Why to start learning from data science mentors in the 2020s?

April 26th 2020

If you are familiar with jobs in data science, you may have seen many articles on how these positions are growing in popularity among employers. The number of expert educational options has grown extensively over recent years and the labor market has expanded as well. However, the promotional articles can be misleading, creating misconceptions, and can be discouraging when reality appears to be very different.

It is not 2000s anymore

When Data Science was introduced to the world, the labor market was not ready for this new highly specialized field. The term data science was presented as an alternative to computer science in 1974. However, data scientist as a job title was not used until 2008.

In the beginning, companies hired inexperienced people, mostly right after universities, which did not offer relevant education yet. In the years of 20016-2019, data scientist was considered the best job and the third-best ranked for 2020.

With the increasing number of investments in building (or shifting) to data-driven solutions, the need for experts in the filed rises as well. Even though the number of open positions is growing, there aren’t enough skilled professionals, it is hard to land a good job and develop yourself. The gap between supply and demand within the respective field is quite deep, yet those who try to find it difficult.

It is getting harder to get noticed

With the progress of the field and technology, companies also raise their expectations.

It can be compared to trying to fish in the very small pool with other 100 fishermen and still not every fish will be picked out. Many people know what data science is, have basic skills, want to find a job in this area, see hundreds of job postings on lower levels and despite that still don’t get employed.

From many discussions with potential candidates, hiring companies, and our market observations, that the problem is a lack of particular skills, not just general understanding, which are hard to acquire independently, and it takes too long to get on the entry-level. Unless you have a mentor that directs you the right way.

Applied theory

Advice and information from an expert can really make a change and help to level up on the career path. And not just that. Many studies have shown that learning something new efficiently is when it is accompanied by practice, seeing the real examples. It makes the information more “tangible” when only reading a book can be too abstract, especially in such a complex field that is data science.

What are the main concerns?

Usually, when we talk to data science newbies, they face different challenges on their way to becoming data science specialists, but they agree on a few things when it comes to professional mentorship.

Problem #1: Who do you need? Mentor or a tutor?

Many definitions describe a professional, who can help you develop yourself in various fields. They might seem all the same, but there is a difference in the meaning. Just to briefly demonstrate the distinction of a few:

  • A mentor provides support, advice, and guidance in navigating work situations, shares knowledge, resources, and expertise, gives feedback, and constructive criticism, helps to look at problems from a different perspective and find an appropriate solution. In general, a mentor is an example to emulate and helps to develop relevant skills for the present project and future career path.

  • A more structured and formal way of development is provided by a coach in a shorter time. A coach helps to defined goals and particular tasks to be followed, however, they do not necessarily have first-hand experience in the field they coach. Their main goal normally is to increase your performance.

  • More established senior in the field that provides connections to important people and resources, advocates for you and opens new doors regarding significant career advancement is known as a sponsor.

  • A term tutor is used more commonly in academic circles. They are proficient in a specific subject and educate that specific technical expertise, help to fix immediate or short-term issues.

Problem #2: Where do you find the expert?

Another obstacle to overcome is to find a reliable expert you can trust, who will be approachable and willing to guide you. Commonly, people seek advice in their circles, on the job, within a community, but when a person is just starting, the network is very small. Survey on Mentorship, help in 2019, revealed, that the demand for mentors is high, yet the supply is quite low. Which means, not only it is hard to find the person with the particular expertise willing to mentor you, they might not be available when you need them. And professional mentoring is very expensive

Problem #3: Will the acquired knowledge be useful for my new job/project?

If you ever tried to learn something new, you most probably found too many articles to read, too many courses to take, classes, podcasts, videos, books, etc. You see lots of similarities within all the content, sometimes find a new perspective. And not to mention how many different paths you can choose, e.g. what programing language you need? What tool do you need to use? But what is relevant and needed and what is not that important?

We have found out, that many of people who wanted to learn basics of data science spend too much time on actually finding the right material, 43% of them gives it up and those, you perceived in studying realize after, that what they learned is not what they need to land a good job. An example, you learn SQL and visualization in Tableau, and the employers need Python, R, and MS PowerBI. A natural response might be to learn the other combination. But how long can you really do that?

However, if you could turn it around, start with the project, and then learn all the requirements, it would be not only faster but more importantly effective.

Incubating future Data Masters

We noticed a gap in the labor market and applied our know-how, experience, and skills to develop others. It not only lets companies progress toward more data-driven solutions and also lets not so skilled people to find new interesting projects to work on.

Data Master Incubator helps ambitious people to dive in and advance in their career in data science, and deliver new projects successfully. The uniqueness of our approach lays in the reverse strategy. We seek the potential to develop, not years of experience. We connect interesting projects and people, who want to learn. Companies trust us to contribute to their internal team by sharing our extensive experience of custom-built solutions and technological skills and thus support their growth.

During the incubating period, you work on the given project, which usually is challenging and more advance that what you would normally do. However, our experts guide you to deliver better results. This way, you learn, the project progresses faster, and you are more successful in your job in front of your team and employers. On the other side, the company is sure, the project will be executed the best way possible, thanks to the supervision of our mentors, who are also working on similar projects therefore not out of touch or just speaking theoretically. However, the newly gained knowledge stays in your team afterward. Typically, the cooperation doesn’t end with one project and we continue to grow together.

Benefits of having a guidance

Experienced data professionals have navigated their own journey with success.

Shared knowledge and experience

Mentor advice and support can guide you, help you to develop the necessary skills, soft and hard skills. One of the most valued areas within a mentoring is when you are giving a new very challenging project that required substantial knowledge and advanced skills. Creating strategies for executing the project efficiently can be difficult when you need to first understand the problem, figure out the right approach, and execute it.

Constructive and continuous feedback

A good mentor tells you if you do something well or not. A great mentor will explain why and steer you back on the right track if you deviate. They do not do your work and definitely do not micromanage. Freedom within a framework while gaining independence and confidence. Only praise won’t get you far, but constructive criticism helps you to sharpen your skills.

Real rules

What is not written in any book or brochure, are the unofficial rule, how it really works. An experienced professional has walked the lengths for years, can tell you who is the person to talk to in different situations, especially when working for big clients. That is why honesty is important when trying to learn and the well-known, yet unrecorded policies will get you ahead.

Diversity

One kind of guidance can be limiting. The most common approach is to have one main mentor and also sometimes discuss the matters with someone from a different expert field to add a perspective. For example, Data Master Incubator provides quite a few mentors with a diverse focus, which provides an effective way of how to achieve such a variety.

Different views, opinions, and skills to broaden horizons

The best mentors fill the gap where you struggle. They indeed make your strengths stronger but more importantly, the weaknesses. The mentors, who have the exact skill sets hardly can develop you further. The quickly evolving area that data science requires constant learning, while mentorship is crucial to learn faster.

Encouraging new ideas and challenging discussion

Everyone has experienced a situation when somebody’s comment gave them a great idea, and it did not have to be anything too specific. And imagine having a constant flow of inspiration discussions. Mentorship is also about bringing inspiration, encouraging new fresh ideas, and helping you to turn them into reality.

Conclusion

In conclusion, mentorship is a very effective way of learning and developing new skills. Practice gives more than just a book and a few expert advice can be worth more than a professional course. Especially in the age of almost unlimited options of finding information, it is hard to recognize what is relevant and important, where to invest the energy and whom to talk to understand better. And of course, getting feedback on your performance that is truly helpful.

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