Crossing Economics and Data Science: Yeow Chong Goh’s Story

With an extensive background in economics, Yeow Chong Goh’s interests lie in the intersection of technology and labour market research.

After completing his master’s, Yeow Chong decided to transition into data science, so that he could continue to capitalise on his experience with statistical analysis.

We spoke with Yeow Chong recently, keen to learn about his journey into data science and to discuss his experience with the Institute of Data’s Data Science & AI Programme.

Learn how our programme helped Yeow Chong secure his current role and the advice he has for those considering a career move into the exciting field of data science.

1. Hello Yeow, please tell us about yourself.

I’m from Singapore.

My background is in economics, where I focused on labour.

I come from more of a classical statistical training background, namely econometrics.

I’m passionate about specific issues surrounding technology and the labour market.

2. Why did you decide to study Data Science & AI with the Institute of Data?

I initially thought I’d like to do economics research, so I went to graduate school and completed my master’s.

Around this time, there was a lot of hype around artificial intelligence (AI) and machine learning (ML), and I could see how analysts would need to know how to apply these technologies.

That’s when I decided to make a career transition and discovered the Data Science & AI Programme under the IMDA‘s Partial Grant.

Economics has clear applications in the public sector, especially for cost-benefit modelling, which was a big driver of my interest in economics, thinking about problems and concerns about the labour market.

My curiosity about AI and ML is driven by a similar interest in applying these tools to analysing data to answer important questions.

In my current role working at a research centre, I’m analysing how children form early skills and what predicts their early academic starting points, contributing toward the centre’s broader goal of informing the Ministry of Education (in Singapore) in preparing our children to lead gainful and productive lives.

I realised that I can harness my affinity for technology to help solve these kinds of issues.

[su_quote]From data, you can solve problems and infer conclusions about the hidden underlying processes of how things work in reality.[/su_quote]

3. How did you find the programme overall?

The programme is very well structured.

Once we moved beyond the basics, nearly everything else was new to me.

We were introduced to cloud tools such as AWS and Google Cloud, and Python, which was very useful.

[su_quote]I appreciate the Python training at the Institute of Data, where you start with the basics and progress to tackling classic supervised problems and then to unsupervised problems and complex models. Also, the Institute of Data’s Job Outcomes Programme was especially valuable. They were incredibly helpful as I tried to navigate the job-hunting process.[/su_quote]

They provided great support with writing effective resumes, knowledge of the recruitment process, and skills like structuring cover letters to ensure we were saying the right thing to potential employers.

4. Did the IMDA subsidy scheme support you in your pursuit?

The IMDA subsidy helped me greatly. I’d just finished university, so I didn’t have much savings.

Being able to pursue my certification free of financial worry was invaluable.

5. That’s great to hear. Can you tell us about your current role?

Luckily, I got an interview for the role I’m currently in relatively soon after applying.

They assigned some tasks as part of the interview process, from data cleaning and data processing to simple feature engineering and model estimation, after which I presented my findings to a panel.

After that, the usual technical questions and personal competency questions were asked.

And I now have a job.

My title is Senior Research Assistant, and I work in the Center for Research in Child Development.

As a research assistant, I’m responsible for data processing, cleaning, and analysis.

6. What do you enjoy the most about working in the Data Science Industry?

[su_quote]I enjoy applying the various statistical tools I have picked up to delve deep into relationships between variables and extract insights into specific groups in the data.[/su_quote]

It’s interesting to see the insights you can draw, and I like the challenge of figuring out the best tool to employ, be it ML or AI.

7. What guidance would you give someone applying for jobs in the Data Science or AI domains?

For those in the middle of job-searching, one of my recommendations is that it’s better to rely on your networks (especially for those with two to five years or more experience).

[su_quote]Networking is the fastest way to get a higher-quality job.[/su_quote]

Of course, that requires a reputation, so networking events and participating in various industry opportunities are essential.

If you’re looking for an entry-level role, you can also consider starting with an internship.

The key idea with internships is to gain an entry point into the market.

Once you get that first job and have quantifiable achievements, you can present them to your next employer.

That’s when you can start moving up the job ladder and set your net a bit wider.

8. Is there anything else you would like to share about your experience?

My journey hasn’t been entirely conventional, but where I’ve ended up is so interesting.

For example, the Research Centre’s research is so varied that it allows us to use a wide range of data science tools.

We get to use many different applications in the most unexpected areas – I really like that.

Conclusion

If you’d like to learn more about our Data Science & AI Programmes, book a career consultation with one of our experts at the Institute of Data and start your journey with an actionable plan.

You can connect with Yeow Chong Goh and follow his professional journey on LinkedIn.