Recent events in industry have heralded an avalanche of interest in all things data. Stakeholders, both public, private, and everything in-between are racing to cash in on the tsunami of freshly collected data, and companies, government agencies, and a litany of others are clamoring and scraping for more expertise in the nascent field of machine learning, and its proper forefather discipline, statistics. Though predictions may vary, McKinsey Global predicting a demand of 2.9 million jobs requiring data analytic skills this year, Forbes reporting a 650% increase in data science positions appearing on LinkedIn in the last few years, the evidence is overwhelming that demand is skyrocketing and talent is scarce. For those of us already in the field, it’s very good news indeed.
I’ve noticed in particular a peculiar proliferation of data programs, Udacity and Coursera-style mini-courses designed to generate more and more data scientists, and a surge of LinkedIn content geared toward conversational data science and mutual-congratulatory reverie. Connections of mine suddenly are brandishing their shiny-new course certifications, ready and able to dive into a sea of messy, unwieldy data to mine for the sparse nugget of value. Their stories are interesting.
As the data science fever has raged upward and onward, I’m increasingly cognizant of something truly unique, a convergence of public and private interest in what automation, data, and the science behind it can do. Those of us in this space are uniquely situated to mentor and raise up the next generation of scientists in artificial intelligence. And so I come to the rationale for this blog. Advocacy and mentoring are important objectives for me, as those of you who’ve read my political blog know. I’ve also recently weathered a health crisis locking me face-to-face with mortality, so I have a heightened sense of urgency around accomplishing my key objectives. Further, the kind of data science I do is unique, even within the trade, as I enjoy dusting off and leveraging techniques from statistics lost in the excitement of machine learning, and all that goes with it. My aim here is to tell a story, teach some concepts, and share with data scientists and enthusiasts alike discussions with authors, experts, social scientists, and many others.
Welcome to Algo-Stats!