Clever Geek Handbook

Master's Degree for Modeling / Statistics / Forecast?

I am wondering if anyone has any idea of ​​this. I am considering going to Gradient School to get a related degree in Computer Science. I have always been intrigued by people who work on problems using statistical packages or simulations to solve problems. What will I study to get good information about these things? Are they getting into machine learning? Thanks to

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There are many possibilities here. Let me add the following options:

  • Physics focuses on complex networks. It has applications in biology, epidemiology, sociology, finance, and computer science.
  • 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 would be happy to talk about this, please ask questions in the comments.

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A friend of mine is getting her degree in mathematics with an emphasis in statistics and Operations Research.



She works a lot with SAS and other statistical software to maximize certain features and predict the likelihood of future events. It might be more math than you like, but you can try finding CS program masters with a focus on Operations Research or Statistics.

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I would suggest that your school will offer some up-to-date statistics courses, perhaps in the math department, that you could study to learn all about it.

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Learn a lot of math, especially probability and statistics. I now have a graduate modeling course and I wish I knew more of the problems / characteristics.

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At Biostatics (in Minnesota) we have done a lot of simulations in areas like Bayesian statistics, genetics, and others. Any strong analytical program is a good candidate for teaching the skills you want, including: economics, econometrics, agronomy, statistical genetics ... etc. Etc.,:)

While you wait, grab R, Matlab (Octave is a free implementation), or your Turing-Complete language, dig it into Wikipedia and get it working :)

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I would like to reiterate Gregg Lind's recommendation to think about statistics in the biological sciences. It's well funded, there's a lot of interesting work out there (both theoretical and applied!), And you can sound great at parties because somehow you can always make some kind of connection from your job to cancer treatment. :)

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