Just try to add some more words.
As a PhD student in Biostatistics, I feel great with what @Frank-Harrell said. And that's quite correct!!! Students from our department have great job placements after graduation.
On the other hand, @StasK cited the article "Aren’t We Data Science?", but titled it as "statisticians are not recognized as data scientists". This is somewhat misleading to me. Statisticians might not be titled as data scientists. But who else can formally claim that? Anyway, what the article states, at least to me, is that statistics has the great potential to contribute to data science. The main issue, if there are, that impede Statistics to promote data science is that people from Statistics are not well trained for large-scale computation and efficient programming. Cited from that article is following.
And to statistics. Statistics has enormous potential to contribute to
data science. There are open research problems requiring that
classical statistical methods in sampling, design, and causal
inference be “scaled up” to be feasible with massive data sets. Few of
the computer scientists and others who dominate the data science
landscape are well-versed in these concepts, and many take an
“algorithmic” view of data analysis. Data science needs statistical
thinking and new foundational frameworks—for example, what is the
“population” when one confronts the Big Data generated by Google?
In fact, many businesses are beginning to collect data prospectively
for internal testing and validation, and there is little appreciation
for the power of design principles. Statisticians could propel major
advances through development of “experimental design for the 21st
One can arguably say that Computer Sciences are at better position but just lacks the statistics thinking. But to me, I regard the two main component as the "brain" and "hands"! If the experiment design is flawed in the very beginning, or if the inference is
biased at the very end, we will end up with a totally different story about the conclusion and business strategy.
To put it simple for all I wish to convey here, data science practitioners really need both great statistical thinking and programming.
To decide which degree you are going to pursue, you have to get to know what skill sets that qualify you to work in data science area. Based on what I've known, if you wish to enter the data science field, what "hard" skills you would wish to be equipped are basically twofolds: the strong analytical ability, and good computation and programming skills. You can go to Quora and search like "data science", "data scientist", etc, to get some feelings about what the
field looks like, and what you need to prepare for that area. Here are two questions from Quora you might wish to go through:
- What is data science?
- How do I become a data scientist?
Some questions like that, you get my point.
(The soft skills, like oral and written communications skills and team-work ability are also very important. And in some circumstances, even more important than your analytical skills to some extend. But certainly the discussion on soft skills is certainly off topic for the sake of your questions.)
Now back to your questions.
What are the graduate degree choices that would get me to where I want to go?
Once you have clear vision and deep thinking about what you need to learn, you should be able to answer this by yourself. My suggestion would be Computer Sciences, Applied mathematics or statistics, Biostatistics, Physics, Engineering, or any other degrees that heavily involve analytics and computation. Essentially, an interdisciplinary degree that help you train both data analysis and programming will definitely win you a great position for working in data science area.
Is there a consensus as to whether a graduate degree in applied mathematics or statistics would put me in a better position to enter the field of data science?
I am not aware of whether there is such consensus formally acknowledged by academic researchers or industrial practitioners, but I can give you some news/reports from websites which show how Statistics will have a great role to play as the "Era of Big Data" evolves. I believe these articles will at least give you confidence that statistics should be a good choice.
- For Today’s Graduate, Just One Word: Statistics
- How Statistical Science Can Advance Big Data Research Projects?
- Discovery with Data: Leveraging Statistics with Computer Science to Transform Science and Society
- We Are Data Science
- [The Era of Big Data] Must-read articles about Big Data
The last one is from my blog, in which I collected some important articles from media and famous webs, like NYTimes, Forbes, McKinsey, Harvard Business Review, etc. You can find some that outline the future of data science field, and the skills one needs for that field. For example, here is the Quote from NYTimes, the words from Hal Varian.
“I keep saying that the sexy job in the next 10 years will be statisticians,” said Hal Varian, chief economist at Google. “And I’m not kidding.”
What most of the articles elaborate is that as a discipline that studies data -"the science of data", the Statistics field is booming at this historical point. So if there is a consensus, these articles would be the signs of it.
Last, as it might appear to you that I am convincing you to obtain a graduate degree in Statistics or Biostatistics, I don't have that intention, though they are great choices as I indicated previously. Any degrees that fit your interests (like machine learning in Computer Sciences) are good to consider, as long as you know you're preparing your analytical and computation skills. You can even learn those skills by yourself through Open courses on Coursera.