Here's a letter I wrote in response to the article below (which was never published):
I read with great interest the recent "Scientist at Work" piece in Science Times focusing on Nick Patterson. I work in a similar area to Dr. Patterson and liked the way the piece developed the concept of a "Data Analyst". In particular, I thought it important that it showed that in seemingly disparate fields such as Military Intelligence, Finance, and Genomics, there was a common thread of having to grapple with and analyze large amounts of data. Increasingly, the modern world is being transformed by vast sea of social and commercial information being tracked on the internet and by the huge amounts of data being generated by high-throughput scientific experimentation. Because of this, we are increasingly being confronted with large data sets in many fields. The challenge is how to mine them as Dr. Patterson does. A related issue is how we should educate a new generation of ace analysts who can take a more straightforward path to their problem than Dr. Patterson has.
http://www.nytimes.com/2006/12/12/science/12prof.html
Scientist at Work | Nick Patterson
A Cryptologist Takes a Crack at Deciphering DNA's Deep Secrets
By INGFEI CHEN
Published: December 12, 2006
Thirty years ago, Nick Patterson worked in the secret halls of the Government
Communications Headquarters, the code-breaking British agency that unscrambles
intercepted messages and encrypts clandestine communications. He applied his
brain to "the hardest problems the British had," said Dr. Patterson, a
mathematician. Today, at 59, he is tackling perhaps the toughest code of all —
the human genome...Genomics is a third career for Dr. Patterson, who confesses
he used to find biology articles in Nature "largely impenetrable." After 20
years in cryptography, he was lured to Wall Street to help build mathematical
models for predicting the markets. His professional zigzags have a unifying
thread, however: "I'm a data guy," Dr. Patterson said. "What I know about is how
to analyze big, complicated data sets." In 2000, he pondered who had the most
interesting, most complex data sets and decided "it had to be the biology
people."...
[L2E]
