Why I’m Pursuing a Data Science Skill Set

Take a look at your hands.

Your hands are your hands… but they aren’t just simply hands. Your hands are made of cells, which are made of molecules, which are made of atoms, which are made of smaller parts that scientists are still constantly learning about even through today.

Our hands are interesting because the “thingness” that comprises our hands is contained of all these little particles, but these particles could have easily become a foot or a head or a raccoon. None of these particles are a hand themselves; it is only in their congregation that they form a hand.

The particles form your hand… but they are also not your hand.
Where does the particle start and the hand end?
This isn’t unlike the question of what comprises you as a person: Are you your hand?
Where does the consciousness lie?

It’s no secret that truth-seeking is something that’s very important to me, as evidenced by this blog. It’s one thing to talk about philosophy and spirituality, but we have to be honest with ourselves: that stuff is all speculation at the end of the day. Speculation isn’t inherently bad and can often be very helpful, but it is not the sort of truth that can be replicated in a lab via the scientific method.

But that doesn’t mean all is futile. Using the phrasing of Rob Bell, there’s still something that “hums” within me that inclines me toward this idea of “something more.” It might be irrational, but I just can’t accept that this is all there is to life. I don’t think it’s fair to dismiss as it’s entirely possible that one day we will have scientific understandings to explain the randomness.

That’s why I don’t buy the whole “randomness of the universe” argument. If these particles were truly random, there should only be one of two outcomes: A) There should only be chaos, and there should be nothing that exists. Or B) We should only have amorphous blobs of matter at best. Consciousness in particular makes no sense in a truly random universe.

And what is the opposite of randomness?

Order and patterns.

Everything is comprised of order and patterns. From the weather across different climates to the way genes encode the color of your eyes, we can’t escape from it. Even the role of a dice isn’t necessarily random in that the “randomness” is based on the die existing to begin with. Functionally speaking of course, it’s still useful for us to consider a non-weighted die to be random.

(Side note: This does NOT at all imply that we should base decisions around inherent order. Basing things around natural, categorical order can be dangerous. Can you say… racism?)

The big question that data science answers, then, is this: What patterns can we find in the chaos?
Can we predict how something will behave given on what we know from past information (data)?
What is the signal in the noise?

Transparently, I didn’t begin on the data science path because of this. I started because somebody told me it would be a good career move, and that is 100% true. Now… I continue on this path because I began to discover that there are much bigger implications and potentials for this work that span far beyond what to watch next on Netflix.

In fact, I think we’re still very much in the infancy of what we’ll be able to do or learn with data since. (And I totally understand that data science is basically a fancy term advanced statistical learning, but the same holds true anyway.) Data science started becoming more prevalent as we began to develop systems that could chug through a ton of information, and simultaneously, we have more electronic data to work with than ever.

Just think about what the technology landscape was like a decade ago. We barely had smartphones, tablets were still a rumor, and big cloud platforms like Amazon Web Services (AWS) were basically unheard of by the rest of the general IT industry. And data? Even some refrigerators gather data today!

If we’ve come this far in ten years… what will it be like in another ten? Another twenty? Another hundred?

We’re on the cutting edge of understanding how these new technologies will transform our future. Some people are figuring out how to better leverage these machine learning (ML) algorithms to better detect for cancer within patients. We recently got our first picture of a black hole using “the equivalent of 5000 years of mp3 files.” And one of my favorite recent discoveries, we’ve even been able to use data science to better understand Picasso’s handiwork.

Like I said, friends… we’re just at the beginning.

And I want to be along for the ride. I want to be at the front lines of these unfolding things to see how we will continue to uncover new truths as we continue to learn more and more about this space. Sure, it’s a great career move and helps me continue to provide for my family, but for me… this is so much more than a regular paycheck.

It’s about pioneering new forms of truth.

It might not be in my lifetime, but maybe one day our work will cure cancer. Maybe one day our work will continue to help find a new form of energy that will help maintain our planet from an environmental perspective. Maybe we’ll be able to optimize food growth and end world hunger.

And maybe one day… we’ll understand why the particles that comprise our hand order themselves in that way.




Machine learning engineer at a Fortune 50 company, 5x AWS certified, 2x HashiCorp certified, 1x GCP certified, M.A. in Organizational Leadership, PMP, ChFC, CSM

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David Hundley

David Hundley

Machine learning engineer at a Fortune 50 company, 5x AWS certified, 2x HashiCorp certified, 1x GCP certified, M.A. in Organizational Leadership, PMP, ChFC, CSM

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