Career change into Data Science in 2023: Was it worth it?

Making career decisions can be flipping hard work.

These days, it seems like every man and his dog are trying to switch into Data Science, but it can be really hard to know whether this is the right move for you. If you’re considering becoming a Data Scientist, chances are that you’ve come across a lot of “hype” about the industry, and also read your fair share of “Why you should NOT become a Data Scientist in 2023”-style articles. Often, however, I think these articles lack a personal touch, and it can be quite difficult as a non-Data Scientist to know which opinions you should trust.

Over the last two years, I have changed from being a Project Manager/Business Analyst to becoming a Data Scientist, and I am writing this article to share my honest reflections on why I chose to leave my old role, why I picked Data Science, and whether I’d recommend you do the same.

Why I became a Data Scientist

Around the start of 2020, I felt stuck on a career path I like to call “business generalism”. I was in the middle of a two-year rotational graduate scheme working for Vodafone, a large telecoms company based in London, UK. The scheme itself was fantastic — it involved completing several placements across different areas of the business, which was a great way to learn about how a large corporate works and make the transition from university to “the real world” of full-time work.

The problem, however, was that I felt thoroughly dissatisfied with the work I was doing. When people asked me what I did for a living, I struggled to give a coherent answer: “It’s sort of like project management, but I also do a bit of data analysis, oh and I also kind of work in Marketing but not really?” Often, I felt like my job was a bit too generalist; like I was a “jack of all trades and master of none”.

Of course, there’s nothing wrong with having multiple specialisms — I loved the variety of my roles, and entire books have been written about the value of having a generalist skill set! For me, the issue was that having a generalist skillset didn’t feel particularly satisfying, and it was hard to pinpoint anything that could give me direction or purpose in my career.

It was around this time that I read an amazing book called Garden City by John Mark Comer, an American author writes a lot about career-related topics. In this book, John Mark Comer claims that the phrase “jack of all trades, master of none” is actually a misquote of Benjamin Franklin, who originally coined the phrase as “jack-of-all-trades, master of one.”

Woah.

That’s a completely different angle, right? I loved this quote because it felt like a really tangible vision for what I could aim for in my career. I realised that, while there’s nothing wrong with being a jack-of-all-trades, it might really help me if I could find that “one” skill I could master in addition to all the other more generalist skills I was learning.

So, why Data Science?

If John Mark Comer was the one who convinced that I needed to find a “specialism”, it was my own experience that convinced me that Data Science could be the right specialism for me.

By mid-2020, I’d worked in a few analytics roles (e.g. Data Analyst, Business Analyst) and had loved them all. It’s hard to put into words, but I’ve always found that there is something immensely satisfying about taking a really complex problem and using data to solve it. At the time, I couldn’t really code, but I had taken a couple of short beginner Python courses including the Open University’s ‘Learn to Code for Data Analysis’ course, and I’d enjoyed these enough to make me consider taking Data Science more seriously.

On a practical note, I was also acutely aware that the Data Science job market was on the rise. Sure, this was 10 years after Harvard Business Review first predicted that Data Science would be the “sexiest job of the 21st century” (yawn), but even at the time I was considering making the move, the number of Data Scientist roles in the UK had more than tripled over the previous five years, and I knew anecdotally (from speaking to two different Chief Data Officers) that many companies were struggling to recruit Data Scientists who understood the business context of their work. It felt a little like the stars were aligning and there might be space in this industry for me to bring my “business generalist” skills and create a unique personal value proposition as a Data Scientist who appreciated the commercial aspects of the role.

How I made the switch

The first thing I did was take some online courses, to test the water and see if I enjoyed the kind of work that Data Scientists do. The courses I found most helpful were:

• The Open University’s ‘Learn to Code for Data Analysis’ course
• The IBM Data Science Professional Certificate, on Coursera

Alongside this, I tried to network with other Data Scientists at my company and connect with other Data Scientists online, and get their advice about breaking into the field. My thinking was: if I’m going to make such a big career change, I’d better make sure that this is actually what I want!

Having established that yes, I did want to be a Data Scientist, I applied to study a master’s degree in Data Science. I really want to stress: this is not for everyone, and is definitely not the only way to get into the field. Having met many Data Scientists over the last couple of years, I can reassure you that there is no “correct” way to get into the field, and there are lots of different routes available to you including part-time study, learning on-the-job, and bootcamps. For me, the main reasons I chose to study a Data Science masters were (1) I got accepted to study at Oxford University, and I felt that this was a bit of a ‘once-in-a-lifetime’ opportunity, (2) I felt it would give me more credibility to have a formal qualification in Data Science, and (3) financially it wasn’t going to be too big of a hit: I won a partial scholarship, and had saved for a couple of years so had enough money to cover the remaining expenses. I also had no financial dependents, so there was no pressure to be earning during that year while I was studying.

During my masters, I worked with the Data Science team at Tripadvisor in order to get a bit more commercial experience of Data Science, and after I finished my master’s I did a four-month internship as a Data Scientist at a fantastic NLP startup called Rewire. Throughout these experiences, I got the feeling that I would like to work in the media/tech sector, which led me to apply to Sky, where I got a job as a Data Scientist (my current role).

Was it worth it?

Honestly, I can say that this transition has been one of the best things I’ve ever done. I have loved all of my Data Science jobs and have never looked back. It feels very exciting to be part of a growing field, and I love the emphasis on problem-solving and continual learning which are part-and-parcel of working in the world of Data and Artificial Intelligence.

Would I recommend that you make the switch?

My boring (but true) answer: it depends. For me, the switch was 100% worth it. For you, it might be, but it might not be. If you’re someone who loves problem-solving and has enjoyed working with data in the past, it could be a fantastic way to find that “specialism” that you can master. If you are considering making the move and don’t have any prior coding experience, I recommend taking some introductory coding courses online to get a flavour of what being a Data Scientist will involve. And, above all, SPEAK TO PEOPLE. It’s really easy to do all your research and thinking “online” (i.e. just reading articles like this), but honestly I would recommend that you reach out to some real-life Data Scientists, tell them a bit of your background/story, and ask for their advice. This was so valuable for me because I was able to receive tailored advice based specifically on my situation.

On that note, feel free to reach out to me or comment on this article; I would be happy to see if I can help.

The final thing I’d like to say is: don’t overthink it. I think that part of what makes it hard to make these big career choices is that we don’t want to “get it wrong” and end up in a career we don’t actually enjoy as much as we thought we would. Something that’s really helped me overcome this fear is some advice shared by a friend a few years ago. He encouraged me to think of my career in five-year chunks, not as one single entity. In that sense, he said, just focus on what you want to do in the next few years, and don’t worry about what you’ll do in five or ten or twenty. If you think Data Science might be something you’d enjoy, I would also advise the same thing to you. Making a career switch into Data Science is a big decision, but you don’t have to be 100% certain that it’s the career you want to do for the rest of your life. All you need is to be above a threshold of certainty where you can say “this is what I want to do for the next few years”.

If that’s you, go for it.

Oh, one more thing —

I’ve started a free newsletter called AI in Five where I share 5 bullet points each week on the latest AI news, coding tips and career stories for Data Scientists/Analysts. There’s no hype, no “data is the new oil” rubbish and no tweets from Elon — just practical tips and insights to help you develop in your career. Subscribe here if that sounds up your street!