Databases Reference
In-Depth Information
Data science, as it's practiced, is a blend of Red-Bull-fueled hacking
and espresso-inspired statistics.
But data science is not merely hacking—because when hackers finish
debugging their Bash one-liners and Pig scripts, few of them care
about non-Euclidean distance metrics.
And data science is not merely statistics, because when statisticians
finish theorizing the perfect model, few could read a tab-delimited
file into R if their job depended on it.
Data science is the civil engineering of data. Its acolytes possess a
practical knowledge of tools and materials, coupled with a theoretical
understanding of what's possible.
Driscoll then refers to
Drew Conway's Venn diagram of data science
from 2010, shown in
Figure 1-1
.
Figure 1-1. Drew Conway's Venn diagram of data science
He also mentions the sexy skills of data geeks from Nathan Yau's 2009
post,
“Rise of the Data Scientist”
,
which include:
• Statistics (traditional analysis you're used to thinking about)
• Data munging (parsing, scraping, and formatting data)
