Mike Anderson (MS Statistics, Miami, 1995) [Senior Research Biostatistician at Battelle Memorial Institute]: When I was young, in elementary and middle school, I quickly learned that I liked mathematics. Math was a subject that I liked because it had a defined answer. I liked working with numbers. I liked working with equations. I liked solving some sort of problem. I preferred that more than, you know, English or history and subjects of that nature.

I must say that Miami did an excellent job of preparing me for my current profession as a statistician. First and foremost, the technical background that I got here, I mean the statistical professors that were here, were topnotch. I learned a lot. They were very challenging. The one thing that I learned other than all the statistics knowledge I needed, was really just kind of a focus of how to work really hard, because this was not an easy program. I struggled a lot at the beginning, but I had really good professors that worked with me and asked me to stick with it and really just it taught me the drive that, "Hey, problems in life aren't as simple as what you learn in high school or middle school. There are real-world problems out there that need statisticians and that need data analysts." And the environment here just enabled me to do that.

The nice thing about being a statistician is that you work in many different fields. Everyone has data, whether you're a biologist, environmentalist, a transportation expert, a computer scientist, everybody has data that they need to analyze. So as a statistician, you have a really good diversity of things to do. I mean you can work in any field you want and not be an expert in it. I'm a biostatistician. I don't know much about biology. I know enough to get along, but it's my statistics knowledge that helps me do that. And I feel that liberal arts education allows you to be exposed to many different areas, and that just makes you more valuable to your current employer, or if you're a graduate student looking for employment, the more that you know and the more areas that you can have some sort of knowledge in, that's really beneficial for your career.

For example, Dr. [John] Bailer in the Department of Statistics hooked me up with a graduate student over in the zoology department. And this was a graduate student that was doing some research for her master's thesis and needed some help, statistically, to analyze some data. And she was actually doing a project to measure the strength of spider webs, and I was like, "Okay, this is something I've not experienced before, not dealt with before. They don't have those types of problems in the textbooks that you have in class." I went over to the department. I met with her. She showed me her experiment. She had all these different kinds of spiders, many different kinds, and they all were building webs, and she was interested in which type of spider can build the strongest web. This was a really good part of my master's thesis. I spent about five months working with her and then developing a report. So that type of thing goes above and beyond just classroom teaching.

I think the two biggest things that I would encourage people to do is, first of all, especially for statisticians, is communicate. Work on your communication skills. And I'd say that both from a written perspective and an oral perspective. I originally got into math and stats when I was young because I liked solving math problems, and I did not like writing term papers or reading a book and writing a book report. It's funny now that most of my job is actually doing writing and talking to people. As a statistician, I still get to solve the problems, but the next step is you have to explain to the client what the answers are. And so from an oral perspective, you need to be able to talk to non-statisticians and explain the results to them. The other thing that I would suggest doing is working with some real data. Textbook problems are not real-world problems. They're in a textbook for a reason. They're easy and they can be done in a small timeframe, but the real world doesn't work that way, so to the extent that students can work with ... maybe do internships, find another grad student from another department that has data that you can analyze for them as part of a project or just a side thing. That helps immensely, because when you go out to interview for jobs, or even when you're at the job that you're at, having that experience is just a really big benefit.

[September 2015]