Why Is 1-on-1 Mentoring Effective in Data Science Learning?
By: Zacharias Voulgaris
Tags: Artificial Intelligence, Machine Learning, Mentorship, Data Science, Bootcamp

The person on the left is the mentor while the one on the right is the mentee / protege

What is mentoring?

Mentoring is the professional relationship that involves the educational and professional guidance of an individual (aka mentee or protege) by another, more experienced and more knowledgeable professional (aka mentor). Mentoring is a commonplace phenomenon nowadays, especially in sophisticated lines of work, like data science.

What does Data Science learning involve?

Data science is a complex field and as such it requires methodical learning. This involves technical skills as well as business-related, and other material, all of which help someone develop the right mindset and the tools to apply it to real-world problems. Naturally, unless someone is particularly talented, this whole process of education in the data science discipline is quite challenging. After all, it’s a hands-on profession so a lot of practice is also required. This is one of the reasons why books and videos are insufficient in providing the education needed to become a (good) data scientist. Other educational strategies are better, but even those often have their gaps.

How does mentoring fit in all this?

Mentoring attempts to fill all the gaps an educational strategy has so that you can learn what it takes to become a good data scientist. On top of that, it clarifies concepts learned already and gives a sense of perspective to all the data science knowledge the mentee has. The mentor also offers useful feedback on various projects, including code-related matters, significantly speeding up learning. Mentoring can take place either on a one-on-one setting or a single mentor with several mentees at once (e.g. in the form of a workshop).

What does one-on-one mentoring entail?

One-on-one mentoring is the most common kind of mentoring and it is the one that yields the most benefit. It is, in essence, mentoring involving just a mentor and a mentee. It is not the same as tutoring though as the mentee makes an effort of her own to learn the material at hand, regardless of the mentor. The latter help cement the former’s understanding, while also providing recommendations about projects and other sources of learning.

The one-on-one mentoring can take place in person or, more commonly, over the internet, particularly through a VoIP system. The latter allows for screen-sharing and other useful features, making mentoring easier and more effective. Oftentimes, the sharing of material is involved, something that can save the mentee hours of work and tons of frustration.

Benefits of having a mentor

1. The mentor can serve as a professional role model. This way, a data science student can have a clear idea of what to aspire to. This can help her focus her efforts accordingly and be more effective in her career choices.

2. Individualized help enables people to learn more quickly. If it were as easy and simple as buying a book, data science authors would be rich. Also, even if a data science course can go a long way in helping a student learn the essential know-how, it is often not enough. Every student is unique and has unique needs. The individualized help a mentor offers can help address this matter.

3. A mentor can keep a student accountable and help him develop a sense of rhythm and professional standard. This make students work harder, smarter, and most importantly, with consistency. In a way, it is like an apprenticeship, something that has been proven to work for centuries, particularly in areas where hands-on work is required (e.g. in the work of an electrician). Data science may be more high-level than this kind of work, but it still has a lot to benefit from this sort of approach, so that the new generation of data scientists are not just knowledgeable but also practical.

4. A mentor can give a holistic view of the field more quickly. Learning the big picture is difficult on your own. This is particularly the case if you are new to learning and haven’t acquired enough work experience yet. Mentoring can help bring about this sense of perspective and down-to-earth attitude that is so much needed in our profession.

How you can put all of the above info into practice

Based on all this, it is clear that mentoring has an important role to play in data science education, especially today, when time is of the essence when it comes to technical learning. Fortunately, Bootrain has your back on all this. Among the various services offered by this data science education company is one-on-one mentoring. This can be either as part of an educational package (online course) or independently. This way, you can adapt this service to your individual needs and optimize your educational journey in the world of data science.