‘Data Science Is Not a Math Skill but a Life Skill’: Noonies Nominee Kirk Borne

Hey Hackers! I’m Kirk Borne and I’m the Chief Science Officer at AI startup DataPrime, and I am founder and sole owner of Data Leadership Group LLC.

As someone in the data science industry, I believe that the most exciting technology of the present age is edge intelligence (edge computing with AI) because edge sensors are being deployed rapidly everywhere (that’s observability).

The most efficient and effective way to derive the quickest insights and fastest value from these sensors is to move the intelligent algorithms to the data at the point of data collection. Learn more about my thoughts and opinions on these topics and my journey in the tech industry via the interview below.

1. What do you do and why do you do it? (tell us your story)

I love learning about the world (and the Universe, as a card-carrying Ph.D. astrophysicist,) through observation and data and to model the things that we see in order to characterize and predict how they work and evolve (including colliding galaxies, plus also any other dynamic interesting thing that moves through space and time, like customers, marketing campaigns, cyber actors, machines, engines, sports, financial markets, supply chains, logistics, health apps, etc.), and then use the data and modeling to increase our understanding of these things through scientific testing of our data-inspired hypotheses. I also love teaching people about this stuff.

I believe that data science is not a math skill but a life skill.

2. Tell us more about the things you create / write / manage / build!

I am very active on social media, primarily Twitter and LinkedIn. I consider Twitter my micro-education platform, where I share knowledge, new trends, new developments, new algorithms, new applications, and ideas associated with emerging digital technologies of all sorts. I write blogs, lots of blogs -- on my own blog site rocketdatascience.org and on Medium, and for many other venues.

I give talks (conferences, webinars, podcasts), lots of talks. I do a lot of mentoring, training, and strategy consulting. I also do a lot of knowledge-sharing and social promotions on LinkedIn of things that I have seen and am excited about.

I have created several different university courses and taught most of them for many years. Well, I love creating and sharing knowledge -- that’s it, in a nutshell. Also, going back a few years, I did many years of research in astronomy and astrophysics -- you can find my research articles on Google Scholar.

3. How did you end up on your current career path? Do you like it?

I became enamored with space and astronomy as a young kid. First, it was the pretty pictures, then I wanted to know how it worked. I learned early on about math, science, data, computing (and that I was really good at that stuff) -- and that took me to a BS in Physics and a PhD in astronomy, then to 20 years as an astrophysicist at NASA, then 12 years as Professor at GMU (teaching data science, computational science, and a little astronomy), then 6 years as Executive Advisor and the first Data Science Fellow and first Principal Data Scientist at a major strategy consulting firm, and now in my current part-time freelance roles.

It’s science, math, computing, data, and modeling all the time, from age 9 to my startup/freelance/influencer activities today. I feel like the proverbial kid in a candy store nearly every day. I couldn’t imagine doing anything else.

4. What tech are you most excited or passionate about right now and why?

I see connections across many technologies, especially how they are inspired by data, informed by data, driven by data, and deliver innovations through data. That includes AI, autonomous systems, AR, VR, digital twins, blockchain, IoT, etc.

That all brings the focus back to my deep interest right now with edge intelligence (AI and sensors at the edge), observability (the strategic placement of these edge sensors), dynamic data-driven application systems, and the corresponding data-driven job creation and value creation.

5. What tech are you most worried about right now and why?

I guess I am a “glass half-full” sort of an optimist. I recognize the risks and the rewards. I teach about these things, both the negatives and the positives. My favorite class that I created and taught at the University was Data Ethics. That’s because this subject touches all sides of the technology equation: the tech, math, the data, the modeling, the science, and the humanity!

I do worry that a lot of these integrated views of our technologies are not taught in schools, and people only encounter them later, outside of school. That goes for both the risks and the benefits (both sides, not just one side or the other) -- a balanced approach to learning and applying our tech is essential.

6. If we gave you 10 million dollars to invest in something today, what would you invest in and why?

I would invest in training programs for people who are not in the usual training pathways, who are being left out (or feeling left out) of the tech revolution (for all sorts of reasons). That includes displaced older workers (victims of ageism), returning veterans, stay-at-home parents returning to work, recovering addicts, released prisoners, people with serious depression (COVID-induced or otherwise), and people suffering imposter syndrome.

My motivations for doing this grew even stronger when my younger brother died of a drug overdose 5 years ago after being terminated from his lifelong job, being at an age where he felt like he had nothing more to offer the world, and he then turned to drugs to escape those feelings, ended up in prison, then ultimately released from prison with nothing to turn to except his drugs again.

I now wish I could have had more time and resources to prove to him (like I showed many of my students) that it is possible for almost anyone to learn these new techie things -- if the jargon and “magic” is removed, and they start to realize they can learn it once they start exercising and strengthening their innate natural-born learning muscles.

I taught calculus-based data science to math-phobic science-phobic freshmen at the university, without ever mentioning the word ‘calculus’ until they had learned it. When I told them what it was, they were stunned, delighted, motivated, and excited to learn more and to consider a completely new career path in STEM that they never ever imagined for themselves. I ran two successful(!) pilots of this in NYC two years ago for “people on the street” -- teaching some statistics, data science, and that “data is not a 4-letter word.”

7. What are you currently learning?

I am learning more about time management. Ha! I have many activities going full-speed every day, and I need better time management skills. More technically, I am learning about blockchain, decentralization, VR/AR, and how those things are changing business, economies, entertainments, personal finances, and so much more!

Of course, I am learning specifically about where those things intersect with AI, machine learning, analytics, and data science. Why? Because it’s all about the data, the math, the science, and the modeling for me.

8. What’s the best advice you’ve ever given someone?

I don’t know about that. Maybe the best advice given to another is the advice that others have given me, that helped me immensely -- I had to learn it the hard way, and so I hope to advise others before they are forced to learn it the hard way.

That includes these pieces of advice:

1. How to get over fear of public speaking in front of a live audience: “You would be surprised how little other people think about you".

2. How to finish a project when you are a perfectionist (i.e., how to finish it when you believe that anything less than 100% perfect is a failure): “Any job worth doing is worth doing poorly” (because, for me as a perfectionist, doing a job at 99% is a poor outcome, which was the wrong mindset when trying to meet real-world job demands and deadlines) -- this is another way of saying “Getting it done is better than getting it perfect.”

3. “Trust your stuff” -- you hear that a lot in sports, but it applies to techies too -- and it helps to overcome imposter syndrome. You’ve learned this stuff. You can do it. You have every right to be doing this, even if you think you are surrounded by geniuses (which is what I was thinking as a young scientist at Caltech). Trust what you’ve learned and what you know. You just might be the smartest person in the room. If you are a PhD candidate, defending your doctoral dissertation, there’s a very good chance that you are indeed the smartest person in the room -- on that day, on that particular topic. You own it!

9. What’s the best advice you’ve ever received?

Refer to my response to the previous question. (Oh, by the way, referring people to other things you’ve done is another great piece of advice received.)

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