Chapter 1. What is data science?
The future belongs to the companies and people that turn data into products
We’ve all heard it: according to Hal Varian, statistics is the next sexy job. Five years ago, in What is Web 2.0, Tim O’Reilly said that “data is the next Intel Inside.” But what does that statement mean? Why do we suddenly care about statistics and about data?
In this post, I examine the many sides of data science -- the technologies, the companies and the unique skill sets.
What is data science?
The web is full of “data-driven apps.” Almost any e-commerce application is a data-driven application. There’s a database behind a web front end, and middleware that talks to a number of other databases and data services (credit card processing companies, banks, and so on). But merely using data isn’t really what we mean by “data science.” A data application acquires its value from the data itself, and creates more data as a result. It’s not just an application with data; it’s a data product. Data science enables the creation of data products.
One of the earlier data products on the Web was the CDDB database. The developers of CDDB realized that any CD had a unique signature, based on the exact length (in samples) of each track on the CD. Gracenote built a database of track lengths, and coupled it to a database of album metadata (track titles, artists, album titles). If you’ve ever used iTunes to rip a CD, you’ve taken advantage of this database. Before it does anything else, iTunes ...
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