Data & Analytics STL

A “Strong” Base: How Strength Training Prepared Shanti Greene to Train Data Science Models

Shanti Greene

At his peak, Shanti Greene could squat 605 lbs. and bench press almost double his body weight.

Since then, he has had new demands on his time – all good things, like family and career – so he isn’t able to hit those numbers anymore, though there are some correlations between what he learned and his role in data science with Daugherty Business Solutions.

Shanti’s strength training journey started with powerlifting and progressed to Strongman and CrossFit. He became a Certified Strength and Conditioning Specialist and held a CrossFit Level 1 certificate as well.

Powerlifting, Strongman and CrossFit all focus on different aspects of strength. Powerlifting centers on three lifts – squat, bench, deadlift. Strongman focuses on lifting and moving with awkwardly shaped objects. CrossFit incorporates metabolic conditioning into the mix.

This isn’t the whole spectrum of strength training, which is a very broad field, just like the field of data science. Both the strength training and data science fields come with trade-offs in the specific areas of focus. For example, when Shanti was good at CrossFit, he wasn’t as good at Strongman and vice versa.

Similarly, data science can include statistical analysis, data engineering, business analysis, data visualization, reporting, and machine learning specialization – all of which to some degree prepare how data is organized, collected and distributed. While in the long run, knowing each area makes Shanti more well-rounded, he can’t specialize in all of them.

You might say data science enables Shanti to capitalize on his – wait for it – strengths. He doesn’t consider himself a great inventor of new ideas, but he loves optimizing existing products.

Similarly, strength training is not about inventing new ways of getting strong – the methodologies are well studied. It’s about considering the end goal, putting measures in place to achieve that goal, then monitoring the measures. Shanti used to keep detailed training journals, jotting down his perceived exertion, heartrate, the reps he did, the time he did them, the supplements he took. All this he used to go back and determine if he was doing better.

Ultimately, it was a combination of research, collaboration and coaching that led him to mastery in both strength training and data science.

In strength training, research involved some of the old standards; the Conjugate Method, Arnold Schwarzenegger’s Encyclopedia of Modern Bodybuilding, Greg Evrett’s Olympic Weightlifting. In data science, it was building a foundation in linear algebra, discrete math, calculus and computer science.

Collaboration meant learning new lifting styles from different people. For example, Shanti got into Strongman when a guy approached him at work and said, “I heard you’re the kind of guy who’s interested in a giant tractor tire.” Similarly, collaborating with other data scientists energizes Shanti about the work he does – considering 10 different approaches to the same problem.

But it wasn’t until Shanti began to run a bootcamp-style CrossFit group that he quickly uncovered what he needed to learn.

“Teaching something is the real test of what you know,” he said. “If you can’t teach it, you don’t really know it. Coaching helps you understand the nuances of various aspects and instills those fundamentals.”

Likewise, in data science, Shanti’s strong foundation of the basics allows him to learn the nuances of multiple domains and articulate data solutions without using jargon.

“If you have a good base, then going deep becomes a little easier,” Shanti said.

Today, at Daugherty Business Solutions, Shanti leads the Innovation Hub, where he and his teammates collaborate to give clients flexible ways to engage with Daugherty: building proofs of concept – taking a big problem, solving it at a small scale, then scaling it up to understand the feasibility of different solutions. By decomposing the problem in this manner, Shanti and team can uncover solutions quickly without trying to bite off more than they can chew.

It’s similar in some ways to the pacing techniques Shanti learned in CrossFit – being able to go fast enough that he could keep up with everyone else, but not so fast that he’d hit his red line and not be able to recover.

By actively managing exertion, Shanti was able to be very competitive without becoming exhausted – performing at a high level over longer periods.

“It’s not just about being good, but about determining the right things to be good at, then perfecting those,” he said.

And, as is true with all things, practice makes perfect. The stronger the workout regimen, the better the payoff – just like with data science.

Do you have any unique quirks you’d like to share with the enterprise? Email us at