I have talked about the value of a mentor in data science in a previous post. The thing is that even the best mentor in the world is bound to be ineffective if she is working with someone who is not embodying the protege role to a decent degree. But what does it mean to be a protege and how is that relevant in the path of development as a data science professional?
Let’s start by what a protege is not, since that’s more straight-forward and it is often a misconception in people’s minds, regarding this topic. So, a protege is not someone who passively receives knowledge and know-how from a mentor, nor is it someone who obeys blindly the instructions of his guide. A protege doesn’t have to be a helper either to the person who is mentoring him, although it is not unheard of. Also, a protege is not bound to given mentor, since he may be learning different things in his life or career, requiring a number of mentors.
A protege is more of a person willing to learn, mainly through his own efforts, yet open to guidelines by people more experienced and more knowledgeable than himself. A protege teaches himself and makes use of his mentor’s suggestions through an intelligent assimilation of them and through a constantly refined comprehension of the stuff he is working on. The mentor is more of a leader figure, who inspires, rather than demands, leading by example. The protege is humble enough to listen to her before judging the validity of what he hears and makes an effort to understand before choosing to go with it, or discard it. We can think of a protege like a bee, bound to a goal, but with the freedom to go about it in the most efficient way he comes up with. Also, if he decides to be an assistant of sorts to the mentor (usually in a company setting, where there is a more formal work relationship between the two), it is out of free will, rather than obligation.
Finally, it is important to note that the mentor is not a know-it-all so if she is true to herself and values mentoring, she is also a protege. Also, the protege himself may also be a mentor to someone else, perhaps some intern in his team. And since no mentor is adept in everything, it is quite common for someone to have several mentors throughout one’s life. In data science, for example, you may have a mentor to guide you through the whole pipeline of insight-derivation and data product development. However, you may find that you want to delve deeper into programming and choose to have another mentor in that aspect of the craft. Also, you may be into other activities, like creative writing and find that you need a different mentor there. So, it’s good to keep an open mind about the whole mentor-protege relationship.
What is your experience in being a protege? What would you expect from a mentor to make the most of your time with them? Where do you see the most value in being a protege?
Zacharias Voulgaris, PhD
Passionate data scientist with a foxy flair when it comes to technology, technique, and tests.