Databases Reference
In-Depth Information
CHAPTER 7
Extracting Meaning from Data
How do companies extract meaning from the data they have?
In this chapter we hear from two people with very different approaches
to that question—namely, William Cukierski from
Kaggle
and David
Huffaker from Google.
William Cukierski
Will went to Cornell for a BA in physics and to Rutgers to get his PhD
in biomedical engineering. He focused on cancer research, studying
pathology images. While working on writing his dissertation, he got
more and more involved in Kaggle competitions (more about Kaggle
in a bit), finishing very near the top in multiple competitions, and now
works for Kaggle.
After giving us some background in data science competitions and
crowdsourcing, Will will explain how his company works for the par‐
ticipants in the platform as well as for the larger community.
Will will then focus on
feature extraction
and
feature selection
. Quickly,
feature extraction refers to taking the raw dump of data you have and
curating it more carefully, to avoid the “garbage in, garbage out” sce‐
nario you get if you just feed raw data into an algorithm without
enough forethought. Feature selection is the process of constructing
a subset of the data or functions of the data to be the predictors or
variables for your models and algorithms.
