Pondering a Career Change? Then Try Data Science

My most Frequently Asked Questions (FAQ) about going into Data Science

When I contemplated switching careers almost a year ago, data science emerged as a natural choice after years of working with statistics and data. However, even after completing a data science boot camp, I found myself re-questioning whether it was the right path for me.

So, why does data science make sense as a career choice, and who is it best suited for? What you are going to read next is not a comprehensive list of everything you need to know about making it into data science. I just compiled some thoughts and hints that can point you in the right direction and suggest whether you are on the right track.

If you’re curious, then keep on reading!

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Why go into Data Science?

The most straightforward answer is: It’s a booming field in the tech industry, offering promising salaries, job security, and, most importantly, long-term relevance. Not only will data not become obsolete anytime soon, but it will probably boost advancement in many areas of knowledge for years to come.

But let’s be real — it’s not for the faint of heart. You’ve got to be dedicated, love problem-solving, and thrive on challenges. Otherwise, it’s best to stick to your current path, become a specialist in it, or explore other fields to avoid disappointment.

Do I need a tech background to be a data scientist?

Don’t worry if you have never studied or worked in tech. Having some exposure to the field certainly helps, but it’s not a deal-breaker. Being exposed to the IT world means knowing about its hurdles, its dynamics, and the endless trials and errors involved in creating a tech product, all of which go a great length when delving into data science. But having a tech or engineering background doesn’t guarantee success, either.

When it comes to programming, knowing some coding prevents you from a hard landing at SQL, Python, or R. Even basic coding knowledge can go a long way in easing your transition into these tools. I’ve witnessed colleagues who already knew other programming languages take projects to the next level after first trying some loops and functions. And that’s expected — after all, they’ve probably been invested in coding for decades. Side thought: I bet they did not have to digest Hegel’s “Phenomenology of Spirit” like I did while at university.

But there’s a saying in the “Four Laws of Spirituality” that can be applied to coding: you start when you start, not before, not after, just when the time is right.

“Any time you start is the right time.”
It all starts at the right time, neither before nor after.
When we are ready to start something new in our lives, it is then when you start.

How do I know if I should go into Data Science?

Here are some key indicators you should consider before pursuing a career in data. You don’t need to have them all, just a few could be enough positive hints:

• You love science and research: You don’t have to be a rocket scientist or a lab master, but a genuine interest in scientific discovery is essential. Whether you’re fascinated by astronomy, enjoy learning about new developments in your field of study, or find joy in nurturing your home garden, a curiosity for science is a strong indicator of a potential fit for data science. Perhaps you were the science fair kid growing up, or you simply appreciate the process of inquiry and exploration. These interests and experiences can serve as valuable clues that data science aligns with (at least some of) your passions.
• You’ve had some exposure to data: Whether through academic study or practical experience, you’ve engaged with statistics, collected figures, and understand why databases are useful. But most importantly: you find that interesting or intriguing.
• You are open to learning (yet again!): Even with years of experience in a data-related field, or after having achieved your PhD, you may still lack knowledge in data engineering, machine learning, or basic data science concepts. Simply working in a data-driven organization, perhaps focusing on tasks like content writing or sales development, may not necessarily equip you with these skills. You need to be prepared to tackle new tools that will seem like Mount Everest and actually suck at it at the beginning. But worry not, that is how every data scientist on Earth learned to be good at their craft.
• You are curious and open to change: Similarly, extensive experience with data and statistics won’t be beneficial if you’re resistant to IT or skeptical about the future of digitalization. Being open to change is one of the key aspects of embracing data science. Or plain and simple, the field makes you curious and you have a history of endeavoring into (long, rocky) learning paths.
• You’ve received career advice or counseling suggesting that data science could be a promising opportunity for you: Sometimes, we overlook our own potential until a third-party observer helps us see it. Fortunately, numerous resources are available online to guide you, including AI-powered tools like Wanderer Space and free career orientation services like MatchTalent.org or Berlin-based NGO Migrapreneur. There are also very capable and supportive career professionals who are willing to offer guidance.

What should I be, a Data Scientist or a Data Analyst?

Countless discussions have attempted to define the roles of these two positions, but the reality is, they can differ significantly from one organization to another.

Some may view data analysts as spreadsheet wizards, while data scientists may find themselves immersed in software departments. However, job titles and responsibilities can vary greatly based on the specific tech skills required and the unique applications of data within each organization. I’ve encountered analysts who excel at market research or data modeling without any data pipeline-building knowledge, and I’ve seen data scientists who’ve never touched Excel or worked extensively with spreadsheets. And that was surprising for me because, in my head, data scientists had to know everything about data. Now I know they are just two separate paths that require different skills.

The more you know, of course, the better. While having a strong foundation of knowledge is valuable, organizations are primarily seeking individuals who can continue to develop their skills and expertise on the job, no matter whether they call themselves analysts or scientists.

Data Scientist: To be or not to be?

Even after going through the whole process of successfully finishing a data science degree or boot camp, you may find it difficult to call yourself a data scientist. Ultimately, what makes a data scientist is experience and not educational background, or so I’ve been constantly told during my training. We can consider ourselves lucky to finish our training aspiring to be one, or at least a “Junior” one. The rest is motivation, enthusiasm, consistency, and lifelong learning.

So, how do you know if data science is right for you? If you’re passionate about science and research, have some experience with statistics, and find databases intriguing, you’re on the right track. But beware, working with data isn’t just about crunching numbers; it’s about embracing digitalization and automation. If you’re curious about the field and enjoy embarking on learning journeys, data science might just be your calling. And remember, there’s no perfect time to start — just dive in when you’re ready.

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