Ask Any Questions - Current CMU Heinz Student

[GradSchoolGrad]

4 hours ago, dspp_grad said:

@Kaz_KV  Does Heinz list the specific roles at each organization? There are definitely some big-name companies like Facebook and IBM listed but I had the impression the roles at those companies were mostly "Business Analyst" or "Data Analyst" rather than "Data Scientist" (but I definitely could be wrong about that). I would also add that McCourt has a phenomenal track record of placing people into research organizations like Mathematica, Brookings, and Urban Institute; being from McCourt by no means guarantees you a job at these places but does give you a huge leg up and is probably somewhat easier than coming from Heinz.

Career-wise I think the most important factor to consider is simply whether you want to work in D.C. afterward. If you know you want to be in D.C., you should probably choose McCourt; if you want to keep your options more geographically open, you should probably choose Heinz. Being able to take PhD courses at Heinz is definitely a point in CMU's favor but, to the best of my knowledge, Public Policy PhD programs definitely aren't expecting you to have already taken PhD courses when you apply.

Yes, McCourt does have a track record of sending people to research organizations, but lets be totally honest how this work.

1. Lets remove all the boutiquey research organizations/institutes/centers (so basically less than 100ish people on staff) + the government research shops (granted I think these are better long term careers, but that is a moot point) and lets focus on the big research think tanks with national/international reach. 

These are the Brookings, Urban, AEI, CATO, and barely squeezing in is Mathmatica (purists won't consider Mathmatica in this group actually, but I do!)

2. Yes McCourt sends about 2 to 4 (among all programs - MPP, MIDP, DSPP). year to ALL of these for full time employment out of a program yearly group of proxy 180 or so (counting all part time students as well). So 1-2% matriculation into the bigs. 

3a. How do you get in Option A - Faculty Referral: This is the primary way McCourt students funnel into them (mainly Mathmatica and Urban). Basically you RA/TA/GA for a prime faculty member with connections. This generally means you to at the very least share similar research interests + or write a thesis that stuns people. 

3b. How do you get in Option B (usually paired with A) - impress McCourt alums at said organization

3c. How you get in Option C (I have only seen this happen once) - networking on your own (I can talk at length on strategies to do this). At large I seen this method when a person is interested in something no professor/McCourt alum is really interested in --> which happens more than you would think because McCourt's policy portfolio of strength is rather limited. 

Edited March 18, 2021 by GradSchoolGrad

[woolscarves]

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:

1. 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.
2. 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.
3. 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.

[Kaz_KV]

On 3/19/2021 at 1:01 AM, woolscarves said:

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:

1. 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.
2. 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.
3. 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.

Thanks! And yeah a lot of this adds up to what I had expected. It's also particularly hard to judge since I don't know a student's undergrad major (i.e., if he majored in stem). 

Would you mind sharing what kind of preparation your friends entering into PhD programs got while at Heinz? Feel free to PM me but I'd love to know if they took a pretty standard path or worked to augment/exempt a chunk of their curriculum.

[woolscarves]

On 3/20/2021 at 12:41 PM, Kaz_KV said:

Thanks! And yeah a lot of this adds up to what I had expected. It's also particularly hard to judge since I don't know a student's undergrad major (i.e., if he majored in stem). 

Would you mind sharing what kind of preparation your friends entering into PhD programs got while at Heinz? Feel free to PM me but I'd love to know if they took a pretty standard path or worked to augment/exempt a chunk of their curriculum.

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.

[Kaz_KV]

14 hours ago, woolscarves said:

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.

Good to know thank you!

[seb_]

On 3/19/2021 at 5:01 AM, woolscarves said:

1. 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.

Hey! 

I have more or less decided to accept my MSPPM-DA offer over CAPP and DSPP, but I'm hoping to waive a couple of first year courses to take harder stuff in DA, CS as well as math and statistics.

What you described in your post above is what I was hoping to do (though maybe a bit less extreme). Do you know what courses best to select in the CS department and to improve my math/stats? Or could you give me the contact of someone you know who did it and could tell me a little me about it? Please feel free to text me privately.

The degree has so many great-sounding courses that I want to choose wisely and any help I get is much appreciated.

Thank you in advance!

[ak_gc]

 @woolscarves

This thread has been very enlightening, thanks for answering all the questions so thoroughly.

I'd like to share my situation and see if you have any perspective you can share. First, some context: I am a recently admitted student to the MSPPM-DA program with 6 years work experience and have no prior masters degree. I am also currently a Full-stack Data Scientist at a fortune 500 company and have been head-hunted several times for roles at the big tech companies that a lot of MSPPM-DA graduates go work at. Further, I have been consulting part-time with academia and public-sector institutions on solving data science problems that serve the greater public good.

I see this program as a way to pivot and fast-track my path to a public-sector leadership role where I can lead the charge on building out full-stack data science capabilities for public service oriented organizations. My conundrum is identifying:
(a) Do I benefit from a full-time masters program like this at all?
(b) If so, is this the right type of program or should I be looking at a top-tier MPP/MBA

I've listed out some pros and cons below.
Pro's:
- Access to work with professors like Rayid Ghani and Chris Goranson on data-centric public sector problems
- Access to the school, alumni and professor network
- Access to top tier advanced machine learning, management science, and policy analysis courses
- Get a Master of Science degree in Analytics related field (a commonly desired trait in private sector)
- Cheaper than Kennedy MPP level program, much cheaper than most full-time MBA programs
Con's:
- Currently growing and learning a lot, both at work and outside, will trade this for more structured learning if I attend 
- Opportunity cost of lost wages and cost of attendance (I have a partial scholarship but the cost is still not insignificant)
- MBA/MPP might offer better outcomes/opportunities through network
- Current student network seems to be less experienced and significantly earlier in their career vs. myself
- An online or part-time masters program will be much cheaper (especially with company paying for most of it)

This program definitely seems good at spring-boarding students who come from less experienced or non-technical backgrounds into great careers, particularly in the private sector. I'm talking to as many folks as I can to get some concrete perspective on if it has a high likelihood of doing the same for me.

[GradSchoolGrad]

15 hours ago, ak_gc said:

 @woolscarves

This thread has been very enlightening, thanks for answering all the questions so thoroughly.

I'd like to share my situation and see if you have any perspective you can share. First, some context: I am a recently admitted student to the MSPPM-DA program with 6 years work experience and have no prior masters degree. I am also currently a Full-stack Data Scientist at a fortune 500 company and have been head-hunted several times for roles at the big tech companies that a lot of MSPPM-DA graduates go work at. Further, I have been consulting part-time with academia and public-sector institutions on solving data science problems that serve the greater public good.

I see this program as a way to pivot and fast-track my path to a public-sector leadership role where I can lead the charge on building out full-stack data science capabilities for public service oriented organizations. My conundrum is identifying:
(a) Do I benefit from a full-time masters program like this at all?
(b) If so, is this the right type of program or should I be looking at a top-tier MPP/MBA

I've listed out some pros and cons below.
Pro's:
- Access to work with professors like Rayid Ghani and Chris Goranson on data-centric public sector problems
- Access to the school, alumni and professor network
- Access to top tier advanced machine learning, management science, and policy analysis courses
- Get a Master of Science degree in Analytics related field (a commonly desired trait in private sector)
- Cheaper than Kennedy MPP level program, much cheaper than most full-time MBA programs
Con's:
- Currently growing and learning a lot, both at work and outside, will trade this for more structured learning if I attend 
- Opportunity cost of lost wages and cost of attendance (I have a partial scholarship but the cost is still not insignificant)
- MBA/MPP might offer better outcomes/opportunities through network
- Current student network seems to be less experienced and significantly earlier in their career vs. myself
- An online or part-time masters program will be much cheaper (especially with company paying for most of it)

This program definitely seems good at spring-boarding students who come from less experienced or non-technical backgrounds into great careers, particularly in the private sector. I'm talking to as many folks as I can to get some concrete perspective on if it has a high likelihood of doing the same for me.

If you already have all the data skills this or any comparable program would teach, I would really think about how you might want to best pivot your future career. If you are interested in the administrative/leadership side of policy or an organization, then you should get an MBA/MPA. If you want to go into analysis for Policy/Government, the importance is to learn about techniques and approach, and an MPP might be more helpful for you to get to new career opportunities. You can refocus your coding skills newly guided on Econ knowledge. I have seen plenty of coding savvy folks take their policy education as new guidance on how best to apply their existing skills.

I don't see any point for you to enter a Data Analytics/Science program where you basically go to class to learn what you already mastered. 

[ak_gc]

@GradSchoolGrad
Thanks for the response your recommendation is definitely one of the pathways I'm considering.

What do you think the likelihood of getting some funding is for an MPA or MBA program? My concern with these pathways is that I'm very likely going to make the same or less in terms of my income and will take on what I consider to be huge debt. For someone who has financial obligations and doesn't come from a background where their parents can foot the bill, I'm trying to figure out the most cost-effective way to make this pivot.

[GradSchoolGrad]

22 minutes ago, ak_gc said:

@GradSchoolGrad
Thanks for the response your recommendation is definitely one of the pathways I'm considering.

What do you think the likelihood of getting some funding is for an MPA or MBA program? My concern with these pathways is that I'm very likely going to make the same or less in terms of my income and will take on what I consider to be huge debt. For someone who has financial obligations and doesn't come from a background where their parents can foot the bill, I'm trying to figure out the most cost-effective way to make this pivot.

You need to do a lot of research on your own to answer that. I don’t know your background, earnings potential, and aspirations. You can figure out clues to all your answers via Google. 
 

I will say that next application cycle will be more generous for funding (most likely). Another pro tip... to get scholarships for MBA, it helps to send in the GMAT. MBA schools will pay for a high GMAT to boost their rankings, but not as much so for GRE.

[ak_gc]

Just now, GradSchoolGrad said:

You need to do a lot of research on your own to answer that. I don’t know your background, earnings potential, and aspirations. You can figure out clues to all your answers via Google. 
 

I will say that next application cycle will be more generous for funding (most likely). Another pro tip... to get scholarships for MBA, it helps to send in the GMAT. MBA schools will pay for a high GMAT to boost their rankings, but not as much so for GRE.

Yeah I'm with you, lots of digging to be done there. I've started connecting with friends who are pursuing MBAs with a public service orientation.

Thanks for the tip

[MPPfall21applicant]

Hi

This thread has been really informative; so thank you all for that. I have been offered admission to the MSPPM two year Pittsburgh track. It will be great to know some of your insights about a few queries I have regarding this course

1. I am an international student and I wanted to know the ROI on this course since it is non-STEM

2. I read somewhere that the MSPPM course won't benefit if one does not have prior knowledge of data science and programming languages since it is not something that can be taught in as little time as Heinz devotes. Is this true? I have humanities background so will I be able to cope with the course and be at par?

3. I wanted to know if there is a way to negotiate for further financial aid without competing offers. I have been wait-listed at a couple of places but haven't received final admits from any anywhere other than Heinz.

 

Any and all suggestions are welcome

[woolscarves]

On 3/23/2021 at 10:25 AM, seb_ said:

Hey! 

I have more or less decided to accept my MSPPM-DA offer over CAPP and DSPP, but I'm hoping to waive a couple of first year courses to take harder stuff in DA, CS as well as math and statistics.

What you described in your post above is what I was hoping to do (though maybe a bit less extreme). Do you know what courses best to select in the CS department and to improve my math/stats? Or could you give me the contact of someone you know who did it and could tell me a little me about it? Please feel free to text me privately.

The degree has so many great-sounding courses that I want to choose wisely and any help I get is much appreciated.

Thank you in advance!

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.

[woolscarves]

On 4/1/2021 at 12:46 PM, ak_gc said:

Yeah I'm with you, lots of digging to be done there. I've started connecting with friends who are pursuing MBAs with a public service orientation.

Thanks for the tip

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.

[woolscarves]

On 4/2/2021 at 12:04 PM, MPPfall21applicant said:

Hi

This thread has been really informative; so thank you all for that. I have been offered admission to the MSPPM two year Pittsburgh track. It will be great to know some of your insights about a few queries I have regarding this course

1. I am an international student and I wanted to know the ROI on this course since it is non-STEM

2. I read somewhere that the MSPPM course won't benefit if one does not have prior knowledge of data science and programming languages since it is not something that can be taught in as little time as Heinz devotes. Is this true? I have humanities background so will I be able to cope with the course and be at par?

3. I wanted to know if there is a way to negotiate for further financial aid without competing offers. I have been wait-listed at a couple of places but haven't received final admits from any anywhere other than Heinz.

 

Any and all suggestions are welcome

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.

[spartan32956]

Hi all

I have received an offer for MSPPM-DA at Heinz and MSCAPP at Harris. I have a strong math, stat and economics background and am looking to build hard computational skills. After school, I want to work in the tech space -- very open to the kind of sector. 

Currently, I am struggling to decide between the 2, but leaning more towards MSPPM-DA (due to financial considerations). Regarding that, I had a few questions. Would be grateful to get some opinions:

1. In terms of electives, I feel Harris has a wide range of rigorous technical classes. For MSPPM-DA, I read on this thread that there are limitations on the number of classes you can take at the CS department. Further, Harris has many economics and stats class that I can waive towards more CS oriented classes, but the same doesn't seem to be possible at Heinz for management classes. Q - Considering overall core + elective options, would joining MSPPM-DA put me at any disadvantage as compared to MSCAPP in terms of gaining hard computational skills (data science, data engineering, data analytics skills) ? 

2. Assuming that I land a job providing the median salary, how easy or difficult would it be to pay off ~50K USD loan? Any experiences?

 

[lostinstatistics]

On 4/11/2021 at 3:59 AM, spartan32956 said:

Hi all

I have received an offer for MSPPM-DA at Heinz and MSCAPP at Harris. I have a strong math, stat and economics background and am looking to build hard computational skills. After school, I want to work in the tech space -- very open to the kind of sector. 

Currently, I am struggling to decide between the 2, but leaning more towards MSPPM-DA (due to financial considerations). Regarding that, I had a few questions. Would be grateful to get some opinions:

1. In terms of electives, I feel Harris has a wide range of rigorous technical classes. For MSPPM-DA, I read on this thread that there are limitations on the number of classes you can take at the CS department. Further, Harris has many economics and stats class that I can waive towards more CS oriented classes, but the same doesn't seem to be possible at Heinz for management classes. Q - Considering overall core + elective options, would joining MSPPM-DA put me at any disadvantage as compared to MSCAPP in terms of gaining hard computational skills (data science, data engineering, data analytics skills) ? 

2. Assuming that I land a job providing the median salary, how easy or difficult would it be to pay off ~50K USD loan? Any experiences?

 

I second these questions if there is anyone from MSPPM-DA who can help!