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  1. Hi, I am planning to go for a CS or Data Science degree in Canada in winter 23 or fall 23. CGPA: 9.10(tier 3 university in India), IELTS: 7.5, Work experience: 10 months full time plus 3 internships(one research based), Decent LORs, Better SOP What universities should I target. I am interested in both MSc and MASc courses. Any help would be beneficial. Thanks.
  2. For context, I'm a second-year DA student at Heinz who came into this program wanting to be a data scientist. During a fall visitation weekend and through multiple emails with staff during the application process, I asked if it was possible to become a data scientist through this program. I got vague assurances at the time that this was possible. From my experience and the experience of my peers, this program is not worth it if you want to be a data scientist. Please do yourself a favor and look elsewhere, because I really wish that I had. Here’s a couple of reasons behind my above assertion: There are too many required classes that provide little to no useful value. For instance, if you have less than 3 years of work experience, you’re required to take a public speaking class for 6 weeks, and you are required to take a 6 week course to learn how to write policy memos. Furthermore, you have to take a 6 week course in “organizational design and implementation” where you read HBS cases and decide how you would have approached them. Finally, you have to take 6 units of a “finance” class that is a really basic class where you just learn how to read financial statements. This is not helpful at all for someone who wants to be a data scientist. The rigor of classes here is… questionable. There are a few classes, namely the required Database class for DA students and Big Data and Large-Scale Computing that are actually rigorous, well-taught, and useful. The rest of the classes either provide too surface-level of an approach to the topics discussed or try to tackle a topic that really should have been covered in a semester in 6 weeks, leaving you with little real comprehension of a topic without a lot of outside work on your own. There is a lack of understanding in many classes of real-world tools that should be incorporated in class. For instance, it would make sense, particularly in the programming intensive classes, to mandate the use of GitHub or similar tools for practice with version control. Anyone who is going to program in a real data science job will use GitHub/GitLab and therefore it would be useful to become much more skilled in it through a grad program. This would also help to easily create a portfolio of work to show future employers. Also, (this has changed) but for my class, we were taught machine learning/data mining in R instead of in Python (scikit-learn) which is more often than not the industry standard. There are a few classes taught by professors who have more non-academic experience who help to clue students into the real world tools but this is pretty rare. Heinz College in general is trying to be too many things at one time and therefore is unsuccessful in being particularly good at one particular thing. The biggest reason peers of mine and myself chose this program over something like UChicago’s MSCAPP program is because of Heinz’s financial aid which is substantial. It was substantial enough for me that I will graduate without any student loans, for context. I am doubting, however, whether the financial savings are worth the lack of educational rigor and course flexibility offered by Heinz. My advice here would be to apply to the MSPPM-DA program for the financial aid package and use it to try to get a better financial aid offer at UChicago (MSCAPP), Georgetown (MSDSPP), or some other comparable program. I'm happy to answer more questions about this! I really wish someone had been much more direct with me in the application process about who this program is best suited for. I don't want someone else to make the same mistake that I did and leave the Heinz program being as frustrated as I am with my overall experience.
  3. Applying for PhD in statistics/biostatistics after working in industry for a few years as a Data Scientist - would appreciate any thoughts, feedback, or advice on programs below given profile/research interests. Undergrad Institution: US Top-5 in Statistics Majors: Statistics, Applied Math GPA: 3.91 Type of Student: International Male Math Courses (All A's): Real Analysis, Complex Analysis, Linear Algebra 1/2, Abstract Algebra, Numerical Analysis, Differential Equations, Calculus 1/2/3, Discrete Math Statistics Courses (All A's): Stochastic Processes, Time Series, Experimental Design, Linear Modelling, Data Science 1/2, Probability Theory, Statistical Computation Computer Science (All A's): Algorithms, Machine Learning, Deep Learning, Databases GRE: 168 Q | 163 V | 5.5 W Research Experience: 2 years in applied statistics (3rd author publication in lower-tier journal - did most of the coding), 1 year in sociology (no publications - mostly database management) Work Experience: 3 years as Data Scientist at large tech company Recommendation Letters: 2 from research advisors (strong letters), 1 from professor with multiple classes and strong performance (mediocre letter) Coding Experience: Python (expert), R (experienced) Research Interests: Causal inference, applications to social sciences (specifically education/public policy), applications to public health policy Programs Considering: UC Berkeley Stats PhD Harvard Stats PhD CMU Stats PhD UCLA Stats PhD UC Santa Barbara Stats PhD Harvard Biostats PhD Penn Biostats PhD Brown Biostats PhD MIT Social & Engineering Systems PhD NYU Data Science PhD Are there programs here which don't sound like a great fit with my interests and profile, or any not here which could be a fit? I recognize my list is a top-heavy, but I'm satisfied at my current industry job and would go back to school only for a relatively well-regarded program, with the end goal of tenure-track professor at a R1.
  4. I'm really uncertain of where my application falls. I was hoping I could get advice on which schools (e.g. rank wise) I should target, and which schools are likely to be a safety or reach. TIA I'm interested in applying to professional Masters in Data Science Programs (online and in person), with a focus in spatial analytics if possible. If your application is similar, feel free to share where you're applying to. GPA: Overall ~3.4-3.5 (1.98 first year, transferred & changed majors, 4.00 2nd - 4th year). Major GPA is 4.0. I had to overcome a lot of personal issues in 1st year which contributed to the low GPA. Major: Data Science (1/2 computer science, 1/2 statistics) Research work 1.5 years in self-directed research supervised by business faculty member. Not very technical, focus on managing implementations of data driven tech in government. I'm the first author, faculty member is the other author. Manuscript in publication, highly renowned journal. 2nd research project supervised by geography faculty member & is conference paper. 3 months work exp. so far, will be 6 months total. Curated and led a workshops about my research topic for an upper level class, with a few more scheduled until the end of the year. Other info: Canadian, first-gen immigrant, female Undergrad description: 3rd tier, low/medium research output, Canadian University GRE: taking my first exam this August, predicted scores are: low/mid 160s quant, high 150s Verbal, 4.5-5.5 Analytical. Really gunning for mid/high 160s for quant but we'll see. Other work experience: 2yrs construction project management, emergency flood restoration. I was working part time before and during first year. Upcoming job placement: (Jan 2022 - Sept 2022) hired as a Data Science/Data Analytics intern (fortune 500, not very well known though) Extracurriculars: professionally accredited classical pianist, volunteered 3x a week 2 years, contracted for a variety of events I've heard people say that you can jump 1 rank at most based on the ranking of your school (e.g. 3rd tier to 2nd tier) - how accurate is this rule of thumb?
  5. I have MS CS admits from ASU and UC Davis , I'm more inclined towards data science , which would be a better choice in terms of courses and opportunities for the same ? (I'm aware that Davis is more reputed , however there seem to be very few data science related courses and not many graduates from here seem to have taken up jobs in this domain)
  6. I was wondering if anyone had any input about deciding between Georgetown and Carnegie Mellon's data science/public policy programs. I've gotten into both and am having a hard time deciding between the two - it seems like the Heinz program is more intense which could be either a positive or negative - I know I want to best prepare myself for a career but also want to be able to have time for a job/social life. I've seen that McCourt is a little lower ranked than Heinz but has the benefit of being in D.C., which appeals to me. Does anyone have any thoughts?
  7. Right now, I am choosing between the following for data science/analytics: -NYU: MS Data Science -Columbia: MS Data Science -USC: MS Applied Data Science -UChicago: MS Analytics -Northwestern: MS Analytics Having lived in the NYC area my entire life, I am leaning towards Northwestern/UChicago to try a new place. Any input specifically to which of these two programs (or any of them) might be better? I have looked extensively at both and think either would be great.
  8. I was wondering if anyone had any input about deciding between Georgetown and Carnegie Mellon's data science/public policy programs. I've gotten into both and am having a hard time deciding between the two - it seems like the Heinz program is more intense which could be either a positive or negative - I know I want to best prepare myself for a career but also want to be able to have time for a job/social life. I've seen that McCourt is a little lower ranked than Heinz but has the benefit of being in D.C., which appeals to me. Does anyone have any thoughts?
  9. Hey all, First and foremost thank you for reading my profile evaluation. I'd love to hear your feedback on my stats, the programs I've selected, my chances of admission, and if there are any programs that you'd suggest I consider given my profile. I am a student with a non-STEM background and I have the bare minimum prerequisite courses to apply to these programs (with good grades thankfully). I am hoping that through my GRE score and LOR/SOP (which I'm still writing) I'll be able to catch the attention of at least one admissions committee, and secure a spot in one of these programs. My ultimate goal is to acquire the technical background necessary to transition from Finance to Data Science. Thanks for your time, and I'm looking forward to hearing what you all think! Undergrad Institution: Large State school Decent/Good Business School GPA: 3.64 Majors: Business Admin - Finance GRE General Test: Q: 168V: 164W: 5.0 Relevant Course Work: Calculus I & II (A-,A) Business Stats (A-) Linear Algebra I (A) Intro to CS (A) By the time of matriculation (As in I'm planning on taking these in the near future): Calculus III, Multivariate Calculus Data Structures and Algorithms Type of Student: Domestic Student (native US) Programs Applying: Masters in Data Science mostly, some Masters in Analytics. My goal is to pivot careers into DS. Research Experience: None (as you probably guessed given my background) Letters of Recommendation: One great one from business capstone professor Two very good ones from internship in market research & Analytics department (One Data Scientist, One Marketing Manager) Work Experience: Analytics Intern (5 months)– became proficient with Alteryx, Tableau/Power BI. Experience in manipulating/transforming/cleaning large datasets which I'd then use to make dashboards and track KPIs. Started learning Python at the end of it under the tutelage of a full time DS. Financial Analyst (1.5 years by matriculation) – several high impact projects that look good on my resume. Lots of face-time with senior management explaining business impacts of whatever data I’m requested to work on. Used some Python scripting to automate reporting, built semi complex models in Power BI. My current list: NYU – Masters in Data Science University of Chicago - Masters in Analytics University of San Francisco - Masters in Data Science Brown – Masters in Data Science Georgia Tech – Masters in Analytics Northwestern – Masters in Analytics MIT – Masters in Business Analytics Carnegie Mellon University – Masters in Data Science Columbia – Masters in Data Science Duke - MIDS (Haven't researched this one that much yet) I feel that I'm reaching quite high, but am hoping that with some luck I'll hear back from at least one program. If anyone has any opinions or advice I'd love to hear it (even if the truth hurts). If you have a program you'd like to suggest, I'd love to hear that as well! Thanks again! Two_Dicey
  10. Hi All, Just wanted help in evaluating my profile for Data Science/Analytics/Business Analytics Programs SCORES: TOEFL: 115 GRE: 165(Q) (Worried about this one), 162(V) Work Ex: Analytics - 18 Months - One Research paper implemented + Preparation of analytical dashboards from large datasets to track KPIs Internship(Data Mining) - 2 Months - Sentiment Analysis + Topic Modeling Education: Graduated from a Tier-1 University(India) with 8.46/10 GPA (B.Tech - Engg. Physics) Current Pool: NYU (Data Science), Columbia University (Data Science), UCLA (MSBA), Georgia Tech (MSBA), University of Michigan (Data Science), MIT (MS Analytics), Imperial College London (MSc Statistics (Data Science)/Msc Business Analytics) University of Pennsylvania (MSE in Data Science) University of Texas Austin (MSBA) University of Warwick (Msc Data Science/Msc Data Analytics) Northwestern University (MS Analytics) University of Chicago (MS Analytics) Harvard University (MS Data Science) University College London (Data Science/Analytics) Northwestern University (MS Analytics) The above pool is quite large. I want to understand how I should evaluate my chances of getting an admit with my profile/target the right universities. Any help will be appreciated. Thanks!
  11. I was wondering if anyone knows anything about how the data science/analytics + public policy Masters programs compare with each other and how they're regarded in the policy world (esp. social policy)? I recently got into the Harris MSCAPP (Computational Analysis and Public Policy) program at UChicago and it's my top choice, but I'm also considering applying to some other programs (below). Let me know what you think! Georgetown McCourt - M.S. Data Science for Public Policy CMU - M.S. Public Policy Management: Data Analytics Track UPenn - M.S. Social Policy + Data Analysis Certificate USC - M.S. Public Policy Data Science
  12. Hi All, Just wanted to understand how I should evaluate my chances of getting an admit with my profile/target the right universities. SCORES: TOEFL: 115 GRE: 165(Q) (Worried about this one), 162V Work Ex: 18 Months(Analytics (One Research paper implemenated/Preparation of analytical dashboards from large datasets to track KPIs) + Internship(2 Months(Data Mining Education: Graduated from a Tier-1 University(India) with 8.46/10 GPA (B.Tech - Engg. Physics) Current Pool: NYU (Data Science), Columbia University (Data Science), UCLA (MSBA), Georgia Tech (MSBA), University of Michigan (Data Science), MIT (MS Analytics), Imperial College London (MSc Statistics (Data Science)/Msc Business Analytics) University of Pennsylvania (MSE in Data Science) University of Texas Austin (MSBA) University of Warwick (Msc Data Science/Msc Data Analytics) Northwestern University (MS Analytics) University of Chicago (MS Analytics) Harvard University (MS Data Science) University College London (Data Science/Analytics) Northwestern University (MS Analytics) The above pool is quite large. I want to understand how I should evaluate my chances of getting an admit with my profile/target the right universities. Any help will be appreciated. Thanks!
  13. Hi all! I'm a nontraditional applicant coming from a biology and engineering background and wondering if I could get some advice on what schools I should apply to given my background? I'm applying to MS in Data Science and Statistics programs for Fall 2021. My goal is to transition to an industry position in Data Science (or related). I've been pretty strong with the math classes I've taken so far, but I worry that a lack of more stats and proof-based courses puts me at a disadvantage. Ethnicity/Demographic: Male Caucasian (European Background) Undergrad 1: ~95-100 ranked US University (known well in region) Major: Bio/biomedical Engineering GPA: 3.76 overall. Transferred out after 2 years. Coursework: Multivariable Calc (A+), Linear Algebra (A+), Differential Equations (A+), Intro MATLAB Courses I, II (A, A) Undergrad 2: ~Top 45 US University (known well in region) Major: Bio/biomedical Engineering GPA: 3.38 overall Coursework: Linear Algebra + Differential Equations (needed to take again due to credit issue) (A-), Statistics and Experimentation (A), Partial Differential Equations and Fourier Analysis (B+) Note: GPA took a hit due to depression junior year for personal reasons - the lower grades were in major-related courses and not in mathematics/stats courses Research: Decently strong - REU in materials engineering, 1+ year work in biological engineering research labs, and 2 research-based internships in industry (total exp ~2.5 years). Letters: Strong letter from a senior advisor/upper level professor who may be able to explain the grade drop and my significant contribution to a team thesis - can attest to computational and quantitative ability; Letter from internship supervisor (worked there for 8-9 months) who I know well and can attest well to my computational ability; Letter from a humanities professor from 1st university I know well who can round out my application/background with a strong letter explaining my writing/communication for industry GRE: Taking soon, but should be around 166+ Q, 160+ V. Currently: Graduated, Taking a Gap Year - Working on computational biology research project with global team of scientists. Enrolled in a Top Ranked Data Science Bootcamp to prepare myself well for grad school (specifically the DS Programs) and to pursue full or part-time jobs prior to Fall 2021. Based on this, I am wondering what tiers of schools would be best to apply for in stats and data science? Is my list too top-heavy, and if so, what other programs should I consider? Again, my goal is to transition into industry (preferably tech) and not to earn a PhD. Thank you! Here is my list so far: UCLA MS Stats Duke MS Stats Duke MIDS UCSD MS ECE + Data Science Columbia MS Data Science JHU MSE Data Science Berkeley MA Stats UW (Seattle) MS Data Science UPenn MSE Data Science Vanderbilt MS Data Science USC MS Applied Data Science USC MS CS (non-CS BS) UMichigan MS Data Science Please let me know if I should trim and/or what other programs to consider in stats or DS! Thank you!
  14. (Please consider scrolling to the bottom for some of my concerns. I suffer from anxiety and I know I'm needlessly stressed, but any pointers are immensely helpful.) Student Type: International Asian (Brown) Female Undergrad: Small university known in academic circles Major: Computer Science (might complete my math minor next sem) GPA: 3.91/4.0 (Major GPA 3.94/4) Relevant Courses: Calc 1, Multivariable Calculus (2&3 combined), Linear Algebra, Probability & Stats, Intro to ML, Advanced ML, independent study (somewhat of a research project, basically assisting a professor) focused on ML. A's in all except calc 1. I'll be completing a Capstone Project in privacy-preserving ML and an intensive math course on data science in the fall and a stat inference course in the Spring. A's in all other CS/math courses except for 2 systems courses. GRE: Taking it next month, hoping for a 166+ in Q, 160+ V Research: The independent study project I assisted on would have resulted in a paper but COVID delayed things terribly. I'm going to continue some work on it if possible over the next few months maybe send it to a conference. If I contribute to the literature during the capstone project I get to convert it into a thesis the sem after that, hopefully get it published (though I doubt that as of now) Work Experience/ Projects: 3 coursework projects using deep learning (are coursework projects given any emphasis?) Data viz/ analysis intern briefly at a GIS lab Data analysis intern in the marketing division of an investment firm/bank Couldn't intern this summer so personally working on mini-projects to integrate prescriptive analytics/ operations research concepts into the DS pipeline (do personal projects matter? I know they help for jobs but not sure about masters) Not too relevant but also: TA for 2 semesters (maybe 3), interned at a consulting firm and was in charge for all the data collection/ scraping for a project with basic analysis using Excel Letters of Recommendation: 2 (hopefully) strong who I've known well and worked with extensively (one of them was a professor at a top 5 CS school in the US), 1 moderate-strong who will write good things but I haven't worked with them as much Possible programs: MS Data Science; MS Analytics (only those with freedom to take computation-heavy courses); MS CS with a concentration/ specialization in ML (course/project based not research based, or professional programs) (I think I should crosspost this in CS?) Possible (REACH) schools I may apply to (not all): UPenn (MSE DS), CMU (MCDS), Northeastern (MSDS); professional masters in CS with ML track programs perhaps at UIUC, UW Madison, UCI, UToronto (MScAC), Cornell MEng; Georgia Tech (MS Analytics) Concerns: I know I have good stats and can get my foot in the door but I'm worried because I don't have significant work experience which seems to be important for some DS/ Analytics/ professional MS programs. Though I do have experience with DS related stuff (I decided early I wanted to get into it), I don't have "proper" research experience/ papers either, which seems to be a hurdle for traditional MS programs, even if course-based. I don't know what to emphasize besides some coursework projects and some limited experience outside school. I am confused where I have a better chance - DS/Analytics or CS, which is why programs with a good mix like Penn or CMU are super appealing. Need suggestions for: Finding good safety/ moderate range options, either for professional/course-based MSCS with ML, or MSDS with computation heavy courses/ freedom to take electives. There are plenty of good analytics options but given that I have studied CS and even ML, paying $5k+ to take intro courses isn't too appealing (one exception is the GaTech course that has business courses alongside a computational track). For example, UW MSDS and Columbia MSDS have lots of stuff I've already studied. On contacting Northwestern's MSiA program they said they may or may not waive the Intro to Python/Java requirement, which is ridiculous. Freedom to take electives would be great. I am open to Canada as well, though I'm not sure yet. Also - I am somewhat concerned with brand names because I may move elsewhere after the STEM OPT visa, though it isn't a top priority. If you know about programs in the UC system that fit and are within reach, those might give me the best brand/quality/buck ratio, besides GaTech, though I would prefer being in California. I am interested in getting into the industry but I'm not restricting myself to just tech, retail/marketing/consulting with good DS departments would be fun too, so some business courses are also welcome though not a dealbreaker. Freedom to take electives from Industrial Eng/OR/MS would be nice.
  15. Hello! I'm currently a rising sophomore with a tentative goal to do graduate studies in Statistics or Data Science. I was wondering what pure/applied math or stats upper division courses you would recommend that would give me a firm foundation? Here is the math course browser: https://www.ucsd.edu/catalog/courses/MATH.html I was thinking about taking one of the Algebra classes with Intro to Numerical Analysis, but I am not sure. Thanks in advance!
  16. Hi everyone! First time posting here. I was looking for some feedback on my profile and what kind of universities I should be targeting. I'm not looking for a PhD, want to gain better knowledge of Data Science to improve my career prospects. Looking for universities in both US and UK. Degree: B.E. (Hons.), Electronics & Instrumentation Engineering, BITS Pilani Goa (Tier 1 university in India), CGPA: 7.8/10 GRE General: 336 (Q 170, V 166, AWA 4.5) Had taken the TOEFL in 2018, will need to take it again this year. Score last time around: 112 (W:29 R:29 L:29 S:25) Grades in Relevant courses, converted to US equivalents: Mathematics - I (Linear Algebra & Geometry): A, Probability & Statistics: B, Mathematics - II (Calculus): B+, Mathematics-III (calculus): B- Work Experience: 3 years so far as a data scientist (2 years in an analytics consulting firm, 1 year at a startup working with a sports franchise - this is the area I would like to proceed with). Another year before I go. Research Experience: None LOR: a professor I had a project under in college, my manager at the consulting firm and my current boss (CEO of the startup). I'm open to consider any programs in the US or the UK. Thanks!
  17. Hello everyone! So I've been accepted into both schools online Data Science/Data Analytics Engineering programs. I'm having a difficult time deciding which to pick. My end goal is to move into a PhD program in computational neuroscience/cognitive psychology upon completing the program. I didn't expect to get accepted into UVA so once I got GMU's acceptance I accepted and was registered for my first class so if I decide to switch programs it would be a bit of process. A few points to consider: - UVA's program will leave me with double the out-of-pocket(~14.5k vs 7.3k) cost even after my current employers generous tuition reimbursement (10k per calendar year). - GMU has a start date of June 1st and UVA is for Fall 2020. - Both programs would have the same graduation date of Spring '22 despite staggered start times - UVA offers two machine learning classes while GMU does not - UVA's curriculum only allows for two electives but GMU's allows for half your course load to be elective I guess I'm kind of caught up on the out of pocket cost to justify making the switch but in the end I want to make the best decision for my future academic goals. Thanks for reading!
  18. 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... 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.
  19. Hi, I did my engineering in electronics and have 3 years of work-ex as a data scientist. I'm interested in working as a data scientist in a social impact firm upon graduating and I'm quite confused between the mentioned programs. I have received 50% scholarship from both programs at UChicago and none from NYU. Any insights into any of these programs would be greatly appreciated!
  20. Hi everyone, I'm confused between two programs - 1. MS in Data Science at NYU CDS (No aid, $80k tuition for 2 years) 2. MS in Computational Analysis and Public Policy at the University of Chicago. ($40k aid, $60k tuition for 2 years) As an international student with background in data science in the tech industry, I'm keen on working as a data scientist in social impact firms. Understandably such jobs are rare in the US and I'm concerned about the financial aspects of repaying the loan. The NYU degree will help me land a well-paying job after graduation using which I can pay off my loans. However, policy is a field on interest to me and I would like to study the same at Harris. Any advice would be much appreciated.
  21. Hi everyone, I am an international student accepted to these two programs for Fall 2020. I am struggling to decide which one to go. 1. Georgetown's MS in Data Science and Analytics program (no financial aid, $70k total for 1.5 years) 2. UChicago's MA in Computational Social Science program (interdisciplinary focus) (2/3 financial aid, $40k total after aid for 2 years). My goal after graduation is to find a data scientist position in consulting/tech industry. I plan to work for a few years in the U.S. first, and then apply to PhD in social science (business, public policy) down the road. (I am interested in the research of the social causes and impact of innovation in the private tech sector). Reason for Georgetown: 1. A data science masters degree will lead to higher income and better job prospect than an MA in Computational Social Science. 2. I currently work in D.C. and have established network here. I personally like D.C. more than Chicago. And the job market at D.C. will be better than the market at Chicago. Reason for UChicago: 1. cheaper tuition ($40k for 2 years vs $70k for 1.5 year) 2. My long term goal is to pursue a Ph.D. in social science, hence MA in Computational Social Science is more relevant in the long run. 3. the teaching quality at the program in UChicago will be significantly higher than that at Georgetown. (1/3 of courses at DS program at Georgetown are taught by adjunct faculty). 4. 40 students cohort at UChicago's program vs 80+ student cohort at Georgetown. And the academic background of student cohort at UChicago's program will be higher than those at Georgetown. I really appreciate advice and insight into these two programs. Thank you!
  22. I received offers of admission from UChicago Harris (MSCAPP) and CMU Heinz (MSPPM: DA track), and I'm certain that there are others out there facing the same choice. For what it's worth, Heinz has offered more funding than Harris (once again, I doubt that I'm the only one in this situation). I'm not even sure how to weigh the pros and cons of the two programs. To people who are currently in one of the programs, how do you feel about it? And to those trying to decide between the two options (or held offers from both places in previous years), what factors did you consider?
  23. I am admitted to the master of data science program at Harvard and MIIS at CMU. I am trying to decide between the two programs. Harvard's brand name is really attractive, but I don't know if a master is considered an alumnus. And also CMU is number 1 in CS field. I went to Umich for undergraduate and I majored in computer science. My current career goal is to become an SDE or data scientist after graduating from grad school. But I didn't completely rule out the possibility of getting a Ph.D. I would really appreciate any advice!
  24. Just wanna start a post for Columbia Data Science 2019 fall applicants. Has anyone heard back yet? Or what is your process?
  25. I'm interested in applying to Stats and Data Science MS programs. The USNWR and AMSTAT top schools list is helpful (for stats), but I'd like to see a larger list. I've come across some interesting programs just by looking up x school (not on the best ranked lists) and looking up programs they're offering. Does anyone know of any resources that post a list of all data science programs and/or statistics programs in the United States? This resource from CSU East Bay is kinda what I'm looking for, but it's dated. Looking up data science programs is trickier-- no USNWR or AMSTAT lists, but instead a lot of "20 Best Value Schools for Data Science Masters" type results. A little bit off-topic, but should I just stray away from considering schools that don't score highly on USNWR (or are even unranked)? It seems like a ton (most?) of the posters on GradCafe are only seriously considering Ivy-League or similar status schools. I'm sure the elite schools consistently beat out lower ranked schools across most metrics, but is it to the point where I shouldn't even consider the lower ranked?
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