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Found 69 results

  1. Hi all, I'm deciding between Columbia's Masters of Science in Business Analytics and University of Michigan's Masters of Science in Data Science program. Since they are in different industries, I'm very conflicted. I got waitlisted from NYU and UW Data Science, got accepted to Cornell's MPS in Applied Statistics (Data Science), ORIE at Cornell Tech, and Georgetown Analytics. Still waiting from Brown, PENN, LSE Data Science. Economics major and Statistics minor at a top 3 liberal arts college, with some cs background. I think my end goal is working as a data scientist at a consulting/finance firm, but I'm open to other data science roles. Not interested in PhD. I was leaning towards Michigan because of technical complexity, so I'll have a wide variety of career options, but everyone's telling me to choose Columbia because of its name value, resources, and geographical advantage (i.e. recruiting and networking). I have a week to decide - any advice/input would be appreciated!!! Thank you!
  2. I can't seem to find much about people applying to data science and analytics masters programs so I thought I'd start one myself. I'm super frustrating with not being able to find information on when decisions will be sent out, so I'm hoping find out if anyone has heard from anywhere. I've applied to: Chicago's MA in Computational Social Science Chicago's Masters in Analytics Georgetown's Masters in Analytics (focus in data science) Northwestern's MS in Analytics LSE Applied Social Data Science and Data Science masters programs I just found out yesterday that I've been accepted to UVAs masters in Data Science. Has anyone heard back from any of these schools??? Just for reference... School: Hillsdale College Major: Psychology and Political Economy, minors in French and Mathematics GPA: 3.53 (4.0) psychology major 3.90 (4.0) GRE: 162 (81%) Q, 165 (96%), 4.5 AW Relevant Courses: Statistics for the Social Sciences, Econometrics, Linear Algebra, Multivariable Calculus, Mathematical Statistics, Statistical Learning Other things: As far as programming I have experience with R and SPSS and have basic knowledge of HTML, C++, and SQL. I'm in the process of publishing psychology research as first author, have leadership experience in my sorority and two other campus organizations, have been my psychology department's statistics and SPSS tutor since sophomore year, am part of both the psychology and economics honoraries, and spent a month at Oxford this summer taking two courses.
  3. Just wanna start a post for Columbia Data Science 2019 fall applicants. Has anyone heard back yet? Or what is your process?
  4. Hi guys! I just got admitted to these school for the data science program. I want to know your thought about these schools in terms of job prospects, education, etc. and let me know what school i should go to pursue my MS. Thank you!
  5. I have an admit from ucsd(no funding) vs kth(tution scholarship). I'm really confused which one to choose. UCSD is more recognized and is near silicon valley, but kth will allow me to be debt free after studies. But wages in sweden are low. Can anyone give their suggestions/share their experiences at any of these?
  6. I (international student) got accepted into these two programs, which are quite different, but I believe both could lead me to a good position as a data scientist, in the US preferably. I am struggling to make a decision. On the one hand, NYU's 2-year Master is a top-10 program in the subject that would allow me to dive deep into data science core subjects as well as to do an internship during the summer. I feel that I would learn a lot in this program, get to know a commnity that is doing cutting edge work on the field and, hopefully, access good job opportunities The program is expensive, though, and I have not received any financial support. On the other hand, Berkeley's 1-year program combines technical courses with business-oriented topics. This means there will be considerably less time to invest in pure data science work. IEOR is a very broad area but ideally I would specialize on analytics, which could get me closer to the kind of jobs I want. I know MEng alumni have pursued careers in data science and similar positions before. Of course, this program has Berkeley's amazing prestige and faculty behind it, plus a lot of networking oppportunities. Besides, it will be significantly less expensive than NYU's MS, since this is a one year program and I have been awarded a $16k grant. Any thoughts on making a decision? Thank you!
  7. Hi everyone, I'm planning on applying to Masters programs at the end of this year specifically ones that intersect in Data Science + Public Policy. I have a BS in Geographic Information Science so my main interest is in spatial analytics and recently its been mainly in urban informatics. My background isn't in computer science, my degree only required minor levels of Python, SQL, & stats so I'm currently taking some comp sci and higher level math courses while I work. I'm looking for any programs that would fulfill these requirements of Data Science + Public Policy (Spatial Analytics/Urban Informatics) I've seen this list https://www.mastersindatascience.org/specialties/best-data-analytics-degrees-for-public-policy/ but I'm searching for more lesser known programs bc my GPA isn't the greatest. I'm actively taking post-bacc courses to remedy this. However I still need safe programs. If you guys know of any please post them below. Some examples: University of Missouri (Mizzou) - Data Science (w geospatial concentration) UMBC - Spatial Analytics Pathway More reputable programs I *might* apply to: Northeastern - Urban Informatics NYU - Applied Urban Science & Informatics (CUSP) University of Chicago - Computational Social Science / Computational Analytics & Public Policy University of Pennsylvania - Urban Spatial Analytics
  8. datasciencenoob

    Any UBC Data Science Admits

    Almost ready to join UBC MDS program for September 2019. (Waiting for Michigan Ann Arbor!) Anyone else joining UBC? Would love to connect. P.S - International Student
  9. I have got admission from BGSU(Ph.D. in Data Science) and CMU (MS in Applied Statistics & Analytics) Which one should I go for? I am referring to learning and future job opportunities. Thanks
  10. ewits

    NYU Data Science PhD F19

    Is anyone else still waiting to hear anything from NYU data science/knows how their process works? I saw someone mention that they did in person interviews last week, does this imply that if we haven't heard anything at this point we should assume rejection?
  11. Faizan Zafar

    Comparison for graduate programs

    Hello, I have got admits in the following universities and programs: TU Kaiserslautern - M.Sc. Computer Science RWTH Aachen - M.Sc. Data Science Paderborn University - M.Sc. Computer Science OVGU Madgeburg - M.Sc. Data & Knowledge Engineering Universitat Bamberg - M.Sc. International Software Systems Science Please suggest the best/suitable program with respect to research prospects, job opportunities, long-term/short-term benefits?
  12. Decisions for M.Sc. Data Science for Summer 2019 at RWTH Aachen are out. What is the expected total incoming strength? Successful applicants are welcome to contact.
  13. Inspired by @bigdatagirl 's idea. If anyone is applying for graduate programs in the Spring or Fall of 2019, feel free to post your profile and the colleges you are applying to. Just interested in what kind of profiles and applicants are out there. Undergraduate Major: Applied Math Institution: TCNJ GPA: 3.1 GRE: 162 Q / 151 V/ 5.0 A Relevant Courses: Regression, Data Mining, Database management, Intro to Data, Data management (taking now), Probability (taking now), Business Analytics (taking now) Applying to: Drexel, Penn State, Rutgers, NJIT, Northeastern, UMDUC, Villanova
  14. Hi all, Hope everything is well for all of you! Congrats for those who got their invitations and offers! I'm not sure if there's anyone in looking at this post and is at the same stage as me: got nothing but only rejection letters (5/9). I applied for 9 PhD programs and haven't heard from Harvard, Princeton, Stanford and UCB, but I see in the cafe that many people have got their invitations, and the interview dates are reasonably close. (One of the disadvatages I have is that I'm an international student, thus we are considered separately because of funding issue.) As a result, I need to move on and work on my plan B. With all the advice I've got from friends, grad students and professors, I don't feel ready to make a solid plan by myself. Thus, I'm posting this up, and, hopefully, we can all share some ideas on how to make an alternative plan. So here's something about me (I'm not sure how detailed should I go for, please let me know if it's not appropriate): I'm currently a senior, international student at UMass Amherst. I'm finishing a dual degree in biology and mathematics (applied/stats track). I have a 3.977/4.000 GPA, and received 40k+ scholarships over the years. In addition to my academic live, I also have 3 years of experience tutoring and 2 years of volunteering (BBBS kid mentoring program). I've only taken GRE once, and I have Q168, V151 and 3.5 (I'll definitely make this looks better if I apply next cycle or later). I'm in the honors college and doing a thesis. I have been in a plant genetics lab since the second month of freshman year, where I had various experience with wet lab experiments and bioinformatics/systems biology analysis. I am co-authoring a paper that will be submitted in February which is about analyzing and interpreting an RNAseq dataset. I'm working with this PI for the 4th year and he said that wrote me a very promising letter. In addition to working with plants, I also had research experience during summers working with mammalian telomeres and interned at MRL at Boston on immuno-oncology targets. As for the computational aspect, my work on analyzing data in lab required me to use R, python and bash scripting. I also had intermediate/entry/entry levels of experience with SAS/Matlab/Java from project-based math/stats courses. My original plan is to go straightly for umbrella PhD programs, which covered computational biology or systems biology. I want to use my advantage where I can do both biology and mathematics and to work in interdisciplinary fields. My passion originated from doing experiments, so I still want to keep up with my web lab skills (i.e. doing gene editing according to the results from computational analysis) which I think would also be valuable when I look for jobs later. For long term goal, I would like to work in the R&D parts of the pharmaceutical industry. From the conversations I had with my co-workers during my internship, career-wise, it would be very helpful to have a PhD degree. Also, I don't want to limit myself to plant biology, so I need the transaction to focus on other systems. Also I want to keep all the lab work I deal with in vitro. However, since the plan going for PhD directly didn't work out well, I need to start thinking alternatives. I think my CV would look better in a year or two when the paper is published (there's another one data analysis based that I'm working on as the first author). Also, there's a gap in between the data analysis I do in the bio lab and what I learned from my math/stats courses: I didn't have experience developing computational/statistical tools. I think it may be a solid plan to do something to fill in that gap. The first thing I'm thinking of is getting a master in biostats. Although the deadlines for submitting applications have passed for a lot of good schools, I'm exploring options that are still available (i.e. Brown, UMich, UMinnesota, UCD, UPittsburgh, CWRU and UMass). One question I have is how much a biostat master degree would help if I want to go back to applying biomedical/compuational PhD programs? I do believe a master in biostats will open a lot of doors if I want to look for jobs, also if I want to switch to tracks such as data science. From what I have seen, all biostats programs offer the opportunity to do a thesis, however, if I want to apply to PhDs during the second year of my master, I don't think the thesis will be ready for publishing and I'm not sure how much points that will add to my application. So should I go for a thesis if I end up going to a master program? The good thing is that, if I stay in the same school, I can finish the master with only one additional year. That being said, if I apply for PhD programs in the next application cycle, a thesis would definitely not in time. Yet, all the courses I take will be very coding heavy and project-oriented so would expand my skill-sets on the computational aspects dramatically. I'm not sure how many bio/mcb master programs are still available now. If not going for biostat programs, I hope to get into schools that may help with my applications later. So please let me know if there's any program worth going for a try. I know the last option I have is the MCB MS at my school, which there's no doubt that I'll get into. One of the reasons I didn't think much of this option is that I need to take classes during the PhD programs anyway so I'd rather do something that I can learn more with the same amount of time and effort. Another option is looking for jobs and gets experiences while working. As an international undergrad, I think it's hard for me to look for jobs in the US (although I have the 36 months OPT available), especially jobs that I can learn as much as a master program. It's hard to imagine finding a job that will allow me to do things that I don't know before (I'm still thinking about filling the gap in my experience/skills). With everything going on in the U.S., I was advised that it's not such a bad idea to look for PhD programs in Europe, since I'll be international anyway. However I have no idea how this would work, so please let me know how I should start looking and what I should be expecting if going to graduate programs in Europe. One addtional note is about grad school funding. My parents are funding me for undergrad (although I tried very hard to get as many scholarships as possible), and they can and are willing to fund for my tuition for master and PhD. However, I find it very not helpful when programs as me to bring my own funding while applying for PhD programs. I completed my undergrad in the U.S. so I'm not eligible for a lot of funding from my own country, also I don't want to sign contracts that force me to go back to work for a few years right after graduation (I'm not against going back but I want to keep all options available). And, to my knowledge, there's no scholarship that I can apply to before being admitted to a program (NSF grant requires citizenship). That leaves me no option to bring my own funding while applying, which makes me less competitive among international or all applicants. I appologized that this is getting way longer that I planned for. Thank you if you have read this far. I'm just going to summary some major questions that I need help with: 1. What can I do better if I apply to PhD programs in the future? (Umbrella programs aiming for computation-based track). Are there any not famous but good phd programs that I can still apply for? I know WPI is still rolling and have a lab that may fit my interest according to a professor I talked to. 2. Is it worth it going for a master in biostats? Is a thesis helpful if it won't be ready as a submitted paper? How much help would it give to a future PhD application (systems bio/computational bio)? What specific programs that are still available? Would I be competitive for such programs? 3. Are there any worthy bio-based (i.e. mcb) master programs still open? 4. Guidelines for looking for jobs as an international undergrad. Is it possible that I can learn how to do more complicated computational analysis even if I had little experience with it before? (Although I can learn from colleagues, I imagine companies will want me to do things that I'm already good at.) 5. Where can I find possible fundings for grad school as an international student? The search engines don't really help much before one is admitted to a program. 6. Any other advice or question? 7. Thanks for reading all these! All the best luck for all of you!
  15. Undergraduate Institution: Cornell University Majors: Statistics Minor: Computer Science Cumulative GPA: 3.61 Student Type: Domestic Asian Male GRE General Test: Quant:169, Verbal: 166, AW: 4.0 Classes: Statistics: Statistical Methods I (A), Statistical Methods II (B+), Probability Models and Inference (A), Statistical Data Mining (B+), Stochastic Processes (A), Theory of Statistics (A), Linear Models with Matrices (B+) Mathematics: Linear Algebra (A), Calculus III (B+), Differential Equations (B) Computer Science: Machine Learning for Intelligent Systems (A), Artificial Intelligence (A), Discrete Mathematics (B+), Java Programming (B) Intern Experience: Two internships in risk and venture capital Teaching Experience: Teaching assistant for intro statistics, and also probability class Other Information: Took several online MOOCs, Officer of Actuarial Society (passed SOA Exam P), did several data science projects for class and in own time Letters of Recommendation: 3 from stats professors, one from machine learning professor Schools planning to apply: Stanford (M.S.), Penn (M.S.), Columbia (M.S.), NorthWestern(M.S.), NYU (M.S.) My Concern: Intern experience doesn't directly relate to data science analysis, GPA is a little bit low (although I believe our school is known for grade deflation?) Programming skill: Python, R, some experience with Java, C++, Matlab Thanks in advance
  16. International student who lacks math/CS coursework looking at PhD bioinformatics programs. I have too many top tier programs in my list, and am looking for "safe schools" to add. However, given where I'm coming from, are any schools really "safe"? Also, any suggestions as to how I should proceed with my non-math/CS background are welcome. Thanks for the help! Undergrad Institution: International university, fairly average Major(s): Life sciences, biochemistry GPA: 3.5 Masters Institution: Top 5 private school, USA Major(s): Molecular biology GPA: 3.88 Type of Student: International Male GRE Scores: Q: 96% V: 90% W: 98% Research Experience: 2 summers of research during undergrad (~ 6 months total) at institutes equivalent to the NIH. in vitro and ex vivo studies (clinical research) 2 year project during Masters (preclinical drug discovery). 1 publication (average journal, impact factor ~4.5) and 1 more on the way (top tier journal). in vitro and in vivo studies 7 months (to date) after graduating (preclinical drug discovery and clinical research). in vitro, ex vivo, in vivo studies. additional experience in bioinformatics. 1 publication on the way Programming languages: R (intermediate), Python(novice) Any Other Info That Shows Up On Your App and Might Matter: Multiple leadership roles Weaknesses: While I do not have formal coursework in math (linear algebra, calc, real analysis) or computer science, I have taken statistics classes in college. My undergrad institution did not allow for that flexibility and I could not take these classes during my masters. Learned those subjects (linear algebra, calc, r, python) through coursera/edX. Recommendations: 1-2 extremely personalized recommendations, third should be average Essay: Working on how to tie my background to my interests Areas of interest: Bioinformatics/genomics/computational biology (Ideally biostat, but thats out of reach given my non math background in my opinion) PhD programs: 1. Washington University in St. Louis - Human and statistical genetics 2. Johns Hopkins - Human genetics, Biomedical engineering, Pathobiology 3. Boston University - Bioinformatics 4. UNC Chapel Hill - BSSP 5. Tri-institutional program (Cornell, Sloan Kettering, Rockefeller) - Computational Biology 6. Columbia - Biomedical informatics + PIBS umbrella program 7. University of Pennsylvania - GCB 8. Emory - Biostatistics 9. University of Washington - Genome Sciences 10. NYU - Systems and computational biomedicine 11. CMU-Pitt - Computational biology 12. Harvard - BBS
  17. Hi guys, I'm from New Zealand and worked for Deloitte Consulting since graduation for the previous 2 years. Undergraduate Institution: University of Auckland (top 1 in NZ but around ~120 globally) Majors: Statistics, Economics and Finance Cumulative GPA: our GPA uses different scale I got 7.5 - 7 stands for A-; 8 for A and 9 for A+. I assumed it was around 3.8? Major GPA: 7.9 Student Type: Asian female GRE General Test: Quant:166, Verbal: 158, AW: 4.0 (will retake again next week as I dont think my GRE is higher enough to get me to top tier?) Classes: Statistics: Mathematical Statistics (A+), Statistical Modelling (A); Introduction to Statistical Inference (A), Stochastic Prcess (B+), Data Analysis for Commerce (A-); Statistical Programming and Modelling using SAS (B+) Mathematics: Differential Equations (A-), Advancing Mathematics 2 (A-), General Mathematics 2 (A+) Research Experience: Worked on a ICU discharge policy under information system departmenet - summer research scholarship, no publication Intern Experience: 1 - Group Strategy Analyst at a global dairy company doing market strategy, digital strategy etc project support 2- Consulting analyst at a leading consulting firm in market research doing market resarch projects Work Experience: Deloitte Consulting in Information & Data Analytics team for 2 years. Project involved: data strategy, data platform, data visualisation and other strategy projects Teaching Experience: Teaching assistant for stage 1 Statistics course Letters of Recommendation: 4 options - havent decided yet! Help please 1: Director of analytics team - strong recommend (team lead) 2: Director of digital team - strong recommend (my coach) 3: Senior lecturer in Economic Department - strong recommend & close; did a few ecnometrics paper with him 4: Profession in Stats - globally well known statistician however not very close; got A+ from one of his class. might be just strong? Schools planning to apply: Stanford (M.S.), Harvard (M.S.), MIT - business analytics (M.S.), Penn (M.S.), Columbia (M.S.), NorthWestern(M.S.), Duke (M.S.), CMU (M.S.), NYU (M.S.) My Concern: work experience doesn't directly relate to data science & data analysis. more relate to data management, strategy (i.e. data migration, data quality, data visualisatoin, overall strategy type of projects) Programming skill: R, SAS, SQL; self-learning Python at the moment Question: 1: any safe schools for recommendations? looking for 1.5 / 2 years programme for data science as I want to build my programming skills & career progression to a data scientist in consulting firm 2: am I over applying to top tier schools? Stanford would be dream! But I think I reached the bar for Columbia / Duke / Upenn - not too sure though 3: help for LOR selection Many thanks!! Appreciate your assitance in advance.
  18. Is it worthwhile to quit my job to focus fully on my master's degree? My master's is currently unfunded, but I have a decent amount in savings and plan to hopefully work a part-time job to offset any loans I may have to take out. My undergrad was in Psychology, and I made the jump to Statistics and CS and the transition has been difficult, but I love it. My current job is unrelated to my field, but it is flexible and is funding my education, which is a huge incentive to stay. However, balancing both graduate school and full-time work is taking a toll on my mental and physical health. I have started having back problems and neck problems from sitting all day at work and sitting all night working on homework. The lack of free-time has also started triggering panic attacks. I've been seeking treatment for these conditions, but it all boils down to stress triggering these issues. I know if I really buckle down, I can get through the semester, but at what cost? Once I graduate, the types of jobs I can apply for pay a lot more than my current job. I'm feeling stuck because the situation I'm in is one that most would envy, but it's seriously effecting my health. Should I quit and focus on graduate school full-time at the risk of incurring debt, or should I stay at the risk of degrading health? Any advice, personal experiences, etc. would be extremely helpful.
  19. Hello everyone, I am applicant from India wanting to apply for Masters program in Data Science/Data Analytics/Computational Chemical Engineering. My humble request is to review my profile and suggest me some universities I should target for applying to the Masters programs. Also, if someone could suggest any chances of obtaining a funded masters opportunity at some universities, it would be awesome ! Thanks in advance ! Profile: - Bachelor of Technology (B.Tech.) from Indian Institute of Technology Kanpur (IIT Kanpur) with GPA of 9.3/10 - Major in Chemical Engineering with Minor in Computer Science and Engineering - 2 years and 3 months work experience as a Data Scientist and Analyst at a BigData Analytics startup - GRE Score : 320 (153 Verbal, 167 Quant, 5.0 in AWA ) out of 340 - TOEFL iBT Score : 116 out of 120
  20. I got accepted into both the MS. Data Science program at the University of Virginia and the MS Analytics program at NC State. Both are 10 months and are rigorous. NCSU's program is more established, but UVA's brand name seems bigger and more focused on emerging tech like R. I hear NCSU is primarily focused on SAS training. Both seem good job opportunity wise, UVA has a 100% employment rate with good companies and NCSU is up there as well, and I'm international so job opportunities after graduating is important to me. Any thoughts on both programs? How are they? Any help is appreciated. Thanks!
  21. Hello there. I've managed to apply to Schengen universities for this fall semester and I've got accepted, roughly into 2 of the best among them, thanks to this forum and their guidance. Now there is some serious head-scratching going on here as to which of these 2 I should choose. Their CS department of these 2 universities seem to be very close to each other in terms of merit. However, my ultimate objective is to pursue a PhD or possibly get directly admitted to work in one of the north american countries (USA or Canada), preferably in data science field. With that in mind, which of these 2 universities are better for my goals, In terms of recognition among north american companies and universities? Is it possible for an international student to exchange with a reputable north american university or company while studying in either of these universities?
  22. Hello Guys, I have admits from KU Leuven for MS in CS as well as Advanced Masters in AI and MSc in Computer Science (Data Science Track) from TU Delft. I am highly confused on which one to accept. I am interested in Machine Learning and AI and want to keep both options of getting a job or pursuing research. Also KU Leuven is inexpensive whereas TU Delft is very much. I have heard getting jobs as a non EU candidate is difficult in Belgium compared to Netherlands. Meanwhile i am also waiting for results from RWTH Simulation Science. I have to decide between KUL or TUD in a couple of weeks if i don't receive any communication from RWTH. I am freaking out. Kindly give your valuable inputs which will help me decide. Thanks.
  23. Zoumana

    Need advices to apply in 2019

    Hi Everyone, I'll be graduated in August 2019 from my engineer degree in computer science. This engineer degree is a study of 3 years in apprenticeship. I did the first year as a software developer, and since september 2017, I continue as an artificial intelligence engineer with IBM. After that, I would like to apply for another master of research degree with a major in computer engineering (Machine Learning). So, I need your advices. Thank you in advance for your help. Regards Zoumana KEITA
  24. Hi everyone, I'm planning to apply for Data Science programs in the Fall, and I was wondering if I have a chance at a Data Science Program. Major: Applied Math Minor: Computer Science GPA: 3.08/4 GRE: Verbal: 151 (52th percentile) Quant: 162 (81st percentile) Analytical Writing: 5 (93th percentile) Relevant Courses taken/taking: Linear Algebra, Databases, Data mining, Intro to Data, Regression analysis, Greatly appreciated, Blue Lion
  25. Hi all, I got accepted to some great data science masters programs and I've narrowed down my choices to NYU's master of data science and Duke's new masters of interdisciplinary data science programs. Background: At a high level, I come from the social sciences/market research analytics world and am thinking that's where I want to continue applying myself in the future. That said, I am open to exploring other applications of data science during my studies if I find another stream of research that catches my interest. Dilemma: Duke's program feels very comfortable for me. The program is housed in their Social Sciences Research Institute (SSRI) and they seem to value a really diverse class of students beyond the traditional CS, Econ, and Tech folk (which is nice for someone like me who does not have a degree in math, stats, cs, or econ). Overall, I think the research is more aligned to my background and my interests than the research at NYU. They also have been extremely welcoming and attentive to me during the entire application process which I can only hope would carry over into the program itself. On the other hand, the Duke program is entire new and seems to still have not worked out a lot of the kinks yet (a required summer online bootcamp that has not been finalized, lack of solid funding budget, etc.) I'm a bit afraid that my class would be a test run with a lot of disorganization. Also, it's no surprise that Durham is not the metropolis that NYC is. When comparing Duke to NYU I have to take into account the networking and internship opportunities available in NYC that may not be available in a mid-size city in NC. Pros: Research fit, welcoming department, price Cons: New program, lack of networking/internships NYU's program is very highly regarded and I'm confident I'd receive a great education and has a track record of successful graduates. I'm very drawn to the energy of NY, and all the culture that comes with it. The accepted student body is very heavy on mathematics and computer science and, to be completely honest, I'm afraid I'll be a way out of my league. NYU's program seems to be much more focused on the science of data science, as opposed to the application. Also, I think it goes without saying but NYC is way more expensive city to live in compared to Durham. I haven't received any funding so it would be all money out of my savings at this point. Pros: Proven program, NYC Cons: Research interests, price I know that either program will be a great choice, but I'd love some thoughts on what you all think would be the best move. Thanks!!
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