Stanford Master's in Data Science or UCLA PhD in Statistics

[hiro722]

I got accepted into both Stanford's Master's in Data Science and UCLA's PhD program in Statistics and am trying to decide between the two.

I would be able to pay my way through Stanford's program (around 50k in tuition) if necessary, but I am trying to weigh the two options. Regardless of which option I choose, I will want to pursue a career in industry afterwards. I'm open to reapplying for a PhD after completing a Masters as well. 

I know that a lot of this is based on my own personal preferences and goals, but do you guys have any advice regarding how people in industry, academia, etc may perceive the two degrees and how you would approach this decision?  

Edited March 16, 2016 by hiro722

[hiro722]

4 hours ago, Tsilaeri said:

This is really a personal opinion, but one more thing to consider is that, Master in Data Science is becoming a fad these days like MBA once used to be, and it's a bit overrated.
All these hypes about big data as the next big thing will end within 5 years when so-called data scientists finish building solutions in each industry. 
It is similar to when the railroad business was hot in the 19s - there were a lot of demands for railroad workers(=data scientists), but who took profit in the end? Not the railroad workers, it was the merchants who utilized them. Moreover, data science skills learned in master level can be easily substituted by Udacity or Coursera these days, and it will not get you really far. 

Thanks for the informative reply! I have a couple of questions though. 

Do you think that this is true even at a top program like Stanford? For example, even if an MBA is a fad, getting one from Harvard Business School still has value. 

Also, are you saying that a PhD will allow me to better adapt to changing demand in the job market? Since a PhD will take 4-5 years anyways, it seems like if I wanted to go into a data science related fields after a PhD, I would be in an even worse position. 

[Pleaaa]

Congratulations first! 

To me, if the ultimate goal is doing data science, I prefer to get a PHD first. From what I got from data scientist recruiters, people with a master in DS basically work under the lead of PHD researchers and masters usually take up the validation or the programming role. The description on Stanford's website gave me an impression that the program provides a good range of career paths (CS, DS, stats, etc.) and students can also choose to pursue a PHD after the program.

If you are worried about the future job market, I think PHD is far less risky than a master unless you are a good programmer (yeah, programming skill is important for masters) and the skills you learn from PHD are transferable (IT industry and even hedge funds!). UCLA is also a great university in terms of statistics. But, as you mention, getting a PHD takes a long time, so it is important for you to know if the supervisor and his\her projects are of your interests. 

On the other hand, if you want to have a career path other than quantitative fields, Stanford is possibly better because it provides you networks to do something like starting your own business.

 

[Tsilaeri]

Uh, I was gonna edit my post, but somehow hid it. Let me add more here.

If you got an admission from top programs like those two, I assume you already have fair amount of statistics education from undergrad?
I will assume that, correct me if I'm wrong.

Consider a business undergrad going for an MBA. 
Total waste of time in terms of gaining additional knowledge, but people still do that for the value of networking.
Yes, top tier MBA such as HBS will give you a splendid network that can far outweigh any form of academic knowledge.
You are basically doing the same thing with this guy, but without the networking advantage. 

As Pleaaa put it nicely, the role you will get as a master's student in the industry will be mostly validation or programming - which will not be far different from the role that you can get after a BS. PhD belongs to another league.

The best path you can take, in my opinion, is to defer UCLA admission, work in the industry for 1 or 2 years to have some practical experience, then go for a PhD.
Data science is a very practical field. Having an industry experience will make a big difference in terms of your future choices and paths. 

(But... to be honest, in the end of the day, a smart guy like you can never go wrong, whichever path you take. Congratulations!)

[Dawnbreaker]

• Range of opportunities with a PhD is much much more than range of opportunities with an MS. If you really want to do the exciting work that is hyped up in big data, you need to be the head of a team with 3-4 people working under you (the MS in DS type people). For that you need a PhD.
• I perceive MS in data science as too short sighted. An MS in data science over specializes on the type of skills that are currently in demand in industry. If the demands or methods change, much of the DS curriculum is obsolete. An MS in CS or Statistics on the other hand provides the necessary foundations, in addition to skills of value in industry.
• When applying for a PhD program, I am pretty certain that strong departments would prefer an MS in CS or MS in Statistics. If you go the MS in DS route, you might be forced to do a PhD in data science with some umbrella program as your home unit (similar to Stanford where ICME is not a department). This is far from ideal if you decide to go the academia route or even top industrial labs.
• I perceive interdisciplinary and data science programs as weaker compared to an established department. Given a choice between MS in CS, MS in Statistics, and MS in DS: nearly everyone will choose the former two. Hence, your cohort is going to be weaker. 

I'll pick UCLA for all the above reasons. In addition, I'd take at least 3-4 CS courses (data structures & algorithms, software development, graph theory, and an applied ML course like vision or speech processing). It'd be ideal if you can get an MS in CS along the way too!