I've always been interested in data, how it's interpretted and the different ways it can be sliced. However, I've always considered statstics itself to be a math that I didn't like. As "data science" and "big data" became more popular, however, I started to look into ways to learn more about it and possibly use it as a entry into another act of my career.
The advent of MOOCs has opened up the possibility of learning new subjects and subject-focused websites like Data Science Central here make diving into new subjects more accessible than in the past. I wanted to detail the start of my journey here - in a couple of months, I'll update where I am on my path and what else I've learned along the way.
The hardest part of a learning journey is where to start. If you don't know anything, there's so much to learn that it's difficult to know what order to take things in. Google sent me down a few directions with a solid Quora page, following prolific data scientists on Twitter that I got off a Hilary Mason post (Hilary herself being a prolific user of Twitter) and just general hunting around.
However, again, that gave me lots of places to start but not any notion as to what the right start was. As I was reading, though, it became more and more apparant that no data science language, technique or tool was going to be useful unless I had an understanding of basic statistics. This led me back to Coursera, where I had dabbled in classes but never dove in. Picking through the catalog led me to Dr. Mine Çetinkaya-Rundel's Duke course on Data Analysis and Statistical Inference. Dr. Çetinkaya-Rundel's approach was comprehensive yet approachable. Sometimes in the college courses, the professors bury you in mathematical theory and you get so busy memorizing Greek letters that you get frustrated. The approach here was to keep the theory light by explaining it through application. I took the class with no issues and am no longer intimidated by statistics.
In the meantime, Udacity was pushing its Data Science specialization and Coursera had published theirs through John Hopkins. Both had a free and paid component but the course list looked a bit tighter and better integrated on Coursera, even though I chose the free option. I also reviewed Dr. Granville's Data Science Apprenticeship here. I eventually ruled out the apprenticeship, for now, because I felt I had more learning to do.
Right now, I'm in the second of three sections in the Johns Hopkins track. Those will wrap up by July. I'm also signed up for the Machine Learning course that may have started all this, at least in the MOOC space, from Stanford. The Johns Hopkins track has a Machine Learning course but I expect it to be a lighter treatment than the Stanford one and a good grounding in Machine Learning is necessary to play in that space as well.
I don't know where this journey will take me yet but it's been a great learning experience so far. I look forward to continuing and then maybe jumping in on the apprenticeship at some later date!