Jump to content

MS Data Science vs. MS Stats - Opinions?


Recommended Posts

I am looking to apply to either stats MS programs or data science MS programs, but I need more information about both in order to decide which to apply to, and so I have a few questions:

  • In your opinion, do stats MS programs provide enough training in applied and technical skills to work as a statistician or data scientist in industry? In looking at the curricula of many stat MS programs, it seems there is a heavy focus on theory and not as much focus on actually preparing one to work as a data scientist. E.g., not very much focus on how to actually apply machine learning models practically or much focus on computer science skills such as data visualization, software engineering, data mining, databases, cloud computing, etc. Is this perception wrong? Do many stat MS programs actually provide these types of skills within stat courses? Or do most stat MS grads pick up these skills on their own? It is somewhat difficult to tell just by looking at the program curricula. I would rather not have to learn these skills on my own, if possible.
  • I am also wondering if data science MS programs would leave me as a good candidate for PhD programs in stats? Looking at some data science curricula, some or all of the courses are not offered in the stat department but offered in a separate data science department. Therefore I am wondering if stat PhD programs would view these courses as watered down and it would leave me as not a very strong stat PhD candidate. Would it be different if as a data science MS student you took some courses in the stat department? For example, Harvard's data science MS allows up to 4 stat electives.

My primary goal with an MS program is to be prepared to work in industry as a statistician or data scientist, although I do enjoy math. My secondary goal is for the MS program to leave me as a good candidate for stats PhD programs as I am considering pursuing a PhD at some point but very well may not. Any insight you may have regarding either question or relevant to this topic at all would be very helpful. Thanks

Edited by fujigala
giving an example
Link to post
Share on other sites

If you are contemplating getting a PhD in Statistics and your profile is competitive enough *without* the Masters, then I would recommend just applying directly to PhD programs.

But if you do insist on going the Masters route first, then the Masters in Statistics (or in Math/Applied Math where you can take the stats classes) would be the best preparation for a Statistics PhD program. For one, it might save time later as far as fulfilling coursework requirements -- you might be able to place out of all the first year classes. I have a MS in Applied Math but I took 4 statistics classes in my MS program, including both semesters of Casella & Berger and the applied statistics classes. As a result of this, I decided to try my PhD department's qualifying exam upon arrival (after spending maybe hundreds of hours practicing old qualifying exam questions), and I passed it so I was able to skip all the first year classes. That saved some time as far as degree completion. 

But even if you do repeat the first-year classes (applied stats and theoretical stats sequences) once you enter a PhD program, you will be completely prepared because you will have seen the material previously. 

Edited by Stat Assistant Professor
Link to post
Share on other sites
10 hours ago, fujigala said:

Thanks for the reply and info. Not sure if I want to commit 4 years to a PhD yet.

You do not have to commit to doing the PhD. You can enter the PhD program and leave with a master's. It's becoming more and more common.

Also, 4 years for a PhD (especially without a master's) is very fast. It's more like 5-6 years.

Link to post
Share on other sites

As someone in industry, an MS is more than enough to get a job as a data scientist or biostatistician. If you're still hesitant about doing research then I highly recommend you go the MS route first then decide after working whether you want to pursue a PhD. 

I personally don't think a PhD is worth it for industry career growth. 

 

EDIT: 

I'd like to add that if you're looking for a PhD program than I'd look into theoretical MS Stats programs. If you're looking for industry then go to well known CS programs that have MS Data Science. Tech companies will be recruiting from there. From what my friends have experienced, the MS Data Science programs are a lot more applied than most MS stats programs. 

Edited by bernoulli_babe
Link to post
Share on other sites
2 hours ago, bernoulli_babe said:

As someone in industry, an MS is more than enough to get a job as a data scientist or biostatistician. If you're still hesitant about doing research then I highly recommend you go the MS route first then decide after working whether you want to pursue a PhD. 

I personally don't think a PhD is worth it for industry career growth. 

 

Well, depends... if you're a domestic student, then you might be able to get one of those jobs without a PhD -- and sometimes with only a Bachelor's. I have a friend who has BS in Biochemistry but he taught himself programming/hacking/etc., and with the "right" connections, he was able to enter the field of data science. Now he has been working in the field for quite some time, and managing data science/engineering teams. So if you manage to get your foot in the door and obtain the right experience, your degree may not even matter that much.

But if you're an international student, then it is *much* easier to get an industry job in the U.S. with a PhD. This is because it is easier to get an H1B visa with a doctorate rather than only a Masters.

Link to post
Share on other sites

Create an account or sign in to comment

You need to be a member in order to leave a comment

Create an account

Sign up for a new account in our community. It's easy!

Register a new account

Sign in

Already have an account? Sign in here.

Sign In Now
×
×
  • Create New...

Important Information

By using this site, you agree to our Terms of Use and Privacy Policy.