fbpx

I switched careers at 34 and became a Data Analyst. Here’s how.

DISCLAIMER: This article is sponsored by SkillsFuture Singapore, the government agency leading the SkillsFuture movement. This writer took data courses [to switch careers to become a Data Analyst] funded by SkillsFuture before committing to a more expensive course down the line.

SSG_Data Analyst_001

About a year ago, I packed up my belongings, returned the company key card, and said my farewells to colleagues. I had spent seven years as a journalist. Two in corporate communications. My next destination was something quite different – a data science bootcamp.  

I had resigned from jobs before, but this was the first time I was quitting without another position already lined up. 

Many would question the need for such a radical change. After all, why leave a stable career to pursue something that I had absolutely no experience in? 

If I were to be absolutely honest, fear was a big factor.

SSG_Data Analyst_002

The career path in communications was seemingly more straightforward, but the relentless march of technology had unsettled me. 

I had always thought as a writer, I’d not be affected. Even as all the talk about AI replacing jobs in Singapore was ongoing, there were always comforting examples of machines epically failing at producing even passable writing. 

Then I read about the rise of robot writers and Natural Language Processing, which has made vast improvements to Google Translate and Siri. Given how thoroughly the media industry has already been disrupted by technology, picking up digital skills suddenly sounded crucial.

One thing I’ve learnt is when change looks impossible, it will come drastically and abruptly when it eventually happens.

Case in point: For years, people had trouble getting a taxi at certain times and in certain areas of Singapore. Countless tweaks were made to the taxi system to no avail. Then ride-hailing apps stormed onto the scene and hailing a taxi became quaint overnight.  

Like most salarymen, I was faced with two choices.

Option 1:  Deepen and ride on my existing credentials for as long as possible and hope disruption doesn’t hit me, or

 Option 2: bite the bullet and upskill. 

If I were just a decade older and a tad closer to retirement, say, 45 – Option 1 might have been a tempting choice. But I chose the latter as I was still only 34 and had my whole working life ahead of me. 

So I chose a new career that built digital skills upon my existing analogue abilities. 

SSG_Data Analyst_003

You might think I’m just jumping on the data science bandwagon as it’s the hottest thing and is a growing industry. That’s only half true.

While my career thus far (dealing mostly with words as a writer) may seem far removed from data analytics (with its cold hard numbers), there are actually numerous similarities:

A professional storyteller makes complex issues easily understandable. I saw data as an additional way to explain the world: Reporters are using machine learning in new forms of journalism while corporate communications professionals are vying to showcase their companies’ data abilities. 

Data Analyst Journalist 
Data analysts gather data through multiple sources such as tracking user behaviour and public databases.  Journalists gather data through multiple sources such as expert interviews and public records.
Data analysts turn unstructured, unwieldy data that is often incomplete into easily understandable structured data that paint a whole picture.  Communications professionals simplify lengthy, inscrutable corporate jargon into a sharp pitch that captures the essence of a subject.
Tell coherent stories with data. Tell coherent stories with information. 

Being aware of these was immensely helpful at job interviews when faced with the question of “why should I hire someone with no experience?”

PS: If you’re curious what your career pathway could be, you can check out the Skills Framework for some clarity. The one specifically for people in the ICT sector here. 

The key is to start small

SSG_Data Analyst_004

Many people imagine switching careers as taking an entire year off to complete a degree. This was partly true for me. Except that I used Skillsfuture Credit to take small ‘trials’ before I decided to take the leap. 

These were the specific courses I took (the ones on Udemy can be claimed (or back-claimed) with your SkillsFuture Credit):  

Interactive Python Dashboards with Plotly and Dash

Hands-On Natural Language Processing (NLP) using Python

Python for Data Science and Machine Learning Bootcamp

Programming for Everybody

These short courses enabled me to pick up basic coding skills and knowledge about data science. More importantly, I gained confidence in my ability to learn and started seeing the potential of data analytics beyond the industry I was familiar with. 

Without them, I most certainly would have been unable to cope with more advanced courses. 

Moving beyond self-learning, I considered taking a Masters in Analytics, but ultimately did not take that route due to costs and time as it would have taken at least a year and set me back by about $25,000. 

 Instead, I leaned towards coding bootcamps that were more affordable and could be completed in a few months. 

Eventually, I chose General Assembly’s 12-week Data Science bootcamp because of its reputation and career services, which included career coaching and a “meet and greet” event with prospective employers.

If you’re Singaporean, embrace the monetary perks of citizenship! Many online courses are claimable on SkillsFuture and some are even free

Two-thirds (that’s over $9,000) of my $14,500 bootcamp fee was also subsidised by the government (through an IMDA scheme).

Be prepared to be humbled.

SSG_Data Analyst_005

Going back to school is humbling. For 12 entire weeks, I found myself struggling with statistics, vectors and probability – concepts I thought I had left behind for good after my A levels. 

But frankly, the toughest part of the career transition happened after I graduated. 

I was sending out about 10 resumes a week and tweaking the accompanying cover letters to suit particular roles. Industry veterans back at General Assembly had warned us that 90 percent of applications would not receive a response but experiencing it first-hand was still a visceral gut punch. 

A cursory check of data job openings on LinkedIn showed easily more than 200 applications per position, not surprising given the numerous data bootcamps and Masters programmes out there today. 

Based on my job hunt, a STEM (science, technology, engineering, maths) background definitely helps. Some employers sent strong signals they would much rather be talking to engineers, statisticians, or computer programmers.

As someone without a STEM degree, I had to work doubly hard to demonstrate the requisite aptitude and overcome doubts about my technical ability. I was actually the last person in my cohort to land a job – at 99.co, a real estate portal. 

Half of my classmates who were sponsored to do the course went back to their jobs using their new-found data skills as an employability boost.

The others joined multinational companies, government bodies, and start-ups. 

In many ways, my job search was also eye-opening. 

SSG_Data Analyst_006

On one hand, I experienced the infamous and harrowing ‘whiteboard test’ where interviewers younger than myself pushed me to my limits (and were extremely encouraging, to their credit).

On the other, I was absolutely taken aback by an encounter with someone in a senior tech role who was oblivious to the most basic of tech and data concepts (imagine a biologist who had never heard of DNA).

It drove home the point that in today’s environment of rapid technological change, no one – no matter your seniority or designation – is exempt from lifelong learning.

I may have achieved a small measure of success in my career switch so far, but if I rest on my laurels, I can easily be made obsolete by those who are hungrier and armed with knowledge I don’t possess.

This may seem daunting, but I got my start in data analytics precisely because someone was willing to evaluate me based on whether I had the relevant skills and the capacity to learn, and not based on my seniority or a piece of paper I earned from university years ago. 

And in this world where skills are king, anyone who is willing to learn has a fighting chance. 

TWS: This is a future where people are paid more for their knowledge, skills and contribution, not how long they’ve clung on to the corporate ladder.

And it’s both a terrifying and exciting time to be alive. 

Stay woke, salaryman

 

Liked it? Take a second to support thewokesalaryman on Patreon!

9 replies to “I switched careers at 34 and became a Data Analyst. Here’s how.

  1. Hi! Thank You for this wonderful post. It helps me tremendously. May i request for the author email as i wish to connect with like-minded personnel? I am at a critical juncture of career and will like to understand more of his passion.
    Also, can i know what was the bootcamp course that he mentioned which was subsidies. Will greatly appreciate if this can be furnished.

    1. Hi Rigine, I’m the writer of this article. You can add me on LinkedIn if you wish to have a conversation. I took General Assembly’s data science course and the link is in the article. Hope this helps you with your decision.

      1. Thank you Zi Liang! Love your article, it sharpens my thinking and made a complex issue simple.

  2. Thank you for insight, I am 40 years old and I am also thinking of changing a career, you post has encouraged me more and I will start with a course next month… Thank you!!

  3. Hello there! Thanks for this wonderfully helpful post. I’ve been trying to learn data science and python as well (not for a job switch, but because I think it will help me be more effective at what I do). Coming from someone who has zero knowledge of programming, where do you suggest I start? Should I be looking at a basic python course first, before even delving into data science and analysis?

    1. Hi Julian, I’m the writer of this article, thanks for reading! Two of the online courses I took that are listed in the article (Programming for Everybody, Python for Data Science and Machine Learning Bootcamp) both offer foundational Python training before going more into data science. I found them very useful. Beyond that, I would say establishing an interest in tech and clear purpose of why you want to learn is more important for sustaining the momentum and finishing the courses. I have a newsletter for people who are starting to explore the tech world so you may find that useful too! https://artsciencemillennial.substack.com

  4. Hi Zi Liang, thanks for the inspiration life story. I’m now entering a fork in my life after spending 10yrs in the night entertainment industry. I’m beginning to see no headroom and I’m starting to dread the working lifestyle I’m having. Especially now after 3 months of CB, I don’t see how my livelihood would improve.

    I’m very inspired by your journey and would hope to connect with you in the future as I’m about to embark on the same journey as you. Hopefully with the same guidance, training and support I can work through this difficult times.

Leave a Reply

Enjoy our content?Subscribe to our mailing list to get TWS in your email inbox.
close-alt close collapse comment ellipsis expand gallery heart lock menu next pinned previous reply search share star