As experience and knowledge accumulate in our minds, it’s increasingly easy to lose touch of that original spark that brought us into this journey of learning, in the fascinating field of data science. I’m referring to that sense of wonder that made all this otherwise dry know-how of math, programming, and data, something we could lose sleep over. Because if you are really in a state of wonder, it’s easy to forget to eat, postpone other tasks, and even find sleep somewhat less important, when your other option is delving more into the learning of the craft.

A sense of wonder, however, is much more than curiosity or even interest in data science. It is all that, but it’s also a way of feeling, a higher sentiment if you will. Being at wonder is what incites wondering and going into more depth. It is what makes a seemingly mundane task, such as data cleaning, appear intriguing and valuable. It is what makes learning about a new model something truly interesting, not just as a memory-based activity, but also as something that sparks imagination and innovation. It is wonder that makes us ask “what if?” instead of just being content with what is presented to us.

Naturally, this sense of wonder is fleeting, just like the perspective we have as newcomers to data science. The more we learn, the more limited our wanderings in the vast knowledge that the field entails, since being more focused on specific tasks and time frames are of the essence. That’s normal since as data scientists we need to be practical and akin to the way the world works, otherwise, we’d be unemployable. Yet, at a certain point of aptitude and understanding of the craft, it is this sense of wonder that enables us to go further and grow beyond what we are expected to be.

The sense of wonder can be cultivated through a sincere wish to become better for the sake of being better, a wish nourished by our love for data science. Ambition can only take us so far, plus after a while, it can become stressful. Wanting to become better because of a lasting motivation is therefore essential for bringing about the sense of wonder. However, we also need to make time for it and allocate resources to such endeavors. Learning through a book or a crash course may be efficient but it’s what we do beyond this that enables us to learn deeply and cultivate the sense of wonder. Liaising with people who already have this sense strong in them, such as beginners who are dedicated learners of the craft, can be a great aid too. Finally, we need to think about the craft and experiment with new ideas. If we just rely on what this or the other expert says, we are bound to be limited by them. We need to study existing ideas, but also dare to venture beyond them, exploring new models and new metrics. Most of them are bound to yield nowhere but some of them are bound to work and help us look at data science from a different angle.

Cultivating a sense of wonder isn’t easy and it’s an ongoing challenge. However, through it, new perspectives come about (such as some of the stuff I talk about in this blog periodically) while the connectedness of the various aspects of the field becomes apparent. All in all, it’s this perspective that makes the field truly wonderful, much more than a line of work. That’s something to wonder about...

I've talked about mentoring quite a bit lately, as well as in a video of mine available on Safari. Although this topic is not that much in vogue these days, I'd like to say a few more things about it and why it is relevant in data science and A.I. these days, perhaps more than ever.

First of all, mentoring is good for both parties and it can even be profitable. Although it's doubtful you'll be rich by being a mentor, you have a lot to gain in terms of a deeper understanding of the craft, once you start explaining concepts for your mentees, while there is the opportunity to revive the "beginner's mind" through this whole experience. If you are a mentee, you'll save lots of time when learning data science / A.I. since your mentor will answer your queries and even guide you towards the resources you need. If the mentor is good, they may even help you develop the mindset of data science, something that's hard to do on your own.

Also, if you are part of the Thinkful online school, you have a whole set of benefits too. As a mentor, you'll have an easier time finding a mentee and even get paid to mentor them. Also, you'll have a structured learning path, through the corresponding data science courses, so that your mentee won't need you for everything since the platform provides him/her with plenty of resources for all the basics of data science. As a mentee of the Thinkful school, you'll have access to vetted data science professionals who will help you learn, while your mentor can help you further through tailored hands-on advice and guidance through your data science learning.

On another note, through mentoring, you get to have a chance to stay grounded, regardless of your role in this partnership. As a mentor, it's easy to get detached from the world, due to the more high-level way of experiencing the craft, while as a mentee it's easy to get lost in the math or programming side of things. Mentoring helps you stay close to the essence of the field, which has to do with how it is applied and the various methodologies involved in its use.

Interestingly, with today's web technologies, it's easy to experience mentoring wherever you are, as long as you have a reliable internet connection and a decent computer, particularly one with a webcam. Although mentoring in person is generally better, you don't have to depend on physical proximity in order to mentor or be mentored in the fascinating field of data science.

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...

I've talked about mentoring in the past and what a good mentee looks like. Here I'd like to highlight the differences between a student and a mentee since it's easy to confuse the two.

First of all, a student is someone who is generally more passive than a mentee. The latter takes initiative and feels responsible for her progress in the field of study. The former often outsources this to the instructor(s) and focuses more on passing exams rather than actually learning.

In addition, a mentee has a closer connection with the person helping him learn, namely the mentor. The student's relationship with his instructors is more impersonal, mainly because the latter have lots of students to deal with and can't usually focus on every single one unless it's for their dissertation project or something. The mentor, however, is more dedicated to getting to know the mentee better and coach him accordingly.

Moreover, the mentee-mentor relationship is beyond academia, even though it can exist in a university too (e.g. in the case of a PhD program). More often than not, mentees and mentors are working professionals, while the former tends to already have a degree.

Furthermore, mentees tend to have a more focused approach to learning, usually related to a specific field, much like an apprentice. The student, on the other hand, may study lots of different fields, as part of her curriculum at the college or university.

Interestingly, although there are several university courses on Data Science these days, most people who learn the craft, tend to do so either independently or through the help of a mentor. Perhaps there is something to this, other than a coincidence. That's not to say that university courses are bad, but oftentimes learning data science as a mentee tends to be more cost-effective and efficient, time-wise.

What are your thoughts and experiences on the matter?