Who wants to hear a story about data?!
Understandably, not a lot of people would raise their hands to an intro like that. For me, however, data has sculpted my career path and led to many exciting opportunities over the life of my profession. It hasn't been easy -- I think any entrepreneur would tell you the same and surely the content and media attention around self-starting individuals would concur. But I'm not here to tell you about the hardships of starting a business. Rather, I'm here to tell you about how I have grown (and continue to grow) into a role within an industry I never really set out to enter. We are very lucky to live in the Information Age where autodidacts like me will never run out of resources to learn what they need to achieve success in their given field. If you are interested in entering the machine learning/data science field, I hope this provides you the encouragement to set off or carry on!
How did I get into this industry? No really -- I'm still not entirely sure how I ended up in my first position as a market research project manager. When you are about to graduate college, I think you tend to have a "spray-and-pray" mentality to getting your first job. Fire off those resumes online or drop them off the top of a tall building like a ticker-tape parade and see what sticks.
While I did entertain interviews from more than one business, I ultimately accepted an entry-level position in Indianapolis, Indiana that I still can't recall ever reaching out to initially (must have been one of those ticker-tape resumes). So began my first foray into using data to drive business decisions.
When someone asks me what "consumer market research" is I typically tell them it's surveys and focus groups.
"Oh, yeah! I know what you mean now. Yeah, I NEVER do those."
It's funny how many people over the years have responded the same way to that! It makes me wonder exactly who responds to online surveys and participates on focus groups these days. The whole purpose of consumer market research is to understand the mind of the consumer: what makes them buy? What makes them chose one product over another? What will they buy in the future? We answer these questions by analyzing data.
Initially, I enjoyed this industry because I found it fascinating to peer inside the mind of the consumer. While this was private industry, there were still inklings of academia when it came to study design and statistical analysis. Furthermore, research (whether academic or business-related) is an art and a science -- something that didn't fall short on someone like me who grew up playing with Legos and reading non-fiction history or science books for my elementary school's summer reading programs (which my mother had to argue I'd actually read in order to win my pizza party)!
I continued to work in consumer market research for some time moving eventually to Chicago where I was employed initially at a boutique agency and eventually the largest market research company in the world.
But I was falling out of love with the industry. Why you might ask? Well:
It was around this time that terms like "Big Data" began to enter the conversational lexicon of business executives and journalists alike. I was intrigued and in my typical fashion, I wanted to learn more. What I found seemed to be an alternative means of understanding the consumer mind. Data was being collected literally everywhere around us. Smartphones and the Internet made that a reality. Additionally, people are much more likely to provide better data on their behaviors when they aren't directly questioned or even realize they are providing data in the first place. I think the adage "Actions Speak Louder Than Words" is appropriate here. Since nearly every action we take today, like our FitBit biometrics, what we purchase online or in stores, how we drive our cars, and possibly every button we click on our smartphone apps and websites is registered as a data point somewhere there must be a way to use that data more effectively than unsubstantial survey data.
I approached many executives and sales team leaders at my organization at the time with an array of ideas about how we could utilize these new data sources to transition away from the survey/focus group methodology. I've heard that if you complain about how something is done in a business to "top brass" (or in my case maybe the "middle copper" if there is such a thing) you better have ideas how to improve the situation.
Instead, I was released.
I knew that my interest in data and the potential power it had to answer business questions, generally improve efficiencies via automation, and even provide competitive advantages wasn't unwarranted (although I remember a supervisor of mine once saying many years ago that "Big Data" was just a fad and it wouldn't amount to anything in the long run -- that was probably 8 years ago now). Long before my premature departure from my organization, I had begun to ramp up my self-study on the topic. As I stated earlier, the Internet provided a litany of resources which, while large back then, isn't even comparable to the resources today just a few years later.
I fully inundated myself in the topic. Keep in mind that although I received my MS, I did not pursue a Ph.D. While many might write off a machine learning practitioner without a Ph.D., I beg to differ. While everyone learns differently, I found these resources extremely helpful in the early going in lieu of pursuing an expensive and time-consuming higher degree:
There was nothing easy or quick about the process I went through and not everyone thrives in an environment where the only one accountable for their progress is themselves. It took me over five years to feel comfortable with the foundational aspects of machine learning. Finally, after several small projects (including one I was lucky to snag with the U.S. Department of Energy) and more on the horizon, I incorporated as Expected X.
I'm actually surprised that I don't get asked what "Expected X" means more often. Some people I speak with say "oh, yeah that's clever" to which I can only assume that they are knowledgeable in statistics. I, myself, have never developed proper verbiage so why not now? I suspect it will be a great lead-in to the rest of this article's content.
Expected X really refers to "expected value" which is a term in statistics represented formally by the notation "E[X]." Expected value is just the value we'd expect a variable to take, on average, after several repetitions or iterations of some, say, experiment. In simple terms, you could equate this to saying "what's the expected value of getting tails when I flip a coin?" To which the answer would be "0.5" or "50%" -- we have two possible outcomes from a coin flip (heads/tails) each with an assumed probability of 0.5.
Expected X is more than just a business name, it's a personal philosophy of mine stemming back from my market research days. Turn on the news and you are often presented with a story regarding some new research study out of the University of So-and-So proclaiming something like "a chemical in coffee is linked to cancer" only to be followed a few weeks later by a new study proclaiming "coffee's health benefits!" The point is that a single, one-off research study hardly lays the ground for truth and causality. In fact, most scientists worth their weight would agree that we can only move the needle closer and closer to accepting a given hypothesis and can never actually know what "truth" is. Deep, isn't it?
That's my business philosophy as well -- machine learning solutions are never a magic bullet. It takes iterations many, many times over to improve an algorithm's accuracy, precision, or whatever metric you are using to measure its efficacy. Conveying this philosophy is difficult considering the slew of content available proclaiming machine learning or deep learning or artificial intelligence (all three terms with different meanings frequently used interchangeably by the mass media) as a be-all, end-all future business model. Sound familiar, blockchain?
It's easy to contract "imposter syndrome" in the machine learning business and I am not immune. Imposter Syndrome, for those that don't know, is the doubting of one's abilities and accomplishments compared to other individuals in their given field. Having been taught via "trial-by-fire" within a corporate setting as well as my own self-study, I have frequently questioned if I know enough to master this discipline's exceedingly-technical ecosystem.
So on a final note, I have a few words from my experience that help me push through and elevate my passions:
All that being said, I hope the reader has gained something from my personal perspective. While your own professional interests might lie elsewhere, I believe much of the preceding could still be applicable to your own situation. Now, I suppose I could ask you if you Agree or Disagree with me (maybe on a scale from one to five, for instance), but that might not be a very trustworthy methodology!