A new article out from BusinessWeek entitled "Math Will Rock Your World" is well worth reading. It talks about the huge business opportunities for the mathematically inclined:
The world is moving into a new age of numbers. Partnerships between mathematicians and computer scientists are bulling into whole new domains of business and imposing the efficiencies of math. This has happened before. In past decades, the marriage of higher math and computer modeling transformed science and engineering. Quants turned finance upside down a generation ago. And data miners plucked useful nuggets from vast consumer and business databases. But just look at where the mathematicians are now. They're helping to map out advertising campaigns, they're changing the nature of research in newsrooms and in biology labs, and they're enabling marketers to forge new one-on-one relationships with customers. As this occurs, more of the economy falls into the realm of numbers. Says James R. Schatz, chief of the mathematics research group at the National Security Agency: "There has never been a better time to be a mathematician."
While it gets the vision thing mostly right, it drops the ball on a key point: it's not the people coming out of mathematics departments that are primarily shaking things up. It's the computer scientists who have mathematics training, people with backgrounds in fields like machine learning, data mining, computational statistics, and computational social networks. You just have look at the technical and research heads of companies like Google, Yahoo, Amazon, and Microsoft to see that this is true.
To make this mistake is to miss the radical transformation that has taken place in computer science departments across the country. While a decade ago, 90-95% of all computer science professors were working in the areas of theory (e.g. discrete algorithms, computability), hardware, database, and languages, now you have 20-30% of the people working in new applied fields like computer security, bioinformatics, computer vision and machine learning, many of which require strong mathematical training.
