In the previous article, I talked about the dichotomy of Student – Mentee in a data science context. However, it’s really the dichotomy of teacher – Mentor that is at the root of all this, while the data science field itself has a role to play too, something many learners of the craft have forgotten. In this article, we’ll explore just that, in an attempt to gain a better perspective of how true learning works and what it takes to connect to the essence of this fascinating field that’s being tainted by the ones who see it as merely a career-boosting opportunity.
Although there is nothing wrong with the role of a professor in any field, especially the fields of science, it’s important to highlight a distinct difference between a professor and a mentor. The former is usually geared towards giving a set of lectures, in order to fulfill the requirements of his/her professional position, something that may or may not be adequate for conveying the essence of the field, especially for a field as complex as data science. It’s not that the professor doesn’t care for all this, but the nature of this profession makes it incredibly difficult, if not impossible, to do this justice. After all, most of these professional educators have other priorities, such as their research.
Mentors, on the other hand, help others learn about data science, not because it’s our job, but because we care about the field, while we have other sources of income to cover our daily expenses. Of course, we may still have monetary benefits linked to mentoring, but it’s generally not the key motivation of all this. Also, we share knowledge about the field based on our own experience, rather than some curriculum which may not always align with the field. Finally, the connection with the people we help (mentees) is more direct and tailored to their needs, rather than generic and impersonal.
It’s important to note that these two roles although different, may still have an overlap. There are professors who can act as mentors, though they usually do this outside the classroom, as in the case of their supervisory role for a PhD student. Also, someone can be a mentor and yet also work part-time in a university. So, it’s good to maintain a flexible view of this whole matter.
Anyway, if you are willing to learn data science in depth, it’s definitely better to do so through a mentor, particularly one with a diversity of experiences in the field. But what about the mentor himself? Where does he learn about all this? In many cases, a mentor may have another mentor to learn from, though it is also possible that the data science field itself is that person’s mentor. After all, data science is a living field, dynamic and ever-changing, with plenty of things to teach to those who are willing to learn from it. Many of its secrets have been discovered but there is still a lot that it’s uncharted territory. That’s something data science can teach anyone who is willing to learn from it. All it takes is a solid understanding of the fundamentals, a strong sense of discipline, and the open-mindedness to abandon what you know for what you can know if you maintain a beginner’s mind...
Zacharias Voulgaris, PhD
Passionate data scientist with a foxy approach to technology, particularly related to A.I.