"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:
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.
My favorite place to start with R is John Cook's Introduction to R for programmers.
There are thousands of machine learning algorithms. Luckily for us, a few have risen to prominence. The most popular machine learning algorithms are:
I want to be a data scientist. I want to learn in the most efficient way. I want to learn from the best.
One of the foremost data scientists is Hilary Mason, (blog, @hmason). She has a tremendous ability to make difficult concepts easy to understand. See: An Introduction to Machine Learning in 30 Minutes.
What did I learn from that video? This can be fun!
In addition to learn the necessary math, I should use the most appropriate tools. A little sleuthing found a survey of the data scientists competing at Kaggle.com.
The winner? R , the open-source tool for statistical analysis.
The other tool to learn? Python, due to its ease of use and large number of libraries.
Combined, those two tools make it easy to find, consume, and analyze data from many places. Next up: math.Permalink