I get that a lot. As a born New Yorker, I’m usually inclined to let the pointed — and often rude — question slide by. Often the answer is simply “I’m the guy who’s here to save your ass from something you screwed up yourself.” With the exception of the occasional comedic interludes, I won’t be discussing those situations in this blog.
Professionally, I am a project manager with experience in a wide variety of technologies, using both agile and waterfall methods of development. I’ve also done my share of infrastructure and I’m even pretty handy at pulling wires through walls and reviving ancient hardware when necessary. I’m certified as a Project Management Professional (PMP) and a Certified Scrum Master (CSM). I have a BA in computer science (not engineering), an MBA in operations and technology management, and a small pile of continuing education certificates from the finest universities in the land. I don’t expect to be discussing those things much either. Except, again, where the stories are appropriate as comedic interludes.
Pivoting to Data Science
I started my career as many current project managers did, as a programmer. I was working on “Wall Street” — actually an uptown office — developing systems and algorithms to enable and streamline growing global capital markets. It was fun, interesting allowed me to be extremely productive almost from day one. Had it not been for a poorly-timed market meltdown that forced me and most of my colleagues to look for other options, I might still be there. In the years to follow I’d get my MBA at UCLA, move into project management, move to Silicon Valley, leave Silicon Valley, move to LA in the midst of another recession, get contract work, start my own consulting firm, and eventually find myself wondering what would it be like if I were still writing code to do interesting things, or at the very least, managing projects that to do that?
Looking around, I found myself drawn to the world of data science, machine learning, deep learning and related technologies. As I was discussing this with a younger friend who is deep in the world of biochemical analysis, she said “you should really learn Python and see what you can do.” So I did.
As I did I’ve found myself deeper and deeper in the machine-learning world. I’ve cut back on my ongoing education in project management and management in general, and instead focused on something of a return to my roots, writing interesting code to implement interesting algorithms to solve what I believe to be interesting and important problems. In doing so, I’ve had the opportunity to work with medical data generated by systems that I may very well have helped install or maintain earlier in my career. I’ve recently been looking at traffic and pedestrian safety data generated by the increasing numbers of sensors, cameras and apps that capture the moves of traffic around the LA area. I’ve taken an interest in just about anything geospatial — intially a surprising development for somebody who comes from the finance/operations world — but ultimately one that made sense, when I considered that Google Maps has been my number one time-sink for many years. I’ve started thinking more and more about what I can do with all that data.
Time to write about it
Most of the journey has been undocumented. When it was, it was usually at the darkest moments, in since-deleted Facebook rants to my friends about the sheer pointlessness of data science in specific, technology in general, and modern society overall. Learning is like that sometimes, and nobody ever said it would be easy. Other than a few wishes for the immediate gory death of unspecified individuals at Microsoft, I take it all back.
The purpose of this blog is twofold: to continue documenting the journey, and to occasionally look back and comment on the things I’ve seen and done but did not comment on productively at the time.
I hope you find my journey informative, or at least cautionary.
Santa Monica, CA
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