I’ll try to be quick. It will be difficult.
A brief note on what I’m doing now. My time is roughly spent like this (waking hours):
Let’s say the vertical axis is hours of the day and the horizontal is something like months. And repeat.
My formal education was in chemical engineering, go Missouri S&T! I worked for a specialty chemical company and then for an oilfield services company. Then my wife and I made a dramatic shift. We decided to drive a truck together, over-the-road as it’s known. 49 states and 7 Canadian provinces. We had a plan to do that for a set number of years, give or take few depending on actual results. Now we have a little girl, and life is good. Except for the fact that trucking (which I still do) isn’t particularly favorable to family life.
For a couple of years I taught math at a fantastic school in Northwest Arkansas. Though it was a great experience, teaching wasn’t for me. For the past year I have been teaching myself programming, and building a foundation in data science.
My history with computers.
In the Year of Our Lord, Nineteen and Ninety Two (8th grade), I vaguely remember programming an Apple IIe to draw several frames of a fish jumping out of water. I was proud. Of course I used MS Word and Excel later, but my next real exposure to programming was a C++ class in college. I don’t remember what it was, but I did. Not. Like. It. That was a shame because the instructor also taught Physics and I loved that. I went on to copy and paste static websites and make them my own with small changes. Huzzah! I did some undergraduate research modeling supercritical CO2 on a UNIX system tweaking my professor’s Monte Carlo simulations. Until then I thought it was just a car.
Anyone will agree with me when I say this: After using C (which I love), using Python is a dream. That’s how I got interested in data science. Python has amazing libraries (and a thriving community) for data science. So my focus has been getting better with Python, and learning everything I can about statistics, analytics, visualization, and machine learning.