Data Science

15 August 2012

“Data Science” is a term coined by DJ Patil (@DPatil) and Jeffrey Hammerbacher (@hackingdata). The term became popular after an article, Data Scientist, the Sexiest Job of the 21st Century. It’s a term similar to “webmaster”: it covers multiple roles. It is also not easy, as Joseph Misiti eloquently describes.

Data scientists do 3 things:

Ask Questions
Write Code
Do Math

Math + Code = Machine Learning (ML). It’s such a dynamic field of study that the best sources are often blogs, research papers and conferences instead of books or classes. Here are the resources I’ve found most helpful.

Blogs

Dataists
Kaggle’s No Free Hunch
Pedro Domingos’s Overview of Machine Learning
Jeremy Kun’s Introduction to Machine Learning
BigML’s Everything You Want to Know about ML
Buck Woody’s Setting up a Data Science Laboratory

Books

Machine Learning for Hackers, by Drew Conway (@drewconway) and John Myles White (@johnmyleswhite). * There is also a Python derivative, Will It Python
Data Analysis Using Open Source Tools, by Philipp Janert

Videos

Conferences

Classes

Coursera’s Machine Learning class
Coursera’s Introduction to Data Science

Languages

The most popular languages for machine learning are R and Python. The second tier of popular languages are SQL, Java, Hadoop/Mahout/Hive, and Matlab.

My favorite place to start with R is John Cook’s Introduction to R for programmers.

Python is popular because it’s simple and has more libraries than God. The most popular Python libraries for machine learning are NumPy, SciPy, SciKit-Learn and Pandas.tnu

Algorithms

There are thousands of machine learning algorithms. Luckily for us, a few have risen to prominence. The most popular machine learning algorithms are:

Decision Trees / Random Forests
Linear Regression
Logistic Regression
Association Rules
K-Means Clustering

Misc

Machine learning uses a lot of matrix math. It’s quite easy to do matrix math in SQL. Joe Celko has some great examples of matrix math in SQL.