2020applicant...

I am writing a response because I went through some similar dilemmas while applying to graduate school. A few points: 1. I think that a key point here is that in undergrad you took a "full calculus sequence, linear algebra, probability & statistics" and that at work you "regularly spend a big chunk of [your] work day in Rstudio." Given this background, you have more options than the average MPP applicant. You could realistically apply to statistics/data science masters degrees, certainly policy MPP masters degrees, and almost any PhD in the social sciences (though specifically for the Econ PhD, a course in measure theory/real analysis may be expected depending on the program). Having this range of options is great but intimidating. As a first step I might casually counsel the following: Regarding the masters degree -> - If you were to pursue the masters level, make sure that you go to a program where you can actually leverage all the work you did in undergrad. E.g. you took linear algebra and vector calc, meaning that you have the requisite knowledge to take statistics/econometrics coursework that is mathematically rigorous. Many MPPs do not offer that type of coursework. Some, like Berkeley and Chicago, allow you to waive core coursework and pursue those more mathematically rigorous alternatives, typically PhD level econometrics. If you were to pursue an MPP, make sure that you can access those types of courses and can waive the typical quant offerings. - Many policy+data analytics masters degrees are, at the most basic level, probably seeking to get the average student up to the level of the advanced undergrad in an econ+data science double major. You are, to some extent, already at that baseline. There is, of course, some variation across programs -- E.G. Chicago's MSCAPP has some rigorous discrete math and data structures coursework* that other programs don't have -- but many programs seem to center around some sort of data wrangling course + basic applied machine learning + applied econometrics core which may not be of interest to you. This doesn't mean these programs have no value in your case, but you would need to be discerning -- 1.) don't pay more for one of these 2.) if you go with this route make sure you can waive core classes where appropriate and have access to high quality, graduate-level electives in Computer Science, Econ, and Statistics Departments. This is true of many schools, especially Chicago and Berkeley. Regarding the difference between PhD and masters level -> - social science PhD-level quantitative training is not, as a rule, more rigorous than at the masters level. Econ is certainly different in this respect, as it is standardized in being very mathematically advanced, but Sociology and Political Science core quant sequences typically don't cover more ground than a quantitatively-inclined MPP student with access to electives around campus, indeed many would cover less. That said, the appeal of the PhD is less the coursework and more the research. You would pursue your research agenda for ~3 of the 5 years, and be forced to learn cutting edge quantitative tools to that end. I know several social science PhDs who took at most 1 applied course in NLP, but are experts on the topic because they then spent 3 years self-studying the topic to finish their dissertation. In the masters route, you would get to take that same 1 NLP class, but you wouldn't necessarily have those next 3 years to obsess over the applications of NLP you have in your research. My view - that is generally where the quantitative gap comes up between the masters and PhD level, less so in the coursework. If you got a masters then worked on advanced quantitative problems, either in research or data science for an agency of some sort, you would be able to similarly continue developing, but these pathways are not the most typical for the average MPP student. - "My end goal is to be a research director type of role." Becoming a research director, in academic labs, or the public/private sectors is certainly associated with having a PhD. That said, I have seen directors who have masters degrees leading quantitative research groups in industry and in government The biggest qualification seems to generally be sustained interest in the topic. People with interest in quant policy research seem to sort into PhDs, and so leaders in that space often have PhDs, but they aren't a hard req outside of the academy. Regarding everything you actually said -> Everything you described about yourself seems to be oriented towards research. You work in research now, you want to continue working in research, and the things you are interested in ("how housing policy affects neighborhood dynamics and resident access to public/private amenities [...] how social and political dynamics affect policy-making and implementation [...] how politicians, media, and just regular folks speech effect the policy-making process.") all sound a little bit more like Sociology/Political Science dissertation topics than things one might work on in government. You can certainly do this sort of work with a masters, but if you really love research and have a research agenda, that is traditionally what a PhD is all about. If you love the idea of using quant tools to implement policy, evaluate and design real programs, and change the social world, then one might lean a little bit away from the PhD and towards the masters, but if you love research and want to use quant tools to do research and gain the respect of other researchers via publication in journals, then there you go. General advice -> Go look up authors on papers from Brookings/Urban Institute/etc. that use advanced tools like machine learning, NLP. etc and see what they did, go look up directors of research orgs you would one day want to lead and see what they did, etc. Feel free to dm if you want more of my perspective, though I certainly wrote like a books worth here. good luck!

Hi, I thought I would give my 2 cents as I was admitted to and did a lot of research into the CMU, U of Chicago, and USC programs you listed. - MSCAPP: I was really impressed with their events and they seem to probably be the best program for getting computational/engineering opportunities IMO, with solid elective access in the computer science department, a cohort that includes people who go into software engineering and comp-sci phds, and the ability to test out of a lot of the intro classes if you come in with existing computer science skills. But it's an expensive program. - CMU MSPPM-DA: I think the coursework looked really interesting, but I got the impression that there was more a push to teach data analysis rather than computer science (which seemed to be the focus at MSCAPP). This makes sense, the CMU program is a joint offering between Policy and Information Systems, whereas MSCAPP is between Policy and pure Computer Science. I liked that they also allowed you to test out of intro courses. Pretty generous on aid. - USC MPPDS: The curriculum looked too rushed/limited to me, and overlaps too much with free MOOCs. Other things: - Some of these data science/public policy programs really focus on teaching programming, which is unfortunate if you already have competencies in any Object Oriented language. At the end of the day I ended up electing to go to a well regarded straight MPP program with no direct programming/software engineering coursework written into the program but extensive elective access in comp-sci rather than going with one of the structured data science/public policy degrees. - There are some cool "applied stats with a focus on social science research" degrees out there that you might also look into, depending on what specific skills you are seeking to build.

Also be aware that CMU's renegotiation form takes into account program cost and area cost of living when evaluating your other offers, so even if another school didn't technically give you more aid, check to see if its program cost is lower or the area is cheaper than Pittsburgh - it might still be a cheaper offer and something you can negotiate off of.

Just to encourage everyone to renegotiate I wanted post that I worked my MSPPM-DA offer up from 60% to 75% after submitting offers from other schools using the excel/form they sent admitted students on ~March 10th.

Program Applied To (MPA, MPP, IR, etc.): MPP, MSCAPP, QMSS, MS, MPA, MA Schools Applied To: I got fee waivers so I was a bit excessive. Stanford GSE (MS Education Data Science), Princeton SPIA (MPA), Yale Jackson (MA), Berkeley Goldman (MPP), Chicago Harris (MSCAPP), UCLA Luskin (MPP), UCSD GPS (MPP), USC Price/Viterbi (MS Public Policy & Data Science), Columbia GSAS (QMSS), Carnegie Mellon Heinz (MSPPM-DA), NYU Steinhardt (MS Applied Statistics for Social Science Research) Schools Admitted To (ordered cheapest to most expensive): UCSD MPP (100% + stipend/health insurance), NYU MS Applied Stats (50%), Carnegie Mellon MSPPM-DA (60%), UCLA MPP (in-state), Berkeley MPP (in-state), Harris MSCAPP (30%), Columbia QMSS (0%), USC MSPP&DS (0%) Schools Rejected From: Yale MA (Waitlist), Princeton MPA, Stanford MS Still Waiting: n/a Undergraduate Institution: A highly ranked U.S. public school Undergraduate GPA: 3.3 Undergraduate Major: basically Poli Sci. Took stat1, econ1 in college, took a couple computer sci courses post-college GRE Quantitative/Verbal/AW Scores: 158Q, 164V, 5.5AW (took it in 2017) Years Out of Undergrad (if applicable): ~5 years as of now Years of Work Experience: ~5 years full time as of now Describe Relevant Work Experience: 1.5 years tutoring at a small domestic nonprofit, 2.25 years in Peace Corps, 1 year in evaluation at a large domestic nonprofit Strength of SOP (be honest, describe the process, etc): I had enough relevant work experience to tie my stated interests to professional anecdotes Strength of LOR's (be honest, describe the process, etc): I didn't read them. Peace Corps Post Senior Staff/Current Supervisor/Seminar Professor from undergrad Other: I want to contribute my info for posterity, but with this post I'll add a note @ future applicants: please don't limit yourself based on what folks on this forum (including me) say. Take your time on your app process and aim higher than you think you should - there's always an element of randomness to this stuff.

cool - thank you!

just out of curiosity - did you receive funding information via email or portal? Thanks, and congrats!

I think that one factor could also be that schools are sending out results and final funding numbers a little bit later this year (at least from what I can gather).

I received an acceptance this morning, but it appears that it comes with no funding

There's a zoom Q and A happening right now regarding funding at GPS. I believe the zoom link was sent in an email in ~February

The program is MPP Data Science, so I'm not completely sure if that means MPP results are also forthcoming? For reference, I applied on December 9th. Also I'll note that my portal is not updated to reflect the decision - I received it through email.

I received an acceptance for the MPP-DS program this morning. No funding information included...

I would consider attending GPS with no financial aid if, come April, it ends up being the cheapest option available. Full in-state tuition at UCSD as a CA resident could end up being a cheaper overall package for me than other schools even with aid (for reference, I was admitted to Harris with modest aid and that is still significantly more expensive than UCSD with none). My views are colored by the fact that my career goals involve being in California and I like the program's curriculum. With regards to career outcomes, in my opinion their employment statistics are a bit vague, but I found it useful to browse LinkedIn to get a sense of how many GPS alum work in the fields/geographies of interest to me and then compare that to other schools.

I also received an acceptance with no mention of funding. Take this with a grain of salt, but based on a quick look at past years' UCSD GPS threads, it seems that funding information may come in March.

I also just got my admit to mscapp and have done a fair amount of research on data science oriented policy programs. I would offer the following: - I emailed the USC public policy data science program a few months ago about the funding options in the program and they informed me that the aid options are very limited - they don't offer deans merit (a major funding source that MPP applicants have access to). This raised a red flag for me. Also, the curriculum itself seems pretty similar to the MSCAPP, but it's also only 1.5 years, rather than 2 like the MSCAPP. - I also looked a lot into schools of Information, especially Berkeley's MISM program, where the program's focus is much more on data science and design, but they offer a policy concentration. These may appeal to you as well. Best of luck