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Yes, I was close friends with ~3 students that did it my year. It's a very solid program and is very similar to the MSPPM-DA program, just subtracting the policy courses, adding a sprinkling of healthcare ones, and a couple of additional analytics requirements. My friends that did it all ended up with super cool opportunities (one in an IT rotational program at a big pharma, one as an analytics manager at one of Walgreens / CVS, and one as a data scientist at one of the mega hospitals in Boston). The program seems well run, but can be restrictive given the number of required courses and the fact that you have a much smaller selection of capstone projects in relation to most of the rest of Heinz students.

Congrats on a couple of great offers! If you're not in the DA program at Heinz, you're not going to really get the foundational courses that you need to excel as a data analyst, much less as a data scientist. I understand the concern for not having a strong enough quant foundation, but if you just want to take courses from the DA curriculum, that doesn't really go away. In fact, the problem probably gets worse as you're in the classes with DA students that are taking more classes in the area and those that have some overlap in skills / material. Most flagship MSPPM students that are interested in tech will take 2ish entry-level analytics classes as electives, but they are considered stressful courses that are spread over the semesters. I don't mean to discourage you, but I think I'm just trying to caution you against misaligning your hopes for the program with what it sounds like your proposed path might actually be able to provide. I loved my time in Pittsburgh and did it without a car as well. The bus will generally be sufficient, but definitely make a friend with a car so you can do excursions outside of the main city!

This isn't the right forum for that (this is predominately an MPA/MPP area). You'll be better off here: https://forum.thegradcafe.com/forum/36-political-science-forum/

1. You can go here to access the most recent employment report which includes mean, minimum, and maximum salaries. This page includes past years if you want to look for more data points. 2. I think this is two separate questions. Can the MSPPM course benefit those without prior knowledge of data science and programming? Of course. Nearly everyone who comes into the flagship program is non-quantitative (as opposed to the DA program where most students have some quantitative background). Students come out of the program with pretty solid outcomes, as you can see in the employment reports above. The second question is can you learn to program competently in this program? No, like you said not in as little time as the flagship program devotes to it. You get 2-3 half-semester courses that dabble in different languages (R, mostly but also a bit of database management tools and some students take Python as an elective). This exposure can prepare you to take more classes in a language if you want while you're here and can give you a launch point if you need to use them in a job after Heinz, but this program isn't supposed to turn you into a programmer or a data analyst. That's what the DA program is for. 3. As far as I know, there isn't a great way to negotiate without competing offers. You can of course try, but the spreadsheet template that they send out is their preferred way of evaluating potential increases and that's predicated on having competing offers. You can always make a case without it, but I haven't heard of many success stories.

Going to agree with what GSG has said. It's too much time and money to come here and learn things you've already learned. Before you focus too much on MBAs/MPPs though, I might just see if you can apply for the types of jobs you're interested in in the public sector. There is a dearth of strong data-professionals in the public sector (hence the growth of programs like the MSPPM-DA and MSCAPP, etc.), so finding someone with real-world experience is likely pretty rare. You honestly might be able to bypass graduate school altogether (plus, it's not like it costs you anything). If you get one of the jobs, great! No wasted earning years, quicker career development, and you're getting to make an impact sooner. If not, no worries you can still be working on your MPP/MBA applications and gear up for those next cycle.

A couple main thoughts here: 1. You'll want to take the intro to ML course offered by the CS department. It's foundational and everyone from the DA program that takes it likes it, but also feels a bit overwhelmed by it. If you're serious about DA/ML then taking this is what really sets on the path to true data science as opposed to just being very capable at data analytics. 2. The CS department has limits on the number of classes that you can take there if you're not a member of their programs to attempt people from trying to "sneak in." I believe the limit is 24 units (a whole semester course is 12 units, so after taking the Intro to ML course, you'll have either 2 half-semester courses or 1 full-semester course left in the CS school). However, there are some solid higher level offerings in Heinz that will scratch your itch (Unstructured Data Analytics, Machine Learning Pipeline, Big Data & Large Scale Computing, Machine Learning for Public Policy, and a couple more that I'm sure I'm forgetting). If you have extra units, you can also take ML classes offered outside of CS or Heinz. Engineering in particular is supposed to have some good ones and I know there are some strong computational biology courses with ML as well. The long-and-short of it is that if you exempt enough stuff, you can definitely take some external courses, but you'll be capped how many you can take in the CS department, but if you want to push yourself there are plenty of more intense Heinz classes or elsewhere in CMU if you look hard enough.

They both exempted a few classes each, which is actually pretty important for flexibility in the DA curriculum. You only have 12 units (the equivalent of one full-semester course) of true, fully elective credit. Exempting economics, stats, and the politics course add 30 units of space (there are other things you can exempt, of course, but those are just some common ones). There's a PhD econometrics class that can fill one of the required courses and is definitely something that I would recommend. Other than that, I think it's what you would typically think: do well in courses, try and take challenging courses to give yourself a foundation in your topic of interest, identify influential professors and get to be close with them, find research positions and get published, etc. Besides that, I don't think it's anything too unusual.

Anecdotally, I know 2 friends from the DA program that are going into PhD programs. Having the quant skills of either of these DSPP or MSPPM-DA sets you very well apart from any other applicant to a public policy PhD program tbh (a lot of them will be polisci undergrads that don't know what they want to do with their life). I doubt many AdComs for public policy will differentiate much between a data science and data analysis background (though to be crystal clear, this is pure speculation and not anything that I know for sure). On the note of Heinz alums who get data scientist titles post grad, I think there are three points: Some students come in with a true purpose to learn data science and go all out. They take all of the hardest courses in the area, take some in the CS school, they exempt out of everything they can, and they sandbag their management courses so that they can make the most of their other courses. You can get a lot deeper knowledge this way, but it requires intent and a lot of willpower. There's a lot of blurring in data science around titles and what the various things mean. To be frank, a lot of organizations wouldn't know the difference between a data analyst and a data scientist. Obviously Facebook and the like do, but a random 200 person company with an analytics department of 3 people certainly doesn't and they think a data scientist title sounds better to attract talent, so that's what they do. Related to attracting talent, there simply isn't enough quality data science/analytics talent out there. Demand is huge and not many have skills, so it lowers the threshold a bit lower than you might expect. Rightly or wrongly, the CMU brand carries a lot of weight in this area, and people see "CMU Masters blah blah blah Data Analytics" on the resume and really do give that a tremendous amount of weight in my experience. Again, not trying to sell the DA program as it's definitely not a one-size fits all (even if it tries to be sometimes), but trying to add a bit more context from your last couple comments.

From what it sounds like, this program isn't for you. Anyone trying to be a "real" data scientist should do a true Data Science/ML masters. The curriculum is tailored towards people without any stats or specific computer programming background and getting them to the point of competence in two years. It does a good job of that. While I do have friends that have been able to make the curriculum work for them by exempting basic courses and taking a few more advanced classes from the CS school, it really just won't be the same IMO. This is I think the major source of frustration for some students that come in. You're right, it's a data analytics program, not data science. It's simply not possible to take students with the wide variety of backgrounds that the program does and turn them into capable data scientists within the short timeframe while taking management/policy courses at the same time. Happy to chat about it more if you want, but from the brief bit that I've heard, I wouldn't say it seems like the right fit.

The DA track is about ~50 students each year nowadays I think. Other MSPPM tracks are of varied sizes. The regular and 3-semester PPM tracks probably combine to be about the same size as the DA track and the DC track is like 15-20 students I think. I wouldn't call it a "big family," but it's definitely not unfriendly or competitive. I'd say there's a number of 5-10/person groups that are close friends and some clusters are closer to others. Like I mentioned earlier in the thread, I feel like we were starting to build a better overall cohort 3/4 of the way through our first year but COVID basically stopped that. There's some good overlap between programs too. I had a few friends from the MEIM program, a friend dated a classmate in MAM, one of my roommates is in the healthcare analytics program, etc. Overall, it's a pretty collegial environment and I've made good friends, but don't get the sense that it's stronger than any other comparable program out there.

I don't know of a single grad student at CMU that has ever lived in the dorms. Everyone lives off campus. Details about where students live, how they find housing, and how they find roommates can be found on the admitted students portal: https://www.heinz.cmu.edu/admitted-students/full-time/housing-survey

This is a hot topic among Heinz students! If you go to the handbook and look at the DA curriculum (page 7), you can see the courses. Minis are 6 units, full courses are 12. In general, I think you're exactly right to be questioning how much expertise you can develop within a mini & a semester. In general, I will say minis are really great for exposing you to a topic and getting to a point of understanding it on a bit more of a surface level. For example, I have friends that took a blockchain fundamentals course that really enjoyed it and understand the role it can play much better now, but are far from the point in which they'd be able to create any sort of meaningful blockchain application. You simply aren't going to get meaningfully proficient in a technical skill in only 8 weeks of 8-15 hours/week classes. Despite this, I appreciate the mini system because it exposes you to things without having to commit a whole semester to them. After taking minis, you can decide whether you want to invest more time in them or not. Most of my favorite classes have been minis because they get straight to trying to give you the most important knowledge given the short timeframe. All in all, I think it's important to think about classes at Heinz as layers that you add on to each other. After the completion of my core python course, I didn't feel like a particularly strong programmer, but I've concentrated on technical courses that all use Python, so I've taken courses in Big Data, Unstructured Data, and Data Science for Product Management that all gave me discreet skills in those subject areas, but all used Python. As my time comes to an end here, I do feel like a fairly confident Python programmer in addition to the particular subject-related expertise that you get from the classes.

Nope, it's a pretty new program. From what I gather, the program office doesn't really view it as a competitor currently, likely due to its early-stage nature. @GradSchoolGradhas spoken before (I feel correctly) about how you rarely want to be in a new program because you're a Guinea pig for working out the kinks. It terms of the culture, I honestly find it really hard to say. First semester was very much each person trying to get their feet under them and adjust to the pace of Heinz. We had some social events and stuff, but it was pretty limited. It felt like the cohort was starting to settle into a nice social dynamic before the pandemic (a lot of us went out to a Mamma Mia themed disco night shortly before COVID hit). The pandemic has really stifled that though, my 4-5 good friends are the ones I still keep in touch with and we work together on projects pretty actively. The process of turning people who I was "friendly with" as opposed to "friends with" got stunted which is naturally disappointing. It's not at all a competitive environment and students like to share code/technical resources frequently. To summarize, I liked my classmates a lot and it felt like we were on our way to have a good cohort culture after a tough first semester, but virtual schooling has definitely siloed us (as it has with most people around the world). I wouldn't say it's common, but it's not extremely rare. A good number of students were RAs this summer (including me, for half of the summer). And yeah, I have some friends that work for the Software Engineering Institute. Most Heinz students work for non-profits, gov agencies, or Heinz departments (including as TAs) during the school year, but some students have managed to work in different areas.

Caveat: obviously I haven't attended any other MPP program, so I can only speak to my own experience and the vibe that we get. I think the DA program is much more involved than most MPP programs. I have time for my classes and one 10-15 hour/week internship/job/TAship. I was in one extracurricular for part of my first year but dropped it about halfway through my second semester. It was a big change for me from undergrad where I spent only 15-20 hours/week on class and spent upwards of 30 hours/week on ECs and a job. CMU in general has an attitude as a university of being pretty high-stress and high-workload. My classmates and I definitely feel stressed-out and burned-out pretty frequently. However, I will say that it's doable and there are obviously benefits to it. I feel like I'm learning much more relevant and useful skills at a higher level than I would at most policy schools, so for me it's worth it. I'll also say that the stress has felt particularly exacerbated by the pandemic. Heinz had a monthly happy hour, we would host parties, and there were plenty of interesting talks we could go to at lunch on campus. Removing those means the entire focus of the Heinz experience is on the classes which are the stressors, which heightens the sense of feeling overwhelmed. I'm sure every grad student feels like their program is one of the most intense, but I would be pretty shocked if more than a couple policy programs were comparable to the DA program at Heinz. It's absolutely a doable program and one that I've had a fantastic experience with, but it's also one that's forced me to prioritize much more than I expected. I think it's a great option for people that want to go to a public policy program and learn harder skills, but I also know students that want more freedom and a more relaxed grad school experience. This wouldn't be a program that I would recommend to the latter even though it's absolutely a valid attitude to have. Deciding which kind of experience you want to have before you go to grad school is important in my opinion.

I don't have a lot of exposure to the application process for the DC track, but I'm pretty sure there's an interview component. From what I can gather, I think they like people who are mature/experienced. I know people that came in close to straight out of undergrad, but in the interview I would emphasize how your internships make you ready to be ready for a fellowship and that you're a person that the program can trust to represent it in the government community in DC. I know that's a bit vague, but that's the best I've got. Good luck! Don't know a lot, they use an external company to validate the information you've provided. You need to provide contacts for your jobs/institutions. Not positive if they actually end up contacting each of them or not, but it's definitely a requirement that you provide it all.