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Here's What Data Scientists Can Learn from Triathletes

First off, a disclaimer: I know that many folks in the analytical community have mixed feelings about the term “data scientist.” Personally, I’ve come to like the term as it captures the Swiss army knife skill set you need today as an analytics professional. And it sounds much better than “data miner.”

Mastering the Balancing Act

As you’re probably aware, a triathlon is a multiple-stage competition where you swim, bike, and run sequentially. This means that triathletes always need to be working on endurance and skills across all three disciplines – mastering only one element of the race is not an option. It turns out that this mindset is useful for data scientists as well. To see why, let’s go through each of the three disciplines:

The swim == communication skills

The general consensus is that you cannot win a race during the swim but you can lose it. In other words, this is not the most critical skill but it’s something you have to master. It's also the discipline that most triathletes enjoy the least.

For data scientists, the analogous skill is the ability to communicate. While it’s not as important as the ability to analyze data you’ll never realize your full potential if you cannot convey your ideas effectively. Presenting decks of endless slides that slowly build up to a fuzzy conclusion will leave people confused and drowsy, not convinced. Learning how to communicate – both when it comes to presenting and project management – can help maximize your impact as a data scientist.

There’s a common belief that, unless you grew up swimming, you’ll never be a good at it. I’ve also heard people say that communication skills are inherent – i.e., that you’re born with them. I believe that anyone can learn to be good at both if they work at it and get some coaching.

The bike ride == analytical skills

The bike section is the longest discipline of most triathlon distances. If you’re a strong rider, you can use the bike leg to separate yourself from the pack. Hence, in many ways it’s the core part of the race. If you don’t like riding fast road bikes clad in lycra, then triathlons are not for you.

For a data scientist, the core skill is the analytical chops. This includes the ability to frame a problem and distinguish correlation from causation, as well as having a command of a wide array of statistical and machine learning techniques. It's fair to say that if you're not interested in acquiring these skills, then data science is not for you.

The run == coding

The run is the last part of a triathlon. It’s also the most strenuous part, both from an aerobic and pure physical standpoint. Many triathletes have experienced cracking during the run.

Now, I’m not trying to say that coding is strenuous – in fact, most people find it relaxing and meditative. The point here is that if you can’t code, you can’t finish your ideas. You might be brilliant at mathematical reasoning and a dazzling presenter. But it your code is unscalable, slow or rigid – requiring endless hours sifting through tangled spaghetti – you might crack during implementation.

Putting it all together

If you want to realize your full potential as a data scientist you might want to adopt the mindset of a triathlete. This means not just focusing on what you enjoy the most which, for most data scientists, is building models. Find a way to continuously balance your efforts and personal development across the three main components of the job: communication, analytical skills and coding. This requires a constant awareness of your weaknesses as well as getting coaching and feedback from people outside your field.

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