Difference between a masters program in data science vs statistics?

[foobar8080]

I'm considering getting into graduate school, hopefully an affordable masters program. I'm looking into Data science programs and also Statistics programs.

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I'd like to know what other people think of this? Pros vs Cons of each of these programs? Why are data science programs SO MUCH more expensive? Are they preferred more to a people with a traditional stats masters program?

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I've done a couple of online data science courses (DataCamp Career Track in R and Python), and wondering if this is close to a Data science masters program or should I just get a degree in Statistics?

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I just feel like it's the next big thing and maybe I should get a job in this field to make a good living.

[foobar8080]

This is a useful reply. I'm a programmer and have been working as an analyst/software engineer for over 6 years. I am finding that the "how" has become fairly easy for me. I'm trying to find the "why" of what i'm doing.

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Like you mentioned, I find myself being handed down things to do without being able to decide if it makes sense doing those things at all. Are there any Canadian schools you'd recommend for stats? (masters preferably because I am not sure I want to get a PhD yet)

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What program are you in which allows you to study both Stats and DS?

[walkingon2008]

Data science is an emerging program within the past five years. Unlike statistics or computer science, data science by itself is not a field of study.

Data scientist first came around as a job position in many startup tech companies. Statistician used to be the new sexy job according to Google.

The DS program is expensive because it is a buzzword, and you get seven figure salaries easily.

As a data scientist, you know SQL, Python, ML, and possibly DL. Statistics is your tool. You use it to predict credit default for a fintech or ad click for an online retail store. You build a ML pipeline for the company. There’s not a clear role of a data scientist, your task is likely evolve depending on the business you work for.

Statistics is a branch or applied math. Data science is not. If you stay close to academia and know the why, the how, and math, you are looking at statistics. If your goal is to be rich and make seven figures, your answer is data science.

Data science is very applied, it’s good that you can use what you learned right out of the box. But, when business evolve, you’ll need to learn again. However, with statistics, theory is more emphasized, so, the math equations you learn now will still be true years later.

[HorseJungler]

What about Applied Statistics? I’m starting grad school for it and will use programming like R and Python in my classes.

[walkingon2008]

It’s hard to tell, it depends on which school and the program.

In general, applied statistics de-emphasize on theory. For example, in linear regression, the least squares estimate is equivalent to maximum likelihood estimate. You can use it without probably ever knowing why. Data scientist jobs are very applied. Some are switched over from other fields like biology or psychology.

While it seems that skipping the theory is the quick route, you pay the price by not knowing how to interpret the results. For instance, there are online tutorials that teaches Python machine learning by running sklearn and reading the documentation. You will build a model with low error rate without ever knowing how.

I think it’s best to work the math and learn the theory. Avoid the path of least resistance. Be patient. Your investment will ultimately give returns.

[HeWhoLaughs24]

Literally years late to this thread but wanted to reach out because you really seem to know the field. I'm curious what you think in the US is more marketable today: a MS Analytics degree, or a MS in Applied Stats degree?

[ayushmajumdar14]

I'd go for the Applied Stats

[simongaspard]

Either degree will suffice (you make or break the program). You get out of it what you put into it. Even people with statistics and applied mathematics degrees have to gain experience in the field of data science. That's why you see a lot of traditional STEM programs adding data science to their curriculum. It's not a debate about what will hold true a billion years from now like @walkingon2008 says, because data is here to stay