:155, Q:164, AWA:4.0) TOEFL: 107 CGPA: 3.20 The Ugly: - No publications - Undergrad degree in Mechanical engineering I have completed Andrew Ng's ML course on Coursera and I have the below mentioned tasks in mind before I start my applications for Fall '17 Kaggle - Pick any of the ongoing/completed competitions and showcase the methodology/performance comparison between models on any blog/github Replicate results from research papers - I have found some really cool research papers and I really want to replicate their results with some modifications. But unsure if it would be worth the effort/time and if it would even help my case Also, in parallel to these things I am planning to complete some more online ML courses to try and get a more comprehensive understanding. Does this seem like a good idea OR is there something else that I should be doing? Do I even have a chance? Should I retake GRE? Should I give up looking out for grad admissions and go hunt for jobs directly? I know this is a bit late wrt to the fall applications this year. But at this point I'm getting all out paranoid. Appreciate the help!