Data36

Feeling too old for a career change in your mid-30s? Well, it can be perfect for a data science pivot -- and here's why.

A small misunderstanding often arises when people ask me if it's worth starting a new career in data science in their mid-30s.

*Is it too late?*
*Are they too old?*

First of all, I really hope being in your mid-30s isn't considered "old," because I'm 34 myself. 😅

Secondly, it's certainly not too late. In fact, most of the people in my data science courses *are* in their mid-30s! Quite a few are on paternity or maternity leave, thinking about which new skills to acquire and where to re-enter the job market when they return to work. And that's how they find data science.

Well, okay. I agree that starting in data science in your early 20s has its advantages:
👉 It's easier to secure internship positions.
👉 You likely don't have family responsibilities, so you can accept an entry-level job or an internship with a lower salary, or even an unpaid position, just to learn and gain experience.
👉 You likely have more freedom to spend your days and nights learning, working on hobby projects, and generally hustling.

In your mid-30s, you probably don't have as much free time, and you might be reluctant to take a step back salary-wise. That's understandable. However, compared to those in their early 20s, you have now several advantages:
👉 You probably have roughly 10 years of work experience, possibly in a relevant domain like finance, marketing, or science. (Articulated well, that can look great on a CV.)
👉 With that decade of experience, you have a better understanding of business, something you likely didn't have fresh out of school or university. (And let's be honest, even business schools don't fully prepare you for real-world business challenges — only work does.)
👉 You probably have strong skills and experience in one or more domains (again, like finance, marketing, or science, etc.), which can be invaluable when specializing in data science.

If you leverage these advantages and acquire the necessary data science skills, you can gradually transition into a data science role. I call this "soft repositioning." Just one common example: 1) You might start with a Business Intelligence (BI) position in your field, such as creating dashboards for marketing executives. 2) Later, you could take on more analytical tasks, like analyzing data in SQL or Python before creating those dashboards. 3) Eventually, you might delve into more scientific tasks, like building basic machine learning models.

I know this sounds simplified, every journey is somewhat different. Also, the process of learning and repositioning is never easy. it can also take a lot of time. (Hence my motto: "Learn Data Science the Hard Way!")

But it's very, very rewarding in every sense!

Either way, my point is: being in your mid-30s is definitely not too late for a career in data science! Many others have made this transition at this age, and it has worked out great for them!

***
If you want to start the learning process, check out my blog where I publish a lot of free stuff: Data36 [dot] com

Is Data Science the Right Career for You? 7 Key Indicators in the post👇

Wondering if a career in data science is your calling? There's more to it than just the alluring promise of wealth and intelligence. In reality, it's a challenging field requiring continuous learning and a deep understanding of complex subjects.

QUESTION #1: Could You Learn Data Science?
I don’t believe in talent. I don’t think that anyone was born being better – either in statistics, mathematics, coding, business, communication or anything else – than others.
I believe in skills. I think that some people are better than others in some things because they are continuously practicing and developing their skills – for years, for decades, for their entire life.
Thus I strongly believe that nowadays anyone can acquire any skills if they are really dedicated to it. Simply put: if you want to learn data science, you can learn data science. So the real question is...

QUESTION #2: Would You Enjoy the Process?
I’ve found that one can only work on a specific skill without losing motivation if they enjoy the process of learning and practicing. Working on something always has its ups and downs, but if you generally enjoy the process, that will definitely help a lot to keep you in the flow.

👉 The most important question for me before you start your data science career is: would you enjoy data science at all? 👈

So I collected 7 Indicators that can help you figure out whether you're cut out for Data Science:
1) Analytical Mindset: If you naturally gravitate towards a rational analysis over relying on luck or instincts, that's a good sign.
2) Love for Math and Statistics: You'll be dealing with numbers a lot, so an affinity for mathematics is essential.
3) Business Acumen: Understanding the business implications of your analyses is crucial. Data science isn't just about number crunching; it's about driving value.
4) Coding Enthusiast: Much of your time will be spent coding. If that's something you can see yourself enjoying, you’re on the right track.
5) Team Player: Expect to collaborate with professionals from various departments. If you're comfortable in cross-functional settings, that's a plus.
6) Communication Skills: You'll need to articulate your findings clearly and effectively, often to non-data experts.
7) Lifelong Learner: The field is always evolving, and your ability to continuously learn is pivotal for long-term success.

As you can see, a great data scientist has a good combination of introverted, extroverted, rational and emotional skills. Again, with practice, you can improve all of them… The biggest question is if you want to. I hope that now you have a clearer picture.

If you want to learn more, check out my data science blog with tons of free tutorials at Data36 [dot] com.