Advice for Every Data Enthusiast from My Professional Experiences
Sharing Some Experiences from My Work History
The data field is vast, encompassing many branches within it. Therefore, you could hold various titles in this field, such as Data Scientist, Data Analyst, Data Engineer, Business Intelligence Analyst, Machine Learning Engineer, and many more. However, no matter your current role in data, everyone's journey starts from the same place — education.
Although my current title is Data Scientist, I have previously worked as a Data Educator. I've worked in both industrial and academic environments and even have experience educating people in the industry. This means I'm well-equipped to explain what you can expect when transitioning from a classroom to an industrial setting. Therefore, I'm eager to provide advice to anyone who wants to enter the data field based on my professional experience.
In this newsletter, I will explain what you can learn from my experience and what to expect as you apply your data education in the industry. Let's dive in!
Idealistic vs Realistic — Abandon Idealistic Thinking
My experience as an educator is similar to that of many others. I love seeing my students grow and improve each time I teach them. However, it's also distinct because I had already worked in academic and industry environments before becoming an educator.
As a high school student, I admired my teacher and believed that everything they taught would be applicable in the real world. I maintained an idealistic mindset, thinking things would go smoothly if I achieved good scores and completed my assignments correctly. This perspective continued into my academic career, where I upheld an idealistic belief in absolute theorems.
After graduating with my bachelor's degree, I became a researcher, hoping to further my academic career, which led me to pursue a master's degree. However, during my master's studies, I realized much was happening outside the classroom. My turning point came when I struggled to find the PhD position I desired but could not secure anything. With my academic aspirations seeming more distant, I realized that an academic career isn't the only avenue for experimentation — you need data. Consequently, I decided to pursue a career in data and take control of my own professional development.
Having gained this experience, I transitioned to the industrial sector with enhanced clarity. I initially found it challenging to let go of the academic mindset, where everything is theoretical and rigid. However, with more practical experience, I realized that not everything is as straightforward as it is in textbooks. You have to be more realistic in the industry, especially as a Data Scientist. For example:
Simple statistics can solve many problems.
Business people don't necessarily care if the results are statistically significant.
There is often ambiguity in the problems you aim to solve.
Even if you have the best model in the world, execution truly matters.
I could provide countless other examples, but the main point is that as a professional, you must be flexible and realistic in the business environment you're part of. Your classroom experience will never translate 100% to the industry.
For anyone new to data, my advice when undertaking your data projects or going through the interview process is to solve real problems that bring value to the business. Also, avoid making grand promises during interviews — it can come off as arrogance.
Communication is Everything
A phrase I always clearly remember from my Communication Mentor is, "Everything can be communicated." This implies that you can achieve what you want by effectively communicating your needs.
Indeed, communication can be challenging, especially for introverted and shy individuals. It demands significant energy to interact with others. However, if you never express your needs, how can you expect them to be fulfilled? I'm not suggesting that you must be a master communicator to become a data professional, but communication is essential for success in the data field.
In my professional journey, I would say that my communication skills have propelled me far. How far? They've helped me transition from a novice to a full-fledged data scientist — this transformation was achieved primarily through communication. Unlike many, I seldom send my CV to companies seeking job prospects. Instead, I rely on referrals to secure positions. How do I get these referrals? Of course, through communication.
I don't approach random individuals for referrals because the chances of success are slim. Instead, I network with people and join communities to build trust. Only through mutual trust are people willing to vouch for you. I understand that networking and communication might not come naturally to some, but these skills can significantly aid a newcomer in standing out.
In my line of work, you're mistaken if you believe that the data world doesn't require communication skills. Data is largely about communication, from discussing with business professionals and presenting your results to convincing others that your model is viable. Effective communication is crucial for performing your job in the data field correctly.
Recognizing the importance of communication, I've taken all the communication classes my company offers. I've also participated in many company initiatives to foster good relationships with individuals outside my department. Small efforts can yield significant results, and good communication with others can bolster your data career.
I recall a conversation with my CEO where he stated, "Your career is in your hands." This means that you dictate the trajectory of your career. You cannot passively wait for what you want; you need to communicate with people to advance your career, precisely what I've done.
My advice to all newcomers in data: Possessing good communication skills can take you far. Push yourself outside your comfort zone because you must do so.
Create a Data Portfolio Domain Specific Project to Stand Out
Many people are interested in the data field, yet many fail to stand out. With numerous individuals vying for the same position, competition is intense — that's why you need to leverage your position in a way that helps you distinguish yourself. But how can you achieve this?
One way is through referrals as I did; however, you might need something more to increase your chances of landing a data interview. I would suggest creating a data-specific project to address a business problem within the company you wish to apply to. What does this mean, exactly?
Let's say you want to apply to an insurance company as a data scientist. In this case, you could develop a project centred around an insurance business problem, such as insurance fraud detection, premium pricing, credit scoring, and so forth. You may ask, why a specific project? Isn't any project enough to demonstrate that I have the skills for the job? Indeed, any project could suffice. However, if you present a project that the business professionals cannot understand, they may perceive you as inexperienced in their business realm.
Consider this scenario: you've been working on a face detection project and plan to use this project to apply to the banking industry. What business problem could the company potentially solve with your project? Knowing that the industry could benefit from your face detection project might help you stand out; otherwise, business professionals might not comprehend its relevance.
My advice is this: The more tailored your project is to the business, the higher the likelihood it will stand out. Strive to present your project in a way that addresses a business problem.
Establish Your Presence
What do I mean by establishing your presence? It implies that you need to make yourself known online (or even offline). I'm not suggesting that you need to become an actor, but rather to be more active in the data community.
You could begin by regularly posting useful information or materials on LinkedIn or writing articles on Medium. Small actions can lead to bigger outcomes. People, including recruiters, will recognize your efforts and desire to know more about you.
Individuals in the data community are well-connected and tend to be familiar with each other. The community comprises experts and recruiters who are always searching for fresh talent. I know several data enthusiasts active in online communities and have improved their chances for data opportunities due to their online activities. Online presence truly transformed my career as well. Through writing and engaging on LinkedIn, I have met many new people and found several side hustles I never knew were possible.
My advice: Make your presence known by doing small things like sharing useful information or writing articles. Be consistent with these actions, and you will secure something meaningful.
Conclusion
Many data enthusiasts aspire to transition into the data field but struggle to leap from the classroom to the industry. Furthermore, standing out among the competition can be challenging. That's why I'd like to share my advice based on my professional experience for any newcomers to the data field:
Let go of idealistic thinking
Understand that communication is key
Develop a specific data science project to stand out
Establish your presence in the field
Thank you for reading the Non-Brand Data Newsletter. If you found this post useful, please feel free to share it with your friends. Also, I encourage you to comment on any topics you'd like me to write about!