Graduate Level Degree for Simulation/Statistics/Prediction?

Question

I am wondering if anyone has any insight into this. I am thinking of going to grad school to get some computer science related degree. I have always been intrigued by people who are working on problems using statistical packages or simulation to solve problems. What would I study to get a good breadth of knowledge of these things? Do they fall into machine learning? Thanks

Answer 1

I would assume that your school would offer some actual Statistics courses, probably in the Math department, which you could take to learn all about this.

Answer 2

My girlfriend is getting a degree in mathematics with an emphasis in Statistics and Operations Research.

She does a lot of work with SAS and other statistical software to maximize certain functions and predict the likelihood of future events. It may be more mathematics then you like, but you might try looking for masters of CS programs with an emphasis in Operations Research or Statistics.

Answer 3

Study a lot of mathematics, especially probability and statistics. I have a graduate simulation course right now, and I wish I knew more probs/stats stuff.

Answer 4

There's a wide range of possible opportunities here. Let me add the following choices:

• Physics with a focus on complex networks. This has applications in biology, epidemiology, sociology, finance, and computer science.
• A good machine learning program, with statistics, data mining, text analysis, and computational learning theory.
• Industrial engineering/operations research, with simulation, reliability, and process control.

I'd be happy to talk further about this, please put questions in comments.

Answer 5

In Biostatics (at the U of Minnesota), we did a lot of simulation, in areas like Bayesian statistics, genetics, and others. Any strongly analytical program is a good candidate for teaching the skills you want, including: econ, econometrics, agronomics, statistical genetics... etc., etc., :)

While you're waiting, pick up R, Matlab (Octave is the free implementation), or your Turing-Complete language of choice, dig into Wikipedia, and get to work :)

Answer 6

I'd like to second Gregg Lind's recommendation of thinking about statistics in the biological sciences. It's well-funded, there's a lot of interesting work going on (both theoretical and applied!), and you can sound really cool at parties because somehow, someway you can always make some sort of connection from your work back to curing cancer. :)

Seriously though, a lot of great statistical work was done in the early 20th century by people like Haldane, Fiscer and Wright. More recent interesting work has been done on analysis or large data sets, multiple hypothesis testing, and applied machine learning. It's super exciting. Come join us!