The Myth of 'Those Who Can't Do, Teach' - Separating Fact from Fiction

The relationship between knowledge and action is more nuanced than some people think...

Meet Bob. A hands-on data scientist who gives classes in his spare time to help others enter the field and give a human touch to their learning experience.

As data professionals, we’ve all heard the age-old adage: “Those who can’t do, teach.” It’s a phrase that’s been bandied about for centuries, and one that’s often attributed to George Bernard Shaw. But where did this fallacy originate? And more importantly, is it truly accurate in our field?

The answer, much like the origin of the phrase itself, remains unclear. What we do know is that it first appeared in print at the end of the 19th century and gained popularity in the mid-20th century. Since then, it’s become a stereotype that doesn’t add any real value or understanding on the topic at hand.

But let’s be real – teaching, especially in a field like data science where complexity and nuance are paramount, is far more challenging than most practitioners realize. In fact, after a certain level of experience, teaching and mentoring come about naturally. It’s not something that can be forced or faked; it requires a deep understanding of the material, as well as the ability to communicate complex concepts in a clear and concise manner.

And yet, many of us data scientists are hesitant to take on a teaching role, often citing the idea that we’re “better off” focusing on our own work rather than trying to teach others. But let’s not forget – without teachers and mentors, our knowledge and aptitude as data practitioners would be significantly lower than they are today.

In fact, working with data on your own or as part of a small team is far more predictable and oftentimes easier than getting involved with teaching, especially on a high level. It’s like comparing apples to oranges – both have their place, but they’re fundamentally different.

And let’s not forget that not all teaching work is the same. Some endeavors, such as doing a workshop or an in-depth mentoring session with a practitioner, are far more challenging than participating in a boot-camp or doing TA work. It’s like comparing a marathon to a sprint – both require effort and dedication, but one demands a level of endurance that the other doesn’t.

So why do we perpetuate this myth? Why do we assume that teaching is somehow “beneath” us as data scientists? The answer lies in our own biases and assumptions. We often view teaching as something that’s done by others – people who can’t or won’t contribute to the field in other ways. But nothing could be further from the truth.

Teaching isn’t just about imparting knowledge; it’s about creating a culture of learning, one where we can all grow and evolve together. And it’s not just limited to formal education settings – it can happen anywhere, anytime. Whether it’s through mentorship, coaching, or simply sharing our expertise with others, teaching is an essential part of what makes us human.

So let’s dispel this myth once and for all. Teaching isn’t something that only certain people do; it’s a vital part of who we are as data scientists. And rather than viewing it as a chore or a burden, let’s see it for the gift that it truly is – one that allows us to give back to our community, to pay it forward, and to leave a lasting legacy.

What are your thoughts on this matter? Let me know in the comments below. Cheers!