Statistics graduate school advice

It’s that time of year again, the time where I find myself meeting with students thinking about graduate school in statistics. Since I often end up sending people the same things, I figured I’d pull them together into a blog post. You probably already know about the grad cafe, the professor is in, and PhD comics. These other links might not be as common.

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Q: What is grad school in statistics like?

A: Here are some resources to help you get a taste:

• When I was still in graduate school, I wrote a three-part blog series for datascience.la on graduate school: Statistics grad school– what is it really like?, Part 2 of what statistics grad school is like, and Part 3: Paying for statistics graduate school.
• I gave some additional advice in this recent interview in AmstatNews.
• Michael Lopez also has a piece called So you want a graduate degree in statistics?, with sections on deciding on a program, thriving once you are there, and much more. He goes in to the difference between biostatistics and statistics, a PhD and an MA, and how to evaluate programs.

Q: Where should I go for grad school?

A: Wherever seems like a good fit!

I really think statistics is such an in-demand field that you can get a job with a degree from virtually any school. These show my personal research interests and biases, but a couple of my favorite programs to recommend to students are:

• Statistics at Iowa State. This is where Hadley Wickham and Yihui Xie got their PhDs, and current students include Carson Sievert, Maggie Johnson, Andee Kaplan, Samantha Tyner, and Eric Hare. Notable faculty include Hieke Hofmann and Ulrike Genschel. [Update, 11/27/17, I believe all of those students now have PhDs!]
• Biostatistics at Johns Hopkins. This is where Hilary Parker, Alyssa Frazee and Jenna Krall got their PhDs. Notable faculty include Roger Peng, Jeff Leek, and Brian Caffo (of Coursera data science specialization fame).
• University of Washington Interactive Data Lab. Not exactly a grad program, because it involves people from both computer science and the school of information, but does awesome work. Notable students include Arvind Satyanarayan [Update 11/27/17, he has his PhD!], and faculty include Jeff Heer of the computer science department and Jessica Hullman in the iSchool.

Fun fact I recently learned– Iowa State was the first US university with a department of statistics, and Johns Hopkins the first (in the world?) to offer biostatistics!

Q: How do I get in to grad school?

A: You need lots of things to be a strong candidate, including:

• good (but not necessarily perfect) grades
• a decent GRE score (particularly on the quantitative section of the regular GRE)
• a compelling story in your personal statement (email me if you want to read mine, I don’t feel like posting it on the web)
• solid letters of recommendation, particularly from faculty members who know you well from a variety of contexts, like class, research, etc.
• I also always recommend reaching out to people from the program you are applying to, whether current grad students, alumni, or professors to get a better sense of a program and whether you would be a good fit. Female Science Professor has a great post about what to say (and not to say) in an email to a professor.

Q: How do I succeed in grad school?

A: I recommend the following:

• How to Prep for Grad School if You’re Poor- a wiki with some information about the application process, but mostly the “cultural capital” you might not have that could be useful when you’re in grad school.
• Doing research- how to do research (the short story: do a little every day).
• A thesis proposal is a contract. Or at least, it should be.
• Write the paper first- an idea about how to frame your work by writing the paper first.
• Oral exams- this is specifically about advancing to candidacy at UCLA in statistics, but I’ve sent it to friends in other programs who don’t have similar guidelines.
• Leek Group Guides to reviewing papers, data sharing, writing your first paper, or reading papers.

Q: How can I learn more?

A: I recommend getting involved in some online communities.

• There is a ton of activity in statistics and data science on twitter. Most of the faculty and students I mentioned above have twitter accounts (some linked above, the others easy to find), and if you are into R you can get a taste by looking at the #rstats and #tidyverse hashtags.
• Roger Peng and Jeff Leek (mentioned above) and Rafa Irizarry have a blog called Simply Statistics with all sorts of interesting statistics discussions.
• Hilary Parker and Roger Peng (mentioned above) have a podcast called Not so standard devations, available on SoundCloud, iTunes, and many more podcast distributors.

What did I miss? What are other questions you have?